cs.IT,"the present work studies quantum and classical correlations in three qubits and four qubits general bell states , produced by operating with braid operators on the computational basis of states the analogies between the general three qubits and four qubits bell states and that of two qubits bell states are discussed the general bell states are shown to be maximal entangled , i e , with quantum correlations which are lost by tracing these states over one qubit , remaining only with classical correlations , as shown by hs decomposition"
cs.IT,"frequency diverse \( fd \) radar waveforms are attractive in radar research and practice by combining two typical fd waveforms , the frequency diverse array \( fda \) and the stepped frequency \( sf \) pulse train , we propose a general fd waveform model , termed the random frequency diverse multi input multi output \( rfd mimo \) in this paper the new model can be applied to specific fd waveforms by adapting parameters furthermore , by exploring the characteristics of the clutter covariance matrix , we provide an approach to evaluate the clutter rank of the rfd mimo radar , which can be adopted as a quantitive metric for the clutter suppression potentials of fd waveforms numerical simulations show the effectiveness of the clutter rank estimation method , and reveal helpful results for comparing the clutter suppression performance of different fd waveforms"
cs.AI,"unsupervised word embeddings have been shown to be valuable as features in supervised learning problems however , their role in unsupervised problems has been less thoroughly explored in this paper , we show that embeddings can likewise add value to the problem of unsupervised pos induction in two representative models of pos induction , we replace multinomial distributions over the vocabulary with multivariate gaussian distributions over word embeddings and observe consistent improvements in eight languages we also analyze the effect of various choices while inducing word embeddings on downstream pos induction results"
cs.IT,"a discrete time wiener phase noise channel with an integrate and dump multi sample receiver is studied an upper bound to the capacity with an average input power constraint is derived , and a high signal to noise ratio \( snr \) analysis is performed if the oversampling factor grows as text snr alpha for 0 le alpha le 1 , then the capacity pre log is at most \( 1 alpha \) 2 at high snr"
cs.IT,"in this paper , we consider a particular class of selective fading channel corresponding to a channel that is selective either in time or in frequency for this class of channel , we propose a systematic way to achieve the optimal dmt derived in coronel and b olcskei , ieee isit , 2007 by extending the non vanishing determinant \( nvd \) criterion to the selective channel case a new code construction based on split nvd parallel codes is then proposed to satisfy the nvd parallel criterion this result is of significant interest not only in its own right , but also because it settles a long standing debate in the literature related to the optimal dmt of selective fading channels"
cs.IT,"the gallager bound is well known in the area of channel coding however , most discussions about it mainly focus on its applications to memoryless channels we show in this paper that the bounds obtained by gallager 's method are very tight even for general sources and channels that are defined in the information spectrum theory our method is mainly based on the estimations of error exponents in those bounds , and by these estimations we proved the direct part of the slepian wolf theorem and channel coding theorem for general sources and channels"
cs.IT,"in a basic related key attack against a block cipher , the adversary has access to encryptions under keys that differ from the target key by bit flips in this short note we show that for a quantum adversary such attacks are quite powerful if the secret key is \( i \) uniquely determined by a small number of plaintext ciphertext pairs , \( ii \) the block cipher can be evaluated efficiently , and \( iii \) a superposition of related keys can be queried , then the key can be extracted efficiently"
cs.IT,"this work considers the smart repeater network where a single source s wants to send two independent packet streams to destinations d 1 , d 2 with the help of relay r the transmission from s or r is modeled by packet erasure channels for each time slot , a packet transmitted by s may be received , with some probabilities , by a random subset of d 1 , d 2 , r and those transmitted by r will be received by a random subset of d 1 , d 2 interference is avoided by allowing at most one of s , r to transmit in each time slot one example of this model is any cellular network that supports two cell edge users when a relay in the middle uses the same downlink resources for throughput safety enhancement in this setting , we study the capacity region of \( r 1 , r 2 \) when allowing linear network coding \( lnc \) the proposed lnc inner bound introduces more advanced packing mixing operations other than the previously well known butterfly style xor operation on overheard packets of two co existing flows a new lnc outer bound is derived by exploring the inherent algebraic structure of the lnc problem numerical results show that , with more than 85 of the experiments , the relative sum rate gap between the proposed outer and inner bounds is smaller than 0 08 under the strong relaying setting and 0 04 under arbitrary distributions , thus effectively bracketing the lnc capacity of the smart repeater problem"
cs.LG,"bike sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility in this paper we are interested in studying and modeling the behavior of features that permit the end user to access , with her his web browser , the status of the bike sharing system in particular , we address features able to make a prediction on the system state we propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage on the one hand , machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features on the other hand , trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost performance trade off of the feature this provides a principled way to assess the runtime behavior of different components before putting them into operation"
cs.IT,consider a polynomial f in m variables and a finite point ensemble s s 1 times times s m when given the leading monomial of f with respect to a lexicographic ordering we derive improved information on the possible number of zeros of f of multiplicity at least r from s we then use this information to design a list decoding algorithm for a large class of affine variety codes
cs.IT,"due to the increasing heterogeneity and deployment density of emerging cellular networks , new flexible and scalable approaches for their modeling , simulation , analysis and optimization are needed recently , a new approach has been proposed it is based on the theory of point processes and it leverages tools from stochastic geometry for tractable system level modeling , performance evaluation and optimization in this paper , we investigate the accuracy of this emerging abstraction for modeling cellular networks , by explicitly taking realistic base station locations , building footprints , spatial blockages and antenna radiation patterns into account more specifically , the base station locations and the building footprints are taken from two publicly available databases from the united kingdom our study confirms that the abstraction model based on stochastic geometry is capable of accurately modeling the communication performance of cellular networks in dense urban environments"
cs.DS,"the cluster editing problem seeks a transformation of a given undirected graph into a disjoint union of cliques via a minimum number of edge additions or deletions a multi parameterized version of the problem is studied , featuring a number of input parameters that bound the amount of both edge additions and deletions per single vertex , as well as the size of a clique cluster we show that the problem remains np hard even when only one edge can be deleted and at most two edges can be added per vertex however , the new formulation allows us to solve cluster editing \( exactly \) in polynomial time when the number of edge edit operations per vertex is smaller than half the minimum cluster size in other words , correlation clustering can be solved efficiently when the number of false positives negatives per single data element is expected to be small compared to the minimum cluster size as a byproduct , we obtain a kernelization algorithm that delivers linear size kernels when the two edge edit bounds are small constants"
cs.AI,"there is a vast supply of prior art that study models for mental processes some studies in psychology and philosophy approach it from an inner perspective in terms of experiences and percepts others such as neurobiology or connectionist machines approach it externally by viewing the mind as complex circuit of neurons where each neuron is a primitive binary circuit in this paper , we also model the mind as a place where a circuit grows , starting as a collection of primitive components at birth and then builds up incrementally in a bottom up fashion a new node is formed by a simple composition of prior nodes when we undergo a repeated experience that can be described by that composition unlike neural networks , however , these circuits take concepts or percepts as inputs and outputs thus the growing circuits can be likened to a growing collection of lambda expressions that are built on top of one another in an attempt to compress the sensory input as a heuristic to bound its kolmogorov complexity"
cs.LG,"robotic systems must be able to quickly and robustly make decisions when operating in uncertain and dynamic environments while reinforcement learning \( rl \) can be used to compute optimal policies with little prior knowledge about the environment , it suffers from slow convergence an alternative approach is model predictive control \( mpc \) , which optimizes policies quickly , but also requires accurate models of the system dynamics and environment in this paper we propose a new approach , adaptive probabilistic trajectory optimization , that combines the benefits of rl and mpc our method uses scalable approximate inference to learn and updates probabilistic models in an online incremental fashion while also computing optimal control policies via successive local approximations we present two variations of our algorithm based on the sparse spectrum gaussian process \( ssgp \) model , and we test our algorithm on three learning tasks , demonstrating the effectiveness and efficiency of our approach"
cs.IT,"in this paper , we develop a new decoding algorithm of a binary linear codes for symbol pair read channels symbol pair read channel has recently been introduced by cassuto and blaum to model channels with high write resolution but low read resolution the proposed decoding algorithm is based on a linear programming \( lp \) it is proved that the proposed lp decoder has the maximum likelihood \( ml \) certificate property , i e , the output of the decoder is guaranteed to be the ml codeword when it is integral we also introduce the fractional pair distance d fp of a code which is a lower bound on the pair distance it is proved that the proposed lp decoder will correct up to lceil d fp 2 rceil 1 pair errors"
cs.IT,"we study the problem of geometric optimization of medium access control in multi hop wireless network we discuss the optimal placements of simultaneous transmitters in the network and our general framework allows us to evaluate the performance gains of highly managed medium access control schemes that would be required to implement these placements in a wireless network consisting of randomly distributed nodes , our performance metrics are the optimum transmission range that achieves the most optimal tradeoff between the progress of packets in desired directions towards their respective destinations and the total number of transmissions required to transport packets to their destinations we evaluate aloha based scheme where simultaneous transmitters are dispatched according to a uniform poisson distribution and compare it with various grid pattern based schemes where simultaneous transmitters are positioned in specific regular patterns our results show that optimizing the medium access control in multi hop network should take into account the parameters like signal to interference ratio threshold and attenuation coefficient for instance , at typical values of signal to interference ratio threshold and attenuation coefficient , the most optimal scheme is based on triangular grid pattern and , under no fading channel model , the most optimal transmission range and network capacity are higher than the optimum transmission range and capacity achievable with aloha based scheme by factors of two and three respectively later on , we also identify the optimal medium access control schemes when signal to interference ratio threshold and attenuation coefficient approach the extreme values and discuss how fading impacts the performance of all schemes we evaluate in this article"
cs.IT,"we consider the problem of soft decision fusion in a bandwidth constrained wireless sensor network \( wsn \) the wsn is tasked with the detection of an intruder transmitting an unknown signal over a fading channel a binary hypothesis testing is performed using the soft decision of the sensor nodes \( sns \) using the likelihood ratio test , the optimal soft fusion rule at the fusion center \( fc \) has been shown to be the weighted distance from the soft decision mean under the null hypothesis but as the optimal rule requires a priori knowledge that is difficult to attain in practice , suboptimal fusion rules are proposed that are realizable in practice we show how the effect of quantizing the test statistic can be mitigated by increasing the number of sn samples , i e , bandwidth can be traded off against increased latency the optimal power and bit allocation for the wsn is also derived simulation results show that sns with good channels are allocated more bits , while sns with poor channels are censored"
cs.IT,"we consider the distributed source coding system for l correlated gaussian observations y i , i 1 , 2 , , l let x i , i 1 , 2 , , l be l correlated gaussian random variables and n i , i 1 , 2 , l be independent additive gaussian noises also independent of x i , i 1 , 2 , , l we consider the case where for each i 1 , 2 , , l , y i is a noisy observation of x i , that is , y i x i n i on this coding system the determination problem of the rate distortion region remains open in this paper , we derive explicit outer and inner bounds of the rate distortion region we further find an explicit sufficient condition for those two to match we also study the sum rate part of the rate distortion region when the correlation has some symmetrical property and derive a new lower bound of the sum rate part we derive a sufficient condition for this lower bound to be tight the derived sufficient condition depends only on the correlation property of the sources and their observations"
cs.DS,"this paper resolves an open problem raised by blocki it et al \( focs 2012 \) , i e , whether other variants of the johnson lindenstrauss transform preserves differential privacy or not \? we prove that a general class of random projection matrices that satisfies the johnson lindenstrauss lemma also preserves differential privacy this class of random projection matrices requires only n gaussian samples and n bernoulli trials and allows matrix vector multiplication in o \( n log n \) time in this respect , this work unconditionally improves the run time of blocki it et al \( focs 2012 \) without using the graph sparsification trick of upadhyay \( asiacrypt 2013 \) for the metric of measuring randomness , we stick to the norm used by earlier researchers who studied variants of the johnson lindenstrauss transform and its applications , i e , count the number of random samples made in concise , we improve the sampling complexity by quadratic factor , and the run time of cut queries by an o \( n o \( 1 \) \) factor and that of covariance queries by an o \( n 0 38 \) factor"
cs.SI,our paper explores contribution patterns of creativity and collaboration of wikipedia editors as manifestations of social dynamics between the editors we find support for existence of four socially constructed personas among the editors and difference in distribution of personas in articles of different qualities
cs.IT,"we consider a stationary and ergodic source p generated symbols x 1 x t from some finite set a and a null hypothesis h 0 that p is markovian source with memory \( or connectivity \) not larger than m , \( m 0 \) the alternative hypothesis h 1 is that the sequence is generated by a stationary and ergodic source , which differs from the source under h 0 in particular , if m 0 we have the null hypothesis h 0 that the sequence is generated by bernoully source \( or the hypothesis that x 1 x t are independent \) some new tests which are based on universal codes and universal predictors , are suggested"
cs.AI,"we show in this paper that , on the one hand , named entities can be designated using different denominations and that , on the second hand , names denoting named entities are polysemous the analysis cannot be limited to reference resolution but should take into account naming strategies , which are mainly based on two linguistic operations synecdoche and metonymy lastly , we present a model that explicitly represents the different denominations in discourse , unifying the way to represent linguistic knowledge and world knowledge"
cs.IT,"we consider information theoretic secret key agreement and secure function computation by multiple parties observing correlated data , with access to an interactive public communication channel our main result is an upper bound on the secret key length , which is derived using a reduction of binary hypothesis testing to multiparty secret key agreement building on this basic result , we derive new converses for multiparty secret key agreement furthermore , we derive converse results for the oblivious transfer problem and the bit commitment problem by relating them to secret key agreement finally , we derive a necessary condition for the feasibility of secure computation by trusted parties that seek to compute a function of their collective data , using an interactive public communication that by itself does not give away the value of the function in many cases , we strengthen and improve upon previously known converse bounds our results are single shot and use only the given joint distribution of the correlated observations for the case when the correlated observations consist of independent and identically distributed \( in time \) sequences , we derive strong versions of previously known converses"
cs.DS,"the paper presents a formula for the reclassification of multidimensional data points \( columns of real numbers , objects , vectors , etc \) this formula describes the change in the total squared error caused by reclassification of data points from one cluster into another and prompts the way to calculate the sequence of optimal partitions , which are characterized by a minimum value of the total squared error e \( weighted sum of within class variance , within cluster sum of squares wcss etc \) , i e the sum of squared distances from each data point to its cluster center at that source data points are treated with repetitions allowed , and resulting clusters from different partitions , in general case , overlap each other the final partitions are characterized by equilibrium stability with respect to the reclassification of the data points , where the term stability means that any prescribed reclassification of data points does not increase the total squared error e it is important that conventional k means method , in general case , provides generation of instable partitions with overstated values of the total squared error e the proposed method , based on the formula of reclassification , is more efficient than k means method owing to converting of any partition into stable one , as well as involving into the process of reclassification of certain sets of data points , in contrast to the classification of individual data points according to k means method"
cs.DS,"a superpermutation on n symbols is a string that contains each of the n ! permutations of the n symbols as a contiguous substring the shortest superpermutation on n symbols was conjectured to have length sum i 1 n i ! the conjecture had been verified for n leq 5 we disprove it by exhibiting an explicit counterexample for n 6 this counterexample was found by encoding the problem as an instance of the \( asymmetric \) traveling salesman problem , and searching for a solution using a powerful heuristic solver"
cs.AI|cs.LG|stat.ML,we develop a technique for generalising from data in which models are samplers represented as program text we establish encouraging empirical results that suggest that markov chain monte carlo probabilistic programming inference techniques coupled with higher order probabilistic programming languages are now sufficiently powerful to enable successful inference of this kind in nontrivial domains we also introduce a new notion of probabilistic program compilation and show how the same machinery might be used in the future to compile probabilistic programs for efficient reusable predictive inference
cs.IT,"we study the classical problem of noisy constrained capacity in the case of the binary symmetric channel \( bsc \) , namely , the capacity of a bsc whose inputs are sequences chosen from a constrained set motivated by a result of ordentlich and weissman in proceedings of ieee information theory workshop \( 2004 \) 117 122 , we derive an asymptotic formula \( when the noise parameter is small \) for the entropy rate of a hidden markov chain , observed when a markov chain passes through a bsc using this result , we establish an asymptotic formula for the capacity of a bsc with input process supported on an irreducible finite type constraint , as the noise parameter tends to zero"
cs.LG,"extracting activation patterns from functional magnetic resonance images \( fmri \) datasets remains challenging in rapid event designs due to the inherent delay of blood oxygen level dependent \( bold \) signal the general linear model \( glm \) allows to estimate the activation from a design matrix and a fixed hemodynamic response function \( hrf \) however , the hrf is known to vary substantially between subjects and brain regions in this paper , we propose a model for jointly estimating the hemodynamic response function \( hrf \) and the activation patterns via a low rank representation of task effects this model is based on the linearity assumption behind the glm and can be computed using standard gradient based solvers we use the activation patterns computed by our model as input data for encoding and decoding studies and report performance improvement in both settings"
cs.IT,"we consider the problem of robustly recovering a k sparse coefficient vector from the fourier series that it generates , restricted to the interval omega , omega the difficulty of this problem is linked to the superresolution factor srf , equal to the ratio of the rayleigh length \( inverse of omega \) by the spacing of the grid supporting the sparse vector in the presence of additive deterministic noise of norm sigma , we show upper and lower bounds on the minimax error rate that both scale like \( srf \) 2k 1 sigma , providing a partial answer to a question posed by donoho in 1992 the scaling arises from comparing the noise level to a restricted isometry constant at sparsity 2k , or equivalently from comparing 2k to the so called sigma spark of the fourier system the proof involves new bounds on the singular values of restricted fourier matrices , obtained in part from old techniques in complex analysis"
cs.SI,"identifying hierarchies and rankings of nodes in directed graphs is fundamental in many applications such as social network analysis , biology , economics , and finance a recently proposed method identifies the hierarchy by finding the ordered partition of nodes which minimizes a score function , termed agony this function penalizes the links violating the hierarchy in a way depending on the strength of the violation to investigate the detectability of ranking hierarchies we introduce an ensemble of random graphs , the hierarchical stochastic block model we find that agony may fail to identify hierarchies when the structure is not strong enough and the size of the classes is small with respect to the whole network we analytically characterize the detectability threshold and we show that an iterated version of agony can partly overcome this resolution limit"
cs.DS,"motivated by the observation that fifo based push relabel algorithms are able to outperform highest label based variants on modern , large maximum flow problem instances , we introduce an efficient implementation of the algorithm that uses coarse grained parallelism to avoid the problems of existing parallel approaches we demonstrate good relative and absolute speedups of our algorithm on a set of large graph instances taken from real world applications on a modern 40 core machine , our parallel implementation outperforms existing sequential implementations by up to a factor of 12 and other parallel implementations by factors of up to 3"
cs.LG,"it has been proven that transfer learning provides an easy way to achieve state of the art accuracies on several vision tasks by training a simple classifier on top of features obtained from pre trained neural networks the goal of this work is to generate better features for transfer learning from multiple publicly available pre trained neural networks to this end , we propose a novel architecture called stacked neural networks which lever ages the fast training time of transfer learning while simul taneously being much more accurate we show that us ing a stacked nn architecture can result in up to 8 im provements in accuracy over state of the art techniques us ing only one pre trained network for transfer learning a second aim of this work is to make network fine tuning retain the generalizability of the base network to un seen tasks to this end , we propose a new technique called joint fine tuning that is able to give accuracies compa rable to finetuning the same network individually over two datasets we also show that a jointly finetuned network gen eralizes better to unseen tasks when compared to a network finetuned over a single task"
cs.LG,"training large scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions this paper studies the impact of multitask and transfer learning for simple question answering a setting for which the reasoning required to answer is quite easy , as long as one can retrieve the correct evidence given a question , which can be difficult in large scale conditions to this end , we introduce a new dataset of 100k questions that we use in conjunction with existing benchmarks we conduct our study within the framework of memory networks \( weston et al , 2015 \) because this perspective allows us to eventually scale up to more complex reasoning , and show that memory networks can be successfully trained to achieve excellent performance"
cs.IT,"intra session network coding has been shown to offer significant gains in terms of achievable throughput and delay in settings where one source multicasts data to several clients in this paper , we consider a more general scenario where multiple sources transmit data to sets of clients and study the benefits of inter session network coding , when network nodes have the opportunity to combine packets from different sources in particular , we propose a novel framework for optimal rate allocation in inter session network coding systems we formulate the problem as the minimization of the average decoding delay in the client population and solve it with a gradient based stochastic algorithm our optimized inter session network coding solution is evaluated in different network topologies and compared with basic intra session network coding solutions our results show the benefits of proper coding decisions and effective rate allocation for lowering the decoding delay when the network is used by concurrent multicast sessions"
cs.DM|cs.DS,"covers being one of the most popular form of regularities in strings , have drawn much attention over time in this paper , we focus on the problem of linear time inference of strings from cover arrays using the least sized alphabet possible we present an algorithm that can reconstruct a string x over a two letter alphabet whenever a valid cover array c is given as an input this algorithm uses several interesting combinatorial properties of cover arrays and an interesting relation between border array and cover array to achieve this our algorithm runs in linear time"
cs.SI,"axelrod 's model for the dissemination of culture contains two key factors required to model the process of diffusion of innovations , namely , social influence \( i e , individuals become more similar when they interact \) and homophily \( i e , individuals interact preferentially with similar others \) the strength of these social influences are controlled by two parameters f , the number of features that characterizes the cultures and q , the common number of states each feature can assume here we assume that the innovation is a new state of a cultural feature of a single individual the innovator and study how the innovation spreads through the networks among the individuals for infinite regular lattices in one and two dimensions , we find that initially the innovation spreads linearly with the time t and diffusively in the long time limit , provided its introduction in the community is successful for finite lattices , the growth curves for the number of adopters are typically concave functions of t for random graphs with a finite number of nodes n , we argue that the classical s shaped growth curves result from a trade off between the average connectivity k of the graph and the per feature diversity q a large q is needed to reduce the pace of the initial spreading of the innovation and thus delimit the early adopters stage , whereas a large k is necessary to ensure the onset of the take off stage at which the number of adopters grows superlinearly with t in an infinite random graph we find that the number of adopters of a successful innovation scales with t gamma with gamma 1 for k 2 and 1 2 gamma 1 for k 2 we suggest that the exponent gamma may be a useful index to characterize the process of diffusion of successful innovations in diverse scenarios"
cs.LG,in this technical report we analyse the performance of diffusion strategies applied to the least mean square adaptive filter we configure a network of cooperative agents running adaptive filters and discuss their behaviour when compared with a non cooperative agent which represents the average of the network the analysis provides conditions under which diversity in the filter parameters is beneficial in terms of convergence and stability simulations drive and support the analysis
cs.AI,"agents and agent systems are becoming more and more important in the development of a variety of fields such as ubiquitous computing , ambient intelligence , autonomous computing , intelligent systems and intelligent robotics the need for improvement of our basic knowledge on agents is very essential we take a systematic approach and present extended classification of artificial agents which can be useful for understanding of what artificial agents are and what they can be in the future the aim of this classification is to give us insights in what kind of agents can be created and what type of problems demand a specific kind of agents for their solution"
cs.DS,"biobjective mixed integer linear programs \( bomilp \) are optimization problems where two linear objectives are optimized over a polyhedron while restricting some of the variables to be integer since many of the techniques for solving bomilp \( or approximating its solution set \) are iterative processes which utilize data discovered during early iterations to aid in the discovery of improved data during later iterations , it is highly desirable to efficiently store the nondominated subset of a given set of data this problem has not received considerable attention in the context of bomilp only naive methods have been implemented we seek to bridge this gap by presenting a new data structure in the form of a modified binary tree that stores , updates , searches and returns nondominated solutions this structure takes points and line segments in mathbb r 2 as input and stores the nondominated subset of this input we note that when used alongside an exact solution procedure , such as branch and bound \( bb \) , at termination the data stored by this structure is precisely the set of pareto optimal solutions we perform two experiments the first is designed to compare the utility of our structure for storing nondominated data to that of a dynamic list which updates via pairwise comparison in the second we use our data structure alongside the biobjective bb techniques available in the literature and solve specific instances of bomilp the results of our first experiment suggest that the data structure performs reasonably well in handling input of up to 10 7 points or segments and does so much more efficiently than a dynamic list the results of the second experiment show that when our structure is utilized alongside bb fathoming is enhanced and running times improve slightly"
cs.IT,"the many to one interference channel has received interest by virtue of embodying the essence of an interference network while being more tractable than the general k user interference channel in this paper , we introduce information theoretic secrecy to this model and consider the many to one interference channel with confidential messages , in which each receiver , in particular , the one subject to interference , is also one from which the interfering users' messages need to be kept secret from we derive the achievable secrecy sum rate for this channel using nested lattice codes , as well as an upper bound on the secrecy sum rate for all possible channel gain configurations we identify several nontrivial cases where the gap between the upper bound and the achieved secrecy sum rate is only a function of the number of the users k , and is uniform over all possible channel gain configurations in each case in addition , we identify the secure degree of freedom for this channel and show it to be equivalent to its degree of freedom , i e , the secrecy in high snr comes for free"
cs.IT,pseudorandom psk 1 enables parallel communication on the same carrier frequency and at the same time we propose different signal processing methods to receive data modulated with pseudorandom psk this includes correlation with the carrier frequency which can be applied to signals in the khz to mhz range and signal processing in the intermediate frequency where the correlation with the carrier frequency is performed analogous in the rf front end we analyze the computation complexity for signal processing with the parameters of symbol length t and number of repetitions of each symbol k with pseudorandom psk and show that the number of operations for each sampling point is theta left \( k right \)
cs.IT,"we consider the group testing problem , in the case where the items are defective independently but with non constant probability we introduce and analyse an algorithm to solve this problem by grouping items together appropriately we give conditions under which the algorithm performs essentially optimally in the sense of information theoretic capacity we use concentration of measure results to bound the probability that this algorithm requires many more tests than the expected number this has applications to the allocation of spectrum to cognitive radios , in the case where a database gives prior information that a particular band will be occupied"
cs.SI,"nature , technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems \( e g , proteins , computers , people \) consequently , one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units \( nodes \) together with their relations \( edges \) most complex systems have an inherently hierarchical organization and , correspondingly , the networks behind them also exhibit hierarchical features indeed , several papers have been devoted to describing this essential aspect of networks , however , without resulting in a widely accepted , converging concept concerning the quantitative characterization of the level of their hierarchy here we develop an approach and propose a quantity \( measure \) which is simple enough to be widely applicable , reveals a number of universal features of the organization of real world networks and , as we demonstrate , is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network the measure we introduce is based on a generalization of the m reach centrality , which we first extend to directed partially directed graphs then , we define the global reaching centrality \( grc \) , which is the difference between the maximum and the average value of the generalized reach centralities over the network we investigate the behavior of the grc considering both a synthetic model with an adjustable level of hierarchy and real networks results for real networks show that our hierarchy measure is related to the controllability of the given system we also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure"
cs.DS,"the price of robustness by bertsimas and sim represented a breakthrough in the development of a tractable robust counterpart of linear programming problems however , the central modeling assumption that the deviation band of each uncertain parameter is single may be too limitative in practice experience indeed suggests that the deviations distribute also internally to the single band , so that getting a higher resolution by partitioning the band into multiple sub bands seems advisable the critical aim of our work is to close the knowledge gap about the adoption of a multi band uncertainty set in robust optimization a general definition and intensive theoretical study of a multi band model are actually still missing our new developments have been also strongly inspired and encouraged by our industrial partners , which have been interested in getting a better modeling of arbitrary distributions , built on historical data of the uncertainty affecting the considered real world problems in this paper , we study the robust counterpart of a linear programming problem with uncertain coefficient matrix , when a multi band uncertainty set is considered we first show that the robust counterpart corresponds to a compact lp formulation then we investigate the problem of separating cuts imposing robustness and we show that the separation can be efficiently operated by solving a min cost flow problem finally , we test the performance of our new approach to robust optimization on realistic instances of a wireless network design problem subject to uncertainty"
cs.IT,"theoretical analysis has long indicated that feedback improves the error exponent but not the capacity of single user memoryless channels recently polyanskiy et al studied the benefit of variable length feedback with termination \( vlft \) codes in the non asymptotic regime in that work , achievability is based on an infinite length random code and decoding is attempted at every symbol the coding rate backoff from capacity due to channel dispersion is greatly reduced with feedback , allowing capacity to be approached with surprisingly small expected latency this paper is mainly concerned with vlft codes based on finite length codes and decoding attempts only at certain specified decoding times the penalties of using a finite block length n and a sequence of specified decoding times are studied this paper shows that properly scaling n with the expected latency can achieve the same performance up to constant terms as with n infty the penalty introduced by periodic decoding times is a linear term of the interval between decoding times and hence the performance approaches capacity as the expected latency grows if the interval between decoding times grows sub linearly with the expected latency"
cs.DM|cs.DS|cs.SI,"in this note we introduce a new randomized algorithm for counting triangles in graphs we show that under mild conditions , the estimate of our algorithm is strongly concentrated around the true number of triangles specifically , if p geq max \( frac delta log n t , frac log n sqrt t \) , where n , t , delta denote the number of vertices in g , the number of triangles in g , the maximum number of triangles an edge of g is contained , then for any constant epsilon 0 our unbiased estimate t is concentrated around its expectation , i e , prob t mean t geq epsilon mean t o \( 1 \) finally , we present a textsc mapreduce implementation of our algorithm"
cs.IT,"necessary conditions for asymptotically optimal sliding block or stationary codes for source coding and rate constrained simulation of memoryless sources are presented and used to motivate a design technique for trellis encoded source coding and rate constrained simulation the code structure has intuitive similarities to classic random coding arguments as well as to ``fake process'' methods and alphabet constrained methods experimental evidence shows that the approach provides comparable or superior performance in comparison with previously published methods on common examples , sometimes by significant margins"
cs.IT,"time reversal , that is prefiltering of transmitted signals with time reversed channel impulse responses , may be used in single user communications in order to move complexity from the receiver to the transmitter , and in multiuser communications to also modify statistical properties of multiuser interference imperfect channel estimation may , however , affect pre vs post filtering schemes in a different way this issue is the object of this paper robustness of time reversal vs all rake \( ar \) transceivers , in multiple access communications , with respect to channel estimation errors , is investigated results of performance analyses in terms of symbol error probability and spectral efficiency when the receiver is structured either by a bank of matched filters or by 1rake , followed by independent decoders , indicate that ar is slightly more robust than time reversal but requires in practice more complex channel estimation procedures since all channels must be simultaneously inferred in the multiuser communication setting"
cs.IT,"this is the second part of a two part paper that studies the problem of jamming in a fixed rate transmission system with fading in the first part , we studied the scenario with a fast fading channel , and found nash equilibria of mixed strategies for short term power constraints , and for average power constraints with and without channel state information \( csi \) feedback we also solved the equally important maximin and minimax problems with pure strategies whenever we dealt with average power constraints , we decomposed the problem into two levels of power control , which we solved individually in this second part of the paper , we study the scenario with a parallel , slow fading channel , which usually models multi carrier transmissions , such as ofdm although the framework is similar as the one in part i cite myself3 , dealing with the slow fading requires more intricate techniques unlike in the fast fading scenario , where the frames supporting the transmission of the codewords were equivalent and completely characterized by the channel statistics , in our present scenario the frames are unique , and characterized by a specific set of channel realizations this leads to more involved inter frame power allocation strategies , and in some cases even to the need for a third level of power control we also show that for parallel slow fading channels , the csi feedback helps in the battle against jamming , as evidenced by the significant degradation to system performance when csi is not sent back we expect this degradation to decrease as the number of parallel channels m increases , until it becomes marginal for m to infty \( which can be considered as the case in part i \)"
cs.IT,"a classification of quaternary hermitian self dual codes of length 20 is given using this classification , a classification of extremal quaternary hermitian self dual codes of length 22 is also given"
cs.LG|stat.ML,we propose a new model for rank aggregation from pairwise comparisons that captures both ranking heterogeneity across users and ranking inconsistency for each user we establish a formal statistical equivalence between the new model and topic models we leverage recent advances in the topic modeling literature to develop an algorithm that can learn shared latent rankings with provable statistical and computational efficiency guarantees the method is also shown to empirically outperform competing approaches on some semi synthetic and real world datasets
cs.AI,"to study the preference of infants for contingency of movements and familiarity of faces during self recognition task , we built , as an accurate and instantaneous imitator , a real time face swapper for videos we present a non constraint face swapper based on 3d visual tracking that achieves real time performance through parallel computing our imitator system is par ticularly suited for experiments involving children with autistic spectrum disorder who are often strongly disturbed by the constraints of other methods"
cs.LG|stat.ML,"we introduce propagation kernels , a general graph kernel framework for efficiently measuring the similarity of structured data propagation kernels are based on monitoring how information spreads through a set of given graphs they leverage early stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels , attributes , and edge information this has two benefits first , off the shelf propagation schemes can be used to naturally construct kernels for many graph types , including labeled , partially labeled , unlabeled , directed , and attributed graphs second , by leveraging existing efficient and informative propagation schemes , propagation kernels can be considerably faster than state of the art approaches without sacrificing predictive performance we will also show that if the graphs at hand have a regular structure , for instance when modeling image or video data , one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes we support our contributions by exhaustive experiments on a number of real world graphs from a variety of application domains"
cs.SI,"online communities are becoming increasingly important as platforms for large scale human cooperation these communities allow users seeking and sharing professional skills to solve problems collaboratively to investigate how users cooperate to complete a large number of knowledge producing tasks , we analyze stackexchange , one of the largest question and answer systems in the world we construct attention networks to model the growth of 110 communities in the stackexchange system and quantify individual answering strategies using the linking dynamics of attention networks we identify two types of users taking different strategies one strategy \( type a \) aims at performing maintenance by doing simple tasks , while the other strategy \( type b \) aims investing time in doing challenging tasks we find that the number of type a needs to be twice as big as type b users for a sustainable growth of communities"
cs.AI,"local consistency techniques such as k consistency are a key component of specialised solvers for constraint satisfaction problems in this paper we show that the power of using k consistency techniques on a constraint satisfaction problem is precisely captured by using a particular inference rule , which we call negative hyper resolution , on the standard direct encoding of the problem into boolean clauses we also show that current clause learning sat solvers will discover in expected polynomial time any inconsistency that can be deduced from a given set of clauses using negative hyper resolvents of a fixed size we combine these two results to show that , without being explicitly designed to do so , current clause learning sat solvers efficiently simulate k consistency techniques , for all fixed values of k we then give some experimental results to show that this feature allows clause learning sat solvers to efficiently solve certain families of constraint problems which are challenging for conventional constraint programming solvers"
cs.IT,"finite resolution digital receiver is recently considered as a potential way to ultra wide band \( uwb \) communication systems due to its ability of mitigating the challenge of analog digital converter \( adc \) in this paper , the effects of narrowband interference \( nbi \) are investigated when finite resolution digital receiver is used for impulse radio uwb \( ir uwb \) system it is shown that finite resolution receiver enlarges the impact of nbi the lower resolution of the uwb receiver is , the more degradations nbi causes"
cs.IT,"we propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects \( i \) we extend the formulation from real non negative amplitude parameters to arbitrary complex ones , and \( ii \) we allow for mismatch between the manifold described by the parameters and its polar approximation to quantify the improvements afforded by the proposed extensions , we evaluate six algorithms for estimation of parameters in sparse translation invariant signals , exemplified with the time delay estimation problem the evaluation is based on three performance metrics estimator precision , sampling rate and computational complexity we use compressive sensing with all the algorithms to lower the necessary sampling rate and show that it is still possible to attain good estimation precision and keep the computational complexity low our numerical experiments show that the proposed algorithms outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to a frequency estimation problem followed by a super resolution algorithm the algorithms studied here provide various tradeoffs between computational complexity , estimation precision , and necessary sampling rate the work shows that compressive sensing for the class of sparse translation invariant signals allows for a decrease in sampling rate and that the use of polar interpolation increases the estimation precision"
cs.IT,"this paper considers the transmission of confidential messages over noisy wireless ad hoc networks , where both background noise and interference from concurrent transmitters affect the received signals for the random networks where the legitimate nodes and the eavesdroppers are distributed as poisson point processes , we study the secrecy transmission capacity \( stc \) , as well as the connection outage probability and secrecy outage probability , based on the physical layer security we first consider the basic fixed transmission distance model , and establish a theoretical model of the stc we then extend the above results to a more realistic random distance transmission model , namely nearest receiver transmission finally , extensive simulation and numerical results are provided to validate the efficiency of our theoretical results and illustrate how the stc is affected by noise , connection and secrecy outage probabilities , transmitter and eavesdropper densities , and other system parameters remarkably , our results reveal that a proper amount of noise is helpful to the secrecy transmission capacity"
cs.SI,"networks such as organizational network of a global company play an important role in a variety of knowledge management and information diffusion tasks the nodes in these networks correspond to individuals who are self interested the topology of these networks often plays a crucial role in deciding the ease and speed with which certain tasks can be accomplished using these networks consequently , growing a stable network having a certain topology is of interest motivated by this , we study the following important problem given a certain desired network topology , under what conditions would best response \( link addition deletion \) strategies played by self interested agents lead to formation of a pairwise stable network with only that topology we study this interesting reverse engineering problem by proposing a natural model of recursive network formation in this model , nodes enter the network sequentially and the utility of a node captures principal determinants of network formation , namely \( 1 \) benefits from immediate neighbors , \( 2 \) costs of maintaining links with immediate neighbors , \( 3 \) benefits from indirect neighbors , \( 4 \) bridging benefits , and \( 5 \) network entry fee based on this model , we analyze relevant network topologies such as star graph , complete graph , bipartite turan graph , and multiple stars with interconnected centers , and derive a set of sufficient conditions under which these topologies emerge as pairwise stable networks we also study the social welfare properties of the above topologies"
cs.SI|stat.ML,"state of the art link prediction utilizes combinations of complex features derived from network panel data we here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions our features are based on social vector clocks , an adaptation of the vector clock concept introduced in distributed computing to social interaction networks in fact , our experiments suggest that by taking into account the order and spacing of interactions , social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date"
cs.AI,"we analyze and exploit some scaling properties of the affinity propagation \( ap \) clustering algorithm proposed by frey and dueck \( 2007 \) first we observe that a divide and conquer strategy , used on a large data set hierarchically reduces the complexity cal o \( n 2 \) to cal o \( n \( h 2 \) \( h 1 \) \) , for a data set of size n and a depth h of the hierarchical strategy for a data set embedded in a d dimensional space , we show that this is obtained without notably damaging the precision except in dimension d 2 in fact , for d larger than 2 the relative loss in precision scales like n \( 2 d \) \( h 1 \) d finally , under some conditions we observe that there is a value s of the penalty coefficient , a free parameter used to fix the number of clusters , which separates a fragmentation phase \( for s s \) from a coalescent one \( for s s \) of the underlying hidden cluster structure at this precise point holds a self similarity property which can be exploited by the hierarchical strategy to actually locate its position from this observation , a strategy based on ap can be defined to find out how many clusters are present in a given dataset"
cs.IT,"let f subset of x x y x z be a relation let the public coin one way communication complexity of f , with worst case error 1 3 , be denoted r 1 , pub 1 3 \( f \) we show that if for computing f k \( k independent copies of f \) , o \( k r 1 , pub 1 3 \( f \) \) communication is provided , then the success is exponentially small in k this settles the strong direct product conjecture for all relations in public coin one way communication complexity we show a new tight characterization of public coin one way communication complexity which strengthens on the tight characterization shown in j , klauck , nayak 08 we use the new characterization to show our direct product result and this may also be of independent interest"
cs.LG,"ml4pg is a machine learning extension that provides statistical proof hints during the process of coq ssreflect proof development in this paper , we use ml4pg to find proof patterns in the coqeal library a library that was devised to verify the correctness of computer algebra algorithms in particular , we use ml4pg to help us in the formalisation of an efficient algorithm to compute the inverse of triangular matrices"
cs.IT,"in this paper we investigate algorithmic randomness on more general spaces than the cantor space , namely computable metric spaces to do this , we first develop a unified framework allowing computations with probability measures we show that any computable metric space with a computable probability measure is isomorphic to the cantor space in a computable and measure theoretic sense we show that any computable metric space admits a universal uniform randomness test \( without further assumption \)"
cs.LG|stat.ML,"latent variable models for network data extract a summary of the relational structure underlying an observed network the simplest possible models subdivide nodes of the network into clusters the probability of a link between any two nodes then depends only on their cluster assignment currently available models can be classified by whether clusters are disjoint or are allowed to overlap these models can explain a flat clustering structure hierarchical bayesian models provide a natural approach to capture more complex dependencies we propose a model in which objects are characterised by a latent feature vector each feature is itself partitioned into disjoint groups \( subclusters \) , corresponding to a second layer of hierarchy in experimental comparisons , the model achieves significantly improved predictive performance on social and biological link prediction tasks the results indicate that models with a single layer hierarchy over simplify real networks"
cs.AI|cs.LG,"characterizing human values is a topic deeply interwoven with the sciences , humanities , art , and many other human endeavors in recent years , a number of thinkers have argued that accelerating trends in computer science , cognitive science , and related disciplines foreshadow the creation of intelligent machines which meet and ultimately surpass the cognitive abilities of human beings , thereby entangling an understanding of human values with future technological development contemporary research accomplishments suggest sophisticated ai systems becoming widespread and responsible for managing many aspects of the modern world , from preemptively planning users' travel schedules and logistics , to fully autonomous vehicles , to domestic robots assisting in daily living the extrapolation of these trends has been most forcefully described in the context of a hypothetical intelligence explosion , in which the capabilities of an intelligent software agent would rapidly increase due to the presence of feedback loops unavailable to biological organisms the possibility of superintelligent agents , or simply the widespread deployment of sophisticated , autonomous ai systems , highlights an important theoretical problem the need to separate the cognitive and rational capacities of an agent from the fundamental goal structure , or value system , which constrains and guides the agent 's actions the value alignment problem is to specify a goal structure for autonomous agents compatible with human values in this brief article , we suggest that recent ideas from affective neuroscience and related disciplines aimed at characterizing neurological and behavioral universals in the mammalian kingdom provide important conceptual foundations relevant to describing human values we argue that the notion of mammalian value systems points to a potential avenue for fundamental research in ai safety and ai ethics"
cs.LG|stat.ML,we study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment our formalism combines multi arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches we propose a new algorithm that exploits the causal feedback and prove a bound on its simple regret that is strictly better \( in all quantities \) than algorithms that do not use the additional causal information
cs.IT,"raptor codes are the first class of fountain codes with linear time encoding and decoding these codes are recommended in standards such as third generation partnership project \( 3gpp \) and digital video broadcasting raptorq codes are an extension to raptor codes , having better coding efficiency and flexibility standard raptor and raptorq codes are systematic with equal error protection of the data however , in many applications such as mpeg transmission , there is a need for unequal error protection \( uep \) namely , some data symbols require higher error correction capabilities compared to others we propose an approach that we call priority based precode ratio \( pbpr \) to achieve uep for systematic raptorq and raptor codes our uep assumes that all symbols in a source block belong to the same importance class the uep is achieved by changing the number of precode symbols depending on the priority of the information symbols in the source block pbpr provides uep with the same number of decoding overhead symbols for source blocks with different importance classes we demonstrate consistent improvements in the error correction capability of higher importance class compared to the lower importance class across the entire range of channel erasure probabilities we also show that pbpr does not result in a significant increase in decoding and encoding times compared to the standard implementation"
cs.IT,"in this paper we investigate the problem of allocating spectrum among radio nodes under sinr requirements this problem is of special interest in dynamic spectrum access networks where topology and spectral resources differ with time and location the problem is to determine the number of radio nodes that can transmit simultaneously while still achieving their sinr requirements and then decide which channels these nodes should transmit on previous work have shown how this can be done for a large spectrum pool where nodes allocate multiple channels from that pool which renders a linear programming approach feasible when the pool is large enough in this paper we extend their work by considering arbitrary individual pool sizes and allow nodes to only transmit on one channel due to the accumulative nature of interference this problem is a non convex integer problem which is np hard however , we introduce a constraint transformation that transforms the problem to a binary quadratic constraint problem although this problem is still np hard , well known heuristic algorithms for solving this problem are known in the literature we implement a heuristic algorithm based on lagrange relaxation which bounds the solution value of the heuristic to the optimal value of the constraint transformed problem simulation results show that this approach provides solutions within an average gap of 10 of solutions obtained by a genetic algorithm for the original non convex integer problem"
cs.IT,"low density parity check \( ldpc \) codes are one of the most promising families of codes to replace the goppa codes originally used in the mceliece cryptosystem in fact , it has been shown that by using quasi cyclic low density parity check \( qc ldpc \) codes in this system , drastic reductions in the public key size can be achieved , while maintaining fixed security levels recently , some proposals have appeared in the literature using codes with denser parity check matrices , named moderate density parity check \( mdpc \) codes however , the density of the parity check matrices to be used in qc ldpc code based variants of the mceliece cryptosystem has never been optimized this paper aims at filling such gap , by proposing a procedure for selecting the density of the private parity check matrix , based on the security level and the decryption complexity we provide some examples of the system parameters obtained through the proposed technique"
cs.IT,"network tomography aims at inferring internal network characteristics based on measurements at the edge of the network in loss tomography , in particular , the characteristic of interest is the loss rate of individual links and multicast and or unicast end to end probes are typically used independently , recent advances in network coding have shown that there are advantages from allowing intermediate nodes to process and combine , in addition to just forward , packets in this paper , we study the problem of loss tomography in networks with network coding capabilities we design a framework for estimating link loss rates , which leverages network coding capabilities , and we show that it improves several aspects of tomography including the identifiability of links , the trade off between estimation accuracy and bandwidth efficiency , and the complexity of probe path selection we discuss the cases of inferring link loss rates in a tree topology and in a general topology in the latter case , the benefits of our approach are even more pronounced compared to standard techniques , but we also face novel challenges , such as dealing with cycles and multiple paths between sources and receivers overall , this work makes the connection between active network tomography and network coding"
cs.DM,we present a new proof of stembridge 's theorem about the enumeration of totally symmetric plane partitions using the methodology suggested in the recent koutschan kauers zeilberger semi rigorous proof of the andrews robbins q tspp conjecture our proof makes heavy use of computer algebra and is completely automatic we describe new methods that make the computations feasible in the first place the tantalizing aspect of this work is that the same methods can be applied to prove the q tspp conjecture \( that is a q analogue of stembridge 's theorem and open for more than 25 years \) the only hurdle here is still the computational complexity
cs.AI|cs.LG,"many learning tasks combine perceptual and cognitive \( e g logical , symbolic \) sub tasks in some cases end to end learning of the perceptual and cognitive sub tasks can be more difficult than learning each sub task individually in other cases such end to end learning can be very efficient as end to end learning is becoming more prevalent in ai , it is important to understand when this approach can work efficiently a toy example is presented end to end visual learning of arithmetic operations from pictures of numbers the perceptual and cognitive sub tasks are ocr and arithmetic respectively the input consists of two pictures , each showing a 7 digit number the output is the picture of the result of the arithmetic operation \( e g addition \) on the two input numbers the concept of a number , or of an operator , is not explicitly introduced learning is carried out by a deep neural network in our experiments , a simple network was successful at visual end to end learning of addition and subtraction other operations , e g multiplication , were not learnable by our network , even when each separate sub task was easily learnable"
cs.IT,"the present work investigates two properties of level crossings of a stationary gaussian process x \( t \) with autocorrelation function r x \( tau \) we show firstly that if r x \( tau \) admits finite second and fourth derivatives at the origin , the length of up excursions above a large negative level gamma is asymptotically exponential as gamma to infty secondly , assuming that r x \( tau \) admits a finite second derivative at the origin and some defined properties , we derive the mean number of crossings as well as the length of successive excursions above two subsequent large levels the asymptotic results are shown to be effective even for moderate values of crossing level an application of the developed results is proposed to derive the probability of successive excursions above adjacent levels during a time window"
cs.IT,"in the wiretap setting it is assumed that the same message is transmitted over two different channels , a channel to an intended legitimate receiver bob and a different channel to an eavesdropper eve three contradicting objectives are simultaneously tried to be achieved a high information rate between the sender and bob , high reliability at the legitimate receiver , and minimal mutual information between the message and the output at the eavesdropper for many channel models asymptotically optimal coding schemes are known , but less effort has been put into actual realizations of wiretap codes for practical parameters bounds on the mutual information and error probability when using coset coding on a rayleigh fading channel were recently established by oggier and belfiore , and the results in this paper build on their work however , instead of using their ultimate inverse norm sum approximation , a more precise expression for the eavesdropper 's probability of correct decision is used in order to determine a general class of good coset codes the code constructions are based on well rounded lattices arising from simple geometric criteria in addition to new coset codes and simulation results , novel number theoretic results on well rounded ideal lattices are presented"
cs.DM|cs.DS,"the rainbow k coloring problem asks whether the edges of a given graph can be colored in k colors so that every pair of vertices is connected by a rainbow path , i e , a path with all edges of different colors our main result states that for any k ge 2 , there is no algorithm for rainbow k coloring running in time 2 o \( n 3 2 \) , unless eth fails motivated by this negative result we consider two parameterized variants of the problem in subset rainbow k coloring problem , introduced by chakraborty et al stacs 2009 , j comb opt 2009 , we are additionally given a set s of pairs of vertices and we ask if there is a coloring in which all the pairs in s are connected by rainbow paths we show that subset rainbow k coloring is fpt when parameterized by s we also study maximum rainbow k coloring problem , where we are additionally given an integer q and we ask if there is a coloring in which at least q anti edges are connected by rainbow paths we show that the problem is fpt when parameterized by q and has a kernel of size o \( q \) for every k ge 2 \( thus proving that the problem is fpt \) , extending the result of ananth et al fsttcs 2011"
cs.LG|stat.ML,"distributed learning is the problem of inferring a function in the case where training data is distributed among multiple geographically separated sources particularly , the focus is on designing learning strategies with low computational requirements , in which communication is restricted only to neighboring agents , with no reliance on a centralized authority in this thesis , we analyze multiple distributed protocols for a large number of neural network architectures the first part of the thesis is devoted to a definition of the problem , followed by an extensive overview of the state of the art next , we introduce different strategies for a relatively simple class of single layer neural networks , where a linear output layer is preceded by a nonlinear layer , whose weights are stochastically assigned in the beginning of the learning process we consider both batch and sequential learning , with horizontally and vertically partitioned data in the third part , we consider instead the more complex problem of semi supervised distributed learning , where each agent is provided with an additional set of unlabeled training samples we propose two different algorithms based on diffusion processes for linear support vector machines and kernel ridge regression subsequently , the fourth part extends the discussion to learning with time varying data \( e g time series \) using recurrent neural networks we consider two different families of networks , namely echo state networks \( extending the algorithms introduced in the second part \) , and spline adaptive filters overall , the algorithms presented throughout the thesis cover a wide range of possible practical applications , and lead the way to numerous future extensions , which are briefly summarized in the conclusive chapter"
cs.IT,"in analogy with the singleton defect for classical codes , we propose a definition of rank defect for delsarte rank metric codes we characterize codes whose rank defect and dual rank defect are both zero , and prove that the rank distribution of such codes is determined by their parameters this extends a result by delsarte on the rank distribution of mrd codes in the general case of codes of positive defect , we show that the rank distribution is determined by the parameters of the code , together the number of codewords of small rank moreover , we prove that if the rank defect of a code and its dual are both one , and the dimension satisfies a divisibility condition , then the number of minimum rank codewords and dual minimum rank codewords is the same finally , we discuss how our results specialize to gabidulin codes"
cs.SI,"we present nearbucket lsh , an effective algorithm for similarity search in large scale distributed online social networks organized as peer to peer overlays as communication is a dominant consideration in distributed systems , we focus on minimizing the network cost while guaranteeing good search quality our algorithm is based on locality sensitive hashing \( lsh \) , which limits the search to collections of objects , called buckets , that have a high probability to be similar to the query more specifically , nearbucket lsh employs an lsh extension that searches in near buckets , and improves search quality but also significantly increases the network cost we decrease the network cost by considering the internals of both lsh and the p2p overlay , and harnessing their properties to our needs we show that our nearbucket lsh increases search quality for a given network cost compared to previous art in many cases , the search quality increases by more than 50"
cs.IT,"the macwilliams identity , which relates the weight distribution of a code to the weight distribution of its dual code , is useful in determining the weight distribution of codes in this paper , we derive the macwilliams identity for linear codes with the rank metric , and our identity has a different form than that by delsarte using our macwilliams identity , we also derive related identities for rank metric codes these identities parallel the binomial and power moment identities derived for codes with the hamming metric"
cs.AI,"humans demonstrate remarkable abilities to predict physical events in complex scenes two classes of models for physical scene understanding have recently been proposed intuitive physics engines , or ipes , which posit that people make predictions by running approximate probabilistic simulations in causal mental models similar in nature to video game physics engines , and memory based models , which make judgments based on analogies to stored experiences of previously encountered scenes and physical outcomes versions of the latter have recently been instantiated in convolutional neural network \( cnn \) architectures here we report four experiments that , to our knowledge , are the first rigorous comparisons of simulation based and cnn based models , where both approaches are concretely instantiated in algorithms that can run on raw image inputs and produce as outputs physical judgments such as whether a stack of blocks will fall both approaches can achieve super human accuracy levels and can quantitatively predict human judgments to a similar degree , but only the simulation based models generalize to novel situations in ways that people do , and are qualitatively consistent with systematic perceptual illusions and judgment asymmetries that people show"
cs.SI,"the structure entropy is an important index to illuminate the structure prop erty of the complex network most of the existing structure entropies are based on the degree distribution of the complex network but the structure entropy based on the degree can not illustrate the structure property of the weighted networks in order to study the structure property of the weighted networks , a new structure entropy of the complex networks based on the betweenness is proposed in this paper comparing with the existing structure entropy , the proposed method is more reasonable to describe the structure property of the complex weighted networks"
cs.IT,"wireless networks can be self sustaining by harvesting energy from radio frequency \( rf \) signals building on classic cognitive radio networks , we propose a novel method for network coexisting where mobiles from a secondary network , called secondary transmitters \( sts \) , either harvest energy from transmissions by nearby transmitters from a primary network , called primary transmitters \( pts \) , or transmit information if pts are sufficiently far away sts store harvested energy in rechargeable batteries with finite capacity and use all available energy for subsequent transmission when batteries are fully charged in this model , each pt is centered at a guard zone and a harvesting zone that are disks with given radiuses a st harvests energy if it lies in some harvesting zone , transmits fixed power signals if it is outside all guard zones or else idles based on this model , the spatial throughput of the secondary network is maximized using a stochastic geometry model where pts and sts are modeled as independent homogeneous poisson point processes \( hppps \) , under the outage constraints for coexisting networks and obtained in a simple closed form it is observed from the result that the maximum secondary throughput decreases linearly with the growing pt density , and the optimal st density is inversely proportional to the derived transmission probability for sts"
cs.IT,"this paper looks into the k user interference channel interference alignment is much likely to be applied with double layered symbol extensions , either for constant channels in the h o st madsen nosratinia conjecture or slowly changing channels in our work , the core idea relies on double layered symbol extensions to artificially construct equivalent time variant channels to provide crucial textit channel randomness or relativity required by conventional cadambe jafar scheme in time variant channels cite ia dof kuser interference"
cs.IT,"imperfect secrecy in communication systems is investigated instead of using equivocation as a measure of secrecy , the distortion that an eavesdropper incurs in producing an estimate of the source sequence is examined the communication system consists of a source and a broadcast \( wiretap \) channel , and lossless reproduction of the source sequence at the legitimate receiver is required a key aspect of this model is that the eavesdropper 's actions are allowed to depend on the past behavior of the system achievability results are obtained by studying the performance of source and channel coding operations separately , and then linking them together digitally although the problem addressed here has been solved when the secrecy resource is shared secret key , it is found that substituting secret key for a wiretap channel brings new insights and challenges the notion of weak secrecy provides just as much distortion at the eavesdropper as strong secrecy , and revealing public messages freely is detrimental"
cs.DS,"this paper analyzes the problem of assigning weights to edges incrementally in a dynamic complete bipartite graph consisting of producer and consumer nodes the objective is to minimize the overall cost while satisfying certain constraints the cost and constraints are functions of attributes of the edges , nodes and online service requests novelty of this work is that it models real time distributed resource allocation using an approach to solve this theoretical problem this paper studies variants of this assignment problem where the edges , producers and consumers can disappear and reappear or their attributes can change over time primal dual algorithms are used for solving these problems and their competitive ratios are evaluated"
cs.SI,"we propose an efficient strategy to suppress epidemic explosion in heterogeneous metapopulation networks , wherein each node represents a subpopulation with any number of individuals and is assigned a curing rate that is proportional to k alpha with k the node degree and alpha an adjustable parameter we have performed stochastic simulations of the dynamical reaction diffusion processes associated with the susceptible infected susceptible model in scale free networks we found that the epidemic threshold reaches a maximum when the exponent alpha is tuned to be alpha opt simeq 1 3 this nontrivial phenomenon is robust to the change of the network size and the average degree in addition , we have carried out a mean field analysis to further validate our scheme , which also demonstrates that epidemic explosion follows different routes for alpha larger or less than alpha opt our work suggests that in order to effectively suppress epidemic spreading on heterogeneous complex networks , subpopulations with higher degrees should be allocated more resources than just being linearly dependent on the degree k"
cs.AI|stat.ML,"in this paper , a geometric framework for neural networks is proposed this framework uses the inner product space structure underlying the parameter set to perform gradient descent not in a component based form , but in a coordinate free manner convolutional neural networks are described in this framework in a compact form , with the gradients of standard and higher order loss functions calculated for each layer of the network this approach can be applied to other network structures and provides a basis on which to create new networks"
cs.LG|cs.SI|stat.ML,"we study a stochastic online problem of learning to influence in a social network with semi bandit feedback , individual observations of how influenced users influence others our problem combines challenges of partial monitoring , because the learning agent only observes the influenced portion of the network , and combinatorial bandits , because the cardinality of the feasible set is exponential in the maximum number of influencers we propose a computationally efficient ucb like algorithm for solving our problem , imlinucb , and analyze it on forests our regret bounds are polynomial in all quantities of interest reflect the structure of the network and do not depend on inherently large quantities , such as the reciprocal of the minimum probability of being influenced and the cardinality of the action set to the best of our knowledge , these are the first such results imlinucb permits linear generalization and therefore is suitable for large scale problems we evaluate imlinucb on several synthetic problems and observe that the regret of imlinucb scales as suggested by our upper bounds a special form of our problem can be viewed as a linear bandit and we match the regret bounds of linucb in this case"
cs.IT,"physical unclonable functions evaluate manufacturing variations to generate secure cryptographic keys for embedded systems without secure key storage it is explained how methods from coding theory are applied in order to ensure reliable key reproduction we show how better results can be obtained using code classes and decoding principles not used for this scenario before these methods are exemplified by specific code constructions which improve existing codes with respect to error probability , decoding complexity and codeword length"
cs.IT,"in this paper , we propose a new paradigm of control , called a maximum hands off control a hands off control is defined as a control that has a short support per unit time the maximum hands off control is the minimum support \( or sparsest \) per unit time among all controls that achieve control objectives for finite horizon control , we show the equivalence between the maximum hands off control and l1 optimal control under a uniqueness assumption called normality this result rationalizes the use of l1 optimality in computing a maximum hands off control we also propose an l1 l2 optimal control to obtain a smooth hands off control furthermore , we give a self triggered feedback control algorithm for linear time invariant systems , which achieves a given sparsity rate and practical stability in the case of plant disturbances an example is included to illustrate the effectiveness of the proposed control"
cs.DM|cs.DS|cs.IT|cs.LG,"counting is among the most fundamental operations in computing for example , counting the pth frequency moment has been a very active area of research , in theoretical computer science , databases , and data mining when p 1 , the task \( i e , counting the sum \) can be accomplished using a simple counter compressed counting \( cc \) is proposed for efficiently computing the pth frequency moment of a data stream signal a t , where 0 p 2 cc is applicable if the streaming data follow the turnstile model , with the restriction that at the time t for the evaluation , a t i 0 , which includes the strict turnstile model as a special case for natural data streams encountered in practice , this restriction is minor the underly technique for cc is what we call skewed stable random projections , which captures the intuition that , when p 1 a simple counter suffices , and when p 1 delta with small delta , the sample complexity of a counter system should be low \( continuously as a function of delta \) we show at small delta the sample complexity \( number of projections \) k o \( 1 epsilon \) instead of o \( 1 epsilon 2 \) compressed counting can serve a basic building block for other tasks in statistics and computing , for example , estimation entropies of data streams , parameter estimations using the method of moments and maximum likelihood finally , another contribution is an algorithm for approximating the logarithmic norm , sum i 1 d log a t i , and logarithmic distance the logarithmic distance is useful in machine learning practice with heavy tailed data"
cs.AI|cs.LG|stat.ML,"we study the expressivity of deep neural networks with random weights we provide several results , both theoretical and experimental , precisely characterizing their functional properties in terms of the depth and width of the network in doing so , we illustrate inherent connections between the length of a latent trajectory , local neuron transitions , and network activation patterns the latter , a notion defined in this paper , is further studied using properties of hyperplane arrangements , which also help precisely characterize the effect of the neural network on the input space we further show dualities between changes to the latent state and changes to the network weights , and between the number of achievable activation patterns and the number of achievable labellings over input data we see that the depth of the network affects all of these quantities exponentially , while the width appears at most as a base these results also suggest that the remaining depth of a neural network is an important determinant of expressivity , supported by experiments on mnist and cifar 10"
cs.AI,"this paper presents an artificial evolutionbased method for stereo image analysis and its application to real time obstacle detection and avoidance for a mobile robot it uses the parisian approach , which consists here in splitting the representation of the robot 's environment into a large number of simple primitives , the flies , which are evolved following a biologically inspired scheme and give a fast , low cost solution to the obstacle detection problem in mobile robotics"
cs.IT,this work presents a new variation of the commonly used least mean squares algorithm \( lms \) for the identification of sparse signals with an a priori known sparsity using a hard threshold operator in every iteration it examines some useful properties of the algorithm and compares it with the traditional lms and other sparsity aware variations of the same algorithm it goes on to examine the application of the algorithm in the problem of spectrum estimation for cognitive radio devices
cs.DS,"we consider a team of k identical , oblivious , asynchronous mobile robots that are able to sense \( emph i e , view \) their environment , yet are unable to communicate , and evolve on a constrained path previous results in this weak scenario show that initial symmetry yields high lower bounds when problems are to be solved by emph deterministic robots in this paper , we initiate research on probabilistic bounds and solutions in this context , and focus on the emph exploration problem of anonymous unoriented rings of any size it is known that theta \( log n \) robots are necessary and sufficient to solve the problem with k deterministic robots , provided that k and n are coprime by contrast , we show that emph four identical probabilistic robots are necessary and sufficient to solve the same problem , also removing the coprime constraint our positive results are constructive"
cs.SI,"social media site \( sms \) usage has grown rapidly in the last few years this sudden increase in sms usage creates an opportunity for data leakage which could compromise personal and or professional life in this work , we have reviewed traditional penetration testing process and discussed the failures of traditional penetration testing process to test the 'people' layer of simple enterprise security architecture \( sesa \) model in order to overcome these failures , we have developed the conceptual idea of online sms user account penetration testing system that could be applied to online sms user account and the user account could be categorised based on the rating points this could help us to avoid leaking information that is sensitive and or damage their reputation finally , we have also listed the best practice guidelines of online sms usage"
cs.DS|cs.LG,"the rough set theory proposed by pawlak , has been widely used in dealing with data classification problems the original rough set model is , however , quite sensitive to noisy data tzung thus proposed deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from quantitative data with a predefined tolerance degree of uncertainty and misclassification this model allowed , which combines the variable precision rough set model and the fuzzy set theory , is thus proposed to solve this problem this paper thus deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from incomplete quantitative data with a predefined tolerance degree of uncertainty and misclassification a new method , incomplete quantitative data for rough set model and the fuzzy set theory , is thus proposed to solve this problem it first transforms each quantitative value into a fuzzy set of linguistic terms using membership functions and then finding incomplete quantitative data with lower and the fuzzy upper approximations it second calculates the fuzzy beta lower and the fuzzy beta upper approximations the certain and possible rules are then generated based on these fuzzy approximations these rules can then be used to classify unknown objects"
cs.LG,"a novel framework of compressed sensing , namely statistical compressed sensing \( scs \) , that aims at efficiently sampling a collection of signals that follow a statistical distribution , and achieving accurate reconstruction on average , is introduced scs based on gaussian models is investigated in depth for signals that follow a single gaussian model , with gaussian or bernoulli sensing matrices of o \( k \) measurements , considerably smaller than the o \( k log \( n k \) \) required by conventional cs based on sparse models , where n is the signal dimension , and with an optimal decoder implemented via linear filtering , significantly faster than the pursuit decoders applied in conventional cs , the error of scs is shown tightly upper bounded by a constant times the best k term approximation error , with overwhelming probability the failure probability is also significantly smaller than that of conventional sparsity oriented cs stronger yet simpler results further show that for any sensing matrix , the error of gaussian scs is upper bounded by a constant times the best k term approximation with probability one , and the bound constant can be efficiently calculated for gaussian mixture models \( gmms \) , that assume multiple gaussian distributions and that each signal follows one of them with an unknown index , a piecewise linear estimator is introduced to decode scs the accuracy of model selection , at the heart of the piecewise linear decoder , is analyzed in terms of the properties of the gaussian distributions and the number of sensing measurements a maximum a posteriori expectation maximization algorithm that iteratively estimates the gaussian models parameters , the signals model selection , and decodes the signals , is presented for gmm based scs in real image sensing applications , gmm based scs is shown to lead to improved results compared to conventional cs , at a considerably lower computational cost"
cs.SI,"most existing literature focuses on the exterior temporal rhythm of human movement to infer the functional regions in a city , but they neglects the underlying interdependence between the functional regions and human activities which uncovers more detailed characteristics of regions in this research , we proposed a novel model based on the low rank approximation \( lra \) to detect the functional regions using the data from about 15 million check in records during a yearlong period in shanghai , china we find a series of latent structures , called urban spatial temporal activity structure \( ustas \) while interpreting these structures , a series of outstanding underlying associations between the spatial and temporal activity patterns can be found moreover , we can not only reproduce the observed data with a lower dimensional representative but also simultaneously project both the spatial and temporal activity patterns in the same coordinate system by utilizing the k means clustering algorithm , five significant types of clusters which are directly annotated with a corresponding combination of temporal activities can be obtained this provides a clear picture of how the groups of regions are associated with different activities at different time of day besides the commercial and transportation dominant area , we also detect two kinds of residential areas , the developed residential areas and the developing residential areas we further verify the spatial distribution of these clusters in the view of urban form analysis the results shows a high consistency with the government planning from the same periods , indicating our model is applicable for inferring the functional regions via social media check in data , and can benefit a wide range of fields , such as urban planning , public services and location based recommender systems and other purposes"
cs.DM,"let mathcal t n denote the set of all unrooted and unlabeled trees with n vertices , and \( i , j \) a double star by assuming that every tree of mathcal t n is equally likely , we show that the limiting distribution of the number of occurrences of the double star \( i , j \) in mathcal t n is normal based on this result , we obtain the asymptotic value of randi 'c index for trees fajtlowicz conjectured that for any connected graph the randi 'c index is at least the average distance using this asymptotic value , we show that this conjecture is true not only for almost all connected graphs but also for almost all trees"
cs.IT,"in this paper , new pairs of binary sequences with three cross correlation values are presented the cross correlation values are shown to be low finally we present some numerical results and some open problems"
cs.DS,"we consider the problem of finding a sparse multiple of a polynomial given f in f x of degree d over a field f , and a desired sparsity t , our goal is to determine if there exists a multiple h in f x of f such that h has at most t non zero terms , and if so , to find such an h when f q and t is constant , we give a polynomial time algorithm in d and the size of coefficients in h when f is a finite field , we show that the problem is at least as hard as determining the multiplicative order of elements in an extension field of f \( a problem thought to have complexity similar to that of factoring integers \) , and this lower bound is tight when t 2"
cs.IT,"we consider the problem of information flow over gaussian relay networks similar to the recent work by avestimehr emph et al 1 , we propose network codes that achieve up to a constant gap from the capacity of such networks however , our proposed codes are also computationally tractable our main technique is to use the codes of avestimehr emph et al as inner codes in a concatenated coding scheme"
cs.IT,"in this paper we describe a new error correcting code \( ecc \) inspired by the naccache stern cryptosystem while by far less efficient than turbo codes , the proposed ecc happens to be more efficient than some established eccs for certain sets of parameters the new ecc adds an appendix to the message the appendix is the modular product of small primes representing the message bits the receiver recomputes the product and detects transmission errors using modular division and lattice reduction"
cs.AI,"the connections among natural language processing and argumentation theory are becoming stronger in the latest years , with a growing amount of works going in this direction , in different scenarios and applying heterogeneous techniques in this paper , we present two datasets we built to cope with the combination of the textual entailment framework and bipolar abstract argumentation in our approach , such datasets are used to automatically identify through a textual entailment system the relations among the arguments \( i e , attack , support \) , and then the resulting bipolar argumentation graphs are analyzed to compute the accepted arguments"
cs.AI,"the maximum density still life problem \( mdslp \) is a hard constraint optimization problem based on conway 's game of life it is a prime example of weighted constrained optimization problem that has been recently tackled in the constraint programming community bucket elimination \( be \) is a complete technique commonly used to solve this kind of constraint satisfaction problem when the memory required to apply be is too high , a heuristic method based on it \( denominated mini buckets \) can be used to calculate bounds for the optimal solution nevertheless , the curse of dimensionality makes these techniques unpractical for large size problems in response to this situation , we present a memetic algorithm for the mdslp in which be is used as a mechanism for recombining solutions , providing the best possible child from the parental set subsequently , a multi level model in which this exact metaheuristic hybrid is further hybridized with branch and bound techniques and mini buckets is studied extensive experimental results analyze the performance of these models and multi parent recombination the resulting algorithm consistently finds optimal patterns for up to date solved instances in less time than current approaches moreover , it is shown that this proposal provides new best known solutions for very large instances"
cs.LG,"neural networks sequentially build high level features through their successive layers we propose here a new neural network model where each layer is associated with a set of candidate mappings when an input is processed , at each layer , one mapping among these candidates is selected according to a sequential decision process the resulting model is structured according to a dag like architecture , so that a path from the root to a leaf node defines a sequence of transformations instead of considering global transformations , like in classical multilayer networks , this model allows us for learning a set of local transformations it is thus able to process data with different characteristics through specific sequences of such local transformations , increasing the expression power of this model w r t a classical multilayered network the learning algorithm is inspired from policy gradient techniques coming from the reinforcement learning domain and is used here instead of the classical back propagation based gradient descent techniques experiments on different datasets show the relevance of this approach"
cs.DS,"given a simple polygon p in the plane , we present new algorithms and data structures for computing the weak visibility polygon from any query line segment in p we build a data structure in o \( n \) time and o \( n \) space that can compute the visibility polygon for any query line segment s in o \( k log n \) time , where k is the size of the visibility polygon of s and n is the number of vertices of p alternatively , we build a data structure in o \( n 3 \) time and o \( n 3 \) space that can compute the visibility polygon for any query line segment in o \( k log n \) time"
cs.AI,"the evolution of the human society raises more and more difficult endeavors for some of the real life problems , the computing time restriction enhances their complexity the matrix bandwidth minimization problem \( mbmp \) seeks for a simultaneous permutation of the rows and the columns of a square matrix in order to keep its nonzero entries close to the main diagonal the mbmp is a highly investigated p complete problem , as it has broad applications in industry , logistics , artificial intelligence or information recovery this paper describes a new attempt to use the ant colony optimization framework in tackling mbmp the introduced model is based on the hybridization of the ant colony system technique with new local search mechanisms computational experiments confirm a good performance of the proposed algorithm for the considered set of mbmp instances"
cs.IT,"asymmetrically clipped optical orthogonal frequency division multiplexing \( aco ofdm \) is theoretically more power efficient but less spectrally efficient than dc bias ofdm \( dco ofdm \) , with less power allocating to the informationless bias component by only using odd index sub carriers layered enhanced asymmetrically clipped optical orthogonal frequency division multiplexing \( l e aco ofdm \) has been proposed to increase the spectral efficiency of aco ofdm in this letter , we experimentally demonstrate a 30 km single mode fiber transmission using l e aco ofdm at 4 375 gbits s using a volterra filter based equalizer , 2 db and 1 5 db q factor improvements for l e aco ofdm comparing with dco ofdm can be obtained in back to back and 30 km fiber transmission respectively"
cs.IT,"point to multipoint communications are expected to play a pivotal role in next generation networks this paper refers to a cellular system transmitting layered multicast services to a multicast group of users reliability of communications is ensured via different random linear network coding \( rlnc \) techniques we deal with a fundamental problem the computational complexity of the rlnc decoder the higher the number of decoding operations is , the more the user 's computational overhead grows and , consequently , the faster the battery of mobile devices drains by referring to several sparse rlnc techniques , and without any assumption on the implementation of the rlnc decoder in use , we provide an efficient way to characterize the performance of users targeted by ultra reliable layered multicast services the proposed modeling allows to efficiently derive the average number of coded packet transmissions needed to recover one or more service layers we design a convex resource allocation framework that allows to minimize the complexity of the rlnc decoder by jointly optimizing the transmission parameters and the sparsity of the code the designed optimization framework also ensures service guarantees to predetermined fractions of users performance of the proposed optimization framework is then investigated in a lte a embms network multicasting h 264 svc video services"
cs.IT,"interference alignment is a transmission technique for exploiting all available degrees of freedom in the interference channel with an arbitrary number of users most prior work on interference alignment , however , neglects interference from other nodes in the network not participating in the alignment operation this paper proposes three generalizations of interference alignment for the multiple antenna interference channel with multiple users that account for colored noise , which models uncoordinated interference first , a minimum interference plus noise leakage algorithm is presented , and shown to be equivalent to previous subspace methods when noise is spatially white or negligible a joint minimum mean squared error design is then proposed that jointly optimizes the transmit precoders and receive spatial filters , whereas previous designs neglect the receive spatial filter this algorithm is shown to be a generalization of previous joint mmse designs for other system configurations such as the broadcast channel finally , a maximum signal to interference plus noise ratio algorithm is developed that is proven to converge , unlike previous maximum sinr algorithms the latter two designs are shown to have increased complexity due to non orthogonal precoders , more required iterations , or more channel state knowledge than the min inl or subspace methods the sum throughput performance of these algorithms is simulated in the context of a network with uncoordinated co channel interferers not participating in the alignment protocol it is found that a network with cochannel interference can benefit from employing precoders designed to consider that interference , but in some cases , ignoring the co channel interference is advantageous"
cs.IT,"based on a stochastic geometry framework , we establish an analysis of the multi hop spatial reuse aloha protocol \( msr aloha \) in ad hoc networks we compare msr aloha to a simple routing strategy , where a node selects the next relay of the treated packet as to be its nearest receiver with a forward progress toward the final destination \( nfp \) in addition , performance gains achieved by employing adaptive antenna array systems are quantified in this paper we derive a tight upper bound on the spatial density of progress of msr aloha our analytical results demonstrate that the spatial density of progress scales as the square root of the density of users , and the optimal contention density \( that maximizes the spatial density of progress \) is independent of the density of users these two facts are consistent with the observations of baccelli et al , established through an analytical lower bound and through simulations"
cs.IT,in this short note we construct codes of length 4n with 8n 8 codewords and minimum distance 2n 2 whenever 4n 4 is the order of a hadamard matrix this generalises work of constantine who obtained a similar result in the special case that n is a prime power
cs.IT,conditions are derived on line of sight channels to ensure the feasibility of interference alignment the conditions involve choosing only the spacing between two subcarriers of an orthogonal frequency division multiplexing \( ofdm \) scheme the maximal degrees of freedom are achieved and even an upper bound on the sum rate of interference alignment is approached arbitrarily closely
cs.DM|cs.DS,"it is well known that when f \( v \) is a constant for each vertex v , the connected f factor problem is np complete in this note we consider the case when f \( v \) geq lceil frac n 2 5 rceil for each vertex v , where n is the number of vertices we present a diameter based characterization of graphs having a connected f factor \( for such f \) we show that if a graph g has a connected f factor and an f factor with 2 connected components , then it has a connected f factor of diameter at least 3 this result yields a polynomial time algorithm which first executes the tutte 's f factor algorithm , and if the output has 2 connected components , our algorithm searches for a connected f factor of diameter at least 3"
cs.IT,"in this paper , we study jamming attacks against wireless networks specifically , we consider a network of base stations \( bs \) or access points \( ap \) and investigate the impact of a fixed number of jammers that are randomly deployed according to a binomial point process we shed light on the network performance in terms of a \) the outage probability and b \) the error probability of a victim receiver in the downlink of this wireless network we derive analytical expressions for both these metrics and discuss in detail how the jammer network must adapt to the various wireless network parameters in order to effectively attack the victim receivers for instance , we will show that with only 1 jammer per bs ap a \) the outage probability of the wireless network can be increased from 1 \( as seen in the non jamming case \) to 80 and b \) when retransmissions are used , the jammers cause the effective network activity factor \( and hence the interference among the bss \) to be doubled furthermore , we show that the behavior of the jammer network as a function of the bs ap density is not obvious in particular , an interesting concave type behavior is seen which indicates that the number of jammers required to attack the wireless network must scale with the bs density only until a certain value beyond which it decreases in the context of error probability of the victim receiver , we study whether or not some recent results related to jamming in the point to point link scenario can be extended to the case of jamming against wireless networks numerical results are presented to validate the theoretical inferences presented"
stat.ML,"a new bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the computational time in results of our experiment , we observe that the proposed method can reduce the computational time to less than one tenth of that taken by conventional bayesian approaches"
cs.DM,"does the n th root of the diagonal ramsey number converge to a finite limit \? the answer is yes a sequence can be shown to converge if it satifies convergence conditions other than or besides monotonicity we show such a property holds for which the sequence of n th roots does converge , even if one has no a priori knowledge as to whether the sequence is monotone or not we show also the n th root of the diagonal ramsey number can be expressed as a product of two factors , the first being a known convergent sequence and the second being an absolutely convergent infinite series one also can express it where one product is convergent and the other has all its values from a uniformly convergent complex function holomorphic within the unit disc on the complex plane our motivation solely is to prove the conjecture as a problem in search of a solution , not to establish some deep theory about graphs a second question is if the limit exists what is it \? at the time of this writing the understanding is the proofs sought need not be constructive here we show by nonconstructive proofs that the n th root of the diagonal ramsey number converges to a finite limit we also show that the limit of the j th root of the diagonal ramsey number is two , where positive integer j depends upon the ramsey number"
cs.AI,"we introduce church , a universal language for describing stochastic generative processes church is based on the lisp model of lambda calculus , containing a pure lisp as its deterministic subset the semantics of church is defined in terms of evaluation histories and conditional distributions on such histories church also includes a novel language construct , the stochastic memoizer , which enables simple description of many complex non parametric models we illustrate language features through several examples , including a generalized bayes net in which parameters cluster over trials , infinite pcfgs , planning by inference , and various non parametric clustering models finally , we show how to implement query on any church program , exactly and approximately , using monte carlo techniques"
cs.IT|stat.ML,"this paper is concerned about sparse , continuous frequency estimation in line spectral estimation , and focused on developing gridless sparse methods which overcome grid mismatches and correspond to limiting scenarios of existing grid based approaches , e g , ell 1 optimization and spice , with an infinitely dense grid we generalize ast \( atomic norm soft thresholding \) to the case of nonconsecutively sampled data \( incomplete data \) inspired by recent atomic norm based techniques we present a gridless version of spice \( gridless spice , or gls \) , which is applicable to both complete and incomplete data without the knowledge of noise level we further prove the equivalence between gls and atomic norm based techniques under different assumptions of noise moreover , we extend gls to a systematic framework consisting of model order selection and robust frequency estimation , and present feasible algorithms for ast and gls numerical simulations are provided to validate our theoretical analysis and demonstrate performance of our methods compared to existing ones"
cs.AI,"in this paper , we consider the setting of graph structured data that evolves as a result of operations carried out by users or applications we study different reasoning problems , which range from ensuring the satisfaction of a given set of integrity constraints after a given sequence of updates , to deciding the \( non \) existence of a sequence of actions that would take the data to an \( un \) desirable state , starting either from a specific data instance or from an incomplete description of it we consider an action language in which actions are finite sequences of conditional insertions and deletions of nodes and labels , and use description logics for describing integrity constraints and \( partial \) states of the data we then formalize the above data management problems as a static verification problem and several planning problems we provide algorithms and tight complexity bounds for the formalized problems , both for an expressive dl and for a variant of dl lite"
cs.IT,"to improve the sum rate \( sr \) of the dual hop relay system , a novel two stage power allocation scheme with non orthogonal multiple access \( noma \) is proposed in this scheme , after the reception of the superposition coded symbol with a power allocation from the source , the relay node forwards a new superposition coded symbol with another power allocation to the destination by employing the maximum ratio combination \( mrc \) , the destination jointly decodes the information symbols from the source and the relay assuming rayleigh fading channels , closed form solution of the ergodic sr at high signal to noise ratio \( snr \) is derived and a practical power allocation is also designed for the proposed noma scheme through numerical results , it is shown that the performance of the proposed scheme is significantly improved compared with the existing work"
cs.IT,"we consider a distributed sensor network in which the single antenna sensor nodes observe a deterministic unknown parameter and after encoding the observed signal with a phase parameter , the sensor nodes transmit it simultaneously to a multi antenna fusion center \( fc \) the fc optimizes the phase encoding parameter and feeds it back to the sensor nodes such that the variance of estimation error can be minimized we relax the phase optimization problem to a semidefinite programming problem and the numerical results show that the performance of the proposed method is close to the theoretical bound also , asymptotic results show that when the number of sensors is very large and the variance of the distance between the sensor nodes and fc is small , multiple antennas do not provide a benefit compared with a single antenna system when the number of antennas m is large and the measurement noise at the sensor nodes is small compared with the additive noise at the fc , the estimation error variance can be reduced by a factor of m"
cs.IT,"we examine and compare several different classes of balanced block codes over q ary alphabets , namely symbol balanced \( sb \) codes , charge balanced \( cb \) codes , and polarity balanced \( pb \) codes known results on the maximum size and asymptotic minimal redundancy of sb and cb codes are reviewed we then determine the maximum size and asymptotic minimal redundancy of pb codes and of codes which are both cb and pb we also propose efficient knuth like encoders and decoders for all these types of balanced codes"
cs.IT,"an important tool to quantify the likeness of two probability measures are f divergences , which have seen widespread application in statistics and information theory an example is the total variation , which plays an exceptional role among the f divergences it is shown that every f divergence is bounded from below by a monotonous function of the total variation under appropriate regularity conditions , this function is shown to be monotonous remark the proof of the main proposition is relatively easy , whence it is highly likely that the result is known the author would be very grateful for any information regarding references or related work"
cs.LG|cs.SI|stat.ML,"this paper studies two important signal processing aspects of equilibrium behavior in non cooperative games arising in social networks , namely , reinforcement learning and detection of equilibrium play the first part of the paper presents a reinforcement learning \( adaptive filtering \) algorithm that facilitates learning an equilibrium by resorting to diffusion cooperation strategies in a social network agents form homophilic social groups , within which they exchange past experiences over an undirected graph it is shown that , if all agents follow the proposed algorithm , their global behavior is attracted to the correlated equilibria set of the game the second part of the paper provides a test to detect if the actions of agents are consistent with play from the equilibrium of a concave potential game the theory of revealed preference from microeconomics is used to construct a non parametric decision test and statistical test which only require the probe and associated actions of agents a stochastic gradient algorithm is given to optimize the probe in real time to minimize the type ii error probabilities of the detection test subject to specified type i error probability we provide a real world example using the energy market , and a numerical example to detect malicious agents in an online social network"
cs.LG|stat.ML,"we study the problem of distributed multi task learning with shared representation , where each machine aims to learn a separate , but related , task in an unknown shared low dimensional subspaces , i e when the predictor matrix has low rank we consider a setting where each task is handled by a different machine , with samples for the task available locally on the machine , and study communication efficient methods for exploiting the shared structure"
cs.IT,we begin a systematic study of the problem of the zero error capacity of noisy binary channels with memory and solve some of the non trivial cases
cs.IT,"this paper focuses on the 1 to k broadcast packet erasure channel \( pec \) , which is a generalization of the broadcast binary erasure channel from the binary symbol to that of arbitrary finite fields gf \( q \) with sufficiently large q we consider the setting in which the source node has instant feedback of the channel outputs of the k receivers after each transmission the capacity region of the 1 to k pec with cof was previously known only for the case k 2 such a setting directly models network coded packet transmission in the downlink direction with integrated feedback mechanisms \( such as automatic repeat request \( arq \) \) the main results of this paper are \( i \) the capacity region for general 1 to 3 broadcast pecs , and \( ii \) the capacity region for two types of 1 to k broadcast pecs the symmetric pecs , and the spatially independent pecs with one sided fairness constraints this paper also develops \( iii \) a pair of outer and inner bounds of the capacity region for arbitrary 1 to k broadcast pecs , which can be easily evaluated by any linear programming solver the proposed inner bound is proven by a new class of intersession network coding schemes , termed the packet evolution schemes , which is based on the concept of code alignment in gf \( q \) that is in parallel with the interference alignment techniques for the euclidean space extensive numerical experiments show that the outer and inner bounds meet for almost all broadcast pecs encountered in practical scenarios and thus effectively bracket the capacity of general 1 to k broadcast pecs with cof"
cs.AI|cs.LG,"the current study examines how adequate coordination among different cognitive processes including visual recognition , attention switching , action preparation and generation can be developed via learning of robots by introducing a novel model , the visuo motor deep dynamic neural network \( vmdnn \) the proposed model is built on coupling of a dynamic vision network , a motor generation network , and a higher level network allocated on top of these two the simulation experiments using the icub simulator were conducted for cognitive tasks including visual object manipulation responding to human gestures the results showed that synergetic coordination can be developed via iterative learning through the whole network when spatio temporal hierarchy and temporal one can be self organized in the visual pathway and in the motor pathway , respectively , such that the higher level can manipulate them with abstraction"
cs.IT,this paper has been withdrawn by the author
cs.DS|cs.IT,"let s be a string whose symbols are solely available through access \( i \) , a read only operation that probes s and returns the symbol at position i in s many compressed data structures for strings , trees , and graphs , require two kinds of queries on s select \( c , j \) , returning the position in s containing the jth occurrence of c , and rank \( c , p \) , counting how many occurrences of c are found in the first p positions of s we give matching upper and lower bounds for this problem , improving the lower bounds given by golynski theor comput sci 387 \( 2007 \) phd thesis and the upper bounds of barbay et al soda 2007 we also present new results in another model , improving on barbay et al soda 2007 and matching a lower bound of golynski soda 2009 the main contribution of this paper is to introduce a general technique for proving lower bounds on succinct data structures , that is based on the access patterns of the supported operations , abstracting from the particular operations at hand for this , it may find application to other interesting problems on succinct data structures"
cs.SI|stat.ML,"community detection using both graphs and social networks is the focus of many algorithms recent methods aimed at optimizing the so called modularity function proceed by maximizing relations within communities while minimizing inter community relations however , given the np completeness of the problem , these algorithms are heuristics that do not guarantee an optimum in this paper , we introduce a new algorithm along with a function that takes an approximate solution and modifies it in order to reach an optimum this reassignment function is considered a 'potential function' and becomes a necessary condition to asserting that the computed optimum is indeed a nash equilibrium we also use this function to simultaneously show partitioning and overlapping communities , two detection and visualization modes of great value in revealing interesting features of a social network our approach is successfully illustrated through several experiments on either real unipartite , multipartite or directed graphs of medium and large sized datasets"
cs.IT,"biomedical signals aid in the diagnosis of different disorders and abnormalities when targeting lossy compression of such signals , the medically relevant information that lies within the data should maintain its accuracy and thus its reliability in fact , signal models that are inspired by the bio physical properties of the signals at hand allow for a compression that preserves more naturally the clinically significant features of these signals in this paper , we illustrate this through the example of eeg signals more specifically , we analyze three specific lossy eeg compression schemes these schemes are based on signal models that have different degrees of reliance on signal production and physiological characteristics of eeg the resilience of these schemes is illustrated through the performance of seizure detection post compression"
cs.SI,"we collect and analyse messages exchanged in twitter using two of the platform 's publicly available apis \( the search and stream specifications \) we assess the differences between the two samples , and compare the networks of communication reconstructed from them the empirical context is given by political protests taking place in may 2012 we track online communication around these protests for the period of one month , and reconstruct the network of mentions and re tweets according to the two samples we find that the search api over represents the more central users and does not offer an accurate picture of peripheral activity we also find that the bias is greater for the network of mentions we discuss the implications of this bias for the study of diffusion dynamics and collective action in the digital era , and advocate the need for more uniform sampling procedures in the study of online communication"
cs.LG,"stochastic structured prediction under bandit feedback follows a learning protocol where on each of a sequence of iterations , the learner receives an input , predicts an output structure , and receives partial feedback in form of a task loss evaluation of the predicted structure we introduce stochastic approximation algorithms that apply this learning scenario to probabilistic structured prediction , with a focus on asymptotic convergence and ease of elicitability of feedback we present simulation experiments for complex natural language processing tasks , showing fastest empirical convergence and smallest empirical variance for stochastic optimization of a non convex pairwise preference learning objective compared to stochastic optimization of related non convex and convex objectives"
cs.LG,"recently , zero shot learning \( zsl \) has received increasing interest the key idea underpinning existing zsl approaches is to exploit knowledge transfer via an intermediate level semantic representation which is assumed to be shared between the auxiliary and target datasets , and is used to bridge between these domains for knowledge transfer the semantic representation used in existing approaches varies from visual attributes to semantic word vectors and semantic relatedness however , the overall pipeline is similar a projection mapping low level features to the semantic representation is learned from the auxiliary dataset by either classification or regression models and applied directly to map each instance into the same semantic representation space where a zero shot classifier is used to recognise the unseen target class instances with a single known 'prototype' of each target class in this paper we discuss two related lines of work improving the conventional approach exploiting transductive learning zsl , and generalising zsl to the multi label case"
cs.DM,"circle graphs are intersection graphs of chords in a circle and k polygon graphs are intersection graphs of chords in a convex k sided polygon where each chord has its endpoints on distinct sides the k polygon graphs , for k ge 2 , form an infinite chain of graph classes , each of which contains the class of permutation graphs the union of all of those graph classes is the class of circle graphs the polygon number gp \( g \) of a circle graph g is the minimum k such that g is a k polygon graph given a circle graph g and an integer k , determining whether gp \( g \) le k is np complete , while the problem is solvable in polynomial time for fixed k in this paper , we show that gp \( g \) is always at least as large as the asteroidal number of g , and equal to the asteroidal number of g when g is a connected distance hereditary graph that is not a clique this implies that the classes of distance hereditary permutation graphs and distance hereditary at free graphs are the same , and we give a forbidden subgraph characterization of that class we also establish the following upper bounds gp \( g \) is at most the clique cover number of g if g is not a clique , at most 1 plus the independence number of g , and at most lceil n 2 rceil where n ge 3 is the number of vertices of g our results lead to linear time algorithms for finding the minimum number of corners that must be added to a given circle representation to produce a polygon representation , and for finding the asteroidal number of a distance hereditary graph , both of which are improvements over previous algorithms for those problems"
cs.DM|cs.DS,we study \( vertex disjoint \) p 2 packings in graphs under a parameterized perspective starting from a maximal p 2 packing p of size j we use extremal arguments for determining how many vertices of p appear in some p 2 packing of size \( j 1 \) we basically can 'reuse' 2 5j vertices we also present a kernelization algorithm that gives a kernel of size bounded by 7k with these two results we build an algorithm which constructs a p 2 packing of size k in time oh \( 2 482 3k \)
cs.IT,"source coding theorems and shannon rate distortion functions were studied for the discrete time wiener process by berger and generalized to nonstationary gaussian autoregressive processes by gray and by hashimoto and arimoto hashimoto and arimoto provided an example apparently contradicting the methods used in gray , implied that gray 's rate distortion evaluation was not correct in the nonstationary case , and derived a new formula that agreed with previous results for the stationary case and held in the nonstationary case in this correspondence it is shown that the rate distortion formulas of gray and hashimoto and arimoto are in fact consistent and that the example of of hashimoto and arimoto does not form a counter example to the methods or results of the earlier paper their results do provide an alternative , but equivalent , formula for the rate distortion function in the nonstationary case and they provide a concrete example that the classic kolmogorov formula differs from the autoregressive formula when the autoregressive source is not stationary some observations are offered on the different versions of the toeplitz asymptotic eigenvalue distribution theorem used in the two papers to emphasize how a slight modification of the classic theorem avoids the problems with certain singularities"
cs.IT,"full rate \( fr \) and full diversity \( fd \) are attractive features in mimo systems we refer to systems which achieve both fr and fd simultaneously as frfd systems non orthogonal stbcs can achieve frfd without feedback , but their ml decoding complexities are high v blast without precoding achieves fr but not fd frfd can be achieved in v blast through precoding given full channel state information at the transmitter \( csit \) however , with limited feedback precoding , v blast achieves fd , but with some rate loss our contribution in this paper is two fold i \) we propose a limited feedback \( lfb \) precoding scheme which achieves frfd in 2 times 2 , 3 times 3 and 4 times 4 v blast systems \( we refer to this scheme as frfd vblast lfb scheme \) , and ii \) comparing the performances of the frfd vblast lfb scheme and non orthogonal stbcs without feedback \( e g , golden code , perfect codes \) under ml decoding , we show that in 2 times 2 mimo system with 4 qam 16 qam , frfd vblast lfb scheme outperforms the golden code by about 0 6 db in 3 times 3 and 4 times 4 mimo systems , the performance of frfd vblast lfb scheme is comparable to the performance of perfect codes the frfd vblast lfb scheme is attractive because 1 \) ml decoding becomes less complex compared to that of non orthogonal stbcs , 2 \) the number of feedback bits required to achieve the above performance is small , 3 \) in slow fading , it is adequate to send feedback bits only occasionally , and 4 \) in most practical wireless systems feedback channel is often available \( e g , for adaptive modulation , rate power control \)"
cs.IT,"compressive sensing \( cs \) has recently emerged as a framework for efficiently capturing signals that are sparse or compressible in an appropriate basis while often motivated as an alternative to nyquist rate sampling , there remains a gap between the discrete , finite dimensional cs framework and the problem of acquiring a continuous time signal in this paper , we attempt to bridge this gap by exploiting the discrete prolate spheroidal sequences \( dpss 's \) , a collection of functions that trace back to the seminal work by slepian , landau , and pollack on the effects of time limiting and bandlimiting operations dpss 's form a highly efficient basis for sampled bandlimited functions by modulating and merging dpss bases , we obtain a dictionary that offers high quality sparse approximations for most sampled multiband signals this multiband modulated dpss dictionary can be readily incorporated into the cs framework we provide theoretical guarantees and practical insight into the use of this dictionary for recovery of sampled multiband signals from compressive measurements"
cs.IT,"an information theoretic approach is proposed to watermark embedding and detection under limited detector resources first , we consider the attack free scenario under which asymptotically optimal decision regions in the neyman pearson sense are proposed , along with the optimal embedding rule later , we explore the case of zero mean i i d gaussian covertext distribution with unknown variance under the attack free scenario for this case , we propose a lower bound on the exponential decay rate of the false negative probability and prove that the optimal embedding and detecting strategy is superior to the customary linear , additive embedding strategy in the exponential sense finally , these results are extended to the case of memoryless attacks and general worst case attacks optimal decision regions and embedding rules are offered , and the worst attack channel is identified"
cs.IT,"in this paper , we study the spectrum usage efficiency by applying wideband methods and systems to the existing analog systems and applications the essential motivation of this work is to define the prospective coexistence between analog fm and digital spread spectrum systems in an efficient way sharing the same frequency band the potential overlaid spread spectrum \( ss \) system can spectrally coincide within the existing narrowband frequency modulated \( fm \) broadcasting system upon several limitations , originating a key motivation for the use of the fm radio frequency band in many applications , encompassing wireless personal and sensors networks the performance of the ss system due to the overlaying analog fm system , consisting of multiple narrowband fm stations , is investigated in order to derive the relevant bit error probability and maximum achievable data rates the ss system uses direct sequence \( ds \) spreading , through maximal length pseudorandom sequences with long spreading codes the ss signal is evaluated throughout theoretical and simulation based performance analysis , for various types of spreading scenarios , for different carrier frequency offset \( delta f \) and signal to interference ratios , in order to derive valuable results for future developing and planning of an overlay scenario"
cs.IT|cs.LG,"we discuss the similarities and differences between training an auto encoder to minimize the reconstruction error , and training the same auto encoder to compress the data via a generative model minimizing a codelength for the data using an auto encoder is equivalent to minimizing the reconstruction error plus some correcting terms which have an interpretation as either a denoising or contractive property of the decoding function these terms are related but not identical to those used in denoising or contractive auto encoders vincent et al 2010 , rifai et al 2011 in particular , the codelength viewpoint fully determines an optimal noise level for the denoising criterion"
cs.AI,"continuous logic extends the multi valued lukasiewicz logic by adding a halving operator on propositions this extension is designed to give a more satisfactory model theory for continuous structures the semantics of these logics can be given using specialisations of algebraic structures known as hoops as part of an investigation into the metatheory of propositional continuous logic , we were indebted to prover9 for finding a proof of an important algebraic law"
cs.SI,"we investigate consensus formation and the asymptotic consensus times in stylized individual or agent based models , in which global agreement is achieved through pairwise negotiations with or without a bias considering a class of individual based models on finite complete graphs , we introduce a coarse graining approach \( lumping microscopic variables into macrostates \) to analyze the ordering dynamics in an associated random walk framework within this framework , yielding a linear system , we derive general equations for the expected consensus time and the expected time spent in each macro state further , we present the asymptotic solutions of the 2 word naming game , and separately discuss its behavior under the influence of an external field and with the introduction of committed agents"
cs.LG,"many real world applications are associated with structured data , where not only input but also output has interplay however , typical classification and regression models often lack the ability of simultaneously exploring high order interaction within input and that within output in this paper , we present a deep learning model aiming to generate a powerful nonlinear functional mapping from structured input to structured output more specifically , we propose to integrate high order hidden units , guided discriminative pretraining , and high order auto encoders for this purpose we evaluate the model with three datasets , and obtain state of the art performances among competitive methods our current work focuses on structured output regression , which is a less explored area , although the model can be extended to handle structured label classification"
cs.SI,"finding a good clustering of vertices in a network , where vertices in the same cluster are more tightly connected than those in different clusters , is a useful , important , and well studied task many clustering algorithms scale well , however they are not designed to operate upon internet scale networks with billions of nodes or more we study one of the fastest and most memory efficient algorithms possible clustering based on the connected components in a random edge induced subgraph when defining the cost of a clustering to be its distance from such a random clustering , we show that this surprisingly simple algorithm gives a solution that is within an expected factor of two or three of optimal with either of two natural distance functions in fact , this approximation guarantee works for any problem where there is a probability distribution on clusterings we then examine the behavior of this algorithm in the context of social network trust inference"
cs.DS,"we develop a new technique for proving cell probe lower bounds for static data structures previous lower bounds used a reduction to communication games , which was known not to be tight by counting arguments we give the first lower bound for an explicit problem which breaks this communication complexity barrier in addition , our bounds give the first separation between polynomial and near linear space such a separation is inherently impossible by communication complexity using our lower bound technique and new upper bound constructions , we obtain tight bounds for searching predecessors among a static set of integers given a set y of n integers of l bits each , the goal is to efficiently find predecessor \( x \) max y in y y x , by representing y on a ram using space s in external memory , it follows that the optimal strategy is to use either standard b trees , or a ram algorithm ignoring the larger block size in the important case of l c lg n , for c 1 \( i e polynomial universes \) , and near linear space \( such as s n poly \( lg n \) \) , the optimal search time is theta \( lg l \) thus , our lower bound implies the surprising conclusion that van emde boas' classic data structure from focs'75 is optimal in this case note that for space n 1 eps , a running time of o \( lg l lglg l \) was given by beame and fich stoc'99"
cs.LG|stat.ML,"probabilistic graphical models combine the graph theory and probability theory to give a multivariate statistical modeling they provide a unified description of uncertainty using probability and complexity using the graphical model especially , graphical models provide the following several useful properties graphical models provide a simple and intuitive interpretation of the structures of probabilistic models on the other hand , they can be used to design and motivate new models graphical models provide additional insights into the properties of the model , including the conditional independence properties complex computations which are required to perform inference and learning in sophisticated models can be expressed in terms of graphical manipulations , in which the underlying mathematical expressions are carried along implicitly the graphical models have been applied to a large number of fields , including bioinformatics , social science , control theory , image processing , marketing analysis , among others however , structure learning for graphical models remains an open challenge , since one must cope with a combinatorial search over the space of all possible structures in this paper , we present a comprehensive survey of the existing structure learning algorithms"
cs.LG|stat.ML,"we present a bayesian nonparametric framework for multilevel clustering which utilizes group level context information to simultaneously discover low dimensional structures of the group contents and partitions groups into clusters using the dirichlet process as the building block , our model constructs a product base measure with a nested structure to accommodate content and context observations at multiple levels the proposed model possesses properties that link the nested dirichlet processes \( ndp \) and the dirichlet process mixture models \( dpm \) in an interesting way integrating out all contents results in the dpm over contexts , whereas integrating out group specific contexts results in the ndp mixture over content variables we provide a polya urn view of the model and an efficient collapsed gibbs inference procedure extensive experiments on real world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains"
cs.DS,"motivated by applications to word of mouth advertising , we consider a game theoretic scenario in which competing advertisers want to target initial adopters in a social network each advertiser wishes to maximize the resulting cascade of influence , modeled by a general network diffusion process however , competition between products may adversely impact the rate of adoption for any given firm the resulting framework gives rise to complex preferences that depend on the specifics of the stochastic diffusion model and the network topology we study this model from the perspective of a central mechanism , such as a social networking platform , that can optimize seed placement as a service for the advertisers we ask given the reported demands of the competing firms , how should a mechanism choose seeds to maximize overall efficiency \? beyond the algorithmic problem , competition raises issues of strategic behaviour rational agents should not be incentivized to underreport their budget demands we show that when there are two players , the social welfare can be 2 approximated by a polynomial time strategyproof mechanism our mechanism is defined recursively , randomizing the order in which advertisers are allocated seeds according to a particular greedy method for three or more players , we demonstrate that under additional assumptions \( satisfied by many existing models of influence spread \) there exists a simpler strategyproof frac e e 1 approximation mechanism notably , this second mechanism is not necessarily strategyproof when there are only two players"
cs.IT,"in this paper we revisit the results of loynes \( 1962 \) on stability of queues for ergodic arrivals and services , and show examples when the arrivals are bounded and ergodic , the service rate is constant , and under stability the limit distribution has larger than exponential tail"
cs.AI,"in this paper we address the problem of decision making within a markov decision process \( mdp \) framework where risk and modeling errors are taken into account our approach is to minimize a risk sensitive conditional value at risk \( cvar \) objective , as opposed to a standard risk neutral expectation we refer to such problem as cvar mdp our first contribution is to show that a cvar objective , besides capturing risk sensitivity , has an alternative interpretation as expected cost under worst case modeling errors , for a given error budget this result , which is of independent interest , motivates cvar mdps as a unifying framework for risk sensitive and robust decision making our second contribution is to present an approximate value iteration algorithm for cvar mdps and analyze its convergence rate to our knowledge , this is the first solution algorithm for cvar mdps that enjoys error guarantees finally , we present results from numerical experiments that corroborate our theoretical findings and show the practicality of our approach"
cs.IT,"we derive a recursive formula determing the weight distribution of the n \( q m 1 \) \( q 1 \) , n m , 3 hamming code h \( m , q \) , when \( m , q 1 \) 1 here q is a prime power the proof is based on moisio 's idea of using pless power moment identity together with exponential sum techniques"
cs.IT,"cyclic codes are an interesting class of linear codes due to their efficient encoding and decoding algorithms as well as their theoretical importance bch codes form a subclass of cyclic codes and are very important in both theory and practice as they have good error correcting capability and are widely used in communication systems , storage devices and consumer electronics however , the dimension and minimum distance of bch codes are not known in general the objective of this paper is to study the dimension and minimum distance of a family of bch codes over finite fields , i e , a class of reversible bch codes"
cs.DM|cs.DS,"the paper is organized as a self contained literate prolog program that implements elements of an executable finite set theory with focus on combinatorial generation and arithmetic encodings the complete prolog code is available at http logic csci unt edu tarau research 2008 phfs zip first , ranking and unranking functions for some mathematically elegant data types in the universe of hereditarily finite sets with urelements are provided , resulting in arithmetic encodings for powersets , hypergraphs , ordinals and choice functions after implementing a digraph representation of hereditarily finite sets we define em decoration functions that can recover well founded sets from encodings of their associated acyclic digraphs we conclude with an encoding of arbitrary digraphs and discuss a concept of duality induced by the set membership relation in the process , we uncover the surprising possibility of internally sharing isomorphic objects , independently of their language level types and meanings"
cs.IT|stat.ML,"we design iterative receiver schemes for a generic wireless communication system by treating channel estimation and information decoding as an inference problem in graphical models we introduce a recently proposed inference framework that combines belief propagation \( bp \) and the mean field \( mf \) approximation and includes these algorithms as special cases we also show that the expectation propagation and expectation maximization algorithms can be embedded in the bp mf framework with slight modifications by applying the considered inference algorithms to our probabilistic model , we derive four different message passing receiver schemes our numerical evaluation demonstrates that the receiver based on the bp mf framework and its variant based on bp em yield the best compromise between performance , computational complexity and numerical stability among all candidate algorithms"
cs.IT,"the evaluation of the performance of clustered cooperative beamforming in cellular networks generally requires the solution of complex non convex optimization problems in this letter , a framework based on a hypergraph formalism is proposed that enables the derivation of a performance characterization of clustered cooperative beamforming in terms of per user degrees of freedom \( dof \) via the efficient solution of a coloring problem an emerging scenario in which clusters of cooperative base stations \( bss \) arise is given by cellular networks with edge caching in fact , clusters of bss that share the same requested files can jointly beamform the corresponding encoded signals based on this observation , the proposed framework is applied to obtain quantitative insights into the optimal use of cache and backhaul resources in cellular systems with edge caching numerical examples are provided to illustrate the merits of the proposed framework"
cs.IT,"this paper presents a novel unified performance analysis of space time block codes \( stbcs \) operating in the multiple input multiple output \( mimo \) network and subjected to generalized shadowed fading and noise scenarios specifically , we derive novel , simple and accurate average bit error rates \( aber \) expressions for coherent modulation schemes in the generalized eta mu and shadowed kappa mu fading channels the noise in the network is assumed to be modeled by the additive white generalized gaussian noise \( awggn \) , which encompasses the laplacian and the gaussian noise environments as special cases the result obviates the need to re derive the error rates for mimo stbc systems under many multipath fading and noise models , while avoiding computationally expensive expressions published results from the literature as well as numerical simulations corroborate the accuracy of our derived generalized expressions"
cs.IT,"we analyse the spatial diversity of a multipath fading process for a finite region or curve in the plane by means of the karhunen lo `eve \( kl \) expansion , this diversity can be characterised by the eigenvalue spectrum of the spatial autocorrelation kernel this justifies to use the term diversity spectrum for it we show how the diversity spectrum can be calculated for any such geometrical object and any fading statistics represented by the power azimuth spectrum \( pas \) we give rigorous estimates for the accuracy of the numerically calculated eigenvalues the numerically calculated diversity spectra provide useful hints for the optimisation of the geometry of an antenna array furthermore , for a channel coded system , they allow to evaluate the time interleaving depth that is necessary to exploit the diversity gain of the code"
cs.DM,"key questions that scientists and engineers typically want to address can be formulated in terms of predictive science questions such as how well does my computational model represent reality \? , what are the most important parameters in the problem \? , and what is the best next experiment to perform \? are fundamental in solving scientific problems mystic is a framework for massively parallel optimization and rigorous sensitivity analysis that enables these motivating questions to be addressed quantitatively as global optimization problems often realistic physics , engineering , and materials models may have hundreds of input parameters , hundreds of constraints , and may require execution times of seconds or longer in more extreme cases , realistic models may be multi scale , and require the use of high performance computing clusters for their evaluation predictive calculations , formulated as a global optimization over a potential surface in design parameter space , may require an already prohibitively large simulation to be performed hundreds , if not thousands , of times the need to prepare , schedule , and monitor thousands of model evaluations , and dynamically explore and analyze results , is a challenging problem that requires a software infrastructure capable of distributing and managing computations on large scale heterogeneous resources in this paper , we present the design behind an optimization framework , and also a framework for heterogeneous computing , that when utilized together , can make computationally intractable sensitivity and optimization problems much more tractable"
cs.IT,"we prove two results on the universality of polar codes for source coding and channel communication first , we show that for any polar code built for a source p x , z there exists a slightly modified polar code having the same rate , the same encoding and decoding complexity and the same error rate that is universal for every source p x , y when using successive cancellation decoding , at least when the channel p y x is more capable than p z x and p x is such that it maximizes i \( x y \) i \( x z \) for the given channels p y x and p z x this result extends to channel coding for discrete memoryless channels second , we prove that polar codes using successive cancellation decoding are universal for less noisy discrete memoryless channels"
cs.IT,"in this paper , we propose a novel algorithm to maximize the sum rate in interference limited scenarios where each user decodes its own message with the presence of unknown interferences and noise considering the signal to interference plus noise ratio it is known that the problem of adapting the transmit and receive filters of the users to maximize the sum rate with a sum transmit power constraint is non convex our novel approach is to formulate the sum rate maximization problem as an equivalent multi convex optimization problem by adding two sets of auxiliary variables an iterative algorithm which alternatingly adjusts the system variables and the auxiliary variables is proposed to solve the multi convex optimization problem the proposed algorithm is applied to a downlink cellular scenario consisting of several cells each of which contains a base station serving several mobile stations we examine the two cases , with or without several half duplex amplify and forward relays assisting the transmission a sum power constraint at the base stations and a sum power constraint at the relays are assumed finally , we show that the proposed multi convex formulation of the sum rate maximization problem is applicable to many other wireless systems in which the estimated data symbols are multi affine functions of the system variables"
cs.SI,"we analyze critical phenomena on networks generated as the union of hidden variables models \( networks with any desired degree sequence \) with arbitrary graphs the resulting networks are general small worlds similar to those a` la watts and strogatz but with a heterogeneous degree distribution we prove that the critical behavior \( thermal or percolative \) remains completely unchanged by the presence of finite loops \( or finite clustering \) then , we show that , in large but finite networks , correlations of two given spins may be strong , i e , approximately power law like , at any temperature quite interestingly , if gamma is the exponent for the power law distribution of the vertex degree , for gamma leq 3 and with or without short range couplings , such strong correlations persist even in the thermodynamic limit , contradicting the common opinion that in mean field models correlations always disappear in this limit finally , we provide the optimal choice of rewiring under which percolation phenomena in the rewired network are best performed a natural criterion to reach best communication features , at least in non congested regimes"
cs.DM,"let alpha \( g \) be the cardinality of a independence set of maximum size in the graph g , while mu \( g \) is the size of a maximum matching g is a konig egervary graph if its order equals alpha \( g \) mu \( g \) the set core \( g \) is the intersection of all maximum independent sets of g \( levit mandrescu , 2002 \) the number def \( g \) v \( g \) 2 mu \( g \) is the deficiency of g \( lovasz plummer , 1986 \) the number d \( g \) max s n \( s \) s in ind \( g \) is the critical difference of g an independent set a is critical if a n \( a \) d \( g \) , where n \( s \) is the neighborhood of s \( zhang , 1990 \) in 2009 , larson showed that g is konig egervary graph if and only if there exists a maximum independent set that is critical as well in this paper we prove that \( i \) d \( g \) core \( g \) n \( core \( g \) \) alpha \( g \) mu \( g \) def \( g \) for every konig egervary graph g \( ii \) g is konig egervary graph if and only if every maximum independent set of g is critical"
cs.AI,"lifelogging devices are spreading faster everyday this growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his her environment in this paper , we propose a semi supervised strategy for easily discovering objects relevant to the person wearing a first person camera given an egocentric video sequence acquired by the camera , our algorithm uses both the appearance extracted by means of a convolutional neural network and an object refill methodology that allows to discover objects even in case of small amount of object appearance in the collection of images a svm filtering strategy is applied to deal with the great part of the false positive object candidates found by most of the state of the art object detectors we validate our method on a new egocentric dataset of 4912 daily images acquired by 4 persons as well as on both pascal 2012 and msrc datasets we obtain for all of them results that largely outperform the state of the art approach we make public both the edub dataset and the algorithm code"
cs.DM,"reed and wood and independently norine , seymour , thomas , and wollan proved that for each positive integer t there is a constant c \( t \) such that every graph on n vertices with no k t minor has at most c \( t \) n cliques wood asked in 2007 if we can take c \( t \) c t for some absolute constant c this question was recently answered affirmatively by lee and oum in this paper , we determine the exponential constant we prove that every graph on n vertices with no k t minor has at most 3 2t 3 o \( t \) n cliques this bound is tight for n geq 4t 3 more generally , let h be a connected graph on t vertices , and x denote the size \( i e , the number edges \) of the largest matching in the complement of h we prove that every graph on n vertices with no h minor has at most max \( 3 2t 3 x 3 o \( t \) n , 2 t o \( t \) n \) cliques , and this bound is tight for n geq max \( 4t 3 2x 3 , t \) by a simple construction even more generally , we determine explicitly the exponential constant for the maximum number of cliques an n vertex graph can have in a minor closed family of graphs which is closed under disjoint union"
cs.IT,"in this paper we study the capacity region of the multi pair bidirectional \( or two way \) wireless relay network , in which a relay node facilitates the communication between multiple pairs of users this network is a generalization of the well known bidirectional relay channel , where we have only one pair of users we examine this problem in the context of the deterministic channel interaction model , which eliminates the channel noise and allows us to focus on the interaction between signals we characterize the capacity region of this network when the relay is operating at either full duplex mode or half duplex mode \( with non adaptive listen transmit scheduling \) in both cases we show that the cut set upper bound is tight and , quite interestingly , the capacity region is achieved by a simple equation forwarding strategy"
cs.IT,"in this paper , novel closed form expressions for the level crossing rate \( lcr \) and average fade duration \( afd \) of kappa mu shadowed fading channels are derived the new equations provide the capability of modeling the correlation between the time derivative of the shadowed dominant and multipath components of the kappa mu shadowed fading envelope verification of the new equations is performed by reduction to a number of known special cases it is shown that as the shadowing of the resultant dominant component decreases , the signal crosses lower threshold levels at a reduced rate furthermore , the impact of increasing correlation between the slope of the shadowed dominant and multipath components similarly acts to reduce crossings at lower signal levels the new expressions for the second order statistics are also compared with field measurements obtained for cellular device to device and body centric communications channels which are known to be susceptible to shadowed fading"
cs.DM,"gremlin is a graph traversal machine and language designed , developed , and distributed by the apache tinkerpop project gremlin , as a graph traversal machine , is composed of three interacting components a graph g , a traversal psi , and a set of traversers t the traversers move about the graph according to the instructions specified in the traversal , where the result of the computation is the ultimate locations of all halted traversers a gremlin machine can be executed over any supporting graph computing system such as an oltp graph database and or an olap graph processor gremlin , as a graph traversal language , is a functional language implemented in the user 's native programming language and is used to define the psi of a gremlin machine this article provides a mathematical description of gremlin and details its automaton and functional properties these properties enable gremlin to naturally support imperative and declarative querying , host language agnosticism , user defined domain specific languages , an extensible compiler optimizer , single and multi machine execution models , hybrid depth and breadth first evaluation , as well as the existence of a universal gremlin machine and its respective entailments"
cs.IT,"this paper introduces a new counting code its design was motivated by distributed video coding where , for decoding , error correction methods are applied to improve predictions those error corrections sometimes fail which results in decoded values worse than the initial prediction our code exploits the fact that bit errors are relatively unlikely events more than a few bit errors in a decoded pixel value are rare with a carefully designed counting code combined with a prediction those bit errors can be corrected and sometimes the original pixel value recovered the error correction improves significantly our new code not only maximizes the hamming distance between adjacent \( or near 1 \) codewords but also between nearby \( for example near 2 \) codewords this is why our code is significantly different from the well known maximal counting sequences which have maximal average hamming distance fortunately , the new counting code can be derived from gray codes for every code word length \( i e bit depth \)"
cs.IT,the diversity product and the diversity sum are two very important parameters for a good performing unitary space time constellation a basic question is what the maximal diversity product \( or sum \) is in this paper we are going to derive general upper bounds on the diversity sum and the diversity product for unitary constellations of any dimension n and any size m using packing techniques on the compact lie group u \( n \)
cs.LG|stat.ML,"in this paper , we investigate the statistical convergence rate of a bayesian low rank tensor estimator our problem setting is the regression problem where a tensor structure underlying the data is estimated this problem setting occurs in many practical applications , such as collaborative filtering , multi task learning , and spatio temporal data analysis the convergence rate is analyzed in terms of both in sample and out of sample predictive accuracies it is shown that a near optimal rate is achieved without any strong convexity of the observation moreover , we show that the method has adaptivity to the unknown rank of the true tensor , that is , the near optimal rate depending on the true rank is achieved even if it is not known a priori"
cs.IT,"characterization of the correlations of a multiparticle system remains an open problem we generalize the notion of quantum discord , using multivariate mutual information , to characterize the quantum properties of a multiparticle system this new measure , called dissension , is a set of numbers quantumness vector there are a number of different possible generalizations we consider two of them the track i generalization is discord type , while the track ii generalization is based on all possible measurements we give expressions for these generalizations for n qubit systems , and explore some of their properties we also consider a set of three qubit and four qubit states to illustrate the usefulness of the dissension vector"
cs.IT,"we analyze two user single antenna fading interference channels with perfect receive channel state information \( csi \) and no transmit csi for the case of very strong interference , we prove that decoding interference while treating the intended signal as noise , subtracting the result out , and then decoding the desired signal , a process known as stripping , achieves the diversity multiplexing tradeoff \( dmt \) outer bound derived in akuiyibo and leveque , int zurich seminar on commun , 2008 the proof is constructive in the sense that it provides corresponding code design criteria for dmt optimality for general interference levels , we compute the dmt of a fixed power split han and kobayashi type superposition coding scheme , provide design criteria for the corresponding superposition codes , and find that this scheme is dmt optimal for certain multiplexing rates"
cs.LG|stat.ML,"boosting is one of the most significant developments in machine learning this paper studies the rate of convergence of l 2 boosting , which is tailored for regression , in a high dimensional setting moreover , we introduce so called textquotedblleft post boosting textquotedblright this is a post selection estimator which applies ordinary least squares to the variables selected in the first stage by l 2 boosting another variant is orthogonal boosting where after each step an orthogonal projection is conducted we show that both post l 2 boosting and the orthogonal boosting achieve the same rate of convergence as lasso in a sparse , high dimensional setting the textquotedblleft classical textquotedblright l 2 boosting achieves a slower convergence rate for prediction , but no assumptions on the design matrix are imposed for this result in contrast to rates e g established with lasso we also introduce rules for early stopping which can easily be implemented and will be used in applied work moreover , our results also allow a direct comparison between lasso and boosting that has been missing in the literature finally , we present simulation studies to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting in the simulation studies post l 2 boosting clearly outperforms lasso"
cs.IT,"in a random key graph \( rkg \) of n nodes each node is randomly assigned a key ring of k n cryptographic keys from a pool of p n keys two nodes can communicate directly if they have at least one common key in their key rings we assume that the n nodes are distributed uniformly in 0 , 1 2 in addition to the common key requirement , we require two nodes to also be within r n of each other to be able to have a direct edge thus we have a random graph in which the rkg is superposed on the familiar random geometric graph \( rgg \) for such a random graph , we obtain tight bounds on the relation between k n , p n and r n for the graph to be asymptotically almost surely connected"
cs.LG,"the letor website contains three information retrieval datasets used as a benchmark for testing machine learning ideas for ranking algorithms participating in the challenge are required to assign score values to search results for a collection of queries , and are measured using standard ir ranking measures \( ndcg , precision , map \) that depend only the relative score induced order of the results similarly to many of the ideas proposed in the participating algorithms , we train a linear classifier in contrast with other participating algorithms , we define an additional free variable \( intercept , or benchmark \) for each query this allows expressing the fact that results for different queries are incomparable for the purpose of determining relevance the cost of this idea is the addition of relatively few nuisance parameters our approach is simple , and we used a standard logistic regression library to test it the results beat the reported participating algorithms hence , it seems promising to combine our approach with other more complex ideas"
cs.IT,"the second weight of the generalized reed muller code of order d over the finite field with q elements is now known for d q and d \( n 1 \) \( q 1 \) in this paper , we determine the second weight for the other values of d which are not multiple of q 1 plus 1 for the special case d a \( q 1 \) 1 we give an estimate"
cs.DS,"we study the power of fractional allocations of resources to maximize influence in a network this work extends in a natural way the well studied model by kempe , kleinberg , and tardos \( 2003 \) , where a designer selects a \( small \) seed set of nodes in a social network to influence directly , this influence cascades when other nodes reach certain thresholds of neighbor influence , and the goal is to maximize the final number of influenced nodes despite extensive study from both practical and theoretical viewpoints , this model limits the designer to a binary choice for each node , with no way to apply intermediate levels of influence this model captures some settings precisely , e g exposure to an idea or pathogen , but it fails to capture very relevant concerns in others , for example , a manufacturer promoting a new product by distributing five 20 off coupons instead of giving away one free product while fractional versions of problems tend to be easier to solve than integral versions , for influence maximization , we show that the two versions have essentially the same computational complexity on the other hand , the two versions can have vastly different solutions the added flexibility of fractional allocation can lead to significantly improved influence our main theoretical contribution is to show how to adapt the major positive results from the integral case to the fractional case specifically , mossel and roch \( 2006 \) used the submodularity of influence to obtain their integral results we introduce a new notion of continuous submodularity , and use this to obtain matching fractional results we conclude that we can achieve the same greedy \( 1 1 e epsilon \) approximation for the fractional case as the integral case in practice , we find that the fractional model performs substantially better than the integral model , according to simulations on real world social network data"
cs.IT,"this paper studies spatial capacity in a stochastic wireless ad hoc network , where multi stage probing and data transmission are sequentially performed we propose a novel signal to interference ratio \( sir \) threshold based scheduling scheme , where by starting with the first probing , each transmitter iteratively decides to further probe or stay idle , depending on whether the estimated sir in the proceeding probing is larger or smaller than a predefined threshold although one can assume that the transmitters are initially deployed according to a homogeneous poisson point process \( ppp \) , the sir based scheduling makes the ppp no longer applicable to model the locations of retained transmitters in the subsequent probing and data transmission phases , due to the interference induced coupling in their decisions we first focus on single stage probing and find that when the sir threshold is set sufficiently small to assure an acceptable interference level in the network , the proposed scheme can greatly outperform the non scheduling reference scheme in terms of spatial capacity we clearly characterize the spatial capacity and obtain exact approximate closed form expressions , by proposing a new approximate approach to deal with the correlated sir distributions over non poisson point processes then we successfully extend to multi stage probing by properly designing the multiple sir thresholds to assure gradual improvement of the spatial capacity furthermore , we analyze the impact of multi stage probing overhead and present a probing capacity tradeoff in scheduling design finally , extensive numerical results are presented to demonstrate the performance of the proposed scheduling as compared to existing schemes"
cs.IT,"network coding permits to deploy distributed packet delivery algorithms that locally adapt to the network availability in media streaming applications however , it may also increase delay and computational complexity if it is not implemented efficiently we address here the effective placement of nodes that implement randomized network coding in overlay networks , so that the goodput is kept high while the delay for decoding stays small in streaming applications we first estimate the decoding delay at each client , which depends on the innovative rate in the network this estimation permits to identify the nodes that have to perform coding for a reduced decoding delay we then propose two iterative algorithms for selecting the nodes that should perform network coding the first algorithm relies on the knowledge of the full network statistics the second algorithm uses only local network statistics at each node simulation results show that large performance gains can be achieved with the selection of only a few network coding nodes moreover , the second algorithm performs very closely to the central estimation strategy , which demonstrates that the network coding nodes can be selected efficiently in a distributed manner our scheme shows large gains in terms of achieved throughput , delay and video quality in realistic overlay networks when compared to methods that employ traditional streaming strategies as well as random network nodes selection algorithms"
cs.IT,"we establish the capacity region of several classes of broadcast channels with random state in which the channel to each user is selected from two possible channel state components and the state is known only at the receivers when the channel components are deterministic , we show that the capacity region is achieved via marton coding this channel model does not belong to any class of broadcast channels for which the capacity region was previously known and is useful in studying wireless communication channels when the fading state is known only at the receivers we then establish the capacity region when the channel components are ordered , e g , degraded in particular we show that the capacity region for the broadcast channel with degraded gaussian vector channel components is attained via gaussian input distribution finally , we extend the results on ordered channels to two broadcast channel examples with more than two channel components , but show that these extensions do not hold in general"
cs.SI,"we analyze properties of apportionment functions in context of the problem of allocating seats in the european parliament necessary and sufficient conditions for apportionment functions are investigated some exemplary families of apportionment functions are specified and the corresponding partitions of the seats in the european parliament among the member states of the european union are presented although the choice of the allocation functions is theoretically unlimited , we show that the constraints are so strong that the acceptable functions lead to rather similar solutions"
cs.AI,"this paper gives an overview of the caderige project this project involves teams from different areas \( biology , machine learning , natural language processing \) in order to develop high level analysis tools for extracting structured information from biological bibliographical databases , especially medline the paper gives an overview of the approach and compares it to the state of the art"
cs.IT,"in 1998 , zhang and yeung found the first unconditional non shannon type information inequality recently , dougherty , freiling and zeger gave six new unconditional non shannon type information inequalities this work generalizes their work and provides a method to systematically derive non shannon type information inequalities an application of this method reveals new 4 variable non shannon type information inequalities"
cs.IT,"rank modulation is a way of encoding information to correct errors in flash memory devices as well as impulse noise in transmission lines modeling rank modulation involves construction of packings of the space of permutations equipped with the kendall tau distance we present several general constructions of codes in permutations that cover a broad range of code parameters in particular , we show a number of ways in which conventional error correcting codes can be modified to correct errors in the kendall space codes that we construct afford simple encoding and decoding algorithms of essentially the same complexity as required to correct errors in the hamming metric for instance , from binary bch codes we obtain codes correcting t kendall errors in n memory cells that support the order of n ! \( log 2n ! \) t messages , for any constant t 1 , 2 , we also construct families of codes that correct a number of errors that grows with n at varying rates , from theta \( n \) to theta \( n 2 \) one of our constructions gives rise to a family of rank modulation codes for which the trade off between the number of messages and the number of correctable kendall errors approaches the optimal scaling rate finally , we list a number of possibilities for constructing codes of finite length , and give examples of rank modulation codes with specific parameters"
cs.SI,"community effects on the behaviour of individuals , the community itself and other communities can be observed in a wide range of applications this is true in scientific research , where communities of researchers have increasingly to justify their impact and progress to funding agencies while previous work has tried to explain and analyse such phenomena , there is still a great potential for increasing the quality and accuracy of this analysis , especially in the context of cross community effects in this work , we propose a general framework consisting of several different techniques to analyse and explain such dynamics the proposed methodology works with arbitrary community algorithms and incorporates meta data to improve the overall quality and expressiveness of the analysis we suggest and discuss several approaches to understand , interpret and explain particular phenomena , which themselves are identified in an automated manner we illustrate the benefits and strengths of our approach by exposing highly interesting in depth details of cross community effects between two closely related and well established areas of scientific research we finally conclude and highlight the important open issues on the way towards understanding , defining and eventually predicting typical life cycles and classes of communities in the context of cross community effects"
cs.IT,"bent functions , which are maximally nonlinear boolean functions with even numbers of variables and whose hamming distance to the set of all affine functions equals 2 n 1 pm 2 frac n 2 1 , were introduced by rothaus in 1976 when he considered problems in combinatorics bent functions have been extensively studied due to their applications in cryptography , such as s box , block cipher and stream cipher further , they have been applied to coding theory , spread spectrum and combinatorial design hyper bent functions , as a special class of bent functions , were introduced by youssef and gong in 2001 , which have stronger properties and rarer elements many research focus on the construction of bent and hyper bent functions in this paper , we consider functions defined over mathbb f 2 n by f a , b mathrm tr 1 n \( ax \( 2 m 1 \) \) mathrm tr 1 4 \( bx frac 2 n 1 5 \) , where n 2m , m equiv 2 pmod 4 , a in mathbb f 2 m and b in mathbb f 16 when a in mathbb f 2 m and \( b 1 \) \( b 4 b 1 \) 0 , with the help of kloosterman sums and the factorization of x 5 x a 1 , we present a characterization of hyper bentness of f a , b further , we use generalized ramanujan nagell equations to characterize hyper bent functions of f a , b in the case a in mathbb f 2 frac m 2"
cs.AI,"in this work we start walking the path to a new perspective for viewing cyberwarfare scenarios , by introducing conceptual tools \( a formal model \) to evaluate the costs of an attack , to describe the theater of operations , targets , missions , actions , plans and assets involved in cyberwarfare attacks we also describe two applications of this model autonomous planning leading to automated penetration tests , and attack simulations , allowing a system administrator to evaluate the vulnerabilities of his network"
cs.IT,"spatial coupling has recently emerged as a powerful paradigm to construct graphical models that work well under low complexity message passing algorithms although much progress has been made on the analysis of spatially coupled models under message passing , there is still room for improvement , both in terms of simplifying existing proofs as well as in terms of proving additional properties we introduce one further tool for the analysis , namely the concept of displacement convexity this concept plays a crucial role in the theory of optimal transport and , quite remarkably , it is also well suited for the analysis of spatially coupled systems in cases where the concept applies , displacement convexity allows functionals of distributions which are not convex in the usual sense to be represented in an alternative form , so that they are convex with respect to the new parametrization as a proof of concept we consider spatially coupled \( l , r \) regular gallager ensembles when transmission takes place over the binary erasure channel we show that the potential function of the coupled system is displacement convex due to possible translational degrees of freedom convexity by itself falls short of establishing the uniqueness of the minimizing profile for the spatially coupled \( l , r \) regular system strict displacement convexity holds when a global translation degree of freedom is removed implications for the uniqueness of the minimizer and for solutions of the density evolution equation are discussed"
cs.IT,"we study the problem of channel pairing and power allocation in a multichannel multihop relay network to enhance the end to end data rate both amplify and forward \( af \) and decode and forward \( df \) relaying strategies are considered given fixed power allocation to the channels , we show that channel pairing over multiple hops can be decomposed into independent pairing problems at each relay , and a sorted snr channel pairing strategy is sum rate optimal , where each relay pairs its incoming and outgoing channels by their snr order for the joint optimization of channel pairing and power allocation under both total and individual power constraints , we show that the problem can be decoupled into two subproblems solved separately this separation principle is established by observing the equivalence between sorting snrs and sorting channel gains in the jointly optimal solution it significantly reduces the computational complexity in finding the jointly optimal solution it follows that the channel pairing problem in joint optimization can be again decomposed into independent pairing problems at each relay based on sorted channel gains the solution for optimizing power allocation for df relaying is also provided , as well as an asymptotically optimal solution for af relaying numerical results are provided to demonstrate substantial performance gain of the jointly optimal solution over some suboptimal alternatives it is also observed that more gain is obtained from optimal channel pairing than optimal power allocation through judiciously exploiting the variation among multiple channels impact of the variation of channel gain , the number of channels , and the number of hops on the performance gain is also studied through numerical examples"
cs.IT,"distributed storage systems and associated storage codes can efficiently store a large amount of data while ensuring that data is retrievable in case of node failure the study of such systems , particularly the design of storage codes over finite fields , assumes that the physical channel through which the nodes communicate is error free this is not always the case , for example , in a wireless storage system we study the probability that a subpacket is repaired incorrectly during node repair in a distributed storage system , in which the nodes communicate over an awgn or rayleigh fading channels the asymptotic probability \( as snr increases \) that a node is repaired incorrectly is shown to be completely determined by the repair locality of the dss and the symbol error rate of the wireless channel lastly , we propose some design criteria for physical layer coding in this scenario , and use it to compute optimally rotated qam constellations for use in wireless distributed storage systems"
cs.LG|stat.ML,"we consider the fundamental question of learnability of a hypotheses class in the supervised learning setting and in the general learning setting introduced by vladimir vapnik we survey classic results characterizing learnability in term of suitable notions of complexity , as well as more recent results that establish the connection between learnability and stability of a learning algorithm"
cs.IT,"this paper presents a saddlepoint approximation of the random coding union bound of polyanskiy et al for i i d random coding over discrete memoryless channels the approximation is single letter , and can thus be computed efficiently moreover , it is shown to be asymptotically tight for both fixed and varying rates , unifying existing achievability results in the regimes of error exponents , second order coding rates , and moderate deviations for fixed rates , novel exact asymptotics expressions are specified to within a multiplicative 1 o \( 1 \) term a numerical example is provided for which the approximation is remarkably accurate even at short block lengths"
cs.IT,"by employing the residue polynomials , a construction of constant composition codes is given this construction generalizes the one proposed by xing 16 it turns out that when d 3 this construction gives a lower bound of constant composition codes improving the one in 10 moreover , for d 3 , we give a lower bound on maximal size of constant composition codes in particular , our bound for d 5 gives the best possible size of constant composition codes up to magnitude"
cs.IT,"an interference channel with generalized feedback \( ifc gf \) is a model for a wireless network where several source destination pairs compete for the same channel resources , and where the sources have the ability to sense the current channel activity the signal overheard from the channel provides information about the activity of the other users , and thus furnishes the basis for cooperation in this two part paper we study achievable strategies and outer bounds for a general ifc gf with two source destination pairs we then evaluate the proposed regions for the gaussian channel part i achievable region we propose that the generalized feedback is used to gain knowledge about the message sent by other user and then exploited in two ways \( a \) to em relay the messages that can be decoded at both destinations thus realizing the gains of beam forming of a distributed multi antenna system and \( b \) to em hide the messages that can not be decoded at the non intended destination thus leveraging the interference pre cancellation property of dirty paper type coding we show that our achievable region generalizes several known achievable regions for ifc gf and that it reduces to known achievable regions for some of the channels subsumed by the ifc gf model"
cs.LG|stat.ML,"we show a principled way of deriving online learning algorithms from a minimax analysis various upper bounds on the minimax value , previously thought to be non constructive , are shown to yield algorithms this allows us to seamlessly recover known methods and to derive new ones our framework also captures such unorthodox methods as follow the perturbed leader and the r 2 forecaster we emphasize that understanding the inherent complexity of the learning problem leads to the development of algorithms we define local sequential rademacher complexities and associated algorithms that allow us to obtain faster rates in online learning , similarly to statistical learning theory based on these localized complexities we build a general adaptive method that can take advantage of the suboptimality of the observed sequence we present a number of new algorithms , including a family of randomized methods that use the idea of a random playout several new versions of the follow the perturbed leader algorithms are presented , as well as methods based on the littlestone 's dimension , efficient methods for matrix completion with trace norm , and algorithms for the problems of transductive learning and prediction with static experts"
cs.AI|cs.SI,"email classification is still a mostly manual task consequently , most web mail users never define a single folder recently however , automatic classification offering the same categories to all users has started to appear in some web mail clients , such as aol or gmail we adopt this approach , rather than previous \( unsuccessful \) personalized approaches because of the change in the nature of consumer email traffic , which is now dominated by \( non spam \) machine generated email we propose here a novel approach for \( 1 \) automatically distinguishing between personal and machine generated email and \( 2 \) classifying messages into latent categories , without requiring users to have defined any folder we report how we have discovered that a set of 6 latent categories \( one for human and the others for machine generated messages \) can explain a significant portion of email traffic we describe in details the steps involved in building a web scale email categorization system , from the collection of ground truth labels , the selection of features to the training of models experimental evaluation was performed on more than 500 billion messages received during a period of six months by users of yahoo mail service , who elected to be part of such research studies our system achieved precision and recall rates close to 90 and the latent categories we discovered were shown to cover 70 of both email traffic and email search queries we believe that these results pave the way for a change of approach in the web mail industry , and could support the invention of new large scale email discovery paradigms that had not been possible before"
cs.IT,"we construct a class of linear space time block codes for any number of transmit antennas that have controllable ml decoding complexity with a maximum rate of 1 symbol per channel use the decoding complexity for m transmit antennas can be varied from ml decoding of 2 lceil log 2m rceil 1 symbols together to single symbol ml decoding for ml decoding of 2 lceil log 2m rceil n \( n 1 , 2 , \) symbols together , a diversity of min \( m , 2 lceil log 2m rceil n 1 \) can be achieved numerical results show that the performance of the constructed code when 2 lceil log 2m rceil 1 symbols are decoded together is quite close to the performance of ideal rate 1 orthogonal codes \( that are non existent for more than 2 transmit antennas \)"
cs.DM|cs.DS,"a path relinking algorithm is proposed for the bandwidth coloring problem and the bandwidth multicoloring problem it combines a population based relinking method and a tabu search based local search procedure the proposed algorithm is assessed on two sets of 66 benchmark instances commonly used in the literature computational results demonstrate that the proposed algorithm is highly competitive in terms of both solution quality and efficiency compared to the best performing algorithms in the literature specifically , it improves the previous best known results for 15 out of 66 instances , while matching the previous best known results for 47 cases some key elements of the proposed algorithm are investigated"
cs.DM,"sequence pattern avoidance is a central topic in combinatorics a sequence s contains a sequence u if some subsequence of s can be changed into u by a one to one renaming of its letters if s does not contain u , then s avoids u a widely studied extremal function related to pattern avoidance is ex \( u , n \) , the maximum length of an n letter sequence that avoids u and has every r consecutive letters pairwise distinct , where r is the number of distinct letters in u we bound ex \( u , n \) using the formation width function , fw \( u \) , which is the minimum s for which there exists r such that any concatenation of s permutations , each on the same r letters , contains u in particular , we identify every sequence u such that fw \( u \) 4 and u contains ababa the significance of this result lies in its implication that , for every such sequence u , we have ex \( u , n \) theta \( n alpha \( n \) \) , where alpha \( n \) denotes the incredibly slow growing inverse ackermann function we have thus identified the extremal function of many infinite classes of previously unidentified sequences"
cs.SI,"human mobility has been empirically observed to exhibit levy flight characteristics and behaviour with power law distributed jump size the fundamental mechanisms behind this behaviour has not yet been fully explained in this paper , we analyze urban human mobility and we propose to explain the levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes , such as walk run , bicycle , train subway or car taxi bus our analysis is based on two real life gps datasets containing approximately 10 and 20 million gps samples with transportation mode information we show that human mobility can be modelled as a mixture of different transportation modes , and that these single movement patterns can be approximated by a lognormal distribution rather than a power law distribution then , we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power law distribution , providing an explanation to the emergence of levy walk patterns that characterize human mobility patterns"
cs.SI,"this paper investigates human dynamics in a large online dating site with 3 , 000 new users daily who stay in the system for 3 months on the average the daily activity is also quite large such as 500 , 000 massage transactions , 5 , 000 photo uploads , and 20 , 000 votes the data investigated has 276 , 210 male and 483 , 963 female users based on the activity that they made , there are clear distinctions between men and women in their pattern of behavior men prefer lower , women prefer higher qualifications in their partner"
cs.IT,"in this paper , the capacity region of the two user linear deterministic \( ld \) interference channel with noisy output feedback \( ic nof \) is fully characterized this result allows the identification of several asymmetric scenarios in which imple menting channel output feedback in only one of the transmitter receiver pairs is as beneficial as implementing it in both links , in terms of achievable individual rate and sum rate improvements w r t the case without feedback in other scenarios , the use of channel output feedback in any of the transmitter receiver pairs benefits only one of the two pairs in terms of achievable individual rate improvements or simply , it turns out to be useless , i e , the capacity regions with and without feedback turn out to be identical even in the full absence of noise in the feedback links"
cs.LG|stat.ML,"the icml 2013 workshop on challenges in representation learning focused on three challenges the black box learning challenge , the facial expression recognition challenge , and the multimodal learning challenge we describe the datasets created for these challenges and summarize the results of the competitions we provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions"
cs.IT,the paper studies a class of three user gaussian interference channels a new layered lattice coding scheme is introduced as a transmission strategy the use of lattice codes allows for an alignment of the interference observed at each receiver the layered lattice coding is shown to achieve more than one degree of freedom for a class of interference channels and also achieves rates which are better than the rates obtained using the han kobayashi coding scheme
cs.IT,"the motivation for extending secret sharing schemes to cases when either the set of players is infinite or the domain from which the secret and or the shares are drawn is infinite or both , is similar to the case when switching to abstract probability spaces from classical combinatorial probability it might shed new light on old problems , could connect seemingly unrelated problems , and unify diverse phenomena definitions equivalent in the finitary case could be very much different when switching to infinity , signifying their difference the standard requirement that qualified subsets should be able to determine the secret has different interpretations in spite of the fact that , by assumption , all participants have infinite computing power the requirement that unqualified subsets should have no , or limited information on the secret suggests that we also need some probability distribution in the infinite case events with zero probability are not necessarily impossible , and we should decide whether bad events with zero probability are allowed or not in this paper , rather than giving precise definitions , we enlist an abundance of hopefully interesting infinite secret sharing schemes these schemes touch quite diverse areas of mathematics such as projective geometry , stochastic processes and hilbert spaces nevertheless our main tools are from probability theory the examples discussed here serve as foundation and illustration to the more theory oriented companion paper"
cs.DM,"the genome median problem is an important problem in phylogenetic reconstruction under rearrangement models it can be stated as follows given three genomes , find a fourth that minimizes the sum of the pairwise rearrangement distances between it and the three input genomes recently , feijao and meidanis extended the algebraic theory for genome rearrangement to allow for linear chromosomes , thus yielding a new rearrangement model \( the algebraic model \) , very close to the celebrated dcj model in this paper , we study the genome median problem under the algebraic model , whose complexity is currently open , proposing a more general form of the problem , the matrix median problem it is known that , for any metric distance , at least one of the corners is a 4 3 approximation of the median our results allow us to compute up to three additional matrix median candidates , all of them with approximation ratios at least as good as the best corner , when the input matrices come from genomes from the application point of view , it is usually more interesting to locate medians farther from the corners we also show a fourth median candidate that gives better results in cases we tried however , we do not have proven bounds for this fourth candidate yet"
cs.DS,"this article reviews the 2008 quantum algorithm for linear systems of equations due to harrow , hassidim and lloyd , as well as some of the followup and related work it was submitted to the springer encyclopedia of algorithms"
cs.IT,"in the cloud radio access network \( c ran \) architecture , a control unit \( cu \) implements the baseband processing functionalities of a cluster of base stations \( bss \) , which are connected to it through a fronthaul network this architecture enables centralized processing at the cu , and hence the implementation of enhanced interference mitigation strategies , but it also entails an increased decoding latency due to the transport on the fronthaul network the fronthaul latency may offset the benefits of centralized processing when considering the performance of protocols at layer 2 and above this letter studies the impact of fronthaul latency on the performance of standard automatic retransmission request \( arq \) protocols , namely stop and wait , go back n and selective repeat the performance of the c ran architecture in terms of throughput and efficiency is compared to the that of a conventional cellular system with local processing , as well as with that of a proposed hybrid c ran system in which bss can perform decoding the dynamics of the system are modeled as a multi dimensional markov process that includes sub chains to capture the temporal correlation of interference and channel gains numerical results yield insights into the impact of system parameters such as fronthaul latency and signal to interference ratio on different arq protocols"
cs.IT,"we describe several polygon compression techniques to enable efficient transmission of polygons representing geographical targets the main application is to embed compressed polygons to emergency alert messages that have strict length restrictions , as in the case of wireless emergency alert messages we are able to compress polygons to between 9 7 and 23 6 of original length , depending on characteristics of the specific polygons , reducing original polygon lengths from 43 331 characters to 8 55 characters the best techniques apply several heuristics to perform initial compression , and then other algorithmic techniques , including higher base encoding further , these methods are respectful of computation and storage constraints typical of cell phones two of the best techniques include a enquote bignum quadratic combination of integer coordinates and a variable length encoding , which takes advantage of a strongly skewed polygon coordinate distribution both techniques applied to one of two enquote delta representations of polygons are on average able to reduce the size of polygons by some 80 a repeated substring dictionary can provide further compression , and a merger of these techniques into a enquote polyalgorithm can also provide additional improvements"
cs.SI,"online social networks represent a popular and highly diverse class of social media systems despite this variety , each of these systems undergoes a general process of online social network assembly , which represents the complicated and heterogeneous changes that transform newly born systems into mature platforms however , little is known about this process for example , how much of a network 's assembly is driven by simple growth \? how does a network 's structure change as it matures \? how does network structure vary with adoption rates and user heterogeneity , and do these properties play different roles at different points in the assembly \? we investigate these and other questions using a unique dataset of online connections among the roughly one million users at the first 100 colleges admitted to facebook , captured just 20 months after its launch we first show that different vintages and adoption rates across this population of networks reveal temporal dynamics of the assembly process , and that assembly is only loosely related to network growth we then exploit natural experiments embedded in this dataset and complementary data obtained via internet archaeology to show that different subnetworks , e g , among students and among alumni , matured at different rates toward similar end states these results shed new light on the processes and patterns of online social network assembly , and may facilitate more effective design for online social systems"
cs.IT,"in this paper we analyze several strategies for the estimation of the roughness parameter of the mathcal g i 0 distribution it has been shown that this distribution is able to characterize a large number of targets in monopolarized sar imagery , deserving the denomination of universal model it is indexed by three parameters the number of looks \( which can be estimated in the whole image \) , a scale parameter , and the roughness or texture parameter the latter is closely related to the number of elementary backscatters in each pixel , one of the reasons for receiving attention in the literature although there are efforts in providing improved and robust estimates for such quantity , its dependable estimation still poses numerical problems in practice we discuss estimators based on the minimization of stochastic distances between empirical and theoretical densities , and argue in favor of using an estimator based on the triangular distance and asymmetric kernels built with inverse gaussian densities we also provide new results regarding the heavytailedness of this distribution"
cs.DM,"relative correctness is the property of a program to be more correct than another with respect to a given specification whereas the traditional definition of \( absolute \) correctness divides candidate program into two classes \( correct , and incorrect \) , relative correctness arranges candidate programs on the richer structure of a partial ordering in other venues we discuss the impact of relative correctness on program derivation , and on program verification in this paper , we discuss the impact of relative correctness on program testing specifically , we argue that when we remove a fault from a program , we ought to test the new program for relative correctness over the old program , rather than for absolute correctness we present analytical arguments to support our position , as well as an empirical argument in the form of a small program whose faults are removed in a stepwise manner as its relative correctness rises with each fault removal until we obtain a correct program"
cs.DM|cs.LG,"a boolean function is called read once over a basis b if it can be expressed by a formula over b where no variable appears more than once a checking test for a read once function f over b depending on all its variables is a set of input vectors distinguishing f from all other read once functions of the same variables we show that every read once function f over b has a checking test containing o \( n l \) vectors , where n is the number of relevant variables of f and l is the largest arity of functions in b for some functions , this bound cannot be improved by more than a constant factor the employed technique involves reconstructing f from its l variable projections and provides a stronger form of kuznetsov 's classic theorem on read once representations"
cs.AI|cs.LG,"this paper presents an information theoretic approach to the concept of intelligence in the computational sense we introduce a probabilistic framework from which computational intelligence is shown to be an entropy minimizing process at the local level using this new scheme , we develop a simple data driven clustering example and discuss its applications"
cs.AI,"tackling the problem of ordinal preference revelation and reasoning , we propose a novel methodology for generating an ordinal utility function from a set of qualitative preference statements to the best of our knowledge , our proposal constitutes the first nonparametric solution for this problem that is both efficient and semantically sound our initial experiments provide strong evidence for practical effectiveness of our approach"
cs.AI|cs.DS,"this paper addresses combinatorial optimization scheme for solving the multicriteria steiner tree problem for communication network topology design \( e g , wireless mesh network \) the solving scheme is based on several models multicriteria ranking , clustering , minimum spanning tree , and minimum steiner tree problem an illustrative numerical example corresponds to designing a covering long distance wi fi network \( static ad hoc network \) the set of criteria \( i e , objective functions \) involves the following total cost , total edge length , overall throughput \( capacity \) , and estimate of qos obtained computing results show the suggested solving scheme provides good network topologies which can be compared with minimum spanning trees"
cs.IT,"berger 's paper `the source coding game' , ieee trans inform theory , 1971 , considers the problem of finding the rate distortion function for an adversarial source comprised of multiple known iid sources the adversary , called the `switcher' , was allowed only causal access to the source realizations and the rate distortion function was obtained through the use of a type covering lemma in this paper , the rate distortion function of the adversarial source is described , under the assumption that the switcher has non causal access to all source realizations the proof utilizes the type covering lemma and simple conditional , random `switching' rules the rate distortion function is once again the maximization of the r \( d \) function for a region of attainable iid distributions"
cs.DS,"this paper proposes an efficient and novel method to address range search on multidimensional points in theta \( t \) time , where t is the number of points reported in re k space this is accomplished by introducing a new data structure , called bits k d tree this structure also supports fast updation that takes theta \( 1 \) time for insertion and o \( log n \) time for deletion the earlier best known algorithm for this problem is o \( log k n t \) time in the pointer machine model"
cs.IT,"in this paper , a novel technique is proposed to address the joint sampling timing acquisition for baseband and broadband power line communication \( bb plc \) systems using orthogonal frequency division multiplexing \( ofdm \) , including the sampling phase offset \( spo \) and the sampling clock offset \( sco \) under pairwise correlation and joint gaussian assumption of received signals in frequency domain , an approximated form of the log likelihood function is derived instead of a high complexity two dimension grid search on the likelihood function , a five step method is employed for accurate estimations several variants are presented in the same framework with different complexities unlike conventional pilot assisted schemes using the extra phase rotations within one ofdm block , the proposed technique turns to the phase rotations between adjacent ofdm blocks analytical expressions of the variances and biases are derived extensive simulation results indicate significant performance improvements over conventional schemes additionally , effects of several noise models including non gaussianity , cyclo stationarity , and temporal correlation are analyzed and simulated robustness of the proposed technique against violation of the joint gaussian assumption is also verified by simulations"
cs.IT,"the problem of signal recovery from the autocorrelation , or equivalently , the magnitudes of the fourier transform , is of paramount importance in various fields of engineering in this work , for one dimensional signals , we give conditions , which when satisfied , allow unique recovery from the autocorrelation with very high probability in particular , for sparse signals , we develop two non iterative recovery algorithms one of them is based on combinatorial analysis , which we prove can recover signals upto sparsity o \( n 1 3 \) with very high probability , and the other is developed using a convex optimization based framework , which numerical simulations suggest can recover signals upto sparsity o \( n 1 2 \) with very high probability"
cs.DM,"in an undirected graph g , a subset c subseteq v \( g \) such that c is a dominating set of g , and each vertex in v \( g \) is dominated by a distinct subset of vertices from c , is called an identifying code of g the concept of identifying codes was introduced by karpovsky , chakrabarty and levitin in 1998 for a given identifiable graph g , let m \( g \) be the minimum cardinality of an identifying code in g in this paper , we show that for any connected identifiable triangle free graph g on n vertices having maximum degree delta geq 3 , m \( g \) le n tfrac n delta o \( delta \) this bound is asymptotically tight up to constants due to various classes of graphs including \( delta 1 \) ary trees , which are known to have their minimum identifying code of size n tfrac n delta 1 o \( 1 \) we also provide improved bounds for restricted subfamilies of triangle free graphs , and conjecture that there exists some constant c such that the bound m \( g \) le n tfrac n delta c holds for any nontrivial connected identifiable graph g"
cs.IT|cs.LG,"given a random binary sequence x \( n \) of random variables , x t , t 1 , 2 , , n , for instance , one that is generated by a markov source \( teacher \) of order k \( each state represented by k bits \) assume that the probability of the event x t 1 is constant and denote it by beta consider a learner which is based on a parametric model , for instance a markov model of order k , who trains on a sequence x \( m \) which is randomly drawn by the teacher test the learner 's performance by giving it a sequence x \( n \) \( generated by the teacher \) and check its predictions on every bit of x \( n \) an error occurs at time t if the learner 's prediction y t differs from the true bit value x t denote by xi \( n \) the sequence of errors where the error bit xi t at time t equals 1 or 0 according to whether the event of an error occurs or not , respectively consider the subsequence xi \( nu \) of xi \( n \) which corresponds to the errors of predicting a 0 , i e , xi \( nu \) consists of the bits of xi \( n \) only at times t such that y t 0 in this paper we compute an estimate on the deviation of the frequency of 1s of xi \( nu \) from beta the result shows that the level of randomness of xi \( nu \) decreases relative to an increase in the complexity of the learner"
cs.DM|cs.SI,"randomized gossip is one of the most popular way of disseminating information in large scale networks this method is appreciated for its simplicity , robustness , and efficiency in the push protocol , every informed node selects , at every time step \( a k a round \) , one of its neighboring node uniformly at random and forwards the information to this node this protocol is known to complete information spreading in o \( log n \) time steps with high probability \( w h p \) in several families of n node static networks the push protocol has also been empirically shown to perform well in practice , and , specifically , to be robust against dynamic topological changes in this paper , we aim at analyzing the push protocol in dynamic networks we consider the edge markovian evolving graph model which captures natural temporal dependencies between the structure of the network at time t , and the one at time t 1 precisely , a non edge appears with probability p , while an existing edge dies with probability q in order to fit with real world traces , we mostly concentrate our study on the case where p omega \( 1 n \) and q is constant we prove that , in this realistic scenario , the push protocol does perform well , completing information spreading in o \( log n \) time steps w h p note that this performance holds even when the network is , w h p , disconnected at every time step \( e g , when p \( log n \) n \) our result provides the first formal argument demonstrating the robustness of the push protocol against network changes we also address other ranges of parameters p and q \( e g , p q 1 with arbitrary p and q , and p 1 n with arbitrary q \) although they do not precisely fit with the measures performed on real world traces , they can be of independent interest for other settings the results in these cases confirm the positive impact of dynamism"
cs.IT,"we develop a flexible and accurate framework for device to device \( d2d \) communication in the context of a conventional cellular network , which allows for time frequency resources to be either shared or orthogonally partitioned between the two networks using stochastic geometry , we provide accurate expressions for sinr distributions and average rates , under an assumption of interference randomization via time and or frequency hopping , for both dedicated and shared spectrum approaches we obtain analytical results in closed or semi closed form in high snr regime , that allow us to easily explore the impact of key parameters \( e g , the load and hopping probabilities \) on the network performance in particular , unlike other models , the expressions we obtain are tractable , i e , they can be efficiently optimized without extensive simulation using these , we optimize the hopping probabilities for the d2d links , i e , how often they should request a time or frequency slot this can be viewed as an optimized lower bound to other more sophisticated scheduling schemes we also investigate the optimal resource partitions between d2d and cellular networks when they use orthogonal resources"
cs.LG|stat.ML,"this work provides a computationally efficient and statistically consistent moment based estimator for mixtures of spherical gaussians under the condition that component means are in general position , a simple spectral decomposition technique yields consistent parameter estimates from low order observable moments , without additional minimum separation assumptions needed by previous computationally efficient estimation procedures thus computational and information theoretic barriers to efficient estimation in mixture models are precluded when the mixture components have means in general position and spherical covariances some connections are made to estimation problems related to independent component analysis"
cs.LG,"a valuation for a player in a game in extensive form is an assignment of numeric values to the players moves the valuation reflects the desirability moves we assume a myopic player , who chooses a move with the highest valuation valuations can also be revised , and hopefully improved , after each play of the game here , a very simple valuation revision is considered , in which the moves made in a play are assigned the payoff obtained in the play we show that by adopting such a learning process a player who has a winning strategy in a win lose game can almost surely guarantee a win in a repeated game when a player has more than two payoffs , a more elaborate learning procedure is required we consider one that associates with each move the average payoff in the rounds in which this move was made when all players adopt this learning procedure , with some perturbations , then , with probability 1 , strategies that are close to subgame perfect equilibrium are played after some time a single player who adopts this procedure can guarantee only her individually rational payoff"
cs.DS,"we study the makespan minimization problem with unrelated selfish machines under the assumption that job sizes are stochastic we design simple truthful mechanisms that under various distributional assumptions provide constant and sublogarithmic approximations to expected makespan our mechanisms are prior independent in that they do not rely on knowledge of the job size distributions prior independent approximation mechanisms have been previously studied for the objective of revenue maximization dhangwatnotai , roughgarden and yan'10 , devanur , hartline , karlin and nguyen'11 , roughgarden , talgam cohen and yan'12 in contrast to our results , in prior free settings no truthful anonymous deterministic mechanism for the makespan objective can provide a sublinear approximation ashlagi , dobzinski and lavi'09"
cs.AI,"inspired by the recent success of methods that employ shape priors to achieve robust 3d reconstructions , we propose a novel recurrent neural network architecture that we call the 3d recurrent reconstruction neural network \( 3d r2n2 \) the network learns a mapping from images of objects to their underlying 3d shapes from a large collection of synthetic data our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3d occupancy grid unlike most of the previous works , our network does not require any image annotations or object class labels for training or testing our extensive experimental analysis shows that our reconstruction framework i \) outperforms the state of the art methods for single view reconstruction , and ii \) enables the 3d reconstruction of objects in situations when traditional sfm slam methods fail \( because of lack of texture and or wide baseline \)"
cs.IT,"compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements in this paper , a k times n measurement matrix for compressed sensing is deterministically constructed via multiplicative character sequences precisely , a constant multiple of a cyclic shift of an m ary power residue or sidelnikov sequence is arranged as a column vector of the matrix , through modulating a primitive m th root of unity the weil bound is then used to show that the matrix has asymptotically optimal coherence for large k and m , and to present a sufficient condition on the sparsity level for unique sparse solution also , the restricted isometry property \( rip \) is statistically studied for the deterministic matrix numerical results show that the deterministic compressed sensing matrix guarantees reliable matching pursuit recovery performance for both noiseless and noisy measurements"
cs.IT,"multi hop relaying is an economically efficient architecture for coverage extension and throughput enhancement in future wireless networks ofdm , on the other hand , is a spectrally efficient physical layer modulation technique for broadband transmission as a natural consequence of combining ofdm with multi hop relaying , the allocation of per hop subcarrier power and per hop transmission time is crucial in optimizing the network performance this paper is concerned with the end to end information outage in an ofdm based linear relay network our goal is to find an optimal power and time adaptation policy to minimize the outage probability under a long term total power constraint we solve the problem in two steps first , for any given channel realization , we derive the minimum short term power required to meet a target transmission rate we show that it can be obtained through two nested bisection loops to reduce computational complexity and signalling overhead , we also propose a sub optimal algorithm in the second step , we determine a power threshold to control the transmission on off so that the long term total power constraint is satisfied numerical examples are provided to illustrate the performance of the proposed power and time adaptation schemes with respect to other resource adaptation schemes"
cs.DS,"we show an improved lower bound for the fp estimation problem in a data stream setting for p 2 a data stream is a sequence of items from the domain n with possible repetitions the frequency vector x is an n dimensional non negative integer vector x such that x \( i \) is the number of occurrences of i in the sequence given an accuracy parameter omega \( n 1 p \) epsilon 1 , the problem of estimating fp is to estimate norm x p p sum i in n abs x \( i \) p correctly to within a relative accuracy of 1 pm epsilon with high constant probability in an online fashion and using as little space as possible the current space lower bound for this problem is omega \( n 1 2 p epsilon 2 p n 1 2 p epsilon 4 p log o \( 1 \) \( n \) \( epsilon 2 log \( n \) \) \) the first term in the lower bound expression was proved in cite b yjks stoc02 , cks ccc03 , the second in cite wz arxiv11 and the third in cite wood soda04 in this note , we show an omega \( p 2 n 1 2 p epsilon 2 log \( n \) \) bits space bound , for omega \( pn 1 p \) le epsilon le 1 10"
cs.IT,a novel general ready to use bit error rate \( ber \) expression for one dimensional constellations is developed the ber analysis is performed for bit patterns that form a labeling the number of patterns for equally spaced m pam constellations with different ber is analyzed
cs.SI,"researchers use community detection algorithms to reveal large scale organization in biological and social networks , but community detection is useful only if the communities are significant and not a result of noisy data to assess the statistical significance of the network communities , or the robustness of the detected structure , one approach is to perturb the network structure by removing links and measure how much the communities change however , perturbing sparse networks is challenging because they are inherently sensitive they shatter easily if links are removed here we propose a simple method to perturb sparse networks and assess the significance of their communities we generate resampled networks by adding extra links based on local information , then we aggregate the information from multiple resampled networks to find a coarse grained description of significant clusters in addition to testing our method on benchmark networks , we use our method on the sparse network of the european court of justice \( ecj \) case law , to detect significant and insignificant areas of law we use our significance analysis to draw a map of the ecj case law network that reveals the relations between the areas of law"
cs.IT,"in this note , we present an information diffusion inequality derived from an elementary argument , which gives rise to a very general fano type inequality the latter unifies and generalizes the distance based fano inequality and the continuous fano inequality established in corollary 1 , propositions 1 and 2 , arxiv 1311 2669v2 , as well as the generalized fano inequality in equation following \( 10 \) t s han and s verd 'u generalizing the fano inequality ieee transactions on information theory , 40 \( 4 \) 1247 1251 , july 1994"
cs.IT,"we address the problem of optimizing the throughput of network coded traffic in mobile networks operating in challenging environments where connectivity is intermittent and locally available memory space is limited random linear network coding \( rlnc \) is shown to be equivalent \( across all possible initial conditions \) to a random message selection strategy where nodes are able to exchange buffer occupancy information during contacts this result creates the premises for a tractable analysis of rlnc packet spread , which is in turn used for enhancing its throughput under broadcast by exploiting the similarity between channel coding and rlnc in intermittently connected networks , we show that quite surprisingly , network coding , when not used properly , is still significantly underutilizing network resources we propose an enhanced forwarding protocol that increases considerably the throughput for practical cases , with negligible additional delay"
cs.IT,"this paper considers reliable communications over a multiple input multiple output \( mimo \) gaussian channel , where the channel matrix is within a bounded channel uncertainty region around a nominal channel matrix , i e , an instance of the compound mimo gaussian channel we study the optimal transmit covariance matrix design to achieve the capacity of compound mimo gaussian channels , where the channel uncertainty region is characterized by the spectral norm this design problem is a challenging non convex optimization problem however , in this paper , we reveal that this problem has a hidden convexity property , which can be exploited to map the problem into a convex optimization problem we first prove that the optimal transmit design is to diagonalize the nominal channel , and then show that the duality gap between the capacity of the compound mimo gaussian channel and the min max channel capacity is zero , which proves the conjecture of loyka and charalambous \( ieee trans inf theory , vol 58 , no 4 , pp 2048 2063 , 2012 \) the key tools for showing these results are a new matrix determinant inequality and some unitarily invariant properties"
cs.LG|stat.ML,"we present a dirichlet process mixture model over discrete incomplete rankings and study two gibbs sampling inference techniques for estimating posterior clusterings the first approach uses a slice sampling subcomponent for estimating cluster parameters the second approach marginalizes out several cluster parameters by taking advantage of approximations to the conditional posteriors we empirically demonstrate \( 1 \) the effectiveness of this approximation for improving convergence , \( 2 \) the benefits of the dirichlet process model over alternative clustering techniques for ranked data , and \( 3 \) the applicability of the approach to exploring large realworld ranking datasets"
cs.IT,"in this paper , we propose a simple procedure to construct \( decodable \) good codes with any given alphabet \( of moderate size \) for any given \( rational \) code rate to achieve any given target error performance \( of interest \) over the additive white gaussian noise \( awgn \) channels we start with constructing codes over groups for any given code rates this can be done in an extremely simple way if we ignore the error performance requirement for the time being actually , this can be satisfied by repetition \( r \) codes and uncoded \( un \) transmission along with time sharing technique the resulting codes are simply referred to as run codes for convenience the encoding decoding algorithms for run codes are almost trivial in addition , the performance can be easily analyzed it is not difficult to imagine that a run code usually performs far away from the corresponding shannon limit fortunately , the performance can be improved as required by spatially coupling the run codes via block markov superposition transmission \( bmst \) , resulting in the bmst run codes simulation results show that the bmst run codes perform well \( within one db away from shannon limits \) for a wide range of code rates color black and outperform the bmst with bit interleaved coded modulation \( bmst bicm \) scheme"
cs.LG|stat.ML,"determinantal point processes \( dpps \) have garnered attention as an elegant probabilistic model of set diversity they are useful for a number of subset selection tasks , including product recommendation dpps are parametrized by a positive semi definite kernel matrix in this work we present a new method for learning the dpp kernel from observed data using a low rank factorization of this kernel we show that this low rank factorization enables a learning algorithm that is nearly an order of magnitude faster than previous approaches , while also providing for a method for computing product recommendation predictions that is far faster \( up to 20x faster or more for large item catalogs \) than previous techniques that involve a full rank dpp kernel furthermore , we show that our method provides equivalent or sometimes better predictive performance than prior full rank dpp approaches , and better performance than several other competing recommendation methods in many cases we conduct an extensive experimental evaluation using several real world datasets in the domain of product recommendation to demonstrate the utility of our method , along with its limitations"
cs.SI,"we present the first comprehensive characterization of the diffusion of ideas on twitter , studying more than 4000 topics that include both popular and less popular topics on a data set containing approximately 10 million users and a comprehensive scraping of all the tweets posted by these users between june 2009 and august 2009 \( approximately 200 million tweets \) , we perform a rigorous temporal and spatial analysis , investigating the time evolving properties of the subgraphs formed by the users discussing each topic we focus on two different notions of the spatial the network topology formed by follower following links on twitter , and the geospatial location of the users we investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on popularity we deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively"
cs.DS,"gossip algorithms spread information by having nodes repeatedly forward information to a few random contacts by their very nature , gossip algorithms tend to be distributed and fault tolerant if done right , they can also be fast and message efficient a common model for gossip communication is the random phone call model , in which in each synchronous round each node can push or pull information to or from a random other node for example , karp et al focs 2000 gave algorithms in this model that spread a message to all nodes in theta \( log n \) rounds while sending only o \( log log n \) messages per node on average recently , avin and els asser disc 2013 , studied the random phone call model with the natural and commonly used assumption of direct addressing direct addressing allows nodes to directly contact nodes whose id \( e g , ip address \) was learned before they show that in this setting , one can break the log n barrier and achieve a gossip algorithm running in o \( sqrt log n \) rounds , albeit while using o \( sqrt log n \) messages per node we study the same model and give a simple gossip algorithm which spreads a message in only o \( log log n \) rounds we also prove a matching omega \( log log n \) lower bound which shows that this running time is best possible in particular we show that any gossip algorithm takes with high probability at least 0 99 log log n rounds to terminate lastly , our algorithm can be tweaked to send only o \( 1 \) messages per node on average with only o \( log n \) bits per message our algorithm therefore simultaneously achieves the optimal round , message , and bit complexity for this setting as all prior gossip algorithms , our algorithm is also robust against failures in particular , if in the beginning an oblivious adversary fails any f nodes our algorithm still , with high probability , informs all but o \( f \) surviving nodes"
cs.SI,"understanding how and how far information , behaviors , or pathogens spread in social networks is an important problem , having implications for both predicting the size of epidemics , as well as for planning effective interventions there are , however , two main challenges for inferring spreading paths in real world networks one is the practical difficulty of observing a dynamic process on a network , and the other is the typical constraint of only partially observing a network using a static , structurally realistic social network as a platform for simulations , we juxtapose three distinct paths \( 1 \) the stochastic path taken by a simulated spreading process from source to target \( 2 \) the topologically shortest path in the fully observed network , and hence the single most likely stochastic path , between the two nodes and \( 3 \) the topologically shortest path in a partially observed network in a sampled network , how closely does the partially observed shortest path \( 3 \) emulate the unobserved spreading path \( 1 \) \? although partial observation inflates the length of the shortest path , the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path we find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path in fact , partial observation may , counterintuitively , make the path seem shorter than it actually is"
cs.IT,"asymptotic expressions of the mutual information between any discrete input and the corresponding output of the scalar additive white gaussian noise channel are presented in the limit as the signal to noise ratio \( snr \) tends to infinity asymptotic expressions of the symbol error probability \( sep \) and the minimum mean square error \( mmse \) achieved by estimating the channel input given the channel output are also developed it is shown that for any input distribution , the conditional entropy of the channel input given the output , mmse and sep have an asymptotic behavior proportional to the gaussian q function the argument of the q function depends only on the minimum euclidean distance \( med \) of the constellation and the snr , and the proportionality constants are functions of the med and the probabilities of the pairs of constellation points at med the developed expressions are then generalized to study the high snr behavior of the generalized mutual information \( gmi \) for bit interleaved coded modulation \( bicm \) by means of these asymptotic expressions , the long standing conjecture that gray codes are the binary labelings that maximize the bicm gmi at high snr is proven it is further shown that for any equally spaced constellation whose size is a power of two , there always exists an anti gray code giving the lowest bicm gmi at high snr"
cs.IT,"a program can be considered as a device that generates discrete time signals , where a signal is an execution shannon information rate , or bit rate , of the signals may not be uniformly distributed when the program is specified by a finite state transition system , algorithms are provided in identifying information rich components for a black box program that has a partial specification or does not even have a specification , a bit rate signal and its spectrum are studied , which make use of data compression and the fourier transform the signal provides a bit rate coverage for testing the black box while its spectrum indicates a visual representation for execution 's information characteristics"
cs.DS,"this paper presents a method to analyze the powers of a given trilinear form \( a special kind of algebraic constructions also called a tensor \) and obtain upper bounds on the asymptotic complexity of matrix multiplication compared with existing approaches , this method is based on convex optimization , and thus has polynomial time complexity as an application , we use this method to study powers of the construction given by coppersmith and winograd journal of symbolic computation , 1990 and obtain the upper bound omega 2 3728639 on the exponent of square matrix multiplication , which slightly improves the best known upper bound"
cs.SI,"motivated by results of henry , pralat and zhang \( pnas 108 21 \( 2011 \) 8605 8610 \) , we propose a general scheme for evolving spatial networks in order to reduce their total edge lengths we study the properties of the equilbria of two networks from this class , which interpolate between three well studied objects the erd h o s r ' e nyi random graph , the random geometric graph , and the minimum spanning tree the first of our two evolutions can be used as a model for a social network where individuals have fixed opinions about a number of issues and adjust their ties to be connected to people with similar views the second evolution which preserves the connectivity of the network has potential applications in the design of transportation networks and other distribution systems"
cs.AI|cs.IT,"this article is a brief personal account of the past , present , and future of algorithmic randomness , emphasizing its role in inductive inference and artificial intelligence it is written for a general audience interested in science and philosophy intuitively , randomness is a lack of order or predictability if randomness is the opposite of determinism , then algorithmic randomness is the opposite of computability besides many other things , these concepts have been used to quantify ockham 's razor , solve the induction problem , and define intelligence"
cs.AI|cs.LG|stat.ML,"the true online td \( lambda \) algorithm has recently been proposed \( van seijen and sutton , 2014 \) as a universal replacement for the popular td \( lambda \) algorithm , in temporal difference learning and reinforcement learning true online td \( lambda \) has better theoretical properties than conventional td \( lambda \) , and the expectation is that it also results in faster learning in this paper , we put this hypothesis to the test specifically , we compare the performance of true online td \( lambda \) with that of td \( lambda \) on challenging examples , random markov reward processes , and a real world myoelectric prosthetic arm we use linear function approximation with tabular , binary , and non binary features we assess the algorithms along three dimensions computational cost , learning speed , and ease of use our results confirm the strength of true online td \( lambda \) 1 \) for sparse feature vectors , the computational overhead with respect to td \( lambda \) is minimal for non sparse features the computation time is at most twice that of td \( lambda \) , 2 \) across all domains representations the learning speed of true online td \( lambda \) is often better , but never worse than that of td \( lambda \) , and 3 \) true online td \( lambda \) is easier to use , because it does not require choosing between trace types , and it is generally more stable with respect to the step size overall , our results suggest that true online td \( lambda \) should be the first choice when looking for an efficient , general purpose td method"
cs.DS,"given a machine u , a c short program for x is a string p such that u \( p \) x and the length of p is bounded by c \( the length of a shortest program for x \) we show that for any standard turing machine , it is possible to compute in polynomial time on input x a list of polynomial size guaranteed to contain a o \( log x \) short program for x we also show that there exists a computable function that maps every x to a list of size x 2 containing a o \( 1 \) short program for x this is essentially optimal because we prove that for each such function there is a c and infinitely many x for which the list has size at least c x 2 finally we show that for some standard machines , computable functions generating lists with 0 short programs , must have infinitely often list sizes proportional to 2 x"
cs.AI,"we identify the presence of pet fish problem situations and the corresponding guppy effect of concept theory on the world wide web for this purpose , we introduce absolute weights for words expressing concepts and relative weights between words expressing concepts , and the notion of 'meaning bound' between two words expressing concepts , making explicit use of the conceptual structure of the world wide web the pet fish problem occurs whenever there are exemplars in the case of pet and fish these can be guppy or goldfish for which the meaning bound with respect to the conjunction is stronger than the meaning bounds with respect to the individual concepts"
cs.LG,"recognizing analogies , synonyms , antonyms , and associations appear to be four distinct tasks , requiring distinct nlp algorithms in the past , the four tasks have been treated independently , using a wide variety of algorithms these four semantic classes , however , are a tiny sample of the full range of semantic phenomena , and we cannot afford to create ad hoc algorithms for each semantic phenomenon we need to seek a unified approach we propose to subsume a broad range of phenomena under analogies to limit the scope of this paper , we restrict our attention to the subsumption of synonyms , antonyms , and associations we introduce a supervised corpus based machine learning algorithm for classifying analogous word pairs , and we show that it can solve multiple choice sat analogy questions , toefl synonym questions , esl synonym antonym questions , and similar associated both questions from cognitive psychology"
cs.IT,"full duplex \( fd \) communications has the potential to double the capacity of a half duplex \( hd \) system at the link level however , fd operation increases the aggregate interference on each communication link , which limits the capacity improvement in this paper , we investigate how much of the potential doubling can be practically achieved in the resource managed , small multi cellular system , similar to the tdd variant of lte , both in indoor and outdoor environments , assuming fd base stations \( bss \) and hd user equipment \( ues \) we focus on low powered small cellular systems , because they are more suitable for fd operation given practical self interference cancellation limits a joint ue selection and power allocation method for a multi cell scenario is presented , where a hybrid scheduling policy assigns fd timeslots when it provides a throughput advantage by pairing ues with appropriate power levels to mitigate the mutual interference , but otherwise defaults to hd operation due to the complexity of finding the globally optimum solution of the proposed algorithm , a sub optimal method based on a heuristic greedy algorithm for ue selection , and a novel solution using geometric programming for power allocation , is proposed with practical self interference cancellation , antennas and circuits , it is shown that the proposed hybrid fd system achieves as much as 94 throughput improvement in the downlink , and 92 in the uplink , compared to a hd system in an indoor multi cell scenario and 54 in downlink and 61 in uplink in an outdoor multi cell scenario further , we also compare the energy efficiency of fd operation"
cs.IT,"fountain codes are rateless erasure correcting codes , i e , an essentially infinite stream of encoded packets can be generated from a finite set of data packets several fountain codes have been proposed recently to minimize overhead , many of which involve modifications of the luby transform \( lt \) code these fountain codes , like the lt code , have the implicit assumption that the probability distribution is fixed throughout the encoding process in this paper , we will use the theory of posets to show that this assumption is unnecessary , and by dropping it , we can achieve overhead reduction by as much as 64 lower than lt codes we also present the fundamental theory of probability distribution designs for fountain codes with non constant probability distributions that minimize overhead"
cs.IT,"in this paper we introduce a nonextensive quantum information theoretic measure which may be defined between any arbitrary number of density matrices , and we analyze its fundamental properties in the spectral graph theoretic framework unlike other entropic measures , the proposed quantum divergence is symmetric , matrix convex , theoretically upper bounded , and has the advantage of being generalizable to any arbitrary number of density matrices , with a possibility of assigning weights to these densities"
cs.DM,"due to implementation constraints the xor operation is widely used in order to combine plaintext and key bit strings in secret key block ciphers this choice directly induces the classical version of the differential attack by the use of xor kind differences while very natural , there are many alternatives to the xor each of them inducing a new form for its corresponding differential attack \( using the appropriate notion of difference \) and therefore block ciphers need to use s boxes that are resistant against these nonstandard differential cryptanalysis in this contribution we study the functions that offer the best resistance against a differential attack based on a finite field multiplication we also show that in some particular cases , there are robust permutations which offers the best resistant against both multiplication and exponentiation base differential attacks we call them doubly perfect nonlinear permutations"
cs.AI|cs.LG|stat.ML,"a key problem in reinforcement learning for control with general function approximators \( such as deep neural networks and other nonlinear functions \) is that , for many algorithms employed in practice , updates to the policy or q function may fail to improve performance or worse , actually cause the policy performance to degrade prior work has addressed this for policy iteration by deriving tight policy improvement bounds by optimizing the lower bound on policy improvement , a better policy is guaranteed however , existing approaches suffer from bounds that are hard to optimize in practice because they include sup norm terms which cannot be efficiently estimated or differentiated in this work , we derive a better policy improvement bound where the sup norm of the policy divergence has been replaced with an average divergence this leads to an algorithm , easy monotonic policy iteration , that generates sequences of policies with guaranteed non decreasing returns and is easy to implement in a sample based framework"
cs.IT,"this paper presents a new construction of maximum distance separable \( mds \) reed solomon erasure codes based on fermat number transform \( fnt \) thanks to fnt , these codes support practical coding and decoding algorithms with complexity o \( n log n \) , where n is the number of symbols of a codeword an open source implementation shows that the encoding speed can reach 150mbps for codes of length up to several 10 , 000s of symbols these codes can be used as the basic component of the information dispersal algorithm \( ida \) system used in a several p2p systems"
cs.LG|stat.ML,"i propose a variational approach to maximum pseudolikelihood inference of the ising model the variational algorithm is more computationally efficient , and does a better job predicting out of sample correlations than l 2 regularized maximum pseudolikelihood inference as well as mean field and isolated spin pair approximations with pseudocount regularization the key to the approach is a variational energy that regularizes the inference problem by shrinking the couplings towards zero , while still allowing some large couplings to explain strong correlations the utility of the variational pseudolikelihood approach is illustrated by training an ising model to represent the letters a j using samples of letters from different computer fonts"
cs.IT,"while most useful information theoretic inequalities can be deduced from the basic properties of entropy or mutual information , up to now shannon 's entropy power inequality \( epi \) is an exception existing information theoretic proofs of the epi hinge on representations of differential entropy using either fisher information or minimum mean square error \( mmse \) , which are derived from de bruijn 's identity in this paper , we first present an unified view of these proofs , showing that they share two essential ingredients 1 \) a data processing argument applied to a covariance preserving linear transformation 2 \) an integration over a path of a continuous gaussian perturbation using these ingredients , we develop a new and brief proof of the epi through a mutual information inequality , which replaces stam and blachman 's fisher information inequality \( fii \) and an inequality for mmse by guo , shamai and verd 'u used in earlier proofs the result has the advantage of being very simple in that it relies only on the basic properties of mutual information these ideas are then generalized to various extended versions of the epi zamir and feder 's generalized epi for linear transformations of the random variables , takano and johnson 's epi for dependent variables , liu and viswanath 's covariance constrained epi , and costa 's concavity inequality for the entropy power"
cs.IT,"this paper investigates the randomness properties of a function of the divisor pairs of a natural number this function , the antecedents of which go to very ancient times , has randomness properties that can find applications in cryptography , key distribution , and other problems of computer science it is shown that the function is aperiodic and it has excellent autocorrelation properties"
cs.SI,"we present the analysis of the interrelation between two processes accounting for the spreading of an epidemics , and the information awareness to prevent its infection , on top of multiplex networks this scenario is representative of an epidemic process spreading on a network of persistent real contacts , and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals the topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other the analysis using a microscopic markov chain approach \( mmca \) reveals the phase diagram of the incidence of the epidemics and allows to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process interestingly , the critical point for the onset of the epidemics has a critical value \( meta critical point \) defined by the awareness dynamics and the topology of the virtual network , from which the onset increases and the epidemics incidence decreases"
cs.AI,"part i describes an intelligent acoustic emission locator , while part ii discusses blind source separation , time delay estimation and location of two continuous acoustic emission sources acoustic emission \( ae \) analysis is used for characterization and location of developing defects in materials ae sources often generate a mixture of various statistically independent signals a difficult problem of ae analysis is separation and characterization of signal components when the signals from various sources and the mode of mixing are unknown recently , blind source separation \( bss \) by independent component analysis \( ica \) has been used to solve these problems the purpose of this paper is to demonstrate the applicability of ica to locate two independent simultaneously active acoustic emission sources on an aluminum band specimen the method is promising for non destructive testing of aircraft frame structures by acoustic emission analysis"
cs.DM|cs.LG,"the goal of the paper is to relate complexity measures associated with the evaluation of boolean functions \( certificate complexity , decision tree complexity \) and learning dimensions used to characterize exact learning \( teaching dimension , extended teaching dimension \) the high level motivation is to discover non trivial relations between exact learning of an unknown concept and testing whether an unknown concept is part of a concept class or not concretely , the goal is to provide lower and upper bounds of complexity measures for one problem type in terms of the other"
cs.IT,"we consider a two hop wireless network where a transmitter communicates with a receiver via m relays with an amplify and forward \( af \) protocol recent works have shown that sophisticated linear processing such as beamforming and distributed space time coding \( dstc \) at relays enables to improve the af performance however , the relative utility of these strategies depend on the available channel state information at transmitter \( csit \) , which in turn depends on system parameters such as the speed of the underlying fading channel and that of training and feedback procedures moreover , it is of practical interest to have a single transmit scheme that handles different csit scenarios this motivates us to consider a unified approach based on dstc that potentially provides diversity gain with statistical csit and exploits some additional side information if available under individual power constraints at the relays , we optimize the amplifier power allocation such that pairwise error probability conditioned on the available csit is minimized under perfect csit we propose an on off gradient algorithm that efficiently finds a set of relays to switch on under partial and statistical csit , we propose a simple waterfilling algorithm that yields a non trivial solution between maximum power allocation and a generalized stc that equalizes the averaged amplified noise for all relays moreover , we derive closed form solutions for m 2 and in certain asymptotic regimes that enable an easy interpretation of the proposed algorithms it is found that an appropriate amplifier power allocation is mandatory for dstc to offer sufficient diversity and power gain in a general network topology"
cs.IT,"the concepts of pseudocodeword and pseudoweight play a fundamental role in the finite length analysis of ldpc codes the pseudoredundancy of a binary linear code is defined as the minimum number of rows in a parity check matrix such that the corresponding minimum pseudoweight equals its minimum hamming distance by using the value assignment of chen and kl o ve we present new results on the pseudocodeword redundancy of binary linear codes in particular , we give several upper bounds on the pseudoredundancies of certain codes with repeated and added coordinates and of certain shortened subcodes we also investigate several kinds of k dimensional binary codes and compute their exact pseudocodeword redundancy"
cs.LG|stat.ML,"the non negative solution to an underdetermined linear system can be uniquely recovered sometimes , even without imposing any additional sparsity constraints in this paper , we derive conditions under which a unique non negative solution for such a system can exist , based on the theory of polytopes furthermore , we develop the paradigm of combined sparse representations , where only a part of the coefficient vector is constrained to be non negative , and the rest is unconstrained \( general \) we analyze the recovery of the unique , sparsest solution , for combined representations , under three different cases of coefficient support knowledge \( a \) the non zero supports of non negative and general coefficients are known , \( b \) the non zero support of general coefficients alone is known , and \( c \) both the non zero supports are unknown for case \( c \) , we propose the combined orthogonal matching pursuit algorithm for coefficient recovery and derive the deterministic sparsity threshold under which recovery of the unique , sparsest coefficient vector is possible we quantify the order complexity of the algorithms , and examine their performance in exact and approximate recovery of coefficients under various conditions of noise furthermore , we also obtain their empirical phase transition characteristics we show that the basis pursuit algorithm , with partial non negative constraints , and the proposed greedy algorithm perform better in recovering the unique sparse representation when compared to their unconstrained counterparts finally , we demonstrate the utility of the proposed methods in recovering images corrupted by saturation noise"
cs.IT,"an important feature of a dynamic game is its monitoring structure namely , what the players effectively see from the played actions we consider games with arbitrary monitoring structures one of the purposes of this paper is to know to what extent an encoder , who perfectly observes the played actions and sends a complementary public signal to the players , can establish perfect monitoring for all the players to reach this goal , the main technical problem to be solved at the encoder is to design a source encoder which compresses the action profile in the most concise manner possible a special feature of this encoder is that the multi dimensional signal \( namely , the action profiles \) to be encoded is assumed to comprise a component whose probability distribution is not known to the encoder and the decoder has a side information \( the private signals received by the players when the encoder is off \) this new framework appears to be both of game theoretical and information theoretical interest in particular , it is useful for designing certain types of encoders that are resilient to single deviations and provide an equilibrium utility region in the proposed setting it provides a new type of constraints to compress an information source \( i e , a random variable \) regarding the first aspect , we apply the derived result to the repeated prisoner 's dilemma"
cs.SI,"we study random graphs with arbitrary distributions of expected degree and derive expressions for the spectra of their adjacency and modularity matrices we give a complete prescription for calculating the spectra that is exact in the limit of large network size and large vertex degrees we also study the effect on the spectra of hubs in the network , vertices of unusually high degree , and show that these produce isolated eigenvalues outside the main spectral band , akin to impurity states in condensed matter systems , with accompanying eigenvectors that are strongly localized around the hubs we also give numerical results that confirm our analytic expressions"
cs.IT,"we study the transport properties of continuous time quantum walks \( ctqw \) over finite two dimensional structures with a given number of randomly placed bonds and with different aspect ratios \( ar \) here , we focus on the transport from , say , the left side to the right side of the structure where absorbing sites are placed we do so by analyzing the long time average of the survival probability of ctqw we compare the results to the classical continuous time random walk case \( ctrw \) for small ar \( landscape configurations \) we observe only small differences between the quantum and the classical transport properties , i e , roughly the same number of bonds is needed to facilitate the transport however , with increasing ar \( portrait configurations \) a much larger number of bonds is needed in the ctqw case than in the ctrw case while for ctrw the number of bonds needed decreases when going from small ar to large ar , for ctrw this number is large for small ar , has a minimum for the square configuration , and increases again for increasing ar we corroborate our findings for large ar by showing that the corresponding quantum eigenstates are strongly localized in situations in which the transport is facilitated in the ctrw case"
cs.DM|cs.DS,"a pair of unit clauses is called conflicting if it is of the form \( x \) , \( bar x \) a cnf formula is unit conflict free \( ucf \) if it contains no pair of conflicting unit clauses lieberherr and specker \( j acm 28 , 1981 \) showed that for each ucf cnf formula with m clauses we can simultaneously satisfy at least pp m clauses , where pp \( sqrt 5 1 \) 2 we improve the lieberherr specker bound by showing that for each ucf cnf formula f with m clauses we can find , in polynomial time , a subformula f' with m' clauses such that we can simultaneously satisfy at least pp m \( 1 pp \) m' \( 2 3 pp \) n 2 clauses \( in f \) , where n is the number of variables in f which are not in f' we consider two parameterized versions of max sat , where the parameter is the number of satisfied clauses above the bounds m 2 and m \( sqrt 5 1 \) 2 the former bound is tight for general formulas , and the later is tight for ucf formulas mahajan and raman \( j algorithms 31 , 1999 \) showed that every instance of the first parameterized problem can be transformed , in polynomial time , into an equivalent one with at most 6k 3 variables and 10k clauses we improve this to 4k variables and \( 2 sqrt 5 4 \) k clauses mahajan and raman conjectured that the second parameterized problem is fixed parameter tractable \( fpt \) we show that the problem is indeed fpt by describing a polynomial time algorithm that transforms any problem instance into an equivalent one with at most \( 7 3 sqrt 5 \) k variables our results are obtained using our improvement of the lieberherr specker bound above"
cs.IT,"the problem of modulation classification for a multiple antenna \( mimo \) system employing orthogonal frequency division multiplexing \( ofdm \) is investigated under the assumption of unknown frequency selective fading channels and signal to noise ratio \( snr \) the classification problem is formulated as a bayesian inference task , and solutions are proposed based on gibbs sampling and mean field variational inference the proposed methods rely on a selection of the prior distributions that adopts a latent dirichlet model for the modulation type and on the bayesian network formalism the gibbs sampling method converges to the optimal bayesian solution and , using numerical results , its accuracy is seen to improve for small sample sizes when switching to the mean field variational inference technique after a number of iterations the speed of convergence is shown to improve via annealing and random restarts while most of the literature on modulation classification assume that the channels are flat fading , that the number of receive antennas is no less than that of transmit antennas , and that a large number of observed data symbols are available , the proposed methods perform well under more general conditions finally , the proposed bayesian methods are demonstrated to improve over existing non bayesian approaches based on independent component analysis and on prior bayesian methods based on the `superconstellation' method"
cs.SI|stat.ML,"many methods have been proposed for community detection in networks , but most of them do not take into account additional information on the nodes that is often available in practice in this paper , we propose a new joint community detection criterion that uses both the network edge information and the node features to detect community structures one advantage our method has over existing joint detection approaches is the flexibility of learning the impact of different features which may differ across communities another advantage is the flexibility of choosing the amount of influence the feature information has on communities the method is asymptotically consistent under the block model with additional assumptions on the feature distributions , and performs well on simulated and real networks"
cs.SI,"mobile networks receive increasing research interest recently due to their increasingly wide applications in various areas mobile ad hoc networks \( manet \) and vehicular ad hoc networks \( vanet \) are two prominent examples mobility introduces challenges as well as opportunities it is known to improve the network throughput as shown in 1 in this paper , we analyze the effect of mobility on the information spreading based on gossip algorithms our contributions are twofold firstly , we propose a new performance metric , mobile conductance , which allows us to separate the details of mobility models from the study of mobile spreading time secondly , we explore the mobile conductances of several popular mobility models , and offer insights on the corresponding results large scale network simulation is conducted to verify our analysis"
cs.IT,"managing interference in a network of macrocells underlaid with femtocells presents an important , yet challenging problem a majority of spatial \( frequency time \) reuse based approaches partition the users based on coloring the interference graph , which is shown to be suboptimal some spatial time reuse based approaches schedule the maximal independent sets \( miss \) in a cyclic , \( weighted \) round robin fashion , which is inefficient for delay sensitive applications our proposed policies schedule the miss in a non cyclic fashion , which aim to optimize any given network performance criterion for delay sensitive applications while fulfilling minimum throughput requirements of the users importantly , we do not take the interference graph as given as in existing works we propose an optimal construction of the interference graph we prove that under certain conditions , the proposed policy achieves the optimal network performance for large networks , we propose a low complexity algorithm for computing the proposed policy we show that the policy computed achieves a constant competitive ratio \( with respect to the optimal network performance \) , which is independent of the network size , under wide range of deployment scenarios the policy can be implemented in a decentralized manner by the users compared to the existing policies , our proposed policies can achieve improvement of up to 130 in large scale deployments"
cs.IT,"this paper studies an uplink multicell joint processing model in which the base stations are connected to a centralized processing server via rate limited digital backhaul links unlike previous studies where the centralized processor jointly decodes all the source messages from all base stations , this paper proposes a suboptimal achievability scheme in which the wyner ziv compress and forward relaying technique is employed on a per base station basis , but successive interference cancellation \( sic \) is used at the central processor to mitigate multicell interference this results in an achievable rate region that is easily computable , in contrast to the joint processing schemes in which the rate regions can only be characterized by exponential number of rate constraints under the per base station sic framework , this paper further studies the impact of the limited capacity backhaul links on the achievable rates and establishes that in order to achieve to within constant number of bits to the maximal sic rate with infinite capacity backhaul , the backhaul capacity must scale logarithmically with the signal to interference and noise ratio \( sinr \) at each base station finally , this paper studies the optimal backhaul rate allocation problem for an uplink multicell joint processing model with a total backhaul capacity constraint the analysis reveals that the optimal strategy that maximizes the overall sum rate should also scale with the log of the sinr at each base station"
cs.LG|stat.ML,"recently , several optimization methods have been successfully applied to the hyperparameter optimization of deep neural networks \( dnns \) the methods work by modeling the joint distribution of hyperparameter values and corresponding error those methods become less practical when applied to modern dnns whose training may take a few days and thus one cannot collect sufficient observations to accurately model the distribution to address this challenging issue , we propose a method that learns to transfer optimal hyperparameter values for a small source dataset to hyperparameter values with comparable performance on a dataset of interest as opposed to existing transfer learning methods , our proposed method does not use hand designed features instead , it uses surrogates to model the hyperparameter error distributions of the two datasets and trains a neural network to learn the transfer function extensive experiments on three cv benchmark datasets clearly demonstrate the efficiency of our method"
cs.IT,"interference is emerging as a fundamental bottleneck in many important wireless communication scenarios , including dense cellular networks and cognitive networks with spectrum sharing by multiple service providers although multipleantenna \( mimo \) signal processing is known to offer useful degrees of freedom to cancel interference , extreme value theoretic analysis recently showed that , even in the absence of mimo processing , the scaling law of the capacity in the number of users for a multi cell network with and without inter cell interference was asymptotically identical provided a simple signal to noise and interference ratio \( sinr \) maximizing scheduler is exploited this suggests that scheduling can help reduce inter cell interference substantially , thus possibly limiting the need for multiple antenna processing however , the convergence limits of interference after scheduling in a multi cell setting are not yet identified in this paper1 we analyze such limits theoretically we consider channel statistics under rayleigh fading with equal path loss for all users or with unequal path loss we uncover two surprisingly different behaviors for such systems for the equal path loss case , we show that scheduling alone can cause the residual interference to converge to zero for large number of users with unequal path loss however , the interference are shown to converge in average to a nonzero constant simulations back our findings"
cs.IT,"this work applies earlier results on quasi cyclic \( qc \) ldpc codes to the codes specified in six separate ieee 802 standards , specifying wireless communications from 54 mhz to 60 ghz first , we examine the weight matrices specified to upper bound the codes' minimum distance independent of block length next , we search for the minimum distance achieved for the parity check matrices selected at each block length finally , solutions to the computational challenges encountered are addressed"
cs.IT,"this letter derives the asymptotic symbol error rate \( ser \) and outage probability of multiple input multiple output \( mimo \) maximum ratio combining \( mrc \) systems we consider rayleigh fading channels with both transmit and receive spatial correlation our results are based on new asymptotic expressions which we derive for the p d f and c d f of the maximum eigenvalue of positive definite quadratic forms in complex gaussian matrices we prove that spatial correlation does not affect the diversity order , but that it reduces the array gain and hence increases the ser in the high snr regime"
cs.LG|stat.ML,"in this paper , we study statistical classification accuracy of two different markov field environments for pixelwise image segmentation , considering the labels of the image as hidden states and solving the estimation of such labels as a solution of the map equation the emission distribution is assumed the same in all models , and the difference lays in the markovian prior hypothesis made over the labeling random field the a priori labeling knowledge will be modeled with a \) a second order anisotropic markov mesh and b \) a classical isotropic potts model under such models , we will consider three different segmentation procedures , 2d path constrained viterbi training for the hidden markov mesh , a graph cut based segmentation for the first order isotropic potts model , and icm \( iterated conditional modes \) for the second order isotropic potts model we provide a unified view of all three methods , and investigate goodness of fit for classification , studying the influence of parameter estimation , computational gain , and extent of automation in the statistical measures overall accuracy , relative improvement and kappa coefficient , allowing robust and accurate statistical analysis on synthetic and real life experimental data coming from the field of dental diagnostic radiography all algorithms , using the learned parameters , generate good segmentations with little interaction when the images have a clear multimodal histogram suboptimal learning proves to be frail in the case of non distinctive modes , which limits the complexity of usable models , and hence the achievable error rate as well all matlab code written is provided in a toolbox available for download from our website , following the reproducible research paradigm"
cs.IT,"it is proved that for memoryless vector channels , maximizing the mutual information over all source distributions with a certain average power or over the larger set of source distributions with upperbounded average power yields the same channel capacity in both cases hence , the channel capacity cannot decrease with increasing average transmitted power , not even for channels with severe nonlinear distortion"
cs.AI,"conflict of interest is the permanent companion of any population of agents \( computational or biological \) for that reason , the ability to compromise is of paramount importance , making voting a key element of societal mechanisms one of the voting procedures most often discussed in the literature and , due to its intuitiveness , also conceptually quite appealing is charles dodgson 's scoring rule , basically using the respective closeness to being a condorcet winner for evaluating competing alternatives in this paper , we offer insights on the practical limits of algorithms computing the exact dodgson scores from a number of votes while the problem itself is theoretically intractable , this work proposes and analyses five different solutions which try distinct approaches to practically solve the issue in an effective manner additionally , three of the discussed procedures can be run in parallel which has the potential of drastically reducing the problem size"
cs.IT,"the use of phasor angle measurements provided by phasor measurement units \( pmus \) in fault detection is regarded as a promising method in identifying locations of power line outages however , communication errors or system malfunctions may introduce errors to the measurements and thus yield bad data most of the existing methods on line outage identification fail to consider such error this paper develops a framework for identifying multiple power line outages based on the pmus' measurements in the presence of bad data in particular , we design an algorithm to identify locations of line outage and recover the faulty measurements simultaneously the proposed algorithm does not require any prior information on the number of line outages and the noise variance case studies carried out on test systems of different sizes validate the effectiveness and efficiency of the proposed approach"
cs.AI,"although the notion of a concept as a collection of objects sharing certain properties , and the notion of a conceptual hierarchy are fundamental to both formal concept analysis and description logics , the ways concepts are described and obtained differ significantly between these two research areas despite these differences , there have been several attempts to bridge the gap between these two formalisms , and attempts to apply methods from one field in the other the present work aims to give an overview on the research done in combining description logics and formal concept analysis"
cs.IT,"this paper considers point to point secure communication over flat fading channels under an outage constraint more specifically , we extend the definition of outage capacity to account for the secrecy constraint and obtain sharp characterizations of the corresponding fundamental limits under two different assumptions on the transmitter csi \( channel state information \) first , we find the outage secrecy capacity assuming that the transmitter has perfect knowledge of the legitimate and eavesdropper channel gains in this scenario , the capacity achieving scheme relies on opportunistically exchanging private keys between the legitimate nodes these keys are stored in a key buffer and later used to secure delay sensitive data using the vernam 's one time pad technique we then extend our results to the more practical scenario where the transmitter is assumed to know only the legitimate channel gain here , our achievability arguments rely on privacy amplification techniques to generate secret key bits in the two cases , we also characterize the optimal power control policies which , interestingly , turn out to be a judicious combination of channel inversion and the optimal ergodic strategy finally , we analyze the effect of key buffer overflow on the overall outage probability"
cs.IT,"this report presents a survey on how to group together in a static way planar nodes , that may belong to a wireless network \( ad hoc or cellular \) the aim is to identify appropriate methods that could also be applied for point processes specifically matching pairs and algorithms are initially discussed next , specifically for point processes , the nearest neighbour and lilypond models are presented properties and results for the two models are stated original bounds are given for the value of the so called generation number , which is related to the size of the nearest neighbour cluster finally , a variation of the nearest neighbour grouping is proposed and an original metric is introduced , named here the ancestor number this is used to facilitate the analysis of the distribution of cluster size based on this certain related bounds are derived the report and the analysis included show clearly the difficulty of working in point processes with static clusters of size greater than two , when these are defined by proximity criteria"
cs.IT,in this paper we investigate the structure of quasi bch codes in the first part of this paper we show that quasi bch codes can be derived from reed solomon codes over square matrices extending the known relation about classical bch and reed solomon codes this allows us to adapt the welch berlekamp algorithm to quasi bch codes in the second part of this paper we show that quasi bch codes can be seen as subcodes of interleaved reed solomon codes over finite fields this provides another approach for decoding quasi bch codes
cs.IT,"crcs have desirable properties for effective error detection but their software implementation , which relies on many steps of the polynomial division , is typically slower than other codes such as weaker checksums a relevant question is whether there are some particular crcs that have fast implementation in this paper , we introduce such fast crcs as well as an effective technique to implement them for these fast crcs , even without using table lookup , it is possible either to eliminate or to greatly reduce many steps of the polynomial division during their computation"
cs.IT,"in this letter we introduce the problem of secrecy reversibility this asks when two honest parties can distill secret bits from some tripartite distribution p xyz and transform secret bits back into p xyz at equal rates using local operation and public communication \( lopc \) this is the classical analog to the well studied problem of reversibly concentrating and diluting entanglement in a quantum state we identify the structure of distributions possessing reversible secrecy when one of the honest parties holds a binary distribution , and it is possible that all reversible distributions have this form these distributions are more general than what is obtained by simply constructing a classical analog to the family of quantum states known to have reversible entanglement an indispensable tool used in our analysis is a conditional form of the g ' a cs k o rner common information"
cs.AI|cs.IT,"we introduce matrix and its block to the dung 's theory of argumentation frameworks it is showed that each argumentation framework has a matrix representation , and the common extension based semantics of argumentation framework can be characterized by blocks of matrix and their relations in contrast with traditional method of directed graph , the matrix way has the advantage of computability therefore , it has an extensive perspective to bring the theory of matrix into the research of argumentation frameworks and related areas"
cs.IT,"wireless communication networks rely heavily on channel state information \( csi \) to make informed decision for signal processing and network operations however , the traditional csi acquisition methods is facing many difficulties pilot aided channel training consumes a great deal of channel resources and reduces the opportunities for energy saving , while location aided channel estimation suffers from inaccurate and insufficient location information in this paper , we propose a novel channel learning framework , which can tackle these difficulties by inferring unobservable csi from the observable one we formulate this framework theoretically and illustrate a special case in which the learnability of the unobservable csi can be guaranteed possible applications of channel learning are then described , including cell selection in multi tier networks , device discovery for device to device \( d2d \) communications , as well as end to end user association for load balancing we also propose a neuron network based algorithm for the cell selection problem in multi tier networks the performance of this algorithm is evaluated using geometry based stochastic channel model \( gscm \) in settings with 5 small cells , the average cell selection accuracy is 73 only an 3 9 loss compared with a location aided algorithm which requires genuine location information"
cs.IT,"fixed size commutative rings are quasi ordered such that all scalar linearly solvable networks over any given ring are also scalar linearly solvable over any higher ordered ring as consequences , if a network has a scalar linear solution over some finite commutative ring , then \( i \) the network is also scalar linearly solvable over a maximal commutative ring of the same size , and \( ii \) the \( unique \) smallest size commutative ring over which the network has a scalar linear solution is a field we prove that a commutative ring is maximal with respect to the quasi order if and only if some network is scalar linearly solvable over the ring but not over any other commutative ring of the same size furthermore , we show that maximal commutative rings are direct products of certain fields specified by the integer partitions of the prime factor multiplicities of the maximal ring 's size finally , we prove that there is a unique maximal commutative ring of size m if and only if each prime factor of m has multiplicity in 1 , 2 , 3 , 4 , 6 in fact , whenever p is prime and k in 1 , 2 , 3 , 4 , 6 , the unique such maximal ring of size p k is the field gf \( p k \) however , for every field gf \( p k \) with k not in 1 , 2 , 3 , 4 , 6 , there is always some network that is not scalar linearly solvable over the field but is scalar linearly solvable over a commutative ring of the same size these results imply that for scalar linear network coding over commutative rings , fields can always be used when the alphabet size is flexible , but alternative rings may be needed when the alphabet size is fixed"
cs.IT,"this work establishes the design , analysis , and fine tuning of a peak to average power ratio \( papr \) reducing system , based on compressed sensing at the receiver of a peak reducing sparse clipper applied to an ofdm signal at the transmitter by exploiting the sparsity of the ofdm signal in the time domain relative to a pre defined clipping threshold , the method depends on partially observing the frequency content of extremely simple sparse clippers to recover the locations , magnitudes , and phases of the clipped coefficients of the peak reduced signal we claim that in the absence of optimization algorithms at the transmitter that confine the frequency support of clippers to a predefined set of reserved tones , no other tone reservation method can reliably recover the original ofdm signal with such low complexity afterwards we focus on designing different clipping signals that can embed a priori information regarding the support and phase of the peak reducing signal to the receiver , followed by modified compressive sensing techniques for enhanced recovery this includes data based weighted ell 1 minimization for enhanced support recovery and phase augmention for homogeneous clippers followed by bayesian techniques we show that using such techniques for a typical ofdm signal of 256 subcarriers and 20 reserved tones , the papr can be reduced by approximately 4 5 db with a significant increase in capacity compared to a system which uses all its tones for data transmission and clips to such levels the design is hence appealing from both capacity and papr reduction aspects"
cs.IT,"linear precoding and cooperative jamming for multiuser broadcast channel is studied to enhance the physical layer security we consider the system where multiple independent data streams are transmitted from the base station to multiple legitimate users with the help of a friendly jammer it is assumed that a normalized linear precoding matrix is given at the base station , whereas the power allocated to each user is to be determined the problem is to jointly design the power allocation across different users for linear precoding and the cooperative jamming at the friendly jammer the goal is to maximize a lower bound of the secrecy rate , provided that a minimum communication rate to the users is guaranteed the optimal solution is obtained when the number of antennas at the friendly jammer is no less than the total number of antennas at the users and eavesdropper moreover , a suboptimal algorithm is proposed , which can be applied for all the scenarios numerical results demonstrate that the proposed schemes are effective for secure communications"
cs.LG|stat.ML,"the goal of unsupervised representation learning methods is to learn a new representation of the original data , such that it makes a certain classification task easier to solve since their introduction in late 2000s , these methods initiated a revolution within machine learning in this paper we present an unsupervised representation learning method for relational data the proposed approach uses a clustering procedure to learn a new representation moreover , we introduce an adaptive clustering method , capable of addressing multiple interpretations of similarity in relational context finally , we experimentally evaluate the proposed approach the preliminary results show the promise of the approach , as the models learned on the new representation often achieve better performance and are less complex than the ones learned on the original data representation"
cs.DM|cs.DS,"vassilevska and williams \( stoc 2009 \) showed how to count simple paths on k vertices and matchings on k 2 edges in an n vertex graph in time n k 2 o \( 1 \) in the same year , two different algorithms with the same runtime were given by koutis and williams \( icalp 2009 \) , and bj orklund emph et al \( esa 2009 \) , via n st 2 o \( 1 \) time algorithms for counting t tuples of pairwise disjoint sets drawn from a given family of s sized subsets of an n element universe shortly afterwards , alon and gutner \( talg 2010 \) showed that these problems have omega \( n lfloor st 2 rfloor \) and omega \( n lfloor k 2 rfloor \) lower bounds when counting by color coding here we show that one can do better , namely , we show that the meet in the middle exponent st 2 can be beaten and give an algorithm that counts in time n 0 45470382 st o \( 1 \) for t a multiple of three this implies algorithms for counting occurrences of a fixed subgraph on k vertices and pathwidth p ll k in an n vertex graph in n 0 45470382k 2p o \( 1 \) time , improving on the three mentioned algorithms for paths and matchings , and circumventing the color coding lower bound we also give improved bounds for counting t tuples of disjoint s sets for s 2 , 3 , 4 our algorithms use fast matrix multiplication we show an argument that this is necessary to go below the meet in the middle barrier"
cs.IT,"the concentration of measure phenomenon may be summarized as follows a function of many weakly dependent random variables that is not too sensitive to any of its individual arguments will tend to take values very close to its expectation this phenomenon is most completely understood when the arguments are mutually independent random variables , and there exist several powerful complementary methods for proving concentration inequalities , such as the martingale method , the entropy method , and the method of transportation inequalities the setting of dependent arguments is much less well understood this chapter focuses on the martingale method for deriving concentration inequalities without independence assumptions in particular , we use the machinery of so called wasserstein matrices to show that the azuma hoeffding concentration inequality for martingales with almost surely bounded differences , when applied in a sufficiently abstract setting , is powerful enough to recover and sharpen several known concentration results for nonproduct measures wasserstein matrices provide a natural formalism for capturing the interplay between the metric and the probabilistic structures , which is fundamental to the concentration phenomenon"
cs.IT,"an analysis of the characteristic function of gaussian quadratic forms is presented in 1 to study the performance of multichannel communication systems this technical report reviews this analysis , obtaining alternative expressions to original ones in compact matrix format"
cs.DM,"we study the activation process in undirected graphs known as bootstrap percolation a vertex is active either if it belongs to a set of initially activated vertices or if at some point it had at least r active neighbors , for a threshold r that is identical for all vertices a contagious set is a vertex set whose activation results with the entire graph being active let m \( g , r \) be the size of a smallest contagious set in a graph g we examine density conditions that ensure that a given n vertex graph g \( v , e \) has a small contagious set with respect to the minimum degree , we prove that if g has minimum degree n 2 then m \( g , 2 \) 2 we also provide tight upper bounds on the number of rounds until all nodes are active for n k l , we denote by m \( n , k , l \) the maximum number of edges in an n vertex graph g satisfying m \( g , l \) k we determine the precise value of m \( n , k , 2 \) and m \( n , k , k \) assuming that n is sufficiently large compared to k"
cs.IT,in this work we investigate unions of lifted mrd codes of a fixed dimension and minimum distance and derive an explicit formula for the cardinality of such codes this will then imply a lower bound on the cardinality of constant dimension codes
cs.IT,"a computer calculation with magma shows that there is no extremal self dual binary code c of length 72 , whose automorphism group contains the symmetric group of degree 3 , the alternating group of degree 4 or the dihedral group of order 8 combining this with the known results in the literature one obtains that aut \( c \) has order at most 5 or isomorphic to the elementary abelian group of order 8"
cs.IT,"in a frequency selective slow fading channel in a mimo system , the channel matrix is of the form of a block matrix we propose a method to calculate the limit of the eigenvalue distribution of block matrices if the size of the blocks tends to infinity we will also calculate the asymptotic eigenvalue distribution of hh , where the entries of h are jointly gaussian , with a correlation of the form e h pj bar h qk sum s 1 t psi \( s \) jk hat psi \( s \) pq \( where t is fixed and does not increase with the size of the matrix \) we will use an operator valued free probability approach to achieve this goal using this method , we derive a system of equations , which can be solved numerically to compute the desired eigenvalue distribution"
cs.IT,"in this letter , we derive an approximate analytical expression for the uplink achievable rate of a massive multi input multi output \( mimo \) antenna system when finite precision analog digital converters \( adcs \) and the common maximal ratio combining technique are used at the receivers to obtain this expression , we treat quantization noise as an additive quantization noise model considering the obtained expression , we show that low resolution adcs lead to a decrease in the achievable rate but the performance loss can be compensated by increasing the number of receiving antennas in addition , we investigate the relation between the number of antennas and the adc resolution , as well as the power scaling law these discussions support the feasibility of equipping highly economical adcs with low resolution in practical massive mimo systems"
cs.LG,"although the internet as level topology has been extensively studied over the past few years , little is known about the details of the as taxonomy an as node can represent a wide variety of organizations , e g , large isp , or small private business , university , with vastly different network characteristics , external connectivity patterns , network growth tendencies , and other properties that we can hardly neglect while working on veracious internet representations in simulation environments in this paper , we introduce a radically new approach based on machine learning techniques to map all the ases in the internet into a natural as taxonomy we successfully classify 95 3 of ases with expected accuracy of 78 1 we release to the community the as level topology dataset augmented with 1 \) the as taxonomy information and 2 \) the set of as attributes we used to classify ases we believe that this dataset will serve as an invaluable addition to further understanding of the structure and evolution of the internet"
cs.DM,"this paper starts the wilf classification of mesh patterns of length 2 although there are initially 1024 patterns to consider we introduce automatic methods to reduce the number of potentially different wilf classes to at most 65 by enumerating some of the remaining classes we bring that upper bound further down to 56 finally , we conjecture that the actual number of wilf classes of mesh patterns of length 2 is 46"
cs.LG,"cascaded adaboost classifier is a well known efficient object detection algorithm the cascade structure has many parameters to be determined most of existing cascade learning algorithms are designed by assigning detection rate and false positive rate to each stage either dynamically or statically their objective functions are not directly related to minimum computation cost these algorithms are not guaranteed to have optimal solution in the sense of minimizing computation cost on the assumption that a strong classifier is given , in this paper we propose an optimal cascade learning algorithm \( we call it icascade \) which iteratively partitions the strong classifiers into two parts until predefined number of stages are generated icascade searches the optimal number ri of weak classifiers of each stage i by directly minimizing the computation cost of the cascade theorems are provided to guarantee the existence of the unique optimal solution theorems are also given for the proposed efficient algorithm of searching optimal parameters ri once a new stage is added , the parameter ri for each stage decreases gradually as iteration proceeds , which we call decreasing phenomenon moreover , with the goal of minimizing computation cost , we develop an effective algorithm for setting the optimal threshold of each stage classifier in addition , we prove in theory why more new weak classifiers are required compared to the last stage experimental results on face detection demonstrate the effectiveness and efficiency of the proposed algorithm"
cs.DM,"we consider a special type of integer partitions in which the parts of the form p aq b , for some relatively prime integers p and q , are restricted by divisibility conditions we investigate the problems of generating and encoding those partitions and give some estimates for several partition functions"
cs.IT,"most practical constructions of lattice codes with high coding gains are multilevel constructions where each level corresponds to an underlying code component construction d , construction d ' , and forney 's code formula are classical constructions that produce such lattices explicitly from a family of nested binary linear codes in this paper , we investigate these three closely related constructions along with the recently developed construction a ' of lattices from codes over the polynomial ring mathbb f 2 u u a we show that construction by code formula produces a lattice packing if and only if the nested codes being used are closed under schur product , thus proving the similarity of construction d and construction by code formula when applied to reed muller codes in addition , we relate construction by code formula to construction a ' by finding a correspondence between nested binary codes and codes over mathbb f 2 u u a this proves that any lattice constructible using construction by code formula is also constructible using construction a ' finally , we show that construction a ' produces a lattice if and only if the corresponding code over mathbb f 2 u u a is closed under shifted schur product"
cs.SI|stat.ML,"while a user 's preference is directly reflected in the interactive choice process between her and the recommender , this wealth of information was not fully exploited for learning recommender models in particular , existing collaborative filtering \( cf \) approaches take into account only the binary events of user actions but totally disregard the contexts in which users' decisions are made in this paper , we propose collaborative competitive filtering \( ccf \) , a framework for learning user preferences by modeling the choice process in recommender systems ccf employs a multiplicative latent factor model to characterize the dyadic utility function but unlike cf , ccf models the user behavior of choices by encoding a local competition effect in this way , ccf allows us to leverage dyadic data that was previously lumped together with missing data in existing cf models we present two formulations and an efficient large scale optimization algorithm experiments on three real world recommendation data sets demonstrate that ccf significantly outperforms standard cf approaches in both offline and online evaluations"
cs.SI,"recent research in industrial organisation has investigated the essential place that middlemen have in the networks that make up our global economy in this paper we attempt to understand how such middlemen compete with each other through a game theoretic analysis using novel techniques from decision making under ambiguity we model a purposely abstract and reduced model of one middleman who pro vides a two sided platform , mediating surplus creating interactions between two users the middleman evaluates uncertain outcomes under positional ambiguity , taking into account the possibility of the emergence of an alternative middleman offering intermediary services to the two users surprisingly , we find many situations in which the middleman will purposely extract maximal gains from her position only if there is relatively low probability of devastating loss of business under competition , the middleman will adopt a more competitive attitude and extract less from her position"
cs.IT,"we initiate the study of zero error communication via quantum channels assisted by noiseless feedback link of unlimited quantum capacity , generalizing shannon 's zero error communication theory with instantaneous feedback this capacity depends only on the linear span of kraus operators of the channel , which generalizes the bipartite equivocation graph of a classical channel , and which we dub non commutative bipartite graph we go on to show that the feedback assisted capacity is non zero \( allowing for a constant amount of activating noiseless communication \) if and only if the non commutative bipartite graph is non trivial , and give a number of equivalent characterizations this result involves a far reaching extension of the conclusive exclusion of quantum states pusey barrett rudolph , nat phys 8 475 , 2012 we then present an upper bound on the feedback assisted zero error capacity , motivated by a conjecture originally made by shannon and proved by ahlswede we demonstrate that this bound is additive and given by a nice minimax formula we also prove a coding theorem showing that this quantity is the entanglement assisted capacity against an adversarially chosen channel from the set of all channels with the same kraus span , which can also be interpreted as the feedback assisted unambiguous capacity the proof relies on a generalization of the postselection lemma \( de finetti reduction \) christandl koenig renner , prl 102 020503 , 2009 that allows to reflect additional constraints , and which we believe to be of independent interest this capacity is a relaxation of the feedback assisted zero error capacity however , we do not know whether they coincide in general we illustrate our ideas with a number of examples , including classical quantum channels and weyl diagonal channels , and close with an extensive discussion of open questions"
cs.DM,"this paper reports about the development of two provably correct approximate algorithms which calculate the euclidean shortest path \( esp \) within a given cube curve with arbitrary accuracy , defined by epsilon 0 , and in time complexity kappa \( epsilon \) cdot cal o \( n \) , where kappa \( epsilon \) is the length difference between the path used for initialization and the minimum length path , divided by epsilon a run time diagram also illustrates this linear time behavior of the implemented esp algorithm"
cs.IT|stat.ML,"most of the existing methods for sparse signal recovery assume a static system the unknown signal is a finite length vector for which a fixed set of linear measurements and a sparse representation basis are available and an l1 norm minimization program is solved for the reconstruction however , the same representation and reconstruction framework is not readily applicable in a streaming system the unknown signal changes over time , and it is measured and reconstructed sequentially over small time intervals in this paper , we discuss two such streaming systems and a homotopy based algorithm for quickly solving the associated l1 norm minimization programs 1 \) recovery of a smooth , time varying signal for which , instead of using block transforms , we use lapped orthogonal transforms for sparse representation 2 \) recovery of a sparse , time varying signal that follows a linear dynamic model for both the systems , we iteratively process measurements over a sliding interval and estimate sparse coefficients by solving a weighted l1 norm minimization program instead of solving a new l1 program from scratch at every iteration , we use an available signal estimate as a starting point in a homotopy formulation starting with a warm start vector , our homotopy algorithm updates the solution in a small number of computationally inexpensive steps as the system changes the homotopy algorithm presented in this paper is highly versatile as it can update the solution for the l1 problem in a number of dynamical settings we demonstrate with numerical experiments that our proposed streaming recovery framework outperforms the methods that represent and reconstruct a signal as independent , disjoint blocks , in terms of quality of reconstruction , and that our proposed homotopy based updating scheme outperforms current state of the art solvers in terms of the computation time and complexity"
cs.IT,"in this paper , we propose a new ensemble of rateless forward error correction \( fec \) codes the proposed codes are serially concatenated codes with reed solomon \( rs \) codes as outer codes and kite codes as inner codes the inner kite codes are a special class of prefix rateless low density parity check \( prldpc \) codes , which can generate potentially infinite \( or as many as required \) random like parity check bits the employment of rs codes as outer codes not only lowers down error floors but also ensures \( with high probability \) the correctness of successfully decoded codewords in addition to the conventional two stage decoding , iterative decoding between the inner code and the outer code are also implemented to improve the performance further the performance of the kite codes under maximum likelihood \( ml \) decoding is analyzed by applying a refined divsalar bound to the ensemble weight enumerating functions \( wef \) we propose a simulation based optimization method as well as density evolution \( de \) using gaussian approximations \( ga \) to design the kite codes numerical results along with semi analytic bounds show that the proposed codes can approach shannon limits with extremely low error floors it is also shown by simulation that the proposed codes performs well within a wide range of signal to noise ratios \( snrs \)"
cs.AI|stat.ML,"opengm is a c template library for defining discrete graphical models and performing inference on these models , using a wide range of state of the art algorithms no restrictions are imposed on the factor graph to allow for higher order factors and arbitrary neighborhood structures large models with repetitive structure are handled efficiently because \( i \) functions that occur repeatedly need to be stored only once , and \( ii \) distinct functions can be implemented differently , using different encodings alongside each other in the same model several parametric functions \( e g metrics \) , sparse and dense value tables are provided and so is an interface for custom c code algorithms are separated by design from the representation of graphical models and are easily exchangeable opengm , its algorithms , hdf5 file format and command line tools are modular and extendible"
cs.IT,"we propose an innovative meteorological radar , which uses reduced number of spatiotemporal samples without compromising the accuracy of target information our approach extends recent research on compressed sensing \( cs \) for radar remote sensing of hard point scatterers to volumetric targets the previously published cs based radar techniques are not applicable for sampling weather since the precipitation echoes lack sparsity in both range time and doppler domains we propose an alternative approach by adopting the latest advances in matrix completion algorithms to demonstrate the sparse sensing of weather echoes we use iowa x band polarimetric \( xpol \) radar data to test and illustrate our algorithms"
cs.LG|stat.ML,"we propose a framework , named aggregated wasserstein , for computing a dissimilarity measure or distance between two hidden markov models with state conditional distributions being gaussian for such hmms , the marginal distribution at any time spot follows a gaussian mixture distribution , a fact exploited to softly match , aka register , the states in two hmms we refer to such hmms as gaussian mixture model hmm \( gmm hmm \) the registration of states is inspired by the intrinsic relationship of optimal transport and the wasserstein metric between distributions specifically , the components of the marginal gmms are matched by solving an optimal transport problem where the cost between components is the wasserstein metric for gaussian distributions the solution of the optimization problem is a fast approximation to the wasserstein metric between two gmms the new aggregated wasserstein distance is a semi metric and can be computed without generating monte carlo samples it is invariant to relabeling or permutation of the states this distance quantifies the dissimilarity of gmm hmms by measuring both the difference between the two marginal gmms and the difference between the two transition matrices our new distance is tested on the tasks of retrieval and classification of time series experiments on both synthetic data and real data have demonstrated its advantages in terms of accuracy as well as efficiency in comparison with existing distances based on the kullback leibler divergence"
cs.LG|stat.ML,"the problem of estimating sparse eigenvectors of a symmetric matrix attracts a lot of attention in many applications , especially those with high dimensional data set while classical eigenvectors can be obtained as the solution of a maximization problem , existing approaches formulate this problem by adding a penalty term into the objective function that encourages a sparse solution however , the resulting methods achieve sparsity at the expense of sacrificing the orthogonality property in this paper , we develop a new method to estimate dominant sparse eigenvectors without trading off their orthogonality the problem is highly non convex and hard to handle we apply the mm framework where we iteratively maximize a tight lower bound \( surrogate function \) of the objective function over the stiefel manifold the inner maximization problem turns out to be a rectangular procrustes problem , which has a closed form solution in addition , we propose a method to improve the covariance estimation problem when its underlying eigenvectors are known to be sparse we use the eigenvalue decomposition of the covariance matrix to formulate an optimization problem where we impose sparsity on the corresponding eigenvectors numerical experiments show that the proposed eigenvector extraction algorithm matches or outperforms existing algorithms in terms of support recovery and explained variance , while the covariance estimation algorithms improve significantly the sample covariance estimator"
cs.IT|cs.LG|stat.ML,"we consider the problem of solving mixed random linear equations with k components this is the noiseless setting of mixed linear regression the goal is to estimate multiple linear models from mixed samples in the case where the labels \( which sample corresponds to which model \) are not observed we give a tractable algorithm for the mixed linear equation problem , and show that under some technical conditions , our algorithm is guaranteed to solve the problem exactly with sample complexity linear in the dimension , and polynomial in k , the number of components previous approaches have required either exponential dependence on k , or super linear dependence on the dimension the proposed algorithm is a combination of tensor decomposition and alternating minimization our analysis involves proving that the initialization provided by the tensor method allows alternating minimization , which is equivalent to em in our setting , to converge to the global optimum at a linear rate"
cs.IT,"the classical bayesian posterior arises naturally as the unique solution of several different optimization problems , without the necessity of interpreting data as conditional probabilities and then using bayes' theorem for example , the classical bayesian posterior is the unique posterior that minimizes the loss of shannon information in combining the prior and the likelihood distributions these results , direct corollaries of recent results about conflations of probability distributions , reinforce the use of bayesian posteriors , and may help partially reconcile some of the differences between classical and bayesian statistics"
cs.IT,"we evaluated the impact of changing the observation scale over the entropy measures for text descriptions midi coded music , computer code and two human natural languages were studied at the scale of characters , words , and at the fundamental scale resulting from adjusting the symbols length used to interpret each text description until it produced minimum entropy the results show that the fundamental scale method is comparable with the use of words when measuring entropy levels in written texts however , this method can also be used in communication systems lacking words such as music measuring symbolic entropy at the fundamental scale allows to calculate quantitatively , relative levels of complexity for different communication systems the results open novel vision on differences among the structure of the communication systems studied"
cs.IT,"we are interested in how to best communicate a real valued source to a number of destinations \( sinks \) over a network with capacity constraints in a collective fidelity metric over all the sinks , a problem which we call joint network source coding it is demonstrated that multiple description codes along with proper diversity routing provide a powerful solution to joint network source coding a systematic optimization approach is proposed it consists of optimizing the network routing given a multiple description code and designing optimal multiple description code for the corresponding optimized routes"
cs.IT,"while full duplex \( fd \) transmission has the potential to double the system capacity , its substantial benefit can be offset by the self interference \( si \) and non ideality of practical transceivers in this paper , we investigate the achievable sum rates \( asrs \) of half duplex \( hd \) and fd transmissions with orthogonal frequency division multiplexing \( ofdm \) , where the non ideality is taken into consideration four transmission strategies are considered , namely hd with uniform power allocation \( upa \) , hd with non upa \( nupa \) , fd with upa , and fd with nupa for each of the four transmission strategies , an optimization problem is formulated to maximize its asr , and a \( suboptimal optimal \) solution with low complexity is accordingly derived performance evaluations and comparisons are conducted for three typical channels , namely symmetric frequency flat selective and asymmetric frequency selective channels results show that the proposed solutions for both hd and fd transmissions can achieve near optimal performances for fd transmissions , the optimal solution can be obtained under typical conditions in addition , several observations are made on the asr performances of hd and fd transmissions"
cs.AI,the proposed feature selection method builds a histogram of the most stable features from random subsets of a training set and ranks the features based on a classifier based cross validation this approach reduces the instability of features obtained by conventional feature selection methods that occur with variation in training data and selection criteria classification results on four microarray and three image datasets using three major feature selection criteria and a naive bayes classifier show considerable improvement over benchmark results
cs.DM,"an identifying code of a graph g is a dominating set c such that every vertex x of g is distinguished from all other vertices by the set of vertices in c that are at distance at most 1 from x the problem of finding an identifying code of minimum possible size turned out to be a challenging problem it was proved by n bertrand that if a graph on n vertices with at least one edge admits an identifying code , then a minimum identifying code has size at most n 1 some classes of graphs whose smallest identifying code is of size n 1 were already known , and few conjectures were formulated to classify all these graphs in this paper , disproving these conjectures , we classify all finite graphs for which all but one of the vertices are needed to form an identifying code we also classify all infinite graphs needing the whole set of vertices in any identifying code new upper bounds in terms of the number of vertices and the maximum degree of a graph are also provided"
cs.SI,"social media users tend to mention entities when reacting to news events the main purpose of this work is to create entity centric aggregations of tweets on a daily basis by applying topic modeling and sentiment analysis , we create data visualization insights about current events and people reactions to those events from an entity centric perspective"
cs.DM,"we investigate the use of linear programming tools for solving semidefinite programming relaxations of quadratically constrained quadratic problems classes of valid linear inequalities are presented , including sparse psd cuts , and principal minors psd cuts computational results based on instances from the literature are presented"
cs.DS|cs.LG,"markov networks are models for compactly representing complex probability distributions they are composed by a structure and a set of numerical weights the structure qualitatively describes independences in the distribution , which can be exploited to factorize the distribution into a set of compact functions a key application for learning structures from data is to automatically discover knowledge in practice , structure learning algorithms focused on knowledge discovery present a limitation they use a coarse grained representation of the structure as a result , this representation cannot describe context specific independences very recently , an algorithm called cspc was designed to overcome this limitation , but it has a high computational complexity this work tries to mitigate this downside presenting csgs , an algorithm that uses the grow shrink strategy for reducing unnecessary computations on an empirical evaluation , the structures learned by csgs achieve competitive accuracies and lower computational complexity with respect to those obtained by cspc"
cs.IT,"this paper proposes an adaptive multi mode transmission strategy to improve the spectral efficiency achieved in the multiple input multiple output \( mimo \) broadcast channel with delayed and quantized channel state information the adaptive strategy adjusts the number of active users , denoted as the transmission mode , to balance transmit array gain , spatial division multiplexing gain , and residual inter user interference accurate closed form approximations are derived for the achievable rates for different modes , which help identify the active mode that maximizes the average sum throughput for given feedback delay and channel quantization error the proposed transmission strategy is combined with round robin scheduling , and is shown to provide throughput gain over single user mimo at moderate signal to noise ratio it only requires feedback of instantaneous channel state information from a small number of users with a feedback load constraint , the proposed algorithm provides performance close to that achieved by opportunistic scheduling with instantaneous feedback from a large number of users"
cs.AI|cs.LG,"embedding learning , a k a representation learning , has been shown to be able to model large scale semantic knowledge graphs a key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities in recent publications the embedding models were extended to also consider temporal evolutions , temporal patterns and subsymbolic representations these extended models were used successfully to predict clinical events like procedures , lab measurements , and diagnoses in this paper , we attempt to map these embedding models , which were developed purely as solutions to technical problems , to various cognitive memory functions , in particular to semantic and concept memory , episodic memory and sensory memory we also make an analogy between a predictive model , which uses entity representations derived in memory models , to working memory cognitive memory functions are typically classified as long term or short term memory , where long term memory has the subcategories declarative memory and non declarative memory and the short term memory has the subcategories sensory memory and working memory there is evidence that these main cognitive categories are partially dissociated from one another in the brain , as expressed in their differential sensitivity to brain damage however , there is also evidence indicating that the different memory functions are not mutually independent a hypothesis that arises out off this work is that mutual information exchange can be achieved by sharing or coupling of distributed latent representations of entities across different memory functions"
cs.LG|stat.ML,"we study the problem of learning markov decision processes with finite state and action spaces when the transition probability distributions and loss functions are chosen adversarially and are allowed to change with time we introduce an algorithm whose regret with respect to any policy in a comparison class grows as the square root of the number of rounds of the game , provided the transition probabilities satisfy a uniform mixing condition our approach is efficient as long as the comparison class is polynomial and we can compute expectations over sample paths for each policy designing an efficient algorithm with small regret for the general case remains an open problem"
cs.IT,"in this paper we study the distribution of the scaled largest eigenvalue of complexwishart matrices , which has diverse applications both in statistics and wireless communications exact expressions , valid for any matrix dimensions , have been derived for the probability density function and the cumulative distribution function the derived results involve only finite sums of polynomials these results are obtained by taking advantage of properties of the mellin transform for products of independent random variables"
cs.IT,"explicit characterization and computation of the multi source network coding capacity region \( or even bounds \) is long standing open problem in fact , finding the capacity region requires determination of the set of all entropic vectors gamma , which is known to be an extremely hard problem on the other hand , calculating the explicitly known linear programming bound is very hard in practice due to an exponential growth in complexity as a function of network size we give a new , easily computable outer bound , based on characterization of all functional dependencies in networks we also show that the proposed bound is tighter than some known bounds"
cs.LG,"in this paper , we present a new multiple instance learning \( mil \) method , called mis boost , which learns discriminative instance prototypes by explicit instance selection in a boosting framework unlike previous instance selection based mil methods , we do not restrict the prototypes to a discrete set of training instances but allow them to take arbitrary values in the instance feature space we also do not restrict the total number of prototypes and the number of selected instances per bag these quantities are completely data driven we show that mis boost outperforms state of the art mil methods on a number of benchmark datasets we also apply mis boost to large scale image classification , where we show that the automatically selected prototypes map to visually meaningful image regions"
cs.AI|cs.LG,"we develop abc logitboost , based on the prior work on abc boost and robust logitboost our extensive experiments on a variety of datasets demonstrate the considerable improvement of abc logitboost over logitboost and abc mart"
cs.IT,"powering mobiles using microwave emph power transfer \( pt \) avoids the inconvenience of battery recharging by cables and ensures uninterrupted mobile operation the integration of pt and emph information transfer \( it \) allows wireless pt to be realized by building on the existing infrastructure for it and also leads to compact mobile designs as a result , emph simultaneous wireless information and power transfer \( swipt \) has emerged to be an active research topic that is also the theme of this paper in this paper , a practical swipt system is considered where two multi antenna stations perform separate pt and it to a multi antenna mobile to accommodate their difference in ranges the mobile dynamically assigns each antenna for either pt or it the antenna partitioning results in a tradeoff between the mimo it channel capacity and the pt efficiency the optimal partitioning for maximizing the it rate under a pt constraint is a np hard integer program , and the paper proposes solving it via efficient greedy algorithms with guaranteed performance to this end , the antenna partitioning problem is proved to be one that optimizes a sub modular function over a matroid constraint this structure allows the application of two well known greedy algorithms that yield solutions no smaller than the optimal one scaled by factors \( 1 1 e \) and 1 2 , respectively"
cs.DM,"we prove that every \( 6k 2l , 2k \) connected simple graph contains k rigid and l connected edge disjoint spanning subgraphs this implies a theorem of jackson and jord 'an 4 and a theorem of jord 'an 6 on packing of rigid spanning subgraphs both these results are generalizations of the classical result of lov 'asz and yemini 9 saying that every 6 connected graph is rigid for which our approach provides a transparent proof our result also gives two improved upper bounds on the connectivity of graphs that have interesting properties \( 1 \) every 8 connected graph packs a spanning tree and a 2 connected spanning subgraph \( 2 \) every 14 connected graph has a 2 connected orientation"
cs.IT,"cyclic codes have been widely used in digital communication systems and consume electronics as they have efficient encoding and decoding algorithms the weight distribution of cyclic codes has been an important topic of study for many years it is in general hard to determine the weight distribution of linear codes in this paper , a class of cyclic codes with any number of zeros are described and their weight distributions are determined"
cs.LG,"most phoneme recognition state of the art systems rely on a classical neural network classifiers , fed with highly tuned features , such as mfcc or plp features recent advances in ``deep learning'' approaches questioned such systems , but while some attempts were made with simpler features such as spectrograms , state of the art systems still rely on mfccs this might be viewed as a kind of failure from deep learning approaches , which are often claimed to have the ability to train with raw signals , alleviating the need of hand crafted features in this paper , we investigate a convolutional neural network approach for raw speech signals while convolutional architectures got tremendous success in computer vision or text processing , they seem to have been let down in the past recent years in the speech processing field we show that it is possible to learn an end to end phoneme sequence classifier system directly from raw signal , with similar performance on the timit and wsj datasets than existing systems based on mfcc , questioning the need of complex hand crafted features on large datasets"
cs.IT,"multi antenna precoding effectively mitigates the interference in wireless networks however , the resultant performance gains can be significantly compromised in practice if the precoder design fails to account for the inaccuracy in the channel state information \( csi \) feedback this paper addresses this issue by considering finite rate csi feedback from receivers to their interfering transmitters in the two user multiple input multiple output \( mimo \) interference channel , called cooperative feedback , and proposing a systematic method for designing transceivers comprising linear precoders and equalizers specifically , each precoder equalizer is decomposed into inner and outer components for nulling the cross link interference and achieving array gain , respectively the inner precoders equalizers are further optimized to suppress the residual interference resulting from finite rate cooperative feedback further more , the residual interference is regulated by additional scalar cooperative feedback signals that are designed to control transmission power using different criteria including fixed interference margin and maximum sum throughput finally , the required number of cooperative precoder feedback bits is derived for limiting the throughput loss due to precoder quantization"
cs.IT,"motivated by the significant performance gains which polar codes experience under successive cancellation list decoding , their scaling exponent is studied as a function of the list size in particular , the error probability is fixed and the trade off between block length and back off from capacity is analyzed a lower bound is provided on the error probability under rm map decoding with list size l for any binary input memoryless output symmetric channel and for any class of linear codes such that their minimum distance is unbounded as the block length grows large then , it is shown that under rm map decoding , although the introduction of a list can significantly improve the involved constants , the scaling exponent itself , i e , the speed at which capacity is approached , stays unaffected for any finite list size in particular , this result applies to polar codes , since their minimum distance tends to infinity as the block length increases a similar result is proved for genie aided successive cancellation decoding when transmission takes place over the binary erasure channel , namely , the scaling exponent remains constant for any fixed number of helps from the genie note that since genie aided successive cancellation decoding might be strictly worse than successive cancellation list decoding , the problem of establishing the scaling exponent of the latter remains open"
cs.SI,"we develop a theoretical framework for the study of epidemic like social contagion in large scale social systems we consider the most general setting in which different communication platforms or categories form multiplex networks specifically , we propose a contact based information spreading model , and show that the critical point of the multiplex system associated to the active phase is determined by the layer whose contact probability matrix has the largest eigenvalue the framework is applied to a number of different situations , including a real multiplex system finally , we also show that when the system through which information is disseminating is inherently multiplex , working with the graph that results from the aggregation of the different layers is flawed"
cs.IT,"we consider the problem of rate allocation in a gaussian multiple access channel , with the goal of maximizing a utility function over transmission rates in contrast to the literature which focuses on linear utility functions , we study general concave utility functions we present a gradient projection algorithm for this problem since the constraint set of the problem is described by exponentially many constraints , methods that use exact projections are computationally intractable therefore , we develop a new method that uses approximate projections we use the polymatroid structure of the capacity region to show that the approximate projection can be implemented by a recursive algorithm in time polynomial in the number of users we further propose another algorithm for implementing the approximate projections using rate splitting and show improved bounds on its convergence time"
cs.IT,"signal processing on graph is attracting more and more attention for a graph signal in the low frequency space , the missing data associated with unsampled vertices can be reconstructed through the sampled data by exploiting the smoothness of graph signal in this paper , two local set based iterative methods are proposed to reconstruct bandlimited graph signal from sampled data in each iteration , one of the proposed methods reweights the sampled residual for different vertices based on a measure corresponding to their local sets , while the other propagates the sampled residue bandlimitedly in their respective local set these algorithms are built on frame theory and a introduced concept of local sets , based on which several frames and contraction operators are proposed we then prove the reconstruction methods converge to the original signal under certain conditions and demonstrate the new methods lead to a significantly faster convergence compared with the baseline method furthermore , the correspondence between graph signal sampling and time domain irregular sampling is analyzed comprehensively , which may be helpful to future works on graph signals computer simulations are conducted the experiment results demonstrate the effectiveness of the reconstruction methods in various sampling geometries , imprecise priori knowledge of cutoff frequency , and noisy scenarios"
cs.IT,"for a sound field observed on a sensor array , compressive sensing \( cs \) reconstructs the direction of arrivals \( doas \) of multiple sources using a sparsity constraint the doa estimation is posed as an underdetermined problem expressing the acoustic pressure at each sensor as a phase lagged superposition of source amplitudes at all hypothetical doas regularizing with an ell 1 norm constraint renders the problem solvable with convex optimization , while promoting sparsity resulting in high resolution doa maps here , the sparse source distribution is derived using maximum a posteriori estimates for both single and multiple snapshots cs does not require inversion of the data covariance matrix and thus works well even for a single snapshot resulting in higher resolution than conventional beamforming for multiple snapshots , cs outperforms conventional high resolution methods , even with coherent arrivals and at low signal to noise ratio the superior resolution of cs is demonstrated with vertical array data from the swellex96 experiment for coherent multi paths"
cs.IT,"now a days , intense research is going on two hop wireless link under different fading conditions with its remedial measures in this paper work , a two hop link under three different conditions is considered \( i \) mimo on both hops , \( ii \) miso in first hop and simo in second hop and finally \( iii \) simo in first hop and miso in second hop the three models used here give the flexibility of using stbc \( space time block coding \) and combining scheme on any of the source to relay \( s r \) and relay to destination \( r d \) link even incorporation of transmitting antenna selection \( tas \) is possible on any link here , the variation of ser \( symbol error rate \) is determined against mean snr \( signal to noise ratio \) of r d link for three different modulation schemes bpsk , 8 psk and 16 psk , taking the number of antennas and snr of s r link as parameters under nakagami m fading condition"
cs.DM,"we consider the problems of finding optimal identifying codes , \( open \) locating dominating sets and resolving sets of an interval or a permutation graph in these problems , one asks to find a subset of vertices , normally called a emph solution set , using which all vertices of the graph are distinguished the identification can be done by considering the neighborhood within the solution set , or by employing the distances to the solution vertices normally the goal is to minimize the size of the solution set then here we give tight lower bounds for the size such solution sets when the input graph is either a interval graph , or a unit interval graph , or a \( bipartite \) permutation graph or a cograph while such lower bounds for the general class of graphs are in logarithmic order \( or order of graph \) , the improved bounds in these special classes are of the order of either quadratic root or linear in terms of number of vertices moreover , the results for cographs lead to linear time algorithms to solve the considered problems on inputs that are cographs"
cs.LG,"whole genome prediction of complex phenotypic traits using high density genotyping arrays has attracted a great deal of attention , as it is relevant to the fields of plant and animal breeding and genetic epidemiology as the number of genotypes is generally much bigger than the number of samples , predictive models suffer from the curse of dimensionality the curse of dimensionality problem not only affects the computational efficiency of a particular genomic selection method , but can also lead to poor performance , mainly due to correlation among markers in this work we proposed the first transductive feature selection method based on the mrmr \( max relevance and min redundancy \) criterion which we call mint we applied mint on genetic trait prediction problems and showed that in general mint is a better feature selection method than the state of the art inductive method mrmr"
cs.LG,"extreme learning machine \( elm \) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers however , its good generalization ability is built on large numbers of hidden neurons , which is not beneficial to real time response in the test process in this paper , we proposed new ways , named constrained extreme learning machines \( celms \) , to randomly select hidden neurons based on sample distribution compared to completely random selection of hidden nodes in elm , the celms randomly select hidden nodes from the constrained vector space containing some basic combinations of original sample vectors the experimental results show that the celms have better generalization ability than traditional elm , svm and some other related methods additionally , the celms have a similar fast learning speed as elm"
cs.DS|cs.IT|cs.LG,"in dictionary learning we observe y ax e for some y in mathbb r n times p , a in mathbb r m times n , and x in mathbb r m times p the matrix y is observed , and a , x , e are unknown here e is noise of small norm , and x is column wise sparse the matrix a is referred to as a em dictionary , and its columns as em atoms then , given some small number p of samples , i e columns of y , the goal is to learn the dictionary a up to small error , as well as x the motivation is that in many applications data is expected to sparse when represented by atoms in the right dictionary a \( e g images in the haar wavelet basis \) , and the goal is to learn a from the data to then use it for other applications recently , sww12 proposed the dictionary learning algorithm er spud with provable guarantees when e 0 and m n they showed if x has independent entries with an expected s non zeroes per column for 1 lesssim s lesssim sqrt n , and with non zero entries being subgaussian , then for p gtrsim n 2 log 2 n with high probability er spud outputs matrices a' , x' which equal a , x up to permuting and scaling columns \( resp rows \) of a \( resp x \) they conjectured p gtrsim n log n suffices , which they showed was information theoretically necessary for em any algorithm to succeed when s simeq 1 significant progress was later obtained in lv15 we show that for a slight variant of er spud , p gtrsim n log \( n delta \) samples suffice for successful recovery with probability 1 delta we also show that for the unmodified er spud , p gtrsim n 1 99 samples are required even to learn a , x with polynomially small success probability this resolves the main conjecture of sww12 , and contradicts the main result of lv15 , which claimed that p gtrsim n log 4 n guarantees success whp"
cs.AI,"our broader goal is to automatically translate english sentences into formulas in appropriate knowledge representation languages as a step towards understanding and thus answering questions with respect to english text our focus in this paper is on the language of answer set programming \( asp \) our approach to translate sentences to asp rules is inspired by montague 's use of lambda calculus formulas as meaning of words and phrases with asp as the target language the meaning of words and phrases are asp lambda formulas in an earlier work we illustrated our approach by manually developing a dictionary of words and their asp lambda formulas however such an approach is not scalable in this paper our focus is on two algorithms that allow one to construct asp lambda formulas in an inverse manner in particular the two algorithms take as input two lambda calculus expressions g and h and compute a lambda calculus expression f such that f with input as g , denoted by f g , is equal to h and similarly g f h we present correctness and complexity results about these algorithms to do that we develop the notion of typed asp lambda calculus theories and their orders and use it in developing the completeness results \( to appear in theory and practice of logic programming \)"
cs.IT,"in this paper , a novel receiver for hexagonal multicarrier transmission \( hmt \) system based on the maximizing signal to interference plus noise ratio \( max sinr \) criterion is proposed theoretical analyses show that there is a timing offset between the prototype pulses of the proposed max sinr receiver and the traditional projection receiver meanwhile , the timing offset should be matched to the channel scattering factor of the doubly dispersive \( dd \) channel the closed form timing offset expressions of the prototype pulse for max sinr hmt receiver over dd channel with different channel scattering functions are derived simulation results show that the proposed max sinr receiver outperforms traditional projection scheme and obtains an approximation to the theoretical upper bound sinr performance consistent with the sinr performance improvement , the bit error rate \( ber \) performance of hmt system has also been further improved by using the proposed max sinr receiver meanwhile , the sinr performance of the proposed max sinr receiver is robust to the channel delay spread estimation errors"
cs.IT,"in this work , energy efficient cooperative protocols are studied for full duplex relaying \( fdr \) with loopback interference in these protocols , relay assistance is only sought under certain conditions on the different link outages to ensure effective cooperation recently , an energy efficient selective decode and forward protocol was proposed for fdr , and was shown to outperform existing schemes in terms of outage here , we propose an incremental selective decode and forward protocol that offers additional power savings , while keeping the same outage performance we compare the performance of the two protocols in terms of the end to end signal to noise ratio cumulative distribution function via closed form expressions finally , we corroborate our theoretical results with simulation , and show the relative relay power savings in comparison to non selective cooperation in which the relay cooperates regardless of channel conditions"
cs.DS,we apply competitive analysis onto the problem of minimizing the number of queries to an oracle to completely reconstruct a given monotone boolean function besides lower and upper bounds on the competitivity we determine optimal deterministic online algorithms for the smallest problem instances
cs.AI|cs.LG|stat.ML,"learning bayesian networks is often cast as an optimization problem , where the computational task is to find a structure that maximizes a statistically motivated score by and large , existing learning tools address this optimization problem using standard heuristic search techniques since the search space is extremely large , such search procedures can spend most of the time examining candidates that are extremely unreasonable this problem becomes critical when we deal with data sets that are large either in the number of instances , or the number of attributes in this paper , we introduce an algorithm that achieves faster learning by restricting the search space this iterative algorithm restricts the parents of each variable to belong to a small subset of candidates we then search for a network that satisfies these constraints the learned network is then used for selecting better candidates for the next iteration we evaluate this algorithm both on synthetic and real life data our results show that it is significantly faster than alternative search procedures without loss of quality in the learned structures"
cs.SI,"the complexity of highly interconnected systems is rooted in the interwoven architecture defined by its connectivity structure in this paper , we develop matrix energy of the underlying connectivity structure as a measure of topological complexity and highlight interpretations about certain global features of underlying system connectivity patterns the proposed complexity metric is shown to satisfy the weyuker criteria as a measure of its validity as a formal complexity metric we also introduce the notion of p point in the graph density space the p point acts as a boundary between multiple connectivity regimes for finite size graphs"
cs.SI,"we consider social peer to peer data management systems \( pdms \) , where each peer maintains both semantic mappings between its schema and some acquaintances , and social links with peer friends in this context , reformulating a query from a peer 's schema into other peer 's schemas is a hard problem , as it may generate as many rewritings as the set of mappings from that peer to the outside and transitively on , by eventually traversing the entire network however , not all the obtained rewritings are relevant to a given query in this paper , we address this problem by inspecting semantic mappings and social links to find only relevant rewritings we propose a new notion of 'relevance' of a query with respect to a mapping , and , based on this notion , a new semantic query reformulation approach for social pdms , which achieves great accuracy and flexibility to find rapidly the most interesting mappings , we combine several techniques \( i \) social links are expressed as foaf \( friend of a friend \) links to characterize peer 's friendship and compact mapping summaries are used to obtain mapping descriptions \( ii \) local semantic views are special views that contain information about external mappings and \( iii \) gossiping techniques improve the search of relevant mappings our experimental evaluation , based on a prototype on top of peersim and a simulated network demonstrate that our solution yields greater recall , compared to traditional query translation approaches proposed in the literature"
cs.SI,"community detection helps us simplify the complex configuration of networks , but communities are reliable only if they are statistically significant to detect statistically significant communities , a common approach is to resample the original network and analyze the communities but resampling assumes independence between samples , while the components of a network are inherently dependent therefore , we must understand how breaking dependencies between resampled components affects the results of the significance analysis here we use scientific communication as a model system to analyze this effect our dataset includes citations among articles published in journals in the years 1984 2010 we compare parametric resampling of citations with non parametric article resampling while citation resampling breaks link dependencies , article resampling maintains such dependencies we find that citation resampling underestimates the variance of link weights moreover , this underestimation explains most of the differences in the significance analysis of ranking and clustering therefore , when only link weights are available and article resampling is not an option , we suggest a simple parametric resampling scheme that generates link weight variances close to the link weight variances of article resampling nevertheless , when we highlight and summarize important structural changes in science , the more dependencies we can maintain in the resampling scheme , the earlier we can predict structural change"
cs.SI,"the rise of social media allowed for rich analyses of their content and their network structure as traditional media \( i e newspapers and magazines \) are being digitized , similar analyses can be undertaken this provides a glimpse of the elite , as the news mostly revolves around the more influential members of society we here focus on a network structure derived from co occurrences of people in the media this network has a strong core with peripheral clusters being connected to the core nonetheless , these characteristics seem to be mainly a result from the bipartite structure of the data we employ a simple growing bipartite model that can qualitatively reproduce such a core periphery structure two self reinforcing processes are vital \( 1 \) more frequently occurring persons are more likely to occur again and \( 2 \) if two people co occur frequently , they are more likely to co occur again this suggests that the core periphery structure is not necessarily reflective of the elite network in society , but might be an artefact of how they are portrayed in the media"
cs.IT,"in this paper , in order to characterize the critical error linear complexity spectrum \( celcs \) for 2 n periodic binary sequences , we first propose a decomposition based on the cube theory based on the proposed k error cube decomposition , and the famous inclusion exclusion principle , we obtain the complete characterization of i th descent point \( critical point \) of the k error linear complexity for i 2 , 3 second , by using the sieve method and games chan algorithm , we characterize the second descent point \( critical point \) distribution of the k error linear complexity for 2 n periodic binary sequences as a consequence , we obtain the complete counting functions on the k error linear complexity of 2 n periodic binary sequences as the second descent point for k 3 , 4 this is the first time for the second and the third descent points to be completely characterized in fact , the proposed constructive approach has the potential to be used for constructing 2 n periodic binary sequences with the given linear complexity and k error linear complexity \( or celcs \) , which is a challenging problem to be deserved for further investigation in future"
cs.IT,"we consider the problem of information aggregation in sensor networks , where one is interested in computing a function of the sensor measurements we allow for block processing and study in network function computation in directed graphs and undirected graphs we study how the structure of the function affects the encoding strategies , and the effect of interactive information exchange we begin by considering a directed graph g \( v , e \) on the sensor nodes , where the goal is to determine the optimal encoders on each edge which achieve function computation at the collector node our goal is to characterize the rate region in r e , i e , the set of points for which there exist feasible encoders with given rates which achieve zero error computation for asymptotically large block length we determine the solution for directed trees , specifying the optimal encoder and decoder for each edge for general directed acyclic graphs , we provide an outer bound on the rate region by finding the disambiguation requirements for each cut , and describe examples where this outer bound is tight next , we address the scenario where nodes are connected in an undirected tree network , and every node wishes to compute a given symmetric boolean function of the sensor data undirected edges permit interactive computation , and we therefore study the effect of interaction on the aggregation and communication strategies we focus on sum threshold functions , and determine the minimum worst case total number of bits to be exchanged on each edge the optimal strategy involves recursive in network aggregation which is reminiscent of message passing in the case of general graphs , we present a cutset lower bound , and an achievable scheme based on aggregation along trees for complete graphs , we prove that the complexity of this scheme is no more than twice that of the optimal scheme"
cs.IT,"pilot aided channel estimation is nowadays a standard component in each wireless receiver enabling coherent transmission of complex valued constellations , only affected by noise and interference whenever these disturbances are sufficiently small and long data frames are used , high data rates can be achieved and the resource overhead due to the pilots vanishes asymptotically on the other , it is expected that for the next generation of mobile networks not only data rate is in the main focus but also low latency , short and sporadic messages , massive connectivity , distributed adhoc processing and robustness with respect to asynchronism therefore a review of several well established principles in communication has been started already a particular implication when using complex valued pilots is that these values have to be known at the receiver and therefore these resources can not be used simultaneously for user data for an ofdm like multicarrier scheme this means that pilot tones \( usually placed equidistantly according to the nyquist theorem \) are allocated with globally known amplitudes and phases to reconstruct the channel impulse response phases are designed and allocated globally which is in contrast to a distributed infrastructure in this work we present therefore a new phaseless pilot scheme where only pilot amplitudes need to be known at the receiver , i e , phases are available again and can be used for various other purposes the idea is based on a phase retrieval result for symmetrized and zero padded magnitude fourier measurements obtained by two of the authors the phases on the pilot tones can now be used to carry additional user specific data or compensate for other signal characteristics , like the papr"
cs.DS,formal verification of complex algorithms is challenging verifying their implementations goes beyond the state of the art of current automatic verification tools and usually involves intricate mathematical theorems certifying algorithms compute in addition to each output a witness certifying that the output is correct a checker for such a witness is usually much simpler than the original algorithm yet it is all the user has to trust the verification of checkers is feasible with current tools and leads to computations that can be completely trusted we describe a framework to seamlessly verify certifying computations we use the automatic verifier vcc for establishing the correctness of the checker and the interactive theorem prover isabelle hol for high level mathematical properties of algorithms we demonstrate the effectiveness of our approach by presenting the verification of typical examples of the industrial level and widespread algorithmic library leda
cs.LG|stat.ML,"in this paper , we investigate a multivariate multi response \( mvmr \) linear regression problem , which contains multiple linear regression models with differently distributed design matrices , and different regression and output vectors the goal is to recover the support union of all regression vectors using l 1 l 2 regularized lasso we characterize sufficient and necessary conditions on sample complexity emph as a sharp threshold to guarantee successful recovery of the support union namely , if the sample size is above the threshold , then l 1 l 2 regularized lasso correctly recovers the support union and if the sample size is below the threshold , l 1 l 2 regularized lasso fails to recover the support union in particular , the threshold precisely captures the impact of the sparsity of regression vectors and the statistical properties of the design matrices on sample complexity therefore , the threshold function also captures the advantages of joint support union recovery using multi task lasso over individual support recovery using single task lasso"
cs.DS,"the capacity of a wireless network is the maximum possible amount of simultaneous communication , taking interference into account formally , we treat the following problem given is a set of links , each a sender receiver pair located in a metric space , and an assignment of power to the senders we seek a maximum subset of links that are feasible in the sinr model namely , the signal received on each link should be larger than the sum of the interferences from the other links we give a constant factor approximation that holds for any length monotone , sub linear power assignment and any distance metric we use this to give essentially tight characterizations of capacity maximization under power control using oblivious power assignments specifically , we show that the mean power assignment is optimal for capacity maximization of bi directional links , and give a tight theta \( log n \) approximation of scheduling bi directional links with power control using oblivious power for uni directional links we give a nearly optimal o \( log n log log delta \) approximation to the power control problem using mean power , where delta is the ratio of longest and shortest links combined , these results clarify significantly the centralized complexity of wireless communication problems"
cs.IT,"two hop security communication with an eavesdropper in wireless environment is a hot research direction the basic idea is that the destination , simultaneously with the source , sends a jamming signal to interfere the eavesdropper near to or co located with the relay similar as physical layer network coding , the friendly jamming signal will prevent the eavesdropper from detecting the useful information originated from the source and will not affect the destination on detecting the source information with the presence of the known jamming signal however , existing investigations are confined to gaussian distributed signals , which are seldom used in real systems when finite constellation signals are applied , the behavior of the secrecy rate becomes very different for example , the secrecy rate depends on phase difference between the input signals with finite constellations , which is not observed with gaussian signals in this paper , we investigate the secrecy capacity and derive its upper bound for the two hop relay model , by assuming an eavesdropper near the relay and the widely used m psk modulation with our upper bound , the best and worst phase differences in high snr region are then given numerical studies verify our analysis and show that the derived upper bound is relatively tight"
cs.IT,"it is well known that in k user constant single antenna interference channels k 2 degrees of freedom \( dof \) can be achieved for almost all channel matrices explicit conditions on the channel matrix to admit k 2 dof are , however , not available the purpose of this paper is to identify such explicit conditions , which are satisfied for almost all channel matrices we also provide a construction of corresponding asymptotically dof optimal input distributions the main technical tool used is a recent breakthrough result by hochman in fractal geometry"
cs.DS,"recent research on mutual exclusion for shared memory systems has focused on local spin algorithms performance is measured using the remote memory references \( rmrs \) metric as common in recent literature , we consider a standard asynchronous shared memory model with n processes , which allows atomic read , write and compare and swap \( short cas \) operations in such a model , the asymptotically tight upper and lower bound on the number of rmrs per passage through the critical section is theta \( log n \) for the optimal deterministic algorithms \( see yang and anderson , 1995 , and attiya , hendler and woelfel , 2008 \) recently , several randomized algorithms have been devised that break the omega \( log n \) barrier and need only o \( log n \) rmrs per passage in expectation \( see hendler and woelfel , 2010 , hendler and woelfel , 2011 , and bender and gilbert , 2011 \) in this paper we present the first randomized abortable mutual exclusion algorithm that achieves a sub logarithmic expected rmr complexity more precisely , against a weak adversary \( which can make scheduling decisions based on the entire past history , but not the latest coin flips of each process \) every process needs an expected number of o \( log n log log n \) rmrs to enter end exit the critical section if a process receives an abort signal , it can abort an attempt to enter the critical section within a finite number of its own steps and by incurring o \( log n log log n \) rmrs"
cs.SI,"zipf 's power law distribution is a generic empirical statistical regularity found in many complex systems however , rather than universality with a single power law exponent \( equal to 1 for zipf 's law \) , there are many reported deviations that remain unexplained a recently developed theory finds that the interplay between \( i \) one of the most universal ingredients , namely stochastic proportional growth , and \( ii \) birth and death processes , leads to a generic power law distribution with an exponent that depends on the characteristics of each ingredient here , we report the first complete empirical test of the theory and its application , based on the empirical analysis of the dynamics of market shares in the product market we estimate directly the average growth rate of market shares and its standard deviation , the birth rates and the death \( hazard \) rate of products we find that temporal variations and product differences of the observed power law exponents can be fully captured by the theory with no adjustable parameters our results can be generalized to many systems for which the statistical properties revealed by power law exponents are directly linked to the underlying generating mechanism"
cs.IT,"to reduce computational complexity and delay in randomized network coded content distribution \( and for some other practical reasons \) , coding is not performed simultaneously over all content blocks but over much smaller subsets known as generations a penalty is throughput reduction we model coding over generations as the coupon collector 's brotherhood problem this model enables us to theoretically compute the expected number of coded packets needed for successful decoding of the entire content , as well as a bound on the probability of decoding failure , and further , to quantify the tradeoff between computational complexity and throughput interestingly , with a moderate increase in the generation size , throughput quickly approaches link capacity as an additional contribution , we derive new results for the generalized collector 's brotherhood problem which can also be used for further study of many other aspects of coding over generations"
cs.IT,"we propose the qos aware bs selection schemes for the distributed wireless mimo links , which aim at minimizing the bs usages and reducing the interfering range , while satisfying diverse statistical delay qos constraints characterized by the delay bound violation probability and the effective capacity technique in particular , based on the channel state information \( csi \) and qos requirements , a subset of bs with variable cardinality for the distributed mimo transmission is dynamically selected , where the selections are controlled by a central server for the single user scenario , we develop two optimization frameworks , respectively , to derive the efficient bs selection schemes and the corresponding resource allocation algorithms one framework uses the incremental bs selection and time sharing \( ibs ts \) strategies , and the other employs the ordered gain based bs selection and probabilistic transmissions \( ogbs pt \) the ibs ts framework can yield better performance , while the scheme developed under the ogbs pt framework is easier to implement for the multi user scenario , we propose the optimization framework applying the priority bs selection , block diagonalization precoding , and probabilistic transmission \( pbs bd pt \) techniques we also propose the optimization framework applying the priority bs selection , time division multiple access , and probabilistic transmission \( pbs tdma pt \) techniques we derive the optimal transmission schemes for all the aforementioned frameworks , respectively also conducted is a set of simulation evaluations which compare our proposed schemes with several baseline schemes and show the impact of the delay qos requirements , transmit power , and traffic loads on the performances of bs selections for distributed mimo systems"
cs.SI,"in recent years , social networking platforms have developed into extraordinary channels for spreading and consuming information along with the rise of such infrastructure , there is continuous progress on techniques for spreading information effectively through influential users in many applications , one is restricted to select influencers from a set of users who engaged with the topic being promoted , and due to the structure of social networks , these users often rank low in terms of their influence potential an alternative approach one can consider is an adaptive method which selects users in a manner which targets their influential neighbors the advantage of such an approach is that it leverages the friendship paradox in social networks while users are often not influential , they often know someone who is despite the various complexities in such optimization problems , we show that scalable adaptive seeding is achievable in particular , we develop algorithms for linear influence models with provable approximation guarantees that can be gracefully parallelized to show the effectiveness of our methods we collected data from various verticals social network users follow for each vertical , we collected data on the users who responded to a certain post as well as their neighbors , and applied our methods on this data our experiments show that adaptive seeding is scalable , and importantly , that it obtains dramatic improvements over standard approaches of information dissemination"
cs.IT,"we study benefits of opportunistic routing in a large wireless ad hoc network by examining how the power , delay , and total throughput scale as the number of source destination pairs increases up to the operating maximum our opportunistic routing is novel in a sense that it is massively parallel , i e , it is performed by many nodes simultaneously to maximize the opportunistic gain while controlling the inter user interference the scaling behavior of conventional multi hop transmission that does not employ opportunistic routing is also examined for comparison our results indicate that our opportunistic routing can exhibit a net improvement in overall power delay trade off over the conventional routing by providing up to a logarithmic boost in the scaling law such a gain is possible since the receivers can tolerate more interference due to the increased received signal power provided by the multi user diversity gain , which means that having more simultaneous transmissions is possible"
cs.IT,"interference between nodes is a critical impairment in mobile ad hoc networks \( manets \) this paper studies the role of multiple antennas in mitigating such interference specifically , a network is studied in which receivers apply zero forcing beamforming to cancel the strongest interferers assuming a network with poisson distributed transmitters and independent rayleigh fading channels , the transmission capacity is derived , which gives the maximum number of successful transmissions per unit area mathematical tools from stochastic geometry are applied to obtain the asymptotic transmission capacity scaling and characterize the impact of inaccurate channel state information \( csi \) it is shown that , if each node cancels l interferers , the transmission capacity decreases as the outage probability to the power of 1 \( l 1 \) as the outage probability vanishes for fixed outage probability , as l grows , the transmission capacity increases as l to the power of \( 1 2 alpha \) where alpha is the path loss exponent moreover , csi inaccuracy is shown to have no effect on the transmission capacity scaling as the outage probability vanishes , provided that the csi training sequence has an appropriate length , which we derived numerical results suggest that canceling merely one interferer by each node increases the transmission capacity by an order of magnitude or more , even when the csi is imperfect"
cs.IT,"consider transmission over a binary additive white gaussian noise channel using a fixed low density parity check code we consider the posterior measure over the code bits and the corresponding correlation between two codebits , averaged over the noise realizations we show that for low enough noise variance this average correlation decays exponentially fast with the graph distance between the code bits one consequence of this result is that for low enough noise variance the gexit functions \( further averaged over a standard code ensemble \) of the belief propagation and optimal decoders are the same"
cs.DS,"drawing network maps automatically comprises two challenging steps , namely laying out the map and placing non overlapping labels in this paper we tackle the problem of labeling an already existing network map considering the application of metro maps we present a flexible and versatile labeling model despite its simplicity , we prove that it is np complete to label a single line of the network for a restricted variant of that model , we then introduce an efficient algorithm that optimally labels a single line with respect to a given weighting function based on that algorithm , we present a general and sophisticated workflow for multiple metro lines , which is experimentally evaluated on real world metro maps"
cs.LG|cs.SI,"precise recommendation of followers helps in improving the user experience and maintaining the prosperity of twitter and microblog platforms in this paper , we design a hybrid recommender system of microblog as a solution of kdd cup 2012 , track 1 task , which requires predicting users a user might follow in tencent microblog we describe the background of the problem and present the algorithm consisting of keyword analysis , user taxonomy , \( potential \) interests extraction and item recommendation experimental result shows the high performance of our algorithm some possible improvements are discussed , which leads to further study"
cs.DM|cs.DS,"paths p1 , , pk in a graph g \( v , e \) are said to be mutually induced if for any 1 i j k , pi and pj have neither common vertices nor adjacent vertices \( except perhaps their end vertices \) the induced disjoint paths problem is to test whether a graph g with k pairs of specified vertices \( si , ti \) contains k mutually induced paths pi such that pi connects si and ti for i 1 , , k we show that this problem is fixed parameter tractable for claw free graphs when parameterized by k several related problems , such as the k in a path problem , are proven to be fixed parameter tractable for claw free graphs as well we show that an improvement of these results in certain directions is unlikely , for example by noting that the induced disjoint paths problem cannot have a polynomial kernel for line graphs \( a type of claw free graphs \) , unless np subseteq conp poly moreover , the problem becomes np complete , even when k 2 , for the more general class of k 1 , 4 free graphs finally , we show that the n o \( k \) time algorithm of fiala et al for testing whether a claw free graph contains some k vertex graph h as a topological induced minor is essentially optimal by proving that this problem is w 1 hard even if g and h are line graphs"
cs.DS,"we present an algorithm called the best trail algorithm , which helps solve the hypertext navigation problem by automating the construction of memex like trails through the corpus the algorithm performs a probabilistic best first expansion of a set of navigation trees to find relevant and compact trails we describe the implementation of the algorithm , scoring methods for trails , filtering algorithms and a new metric called emph potential gain which measures the potential of a page for future navigation opportunities"
cs.IT,"we study distributed optimization where nodes cooperatively minimize the sum of their individual , locally known , convex costs f i \( x \) 's , x in mathbb r d is global distributed augmented lagrangian \( al \) methods have good empirical performance on several signal processing and learning applications , but there is limited understanding of their convergence rates and how it depends on the underlying network this paper establishes globally linear \( geometric \) convergence rates of a class of deterministic and randomized distributed al methods , when the f i 's are twice continuously differentiable and have a bounded hessian we give explicit dependence of the convergence rates on the underlying network parameters simulations illustrate our analytical findings"
cs.SI,"with the rapid development of location based social networks \( lbsns \) , spatial item recommendation has become an important means to help people discover attractive and interesting venues and events , especially when users travel out of town however , this recommendation is very challenging compared to the traditional recommender systems a user can visit only a limited number of spatial items , leading to a very sparse user item matrix most of the items visited by a user are located within a short distance from where he she lives , which makes it hard to recommend items when the user travels to a far away place moreover , user interests and behavior patterns may vary dramatically across different geographical regions in light of this , we propose geo sage , a geographical sparse additive generative model for spatial item recommendation in this paper geo sage considers both user personal interests and the preference of the crowd in the target region , by exploiting both the co occurrence pattern of spatial items and the content of spatial items to further alleviate the data sparsity issue , geo sage exploits the geographical correlation by smoothing the crowd 's preferences over a well designed spatial index structure called spatial pyramid we conduct extensive experiments to evaluate the performance of our geo sage model on two real large scale datasets the experimental results clearly demonstrate our geo sage model outperforms the state of the art in the two tasks of both out of town and home town recommendations"
cs.SI,"clustering , assortativity , and communities are key features of complex networks we probe dependencies between these attributes and find that ensembles with strong clustering display both high assortativity by degree and prominent community structure , while ensembles with high assortativity are much less biased towards clustering or community structure further , clustered networks can amplify small homophilic bias for trait assortativity this marked asymmetry suggests that transitivity , rather than homophily , drives the standard nonsocial social network dichotomy"
cs.AI|cs.LG|stat.ML,"much work has been done refining and characterizing the receptive fields learned by deep learning algorithms a lot of this work has focused on the development of gabor like filters learned when enforcing sparsity constraints on a natural image dataset little work however has investigated how these filters might expand to the temporal domain , namely through training on natural movies here we investigate exactly this problem in established temporal deep learning algorithms as well as a new learning paradigm suggested here , the temporal autoencoding restricted boltzmann machine \( tarbm \)"
cs.SI,"many real world complex systems consist of a set of elementary units connected by relationships of different kinds all such systems are better described in terms of multiplex networks , where the links at each layer represent a different type of interaction between the same set of nodes , rather than in terms of \( single layer \) networks in this paper we present a general framework to describe and study multiplex networks , whose links are either unweighted or weighted in particular we propose a series of measures to characterize the multiplexicity of the systems in terms of i \) basic node and link properties such as the node degree , and the edge overlap and reinforcement , ii \) local properties such as the clustering coefficient and the transitivity , iii \) global properties related to the navigability of the multiplex across the different layers the measures we introduce are validated on a genuine multiplex data set of indonesian terrorists , where information among 78 individuals are recorded with respect to mutual trust , common operations , exchanged communications and business relationships"
stat.ML,"we address the issue of edge detection in synthetic aperture radar imagery in particular , we propose nonparametric methods for edge detection , and numerically compare them to an alternative method that has been recently proposed in the literature our results show that some of the proposed methods display superior results and are computationally simpler than the existing method an application to real \( not simulated \) data is presented and discussed"
cs.DS,we study the problem of hierarchical clustering on planar graphs we formulate this in terms of an lp relaxation of ultrametric rounding to solve this lp efficiently we introduce a dual cutting plane scheme that uses minimum cost perfect matching as a subroutine in order to efficiently explore the space of planar partitions we apply our algorithm to the problem of hierarchical image segmentation
cs.IT,"caching popular contents for frequent access is an attractive way of exploiting the redundancy of user requests exploiting cognition to the cache enabled d2d in the multichannel cellular network is the main focus of this paper we contribute to analyzing the cache based content delivery in a two tier heterogeneous network \( hetnet \) composed of base stations \( bss \) and device to device \( d2d \) pairs , where the d2d accesses the networks with overlay spectrum sharing node locations are first modeled as mutually independent poisson point processes \( ppps \) , and the corresponding tier association and cognitive access protocol are developed the d2d transmitter \( tx \) performs overlay spectrum sensing within its spectrum sensing region \( ssr \) to detect the idleness of cellular channels the number of bss and d2d txs in the ssr are then analyzed we further elaborate the probability mass function \( pmf \) of the delay and queue length , with modeling the traffic dynamics of request arrivals and departures at the bs and d2d tx as the discrete time multiserver queue with priorities moreover , impacts of some key network parameters , e g , the content popularity , the request density and the caching storage , on the system performance are investigated to provide a valuable insight"
cs.SI,"cascading failures and epidemic dynamics , as two successful application realms of network science , are usually investigated separately how do they affect each other is still one open , interesting problem in this letter , we couple both processes and put them into the framework of interdependent networks , where each network only supports one dynamical process of particular interest , they spontaneously form a feedback loop virus propagation triggers cascading failures of systems while cascading failures suppress virus propagation especially , there exists crucial threshold of virus transmissibility , above which the interdependent networks collapse completely in addition , the interdependent networks will be more vulnerable if the network supporting virus propagation has denser connections otherwise the interdependent systems are robust against the change of connections in other layer \( s \) this discovery differs from previous framework of cascading failure in interdependent networks , where better robustness usually needs denser connections finally , to protect interdependent networks we also propose the control measures based on the identification capability the larger this capability , more robustness the interdependent networks will be"
cs.LG|stat.ML,"we address challenges of active learning under scarce informational resources in non stationary environments in real world settings , data labeled and integrated into a predictive model may become invalid over time however , the data can become informative again with switches in context and such changes may indicate unmodeled cyclic or other temporal dynamics we explore principles for discarding , caching , and recalling labeled data points in active learning based on computations of value of information we review key concepts and study the value of the methods via investigations of predictive performance and costs of acquiring data for simulated and real world data sets"
cs.DM|cs.DS,we derive a simple efficient algorithm for abelian periods knowing all abelian squares in a string an efficient algorithm for the latter problem was given by cummings and smyth in 1997 by the way we show an alternative algorithm for abelian squares we also obtain a linear time algorithm finding all `long' abelian periods the aim of the paper is a \( new \) reduction of the problem of all abelian periods to that of \( already solved \) all abelian squares which provides new insight into both connected problems
cs.IT,"we introduce a novel communication strategy for dc micro grids \( mgs \) , termed power talk , in which the devices communicate by modulating the power levels in the dc bus the information is transmitted by varying the parameters that the mg units use to control the level of the common bus voltage , while it is received by processing the bus measurements that units perform this communication is challenged by the fact that the voltage level is subject to random disturbances , as the state of the mg changes with random load variations we develop a corresponding communication model and address the random voltage fluctuations by using coding strategies that transform the mg into some well known communication channels the performance analysis shows that it is possible to mitigate the random voltage level variations and communicate reliably over the mg bus"
cs.IT,"we study the rate distortion relationship in the set of permutations endowed with the kendall tau metric and the chebyshev metric our study is motivated by the application of permutation rate distortion to the average case and worst case analysis of algorithms for ranking with incomplete information and approximate sorting algorithms for the kendall tau metric we provide bounds for small , medium , and large distortion regimes , while for the chebyshev metric we present bounds that are valid for all distortions and are especially accurate for small distortions in addition , for the chebyshev metric , we provide a construction for covering codes"
cs.DS|cs.SI,"the independent set problem is np hard and particularly difficult to solve in large sparse graphs in this work , we develop an advanced evolutionary algorithm , which incorporates kernelization techniques to compute large independent sets in huge sparse networks a recent exact algorithm has shown that large networks can be solved exactly by employing a branch and reduce technique that recursively kernelizes the graph and performs branching however , one major drawback of their algorithm is that , for huge graphs , branching still can take exponential time to avoid this problem , we recursively choose vertices that are likely to be in a large independent set \( using an evolutionary approach \) , then further kernelize the graph we show that identifying and removing vertices likely to be in large independent sets opens up the reduction space which not only speeds up the computation of large independent sets drastically , but also enables us to compute high quality independent sets on much larger instances than previously reported in the literature"
cs.IT,"we consider a spectrum sharing network where n secondary relays are used to increase secondary rate and also mitigate interference on the primary by reducing the required overall secondary emitted power we propose a distributed relay selection and clustering framework , obtain closed form expressions for the secondary rate , and show that secondary rate increases proportionally to log n remarkably , this is on the same order as the growth rate obtained in the absence of a primary system and its imposed constraints our results show that to maximize the rate , the secondary relays must transmit with power proportional to n \( 1 \) \( thus the sum of relay powers is bounded \) and also that the secondary source may not operate at its maximum allowable power the tradeoff between the secondary rate and the interference on the primary is also characterized , showing that the primary interference can be reduced asymptotically to zero as n increases , while still maintaining a secondary rate that grows proportionally to log n finally , to address the rate loss due to half duplex relaying in the secondary , we propose an alternating relay protocol and investigate its performance"
cs.AI,"in this article , an agent based negotiation model for negotiation teams that negotiate a deal with an opponent is presented agent based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process the model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process which offer is sent to the opponent , and whether the offers received from the opponent are accepted the main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round this work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations an empirical evaluation is also performed to study the impact of the different parameters of the model"
cs.IT,"periodic nonuniform sampling is a known method to sample spectrally sparse signals below the nyquist rate this strategy relies on the implicit assumption that the individual samplers are exposed to the entire frequency range this assumption becomes impractical for wideband sparse signals the current paper proposes an alternative sampling stage that does not require a full band front end instead , signals are captured with an analog front end that consists of a bank of multipliers and lowpass filters whose cutoff is much lower than the nyquist rate the problem of recovering the original signal from the low rate samples can be studied within the framework of compressive sampling an appropriate parameter selection ensures that the samples uniquely determine the analog input moreover , the analog input can be stably reconstructed with digital algorithms numerical experiments support the theoretical analysis"
cs.DM|cs.DS,"it is well known that many local graph problems , like vertex cover and dominating set , can be solved in 2 o \( tw \) v o \( 1 \) time for graphs g \( v , e \) with a given tree decomposition of width tw however , for nonlocal problems , like the fundamental class of connectivity problems , for a long time we did not know how to do this faster than tw o \( tw \) v o \( 1 \) recently , cygan et al \( focs 2011 \) presented monte carlo algorithms for a wide range of connectivity problems running in time c tw v o \( 1 \) for a small constant c , e g , for hamiltonian cycle and steiner tree naturally , this raises the question whether randomization is necessary to achieve this runtime furthermore , it is desirable to also solve counting and weighted versions \( the latter without incurring a pseudo polynomial cost in terms of the weights \) we present two new approaches rooted in linear algebra , based on matrix rank and determinants , which provide deterministic c tw v o \( 1 \) time algorithms , also for weighted and counting versions for example , in this time we can solve the traveling salesman problem or count the number of hamiltonian cycles the rank based ideas provide a rather general approach for speeding up even straightforward dynamic programming formulations by identifying small sets of representative partial solutions we focus on the case of expressing connectivity via sets of partitions , but the essential ideas should have further applications the determinant based approach uses the matrix tree theorem for deriving closed formulas for counting versions of connectivity problems we show how to evaluate those formulas via dynamic programming"
cs.IT|cs.LG|stat.ML,"consider a movie recommendation system where apart from the ratings information , side information such as user 's age or movie 's genre is also available unlike standard matrix completion , in this setting one should be able to predict inductively on new users movies in this paper , we study the problem of inductive matrix completion in the exact recovery setting that is , we assume that the ratings matrix is generated by applying feature vectors to a low rank matrix and the goal is to recover back the underlying matrix furthermore , we generalize the problem to that of low rank matrix estimation using rank 1 measurements we study this generic problem and provide conditions that the set of measurements should satisfy so that the alternating minimization method \( which otherwise is a non convex method with no convergence guarantees \) is able to recover back the em exact underlying low rank matrix in addition to inductive matrix completion , we show that two other low rank estimation problems can be studied in our framework a \) general low rank matrix sensing using rank 1 measurements , and b \) multi label regression with missing labels for both the problems , we provide novel and interesting bounds on the number of measurements required by alternating minimization to provably converges to the em exact low rank matrix in particular , our analysis for the general low rank matrix sensing problem significantly improves the required storage and computational cost than that required by the rip based matrix sensing methods cite rechtfp2007 finally , we provide empirical validation of our approach and demonstrate that alternating minimization is able to recover the true matrix for the above mentioned problems using a small number of measurements"
cs.AI,"is knowledge definable as justified true belief \( jtb \) \? we argue that one can legitimately answer positively or negatively , depending on how the notion of justification is understood to facilitate our argument , we introduce a simple propositional logic of reason based belief we show that this logic is sufficiently flexible to accommodate various useful features , including quantification over reasons we use our framework to contrast two notions of jtb one internalist , the other externalist we argue that gettier cases essentially challenge the internalist notion but not the externalist one in particular , we may equate knowledge and jtb if the latter is grounded in what we call adequate reasons"
cs.IT,"the problem of compressive detection of random subspace signals is studied we consider signals modeled as mathbf y mathbf h mathbf x where mathbf h is an n times k matrix with k le n and mathbf x sim mathcal n \( 0 , sigma x 2 mathbf i k \) we say that signal mathbf y lies in or leans toward a subspace if the condition number of mathbf h mathbf h t is strictly greater than one we first design a measurement matrix mathbf phi mathbf phi s t , mathbf phi o t t comprising of two sub matrices mathbf phi s and mathbf phi o where mathbf phi s projects the signals to the strongest left singular vectors , i e , the left singular vectors corresponding to the largest singular values , of subspace matrix mathbf h and mathbf phi o projects it to the weakest left singular vectors we then propose two detectors which work based on the difference in energies of the samples measured by two sub matrices mathbf phi s and mathbf phi o simplified versions of the proposed detectors for the case when the variance of noise is known are also provided furthermore , we study the performance of the detector when measurements are imprecise and show how imprecision can be compensated by employing more measurement devices the problem is then re formulated for the case when the signal lies in the union of a finite number of linear subspaces instead of a single linear subspace finally , we study the performance of the proposed methods by simulation examples"
cs.AI,"in this paper , we determine the complexity of the satisfiability problem for various logics obtained by adding numerical quantifiers , and other constructions , to the traditional syllogistic in addition , we demonstrate the incompleteness of some recently proposed proof systems for these logics"
cs.LG|stat.ML,"in medical risk modeling , typical data are scarce they have relatively small number of training instances \( n \) , censoring , and high dimensionality \( m \) we show that the problem may be effectively simplified by reducing it to bipartite ranking , and introduce new bipartite ranking algorithm , smooth rank , for robust learning on scarce data the algorithm is based on ensemble learning with unsupervised aggregation of predictors the advantage of our approach is confirmed in comparison with two gold standard risk modeling methods on 10 real life survival analysis datasets , where the new approach has the best results on all but two datasets with the largest ratio n m for systematic study of the effects of data scarcity on modeling by all three methods , we conducted two types of computational experiments on real life data with randomly drawn training sets of different sizes , and on artificial data with increasing number of features both experiments demonstrated that smooth rank has critical advantage over the popular methods on the scarce data it does not suffer from overfitting where other methods do"
cs.AI|stat.ML,"we introduce gp fnarx a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model \( narx \) with filtered regressors \( f \) where the nonlinear regression problem is tackled using sparse gaussian processes \( gp \) we integrate data pre processing with system identification into a fully automated procedure that goes from raw data to an identified model both pre processing parameters and gp hyper parameters are tuned by maximizing the marginal likelihood of the probabilistic model we obtain a bayesian model of the system 's dynamics which is able to report its uncertainty in regions where the data is scarce the automated approach , the modeling of uncertainty and its relatively low computational cost make of gp fnarx a good candidate for applications in robotics and adaptive control"
cs.SI,"recent grassroots movements have suggested that online social networks might play a key role in their organization , as adherents have a fast , many to many , communication channel to help coordinate their mobilization the structure and dynamics of the networks constructed from the digital traces of protesters have been analyzed to some extent recently however , less effort has been devoted to the analysis of the semantic content of messages exchanged during the protest using the data obtained from a microblogging service during the brewing and active phases of the 15m movement in spain , we perform the first large scale test of theories on collective emotions and social interaction in collective actions our findings show that activity and information cascades in the movement are larger in the presence of negative collective emotions and when users express themselves in terms related to social content at the level of individual participants , our results show that their social integration in the movement , as measured through social network metrics , increases with their level of engagement and of expression of negativity our findings show that non rational factors play a role in the formation and activity of social movements through online media , having important consequences for viral spreading"
cs.IT,"a position coding pattern is an array of symbols in which subarrays of a certain fixed size appear at most once so , each subarray uniquely identifies a location in the larger array , which means there is a bijection of some sort from this set of subarrays to a set of coordinates the key to fly pentop computer paper and other examples of position codes is a method to read the subarray and then convert it to coordinates position coding makes use of ideas from discrete mathematics and number theory in this paper , we will describe the underlying mathematics of two position codes , one being the anoto code that is the basis of fly paper then , we will present two new codes , one which uses binary wavelets as part of the bijection"
cs.DS,"virtual machine \( vm \) scheduling is an important technique to efficiently operate the computing resources in a data center previous work has mainly focused on consolidating vms to improve resource utilization and thus to optimize energy consumption however , the interference between collocated vms is usually ignored , which can result in very worse performance degradation to the applications running in those vms due to the contention of the shared resources based on this observation , we aim at designing efficient vm assignment and scheduling strategies where we consider optimizing both the operational cost of the data center and the performance degradation of running applications and then , we propose a general model which captures the inherent tradeoff between the two contradictory objectives we present offline and online solutions for this problem by exploiting the spatial and temporal information of vms where vm scheduling is done by jointly consider the combinations and the life cycle overlapping of the vms evaluation results show that the proposed methods can generate efficient schedules for vms , achieving low operational cost while significantly reducing the performance degradation of applications in cloud data centers"
cs.SI,the vertices of an interval graph represent intervals over a real line where overlapping intervals denote that their corresponding vertices are adjacent this implies that the vertices are measurable by a metric and there exists a linear structure in the system the generalization is an embedding of a graph onto a multi dimensional euclidean space and it was used by scientists to study the multi relational complexity of ecology however the research went out of fashion in the 1980s and was not revisited when network science recently expressed interests with multi relational networks known as multiplexes this paper studies interval graphs from the perspective of network science
cs.IT,"this paper deals with the problem of adaptive multidimensional multichannel signal detection in homogeneous gaussian disturbance with unknown covariance matrix and structured \( unknown \) deterministic interference the aforementioned problem extends the well known generalized multivariate analysis of variance \( gmanova \) tackled in the open literature in a companion paper , we have obtained the maximal invariant statistic \( mis \) for the problem under consideration , as an enabling tool for the design of suitable detectors which possess the constant false alarm rate \( cfar \) property herein , we focus on the development of several theoretically founded detectors for the problem under consideration first , all the considered detectors are shown to be function of the mis , thus proving their cfarness property secondly , coincidence or statistical equivalence among some of them in such a general signal model is proved thirdly , strong connections to well known simpler scenarios found in adaptive detection literature are established finally , simulation results are provided for a comparison of the proposed receivers"
cs.IT,"this paper studies an untrusted relay channel , in which the destination sends artificial noise simultaneously with the source sending a message to the relay , in order to protect the source 's confidential message the traditional amplify and forward \( af \) scheme shows poor performance in this situation because of the interference power dilemma providing better security by using stronger artificial noise will decrease the confidential message power from the relay to the destination to solve this problem , a modulo and forward \( mf \) operation at the relay with nested lattice encoding at the source is proposed for this system with full channel state information at the transmitter \( csit \) , theoretical analysis shows that the proposed mf scheme approaches the secrecy capacity within 1 2 bit for any channel realization , and hence achieves full generalized security degrees of freedom \( g sdof \) in contrast , the af scheme can only achieve a small fraction of the g sdof for this system without any csit , the total outage event , defined as either connection outage or secrecy outage , is introduced based on this total outage definition , analysis shows that the proposed mf scheme achieves the full generalized secure diversity gain \( g sdg \) of order one on the other hand , the af scheme can only achieve a g sdg of 1 2 at most"
cs.AI,"the borderline and narcissistic personality disorders are important nosographic entities and have been subject of intensive investigation the currently prevailing psychodynamic theory for mental disorders is based on the repertoire of defense mechanisms employed another fruitful line of research is concerned with the study of psychological traumas and with dissociation as a defensive response both theories can be used to shed light on some aspects of pathological mental functioning , and have many points of contact this work starts from a model of emotional behaviour , inspired by an approach derived from the field of artificial intelligence , and based on neural networks the model is then used to overcome the dichotomy between the two aforementioned psychological theories , and conceive a common framework for the description of personality disorders"
cs.DS|cs.LG|stat.ML,"we generalize newton type methods for minimizing smooth functions to handle a sum of two convex functions a smooth function and a nonsmooth function with a simple proximal mapping we show that the resulting proximal newton type methods inherit the desirable convergence behavior of newton type methods for minimizing smooth functions , even when search directions are computed inexactly many popular methods tailored to problems arising in bioinformatics , signal processing , and statistical learning are special cases of proximal newton type methods , and our analysis yields new convergence results for some of these methods"
cs.IT,"the problem of simultaneously generating a secret key \( sk \) and private key \( pk \) pair among three terminals via public discussion is investigated , in which each terminal observes a component of correlated sources all three terminals are required to generate a common secret key concealed from an eavesdropper that has access to public discussion , while two designated terminals are required to generate an extra private key concealed from both the eavesdropper and the remaining terminal an outer bound on the sk pk capacity region was established in 1 , and was shown to be achievable for one case in this paper , achievable schemes are designed to achieve the outer bound for the remaining two cases , and hence the sk pk capacity region is established in general the main technique lies in the novel design of a random binning joint decoding scheme that achieves the existing outer bound"
cs.LG|stat.ML,learning the parameters of a \( potentially partially observable \) random field model is intractable in general instead of focussing on a single optimal parameter value we propose to treat parameters as dynamical quantities we introduce an algorithm to generate complex dynamics for parameters and \( both visible and hidden \) state vectors we show that under certain conditions averages computed over trajectories of the proposed dynamical system converge to averages computed over the data our herding dynamics does not require expensive operations such as exponentiation and is fully deterministic
cs.IT,using tools from algebraic geometry and groebner basis theory we solve two problems in network coding first we present a method to determine the smallest field size for which linear network coding is feasible second we derive improved estimates on the success probability of random linear network coding these estimates take into account which monomials occur in the support of the determinant of the product of edmonds matrices therefore we finally investigate which monomials can occur in the determinant of the edmonds matrix
cs.IT,"sarvepalli and klappenecker showed how classical one point codes on the hermitian curve can be used to construct quantum codes homma and kim determined the parameters of a larger family of codes , the two point codes in quantum error correction , the observed presence of asymmetry in some quantum channels led to the study of asymmetric quantum codes \( aqeccs \) where we no longer assume that the different types of errors are equiprobable this paper considers quantum codes constructed from the two point codes in the asymmetric case , we show strict improvements over all possible finite fields for a range of designed distances we produce large dimension pure aqecc and small dimension impure aqecc that have better parameters than aqecc from one point codes numerical results for the hermitian curves over f16 and f64 are used to illustrate the gain"
cs.DS,"we present the first streaming algorithm for counting an arbitrary hypergraph h of constant size in a massive hypergraph g our algorithm can handle both edge insertions and edge deletions , and is applicable for the distributed setting moreover , our approach provides the first family of graph polynomials for the hypergraph counting problem because of the close relationship between hypergraphs and set systems , our approach may have applications in studying similar problems"
cs.AI,xnmr is a system designed to explore the results of combining the well founded semantics system xsb with the stable models evaluator smodels its main goal is to work as a tool for fast and interactive exploration of knowledge bases
cs.AI,"dialogue games are a two player semantics for a variety of logics , including intuitionistic and classical logic dialogues can be viewed as a kind of analytic calculus not unlike tableaux can dialogue games be an effective foundation for proof search in intuitionistic logic \( both first order and propositional \) \? we announce kuno , an automated theorem prover for intuitionistic first order logic based on dialogue games"
cs.LG|stat.ML,"we introduce a new recursive aggregation procedure called bernstein online aggregation \( boa \) the exponential weights include an accuracy term and a second order term that is a proxy of the quadratic variation as in hazan and kale \( 2010 \) this second term stabilizes the procedure that is optimal in different senses we first obtain optimal regret bounds in the deterministic context then , an adaptive version is the first exponential weights algorithm that exhibits a second order bound with excess losses that appears first in gaillard et al \( 2014 \) the second order bounds in the deterministic context are extended to a general stochastic context using the cumulative predictive risk such conversion provides the main result of the paper , an inequality of a novel type comparing the procedure with any deterministic aggregation procedure for an integrated criteria then we obtain an observable estimate of the excess of risk of the boa procedure to assert the optimality , we consider finally the iid case for strongly convex and lipschitz continuous losses and we prove that the optimal rate of aggregation of tsybakov \( 2003 \) is achieved the batch version of the boa procedure is then the first adaptive explicit algorithm that satisfies an optimal oracle inequality with high probability"
cs.IT|cs.LG,"the problem of least squares regression of a d dimensional unknown parameter is considered a stochastic gradient descent based algorithm with weighted iterate averaging that uses a single pass over the data is studied and its convergence rate is analyzed we first consider a bounded constraint set of the unknown parameter under some standard regularity assumptions , we provide an explicit o \( 1 k \) upper bound on the convergence rate , depending on the variance \( due to the additive noise in the measurements \) and the size of the constraint set we show that the variance term dominates the error and decreases with rate 1 k , while the term which is related to the size of the constraint set decreases with rate log k k 2 we then compare the asymptotic ratio rho between the convergence rate of the proposed scheme and the empirical risk minimizer \( erm \) as the number of iterations approaches infinity we show that rho leq 4 under some mild conditions for all d geq 1 we further improve the upper bound by showing that rho leq 4 3 for the case of d 1 and unbounded parameter set simulation results demonstrate strong performance of the algorithm as compared to existing methods , and coincide with rho leq 4 3 even for large d in practice"
cs.IT,"we study the degrees of freedom \( dof \) of a multiple input multiple output \( mimo \) two way x relay channel , where there are two groups of source nodes and one relay node , each equipped with multiple antennas , and each of the two source nodes in one group exchanges independent messages with the two source nodes in the other group via the relay node it is assumed that every source node is equipped with m antennas while the relay is equipped with n antennas we first show that the upper bound on the total dof for this network is 2min 2m , n and then focus on the case of n leq 2m so that the dof is upper bounded by the number of antennas at the relay by applying signal alignment for network coding and joint transceiver design for interference cancellation , we show that this upper bound can be achieved when n leq8m 5 we also show that with signal alignment only but no joint transceiver design , the upper bound is achievable when n leq4m 3 simulation results are provided to corroborate the theoretical results and to demonstrate the performance of the proposed scheme in the finite signal to noise ratio regime"
cs.AI|cs.LG,"this paper describes the system of storage , extract and processing of information structured similarly to the natural language for recursive inference the system uses the rules having the same representation , as the data the environment of storage of information is provided with the file mapping \( shm \) mechanism of operating system in the paper the main principles of construction of dynamic data structure and language for record of the inference rules are stated the features of available implementation are considered and the description of the application realizing semantic information retrieval on the natural language is given"
cs.LG,"we introduce the drow detector , a deep learning based detector for 2d range data laser scanners are lighting invariant , provide accurate range data , and typically cover a large field of view , making them interesting sensors for robotics applications so far , research on detection in laser range data has been dominated by handcrafted features and boosted classifiers , potentially losing performance due to suboptimal design choices we propose a convolutional neural network \( cnn \) based detector for this task we show how to effectively apply cnns for detection in 2d range data , and propose a depth preprocessing step and voting scheme that significantly improve cnn performance we demonstrate our approach on wheelchairs and walkers , obtaining state of the art detection results apart from the training data , none of our design choices limits the detector to these two classes , though we provide a ros node for our detector and release our dataset containing 464k laser scans , out of which 24k were annotated for training"
cs.IT,"in this paper we look at a class of random optimization problems that arise in the forms typically known as hopfield models we view two scenarios which we term as the positive hopfield form and the negative hopfield form for both of these scenarios we define the binary optimization problems that essentially emulate what would typically be known as the ground state energy of these models we then present a simple mechanism that can be used to create a set of theoretical rigorous bounds for these energies in addition to purely theoretical bounds , we also present a couple of fast optimization algorithms that can also be used to provide solid \( albeit a bit weaker \) algorithmic bounds for the ground state energies"
cs.IT,"in this correspondence , we focus on the performance analysis of the widely used minimum description length \( mdl \) source enumeration technique in array processing unfortunately , available theoretical analysis exhibit deviation from the simulation results we present an accurate and insightful performance analysis for the probability of missed detection we also show that the statistical performance of the mdl is approximately the same under both deterministic and stochastic signal models simulation results show the superiority of the proposed analysis over available results"
cs.SI,"can we predict the future popularity of a song , movie or tweet \? recent work suggests that although it may be hard to predict an item 's popularity when it is first introduced , peeking into its early adopters and properties of their social network makes the problem easier we test the robustness of such claims by using data from social networks spanning music , books , photos , and urls we find a stronger result not only do predictive models with peeking achieve high accuracy on all datasets , they also generalize well , so much so that models trained on any one dataset perform with comparable accuracy on items from other datasets though practically useful , our models \( and those in other work \) are intellectually unsatisfying because common formulations of the problem , which involve peeking at the first small k adopters and predicting whether items end up in the top half of popular items , are both too sensitive to the speed of early adoption and too easy most of the predictive power comes from looking at how quickly items reach their first few adopters , while for other features of early adopters and their networks , even the direction of correlation with popularity is not consistent across domains problem formulations that examine items that reach k adopters in about the same amount of time reduce the importance of temporal features , but also overall accuracy , highlighting that we understand little about why items become popular while providing a context in which we might build that understanding"
cs.LG|stat.ML,"in this paper we consider the problem of gaussian process classifier \( gpc \) model selection with different leave one out \( loo \) cross validation \( cv \) based optimization criteria and provide a practical algorithm using loo predictive distributions with such criteria to select hyperparameters apart from the standard average negative logarithm of predictive probability \( nlp \) , we also consider smoothed versions of criteria such as f measure and weighted error rate \( wer \) , which are useful for handling imbalanced data unlike the regression case , loo predictive distributions for the classifier case are intractable we use approximate loo predictive distributions arrived from expectation propagation \( ep \) approximation we conduct experiments on several real world benchmark datasets when the nlp criterion is used for optimizing the hyperparameters , the predictive approaches show better or comparable nlp generalization performance with existing gpc approaches on the other hand , when the f measure criterion is used , the f measure generalization performance improves significantly on several datasets overall , the ep based predictive algorithm comes out as an excellent choice for gp classifier model selection with different optimization criteria"
cs.AI,"in newcomb 's paradox you choose to receive either the contents of a particular closed box , or the contents of both that closed box and another one before you choose , a prediction algorithm deduces your choice , and fills the two boxes based on that deduction newcomb 's paradox is that game theory appears to provide two conflicting recommendations for what choice you should make in this scenario we analyze newcomb 's paradox using a recent extension of game theory in which the players set conditional probability distributions in a bayes net we show that the two game theory recommendations in newcomb 's scenario have different presumptions for what bayes net relates your choice and the algorithm 's prediction we resolve the paradox by proving that these two bayes nets are incompatible we also show that the accuracy of the algorithm 's prediction , the focus of much previous work , is irrelevant in addition we show that newcomb 's scenario only provides a contradiction between game theory 's expected utility and dominance principles if one is sloppy in specifying the underlying bayes net we also show that newcomb 's paradox is time reversal invariant both the paradox and its resolution are unchanged if the algorithm makes its `prediction' after you make your choice rather than before"
cs.LG,"in this paper , we have established a unified framework of multistage parameter estimation we demonstrate that a wide variety of statistical problems such as fixed sample size interval estimation , point estimation with error control , bounded width confidence intervals , interval estimation following hypothesis testing , construction of confidence sequences , can be cast into the general framework of constructing sequential random intervals with prescribed coverage probabilities we have developed exact methods for the construction of such sequential random intervals in the context of multistage sampling in particular , we have established inclusion principle and coverage tuning techniques to control and adjust the coverage probabilities of sequential random intervals we have obtained concrete sampling schemes which are unprecedentedly efficient in terms of sampling effort as compared to existing procedures"
cs.DM,"in this paper we deal with additional control structures for decorated pt nets the main contribution are inhibitor arcs and priorities the first ensure that a marking can inhibit the firing of a transition inhibitor arcs force that the transition may only fire when the place is empty an order of transitions restrict the firing , so that an transition may fire only if it has the highest priority of all enabled transitions this concept is shown to be compatible with reconfigurable petri nets"
cs.SI,"complex networks have non trivial characteristics and appear in many real world systems due to their vital importance in a large number of research fields , various studies have offered explanations on how complex networks evolve , but the full underlying dynamics of complex networks are not completely understood many of the barriers to better understanding the evolution process of these networks can be removed with the emergence of new data sources this study utilizes the recently published reddit dataset , containing over 1 65 billion comments , to construct the largest publicly available social network corpus , which contains detailed information on the evolution process of 11 , 965 social networks we used this dataset to study the effect of the patterns in which new users join a network \( referred to as user arrival curves , or uacs \) on the network topology our results present evidence that uacs are a central factor in molding a network 's topology that is , different arrival patterns create different topological properties additionally , we show that it is possible to uncover the types of user arrival patterns by analyzing a social network 's topology these results imply that existing complex network evolution models need to be revisited and modified to include user arrival patterns as input to the models , in order to create models that more accurately reflect real world complex networks"
cs.LG|stat.ML,"this paper considers the problem of learning the parameters in bayesian networks of discrete variables with known structure and hidden variables previous approaches in these settings typically use expectation maximization when the network has high treewidth , the required expectations might be approximated using monte carlo or variational methods we show how to avoid inference altogether during learning by giving a polynomial time algorithm based on the method of moments , building upon recent work on learning discrete valued mixture models in particular , we show how to learn the parameters for a family of bipartite noisy or bayesian networks in our experimental results , we demonstrate an application of our algorithm to learning qmr dt , a large bayesian network used for medical diagnosis we show that it is possible to fully learn the parameters of qmr dt even when only the findings are observed in the training data \( ground truth diseases unknown \)"
cs.IT,"this paper presents a new method , referred to here as the sparsity invariant transformation based ell 1 minimization , to solve the ell 0 minimization problem for an over determined linear system corrupted by additive sparse errors with arbitrary intensity many previous works have shown that ell 1 minimization can be applied to realize sparse error detection in many over determined linear systems however , performance of this approach is strongly dependent on the structure of the measurement matrix , which limits application possibility in practical problems here , we present a new approach based on transforming the ell 0 minimization problem by a linear transformation that keeps sparsest solutions invariant we call such a property a sparsity invariant property \( sip \) , and a linear transformation with sip is referred to as a sparsity invariant transformation \( sit \) we propose the sit based ell 1 minimization method by using an sit in conjunction with ell 1 relaxation on the ell 0 minimization problem we prove that for any over determined linear system , there always exists a specific class of sit 's that guarantees a solution to the sit based ell 1 minimization is a sparsest errors solution besides , a randomized algorithm based on monte carlo simulation is proposed to search for a feasible sit"
cs.IT,"the use of open loop coding can be easily extended to a closed loop concatenated code if the channel has access to feedback this can be done by introducing a feedback transmission scheme as an inner code in this paper , this process is investigated for the case when a linear feedback scheme is implemented as an inner code and , in particular , over an additive white gaussian noise \( awgn \) channel with noisy feedback to begin , we look to derive an optimal linear feedback scheme by optimizing over the received signal to noise ratio from this optimization , an asymptotically optimal linear feedback scheme is produced and compared to other well known schemes then , the linear feedback scheme is implemented as an inner code to a concatenated code over the awgn channel with noisy feedback this code shows improvements not only in error exponent bounds , but also in bit error rate and frame error rate it is also shown that the if the concatenated code has total blocklength l and the inner code has blocklength , n , the inner code blocklength should scale as n o \( c r \) , where c is the capacity of the channel and r is the rate of the concatenated code simulations with low density parity check \( ldpc \) and turbo codes are provided to display practical applications and their error rate benefits"
cs.SI,"this paper proposes a new measure of node centrality in social networks , the harmonic influence centrality , which emerges naturally in the study of social influence over networks using an intuitive analogy between social and electrical networks , we introduce a distributed message passing algorithm to compute the harmonic influence centrality of each node although its design is based on theoretical results which assume the network to have no cycle , the algorithm can also be successfully applied on general graphs"
cs.IT,"in millimeter wave \( mmwave \) mimo systems , while a hybrid digital analog precoding structure offers the potential to increase the achievable rate , it also faces the challenge of the need of a low complexity design in specific , the hybrid precoding may require matrix operations with a scale of antenna size , which is generally large in mmwave communication moreover , the channel estimation is also rather time consuming due to the large number of antennas at both tx rx sides in this paper , a low complexity hybrid precoding and channel estimation approach is proposed in the channel estimation phase , a hierarchical multi beam search scheme is proposed to fast acquire n rm s \( the number of streams \) multipath components \( mpcs \) clusters with the highest powers in the hybrid precoding phase , the analog and digital precodings are decoupled the analog precoding is designed to steer along the n rm s acquired mpcs clusters at both tx rx sides , shaping an equivalent n rm s times n rm s baseband channel , while the digital precoding performs operations in the baseband with the reduced scale channel performance evaluations show that , compared with a state of the art scheme , while achieving a close or even better performance when the number of radio frequency \( rf \) chains or streams is small , both the computational complexity of the hybrid precoding and the time complexity of the channel estimation are greatly reduced"
cs.DM|cs.DS,"we consider the problem of balancing load items \( tokens \) in networks starting with an arbitrary load distribution , we allow nodes to exchange tokens with their neighbors in each round the goal is to achieve a distribution where all nodes have nearly the same number of tokens for the continuous case where tokens are arbitrarily divisible , most load balancing schemes correspond to markov chains , whose convergence is fairly well understood in terms of their spectral gap however , in many applications , load items cannot be divided arbitrarily , and we need to deal with the discrete case where the load is composed of indivisible tokens this discretization entails a non linear behavior due to its rounding errors , which makes this analysis much harder than in the continuous case we investigate several randomized protocols for different communication models in the discrete case as our main result , we prove that for any regular network in the matching model , all nodes have the same load up to an additive constant in \( asymptotically \) the same number of rounds as required in the continuous case this generalizes and tightens the previous best result , which only holds for expander graphs , and demonstrates that there is almost no difference between the discrete and continuous cases our results also provide a positive answer to the question of how well discrete load balancing can be approximated by \( continuous \) markov chains , which has been posed by many researchers"
cs.LG,"we present a method for extracting depth information from a rectified image pair our approach focuses on the first stage of many stereo algorithms the matching cost computation we approach the problem by learning a similarity measure on small image patches using a convolutional neural network training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches we examine two network architectures for this task one tuned for speed , the other for accuracy the output of the convolutional neural network is used to initialize the stereo matching cost a series of post processing steps follow cross based cost aggregation , semiglobal matching , a left right consistency check , subpixel enhancement , a median filter , and a bilateral filter we evaluate our method on the kitti 2012 , kitti 2015 , and middlebury stereo data sets and show that it outperforms other approaches on all three data sets"
cs.LG,"in this new and current era of technology , advancements and techniques , efficient and effective text document classification is becoming a challenging and highly required area to capably categorize text documents into mutually exclusive categories fuzzy similarity provides a way to find the similarity of features among various documents in this paper , a technical review on various fuzzy similarity based models is given these models are discussed and compared to frame out their use and necessity a tour of different methodologies is provided which is based upon fuzzy similarity related concerns it shows that how text and web documents are categorized efficiently into different categories various experimental results of these models are also discussed the technical comparisons among each model 's parameters are shown in the form of a 3 d chart such study and technical review provide a strong base of research work done on fuzzy similarity based text document categorization"
cs.LG|stat.ML,"we develop a model of issue specific voting behavior this model can be used to explore lawmakers' personal voting patterns of voting by issue area , providing an exploratory window into how the language of the law is correlated with political support we derive approximate posterior inference algorithms based on variational methods across 12 years of legislative data , we demonstrate both improvement in heldout prediction performance and the model 's utility in interpreting an inherently multi dimensional space"
cs.DS,"exact byzantine consensus problem requires that non faulty processes reach agreement on a decision \( or output \) that is in the convex hull of the inputs at the non faulty processes it is well known that exact consensus is impossible in an asynchronous system in presence of faults , and in a synchronous system , n 3f 1 is tight on the number of processes to achieve exact byzantine consensus with scalar inputs , in presence of up to f byzantine faulty processes recent work has shown that when the inputs are d dimensional vectors of reals , n max \( 3f 1 , \( d 1 \) f 1 \) is tight to achieve exact byzantine consensus in synchronous systems , and n \( d 2 \) f 1 for approximate byzantine consensus in asynchronous systems due to the dependence of the lower bound on vector dimension d , the number of processes necessary becomes large when the vector dimension is large with the hope of reducing the lower bound on n , we consider two relaxed versions of byzantine vector consensus k relaxed byzantine vector consensus and \( delta , p \) relaxed byzantine vector consensus in k relaxed consensus , the validity condition requires that the output must be in the convex hull of projection of the inputs onto any subset of k dimensions of the vectors for \( delta , p \) consensus the validity condition requires that the output must be within distance delta of the convex hull of the inputs of the non faulty processes , where l p norm is used as the distance metric for \( delta , p \) consensus , we consider two versions in one version , delta is a constant , and in the second version , delta is a function of the inputs themselves we show that for k relaxed consensus and \( delta , p \) consensus with constant delta 0 , the bound on n is identical to the bound stated above for the original vector consensus problem on the other hand , when delta depends on the inputs , we show that the bound on n is smaller when d 3"
cs.IT,"in this paper a unified approach for power allocation \( pa \) problem in multi hop orthogonal frequency division multiplexing \( ofdm \) amplify and forward \( af \) relaying systems has been developed in the proposed approach , we consider short and long term individual and total power constraints at the source and relays , and devise decentralized low complexity pa algorithms when wireless links are subject to channel path loss and small scale rayleigh fading in particular , aiming at improving the instantaneous rate of multi hop transmission systems with af relaying , we develop \( i \) a near optimal iterative pa algorithm based on the exact analysis of the received snr at the destination \( ii \) a low complexity suboptimal iterative pa algorithm based on an approximate expression at high snr regime and \( iii \) a low complexity non iterative pa scheme with limited performance loss since the pa problem in multi hop systems is too complex to solve with known optimization solvers , in the proposed formulations , we adopted a two stage approach , including a power distribution phase among distinct subcarriers , and a power allocation phase among different relays the individual pa phases are then appropriately linked through an iterative method which tries to compensate the performance loss caused by the distinct two stage approach simulation results show the superior performance of the proposed power allocation algorithms"
cs.IT,"in this paper , we introduce a vectorial conception of d fcsrs to build these registers over any finite field we describe the structure of d vectorial fcsrs and we develop an analysis to obtain basic properties like periodicity and the existence of maximal length sequences to illustrate these vectorial d fcsrs , we present simple examples and we compare with those of goresky , klapper and xu keywords lfsr , fcsr , vectorial fcsr , d fcsr , sequences , periodicity , p adic , \? adic , maximal period"
cs.IT,"we consider the distributed function computation problem in asymmetric communication scenarios , where the sink computes some deterministic function of the data split among n correlated informants the distributed function computation problem is addressed as a generalization of distributed source coding \( dsc \) problem we are mainly interested in minimizing the number of informant bits required , in the worst case , to allow the sink to exactly compute the function we provide a constructive solution for this in terms of an interactive communication protocol and prove its optimality the proposed protocol also allows us to compute the worst case achievable rate region for the computation of any function we define two classes of functions lossy and lossless we show that , in general , the lossy functions can be computed at the sink with fewer number of informant bits than the dsc problem , while computation of the lossless functions requires as many informant bits as the dsc problem"
cs.LG|stat.ML,"kernel based methods enjoy powerful generalization capabilities in handling a variety of learning tasks when such methods are provided with sufficient training data , broadly applicable classes of nonlinear functions can be approximated with desired accuracy nevertheless , inherent to the nonparametric nature of kernel based estimators are computational and memory requirements that become prohibitive with large scale datasets in response to this formidable challenge , the present work puts forward a low rank , kernel based , feature extraction approach that is particularly tailored for online operation , where data streams need not be stored in memory a novel generative model is introduced to approximate high dimensional \( possibly infinite \) features via a low rank nonlinear subspace , the learning of which leads to a direct kernel function approximation offline and online solvers are developed for the subspace learning task , along with affordable versions , in which the number of stored data vectors is confined to a predefined budget analytical results provide performance bounds on how well the kernel matrix as well as kernel based classification and regression tasks can be approximated by leveraging budgeted online subspace learning and feature extraction schemes tests on synthetic and real datasets demonstrate and benchmark the efficiency of the proposed method when linear classification and regression is applied to the extracted features"
cs.DS,"hidden markov chains \( hmcs \) are commonly used mathematical models of probabilistic systems they are employed in various fields such as speech recognition , signal processing , and biological sequence analysis we consider the problem of distinguishing two given hmcs based on an observation sequence that one of the hmcs generates more precisely , given two hmcs and an observation sequence , a distinguishing algorithm is expected to identify the hmc that generates the observation sequence two hmcs are called distinguishable if for every varepsilon 0 there is a distinguishing algorithm whose error probability is less than varepsilon we show that one can decide in polynomial time whether two hmcs are distinguishable further , we present and analyze two distinguishing algorithms for distinguishable hmcs the first algorithm makes a decision after processing a fixed number of observations , and it exhibits two sided error the second algorithm processes an unbounded number of observations , but the algorithm has only one sided error the error probability , for both algorithms , decays exponentially with the number of processed observations we also provide an algorithm for distinguishing multiple hmcs finally , we discuss an application in stochastic runtime verification"
cs.AI|cs.LG,"definition of an accurate system model for automated planner \( ap \) is often impractical , especially for real world problems conversely , off the shelf planners fail to scale up and are domain dependent these drawbacks are inherited from conventional transition systems such as finite state machines \( fsms \) that describes the action plan execution generated by the ap on the other hand , behavior trees \( bts \) represent a valid alternative to fsms presenting many advantages in terms of modularity , reactiveness , scalability and domain independence in this paper , we propose a model free ap framework using genetic programming \( gp \) to derive an optimal bt for an autonomous agent to achieve a given goal in unknown \( but fully observable \) environments we illustrate the proposed framework using experiments conducted with an open source benchmark mario ai for automated generation of bts that can play the game character mario to complete a certain level at various levels of difficulty to include enemies and obstacles"
cs.IT,"in this work we propose novel decision feedback \( df \) detection algorithms with error propagation mitigation capabilities for multi input multi output \( mimo \) spatial multiplexing systems based on multiple processing branches the novel strategies for detection exploit different patterns , orderings and constraints for the design of the feedforward and feedback filters we present constrained minimum mean squared error \( mmse \) filters designed with constraints on the shape and magnitude of the feedback filters for the multi branch mimo receivers and show that the proposed mmse design does not require a significant additional complexity over the single branch mmse design the proposed multi branch mmse df detectors are compared with several existing detectors and are shown to achieve a performance close to the optimal maximum likelihood detector while requiring significantly lower complexity"
cs.IT,"we consider the problem of synthetic aperture radar \( sar \) imaging and motion estimation of complex scenes by complex we mean scenes with multiple targets , stationary and in motion we use the usual setup with one moving antenna emitting and receiving signals we address two challenges \( 1 \) the detection of moving targets in the complex scene and \( 2 \) the separation of the echoes from the stationary targets and those from the moving targets such separation allows high resolution imaging of the stationary scene and motion estimation with the echoes from the moving targets alone we show that the robust principal component analysis \( pca \) method which decomposes a matrix in two parts , one low rank and one sparse , can be used for motion detection and data separation the matrix that is decomposed is the pulse and range compressed sar data indexed by two discrete time variables the slow time , which parametrizes the location of the antenna , and the fast time , which parametrizes the echoes received between successive emissions from the antenna we present an analysis of the rank of the data matrix to motivate the use of the robust pca method we also show with numerical simulations that successful data separation with robust pca requires proper data windowing results of motion estimation and imaging with the separated data are presented , as well"
cs.IT,"the goal of this paper is to increase our understanding of the fundamental performance limits of mobile and delay tolerant networks \( dtns \) , where end to end multi hop paths may not exist and communication routes may only be available through time and mobility we use analytical tools to derive generic theoretical upper bounds for the information propagation speed in large scale mobile and intermittently connected networks in other words , we upper bound the optimal performance , in terms of delay , that can be achieved using any routing algorithm we then show how our analysis can be applied to specific mobility and graph models to obtain specific analytical estimates in particular , in two dimensional networks , when nodes move at a maximum speed v and their density nu is small \( the network is sparse and surely disconnected \) , we prove that the information propagation speed is upper bounded by \( 1 o \( nu 2 \) \) v in the random way point model , while it is upper bounded by o \( sqrt nu v v \) for other mobility models \( random walk , brownian motion \) we also present simulations that confirm the validity of the bounds in these scenarios finally , we generalize our results to one dimensional and three dimensional networks"
cs.DS,"the greedy spanner is a high quality spanner its total weight , edge count and maximal degree are asymptotically optimal and in practice significantly better than for any other spanner with reasonable construction time unfortunately , all known algorithms that compute the greedy spanner of n points use omega \( n 2 \) space , which is impractical on large instances to the best of our knowledge , the largest instance for which the greedy spanner was computed so far has about 13 , 000 vertices we present a o \( n \) space algorithm that computes the same spanner for points in r d running in o \( n 2 log 2 n \) time for any fixed stretch factor and dimension we discuss and evaluate a number of optimizations to its running time , which allowed us to compute the greedy spanner on a graph with a million vertices to our knowledge , this is also the first algorithm for the greedy spanner with a near quadratic running time guarantee that has actually been implemented"
cs.DM|cs.DS,"using specializations of unfold and fold on a generic tree data type we derive unranking and ranking functions providing natural number encodings for various hereditarily finite datatypes in this context , we interpret unranking operations as instances of a generic anamorphism and ranking operations as instances of the corresponding catamorphism starting with ackerman 's encoding from hereditarily finite sets to natural numbers we define pairings and tuple encodings that provide building blocks for a theory of hereditarily finite functions the more difficult problem of ranking and unranking hereditarily finite permutations is then tackled using lehmer codes and factoradics the self contained source code of the paper , as generated from a literate haskell program , is available at url http logic csci unt edu tarau research 2008 ffun zip keywords ranking unranking , pairing tupling functions , ackermann encoding , hereditarily finite sets , hereditarily finite functions , permutations and factoradics , computational mathematics , haskell data representations"
cs.SI,"we present smartcrowd , a framework for optimizing collaborative knowledge intensive crowdsourcing smartcrowd distinguishes itself by accounting for human factors in the process of assigning tasks to workers human factors designate workers' expertise in different skills , their expected minimum wage , and their availability in smartcrowd , we formulate task assignment as an optimization problem , and rely on pre indexing workers and maintaining the indexes adaptively , in such a way that the task assignment process gets optimized both qualitatively , and computation time wise we present rigorous theoretical analyses of the optimization problem and propose optimal and approximation algorithms we finally perform extensive performance and quality experiments using real and synthetic data to demonstrate that adaptive indexing in smartcrowd is necessary to achieve efficient high quality task assignment"
cs.DS,"we consider a problem where mutually untrusting curators possess portions of a vertically partitioned database containing information about a set of individuals the goal is to enable an authorized party to obtain aggregate \( statistical \) information from the database while protecting the privacy of the individuals , which we formalize using differential privacy this process can be facilitated by an untrusted server that provides storage and processing services but should not learn anything about the database this work describes a data release mechanism that employs post randomization \( pram \) , encryption and random sampling to maintain privacy , while allowing the authorized party to conduct an accurate statistical analysis of the data encryption ensures that the storage server obtains no information about the database , while pram and sampling ensures individual privacy is maintained against the authorized party we characterize how much the composition of random sampling with pram increases the differential privacy of system compared to using pram alone we also analyze the statistical utility of our system , by bounding the estimation error the expected l2 norm error between the true empirical distribution and the estimated distribution as a function of the number of samples , pram noise , and other system parameters our analysis shows a tradeoff between increasing pram noise versus decreasing the number of samples to maintain a desired level of privacy , and we determine the optimal number of samples that balances this tradeoff and maximizes the utility in experimental simulations with the uci adult data set and with synthetically generated data , we confirm that the theoretically predicted optimal number of samples indeed achieves close to the minimal empirical error , and that our analytical error bounds match well with the empirical results"
cs.IT,"the problem of distributed inference with m ary quantized data at the sensors is investigated in the presence of byzantine attacks we assume that the attacker does not have knowledge about either the true state of the phenomenon of interest , or the quantization thresholds used at the sensors therefore , the byzantine nodes attack the inference network by modifying modifying the symbol corresponding to the quantized data to one of the other m symbols in the quantization alphabet set and transmitting the false symbol to the fusion center \( fc \) in this paper , we find the optimal byzantine attack that blinds any distributed inference network as the quantization alphabet size increases , a tremendous improvement in the security performance of the distributed inference network is observed we also investigate the problem of distributed inference in the presence of resource constrained byzantine attacks in particular , we focus our attention on two problems distributed detection and distributed estimation , when the byzantine attacker employs a highly symmetric attack for both the problems , we find the optimal attack strategies employed by the attacker to maximally degrade the performance of the inference network a reputation based scheme for identifying malicious nodes is also presented as the network 's strategy to mitigate the impact of byzantine threats on the inference performance of the distributed sensor network"
cs.IT,"we consider the problem of determining the power ratio between the training symbols and data symbols in order to maximize the channel capacity for transmission over uncertain channels with a channel estimate available at both the transmitter and receiver the receiver makes an estimate of the channel by using a known sequence of training symbols this channel estimate is then transmitted back to the transmitter the capacity that the transceiver maximizes is the worst case capacity , in the sense that given a noise covariance , the transceiver maximizes the minimal capacity over all distributions of the measurement noise under a fixed covariance matrix known at both the transmitter and receiver we give an exact expression of the channel capacity as a function of the channel covariance matrix , and the number of training symbols used during a coherence time interval this expression determines the number of training symbols that need to be used by finding the optimal integer number of training symbols that maximize the channel capacity as a bi product , we show that linear filters are optimal at both the transmitter and receiver"
cs.IT,"this article proposes novel sparsity aware space time adaptive processing \( sa stap \) algorithms with l 1 norm regularization for airborne phased array radar applications the proposed sa stap algorithms suppose that a number of samples of the full rank stap data cube are not meaningful for processing and the optimal full rank stap filter weight vector is sparse , or nearly sparse the core idea of the proposed method is imposing a sparse regularization \( l 1 norm type \) to the minimum variance \( mv \) stap cost function under some reasonable assumptions , we firstly propose a l 1 based sample matrix inversion \( smi \) to compute the optimal filter weight vector however , it is impractical due to its matrix inversion , which requires a high computational cost when in a large phased array antenna then , we devise lower complexity algorithms based on conjugate gradient \( cg \) techniques a computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted simulation results with both simulated and the mountain top data demonstrate that fast signal to interference plus noise ratio \( sinr \) convergence and good performance of the proposed algorithms are achieved"
cs.LG,"in the past few years , the number of fine art collections that are digitized and publicly available has been growing rapidly with the availability of such large collections of digitized artworks comes the need to develop multimedia systems to archive and retrieve this pool of data measuring the visual similarity between artistic items is an essential step for such multimedia systems , which can benefit more high level multimedia tasks in order to model this similarity between paintings , we should extract the appropriate visual features for paintings and find out the best approach to learn the similarity metric based on these features we investigate a comprehensive list of visual features and metric learning approaches to learn an optimized similarity measure between paintings we develop a machine that is able to make aesthetic related semantic level judgments , such as predicting a painting 's style , genre , and artist , as well as providing similarity measures optimized based on the knowledge available in the domain of art historical interpretation our experiments show the value of using this similarity measure for the aforementioned prediction tasks"
cs.IT,"in a slow fading channel , how to find a cooperative diversity scheme that achieves the transmit diversity bound is still an open problem in fact , all previously proposed amplify and forward \( af \) and decode and forward \( df \) schemes do not improve with the number of relays in terms of the diversity multiplexing tradeoff \( dmt \) for multiplexing gains r higher than 0 5 in this work , we study the class of slotted amplify and forward \( saf \) schemes we first establish an upper bound on the dmt for any saf scheme with an arbitrary number of relays n and number of slots m then , we propose a sequential saf scheme that can exploit the potential diversity gain in the high multiplexing gain regime more precisely , in certain conditions , the sequential saf scheme achieves the proposed dmt upper bound which tends to the transmit diversity bound when m goes to infinity in particular , for the two relay case , the three slot sequential saf scheme achieves the proposed upper bound and outperforms the two relay non orthorgonal amplify and forward \( naf \) scheme of azarian et al for multiplexing gains r 2 3 numerical results reveal a significant gain of our scheme over the previously proposed af schemes , especially in high spectral efficiency and large network size regime"
cs.IT,"self interference \( si \) significantly limits the performance of full duplex \( fd \) radio devices if not properly cancelled state of the art si cancellation \( sic \) techniques at the receive chain implicitly set an upper bound on the transmit power of the device this paper starts from this observation and proposes a transmit beamforming design for fd multiple antenna radios that i \) leverages the inherent sic capabilities at the receiver and the channel state information and ii \) exploits the potential of multiple antennas in terms of spatial sic the proposed solution not only maximizes the throughput while complying with the sic requirements of the fd device , but also enjoys a very low complexity that allows it to outperform state of the art counterparts in terms of processing time and power requirements numerical results show that our transmit beamforming design achieves significant gains with respect to applying zero forcing to the si channel when the number of transmit antennas is small to moderate , which makes it particularly appealing for fd small cell base stations"
cs.IT,"in band full duplex \( ibfd \) operation has emerged as an attractive solution for increasing the throughput of wireless communication systems and networks with ibfd , a wireless terminal is allowed to transmit and receive simultaneously in the same frequency band this tutorial paper reviews the main concepts of ibfd wireless because one the biggest practical impediments to ibfd operation is the presence of self interference , i e , the interference caused by an ibfd node 's own transmissions to its desired receptions , this tutorial surveys a wide range of ibfd self interference mitigation techniques also discussed are numerous other research challenges and opportunities in the design and analysis of ibfd wireless systems"
cs.LG,"in this paper , we propose a new stochastic alternating direction method of multipliers \( admm \) algorithm , which incrementally approximates the full gradient in the linearized admm formulation besides having a low per iteration complexity as existing stochastic admm algorithms , the proposed algorithm improves the convergence rate on convex problems from o \( frac 1 sqrt t \) to o \( frac 1 t \) , where t is the number of iterations this matches the convergence rate of the batch admm algorithm , but without the need to visit all the samples in each iteration experiments on the graph guided fused lasso demonstrate that the new algorithm is significantly faster than state of the art stochastic and batch admm algorithms"
cs.IT,"we extend the notion of gacs quantum algorithmic entropy , originally formulated for finitely many qubits , to infinite dimensional quantum spin chains and investigate the relation of this extension with two quantum dynamical entropies that have been proposed in recent years further , we prove an extension of brudno 's theorem in quantum spin chains with shift dynamics"
cs.IT,"in network coding , information transmission often encounters wiretapping attacks secure network coding is introduced to prevent information from being leaked to adversaries for secure linear network codes \( slncs \) , the required field size is a very important index , because it largely determines the computational and space complexities of a slnc , and it is also very important for the process of secure network coding from theoretical research to practical application in this letter , we further discuss the required field size of slncs , and obtain a new lower bound this bound shows that the field size of slncs can be reduced further , and much smaller than the known results for almost all cases"
cs.IT,"we analyze the performance of tcp and tcp with network coding \( tcp nc \) in lossy wireless networks we build upon the simple framework introduced by padhye et al and characterize the throughput behavior of classical tcp as well as tcp nc as a function of erasure rate , round trip time , maximum window size , and duration of the connection our analytical results show that network coding masks erasures and losses from tcp , thus preventing tcp 's performance degradation in lossy networks , such as wireless networks it is further seen that tcp nc has significant throughput gains over tcp in addition , we simulate tcp and tcp nc to verify our analysis of the average throughput and the window evolution our analysis and simulation results show very close concordance and support that tcp nc is robust against erasures tcp nc is not only able to increase its window size faster but also to maintain a large window size despite losses within the network , whereas tcp experiences window closing essentially because losses are mistakenly attributed to congestion"
cs.IT,"stochastic switching circuits are relay circuits that consist of stochastic switches called pswitches the study of stochastic switching circuits has widespread applications in many fields of computer science , neuroscience , and biochemistry in this paper , we discuss several properties of stochastic switching circuits , including robustness , expressibility , and probability approximation first , we study the robustness , namely , the effect caused by introducing an error of size epsilon to each pswitch in a stochastic circuit we analyze two constructions and prove that simple series parallel circuits are robust to small error perturbations , while general series parallel circuits are not specifically , the total error introduced by perturbations of size less than epsilon is bounded by a constant multiple of epsilon in a simple series parallel circuit , independent of the size of the circuit next , we study the expressibility of stochastic switching circuits given an integer q and a pswitch set s frac 1 q , frac 2 q , , frac q 1 q , can we synthesize any rational probability with denominator q n \( for arbitrary n \) with a simple series parallel stochastic switching circuit \? we generalize previous results and prove that when q is a multiple of 2 or 3 , the answer is yes we also show that when q is a prime number larger than 3 , the answer is no probability approximation is studied for a general case of an arbitrary pswitch set s s 1 , s 2 , , s s in this case , we propose an algorithm based on local optimization to approximate any desired probability the analysis reveals that the approximation error of a switching circuit decreases exponentially with an increasing circuit size"
cs.AI,"we propose a new approach for solving a class of discrete decision making problems under uncertainty with positive cost this issue concerns multiple and diverse fields such as engineering , economics , artificial intelligence , cognitive science and many others basically , an agent has to choose a single or series of actions from a set of options , without knowing for sure their consequences schematically , two main approaches have been followed either the agent learns which option is the correct one to choose in a given situation by trial and error , or the agent already has some knowledge on the possible consequences of his decisions this knowledge being generally expressed as a conditional probability distribution in the latter case , several optimal or suboptimal methods have been proposed to exploit this uncertain knowledge in various contexts in this work , we propose following a different approach , based on the geometric intuition of distance more precisely , we define a goal independent quasimetric structure on the state space , taking into account both cost function and transition probability we then compare precision and computation time with classical approaches"
cs.DM,"the em atom bond connectivity \( abc \) index is a degree based molecular descriptor , that found chemical applications it is well known that among all connected graphs , the graphs with minimal abc index are trees a complete characterization of trees with minimal abc index is still an open problem in this paper , we present new structural properties of trees with minimal abc index our main results reveal that trees with minimal abc index do not contain so called em b k branches , with k geq 5 , and that they do not have more than four b 4 branches"
cs.DM,"an textit r dynamic k coloring of a graph g is a proper k coloring of g such that every vertex in v \( g \) has neighbors in at least min d \( v \) , r different color classes the textit r dynamic chromatic number of a graph g , written chi r \( g \) , is the least k such that g has such a coloring proving a conjecture of jahanbekam , kim , o , and west , we show that the m by n grid has no 3 dynamic 4 coloring when mn equiv2 mod 4 this completes the determination of the r dynamic chromatic number of the m by n grid for all r , m , n"
cs.LG|stat.ML,"this is an index to the papers that appear in the proceedings of the 29th international conference on machine learning \( icml 12 \) the conference was held in edinburgh , scotland , june 27th july 3rd , 2012"
cs.LG,"we propose a novel deep network structure called network in network \( nin \) to enhance model discriminability for local patches within the receptive field the conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input instead , we build micro neural networks with more complex structures to abstract the data within the receptive field we instantiate the micro neural network with a multilayer perceptron , which is a potent function approximator the feature maps are obtained by sliding the micro networks over the input in a similar manner as cnn they are then fed into the next layer deep nin can be implemented by stacking mutiple of the above described structure with enhanced local modeling via the micro network , we are able to utilize global average pooling over feature maps in the classification layer , which is easier to interpret and less prone to overfitting than traditional fully connected layers we demonstrated the state of the art classification performances with nin on cifar 10 and cifar 100 , and reasonable performances on svhn and mnist datasets"
cs.DM,"an edge colored graph g is rainbow connected if any two vertices are connected by a path whose edges have distinct colors the rainbow connectivity of a connected graph g , denoted rc \( g \) , is the smallest number of colors that are needed in order to make g rainbow connected in addition to being a natural combinatorial problem , the rainbow connectivity problem is motivated by applications in cellular networks in this paper we give the first proof that computing rc \( g \) is np hard in fact , we prove that it is already np complete to decide if rc \( g \) 2 , and also that it is np complete to decide whether a given edge colored \( with an unbounded number of colors \) graph is rainbow connected on the positive side , we prove that for every epsilon 0 , a connected graph with minimum degree at least epsilon n has bounded rainbow connectivity , where the bound depends only on epsilon , and the corresponding coloring can be constructed in polynomial time additional non trivial upper bounds , as well as open problems and conjectures are also pre sented"
cs.IT,"in this paper , we consider a multi cell network where every base station \( bs \) serves multiple users with an antenna array each user is associated with only one bs and has a single antenna assume that only long term channel state information \( csi \) is available in the system the objective is to minimize the network downlink transmission power needed to meet the users' signal to interference plus noise ratio \( sinr \) requirements for this objective , we propose an asynchronous distributed beamforming and power control algorithm which provides the same optimal solution as given by centralized algorithms to design the algorithm , the power minimization problem is formulated mathematically as a non convex problem for distributed implementation , the non convex problem is cast into the dual decomposition framework resorting to the theory about matrix pencil , a novel asynchronous iterative method is proposed for solving the dual of the non convex problem the methods for beamforming and power control are obtained by investigating the primal problem at last , simulation results are provided to demonstrate the convergence and performance of the algorithm"
cs.AI,"allocating scarce resources among agents to maximize global utility is , in general , computationally challenging we focus on problems where resources enable agents to execute actions in stochastic environments , modeled as markov decision processes \( mdps \) , such that the value of a resource bundle is defined as the expected value of the optimal mdp policy realizable given these resources we present an algorithm that simultaneously solves the resource allocation and the policy optimization problems this allows us to avoid explicitly representing utilities over exponentially many resource bundles , leading to drastic \( often exponential \) reductions in computational complexity we then use this algorithm in the context of self interested agents to design a combinatorial auction for allocating resources we empirically demonstrate the effectiveness of our approach by showing that it can , in minutes , optimally solve problems for which a straightforward combinatorial resource allocation technique would require the agents to enumerate up to 2 100 resource bundles and the auctioneer to solve an np complete problem with an input of that size"
cs.AI|cs.LG|stat.ML,"we consider the problem of reinforcement learning over episodes of a finite horizon deterministic system and as a solution propose optimistic constraint propagation \( ocp \) , an algorithm designed to synthesize efficient exploration and value function generalization we establish that when the true value function lies within a given hypothesis class , ocp selects optimal actions over all but at most k episodes , where k is the eluder dimension of the given hypothesis class we establish further efficiency and asymptotic performance guarantees that apply even if the true value function does not lie in the given hypothesis class , for the special case where the hypothesis class is the span of pre specified indicator functions over disjoint sets we also discuss the computational complexity of ocp and present computational results involving two illustrative examples"
cs.DM|cs.DS,"for an arbitrary undirected simple graph g with m edges , we give an algorithm with running time o \( m 4 l 2 \) to generate the set l of all minimal edge dominating sets of g for bipartite graphs we obtain a better result we show that their minimal edge dominating sets can be enumerated in time o \( m 4 l \) in fact our results are stronger both algorithms generate the next minimal edge dominating set with incremental polynomial delay o \( m 5 l \) and o \( m 4 l \) respectively , when l is the set of already generated minimal edge dominating sets our algorithms are tailored for and solve the equivalent problems of enumerating minimal \( vertex \) dominating sets of line graphs and line graphs of bipartite graphs , with incremental polynomial delay , and consequently in output polynomial time enumeration of minimal dominating sets in graphs has very recently been shown to be equivalent to enumeration of minimal transversals in hypergraphs the question whether the minimal transversals of a hypergraph can be enumerated in output polynomial time is a fundamental and challenging question in output sensitive enumeration it has been open for several decades and has triggered extensive research in the field"
cs.IT,"an analytical technique for the outage and ber analysis of the nx2 v blast algorithm with the optimal ordering has been presented in 1 , including closed form exact expressions for average ber and outage probabilities , and simple high snr approximations the analysis in 1 is based on the following essential approximations 1 the snr was defined in terms of total after projection signal and noise powers , and the ber was analyzed based on their ratio this corresponds to a non coherent \( power wise \) equal gain combining of both the signal and the noise , and it is not optimum since it does not provide the maximum output snr 2 the definition of the total after projection noise power at each step ignored the fact that the after projection noise vector had correlated components 3 the after combining noises at different steps \( and hence the errors \) were implicitly assumed to be independent of each other under non coherent equal gain combining , that is not the case it turns out that the results in 1 hold also true without these approximations , subject to minor modifications only the purpose of this note is to show this and also to extend the average ber results in 1 to the case of bpsk modulated v blast with more than two rx antennas \( eq 18 20 \) additionally , we emphasize that the block error rate is dominated by the first step ber at the high snr mode \( eq 14 and 21 \)"
cs.IT,"in fiber optic communications , evaluation of mutual information \( mi \) is still an open issue due to the unavailability of an exact and mathematically tractable channel model traditionally , lower bounds on mi are computed by approximating the \( original \) channel with an auxiliary forward channel in this paper , lower bounds are computed using an auxiliary backward channel , which has not been previously considered in the context of fiber optic communications distributions obtained through two variations of the stochastic digital backpropagation \( sdbp \) algorithm are used as auxiliary backward channels and these bounds are compared with bounds obtained through the conventional digital backpropagation \( dbp \) through simulations , higher information rates were achieved with sdbp compared with dbp , which implies that tighter lower bound on mi can be achieved through sdbp"
cs.IT,"we consider multi hop multiple access \( mac \) and broadcast channels \( bc \) where communication takes place with the assistance of relays that amplify and forward \( af \) their received signals for a two hop parallel af relay mac , assuming a sum power constraint across all relays we characterize optimal relay amplification factors and the resulting capacity regions we find that the parallel af relay mac with total transmit power of the two users p 1 p 2 p and total relay power p r is the dual of the parallel af relay bc where the mac source nodes become the bc destination nodes , the mac destination node becomes the bc source node , the dual bc source transmit power is p r and the total transmit power of the af relays is p the duality means that the capacity region of the af relay mac with a sum power constraint p on the transmitters is the same as that of the dual bc the duality relationship is found to be useful in characterizing the capacity region of the af relay bc as the union of mac capacity regions the duality extends to distributed relays with multiple antennas and more than 2 hops as well"
cs.IT,"dissections of polytopes are a well studied subject by geometers as well as recreational mathematicians a recent application in coding theory arises from the problem of parameterizing binary vectors of constant hamming weight which has been shown previously to be equivalent to the problem of dissecting a tetrahedron to a brick applications of dissections also arise in problems related to the construction of analog codes here we consider the rectangle to rectangle dissection due to montucla montucla 's dissection is first reinterpreted in terms of the two tile theorem based on this , a cube to brick dissection is developed in mathbb r n we present a linear time algorithm \( in n \) that computes the dissection , i e determines a point in the cube given a point in a specific realization of the brick an application of this algorithm to a previously reported analog coding scheme is also discussed"
cs.AI|cs.SI,"social status , defined as the relative rank or position that an individual holds in a social hierarchy , is known to be among the most important motivating forces in social behaviors in this paper , we consider the notion of status from the perspective of a position or title held by a person in an enterprise we study the intersection of social status and social networks in an enterprise we study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks to that end , we use two enterprise datasets with three communication channels voice call , short message , and email to demonstrate the social behavioral differences among individuals with different status we have several interesting findings and based on these findings we also develop a model to predict social status on the individual level , high status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another on the community level , the principle of homophily , social balance and clique theory generally indicate a rich club maintained by high status individuals , in the sense that this community is much more connected , balanced and dense our model can predict social status of individuals with 93 accuracy"
cs.IT,we propose a new conjecture on some exponential sums these particular sums have not apparently been considered in the literature subject to the conjecture we obtain the first effective construction of asymptotically good tree codes the available numerical evidence is consistent with the conjecture and is sufficient to certify codes for significant length communications
cs.LG,"collaborative filtering \( cf \) is a successful approach commonly used by many recommender systems conventional cf based methods use the ratings given to items by users as the sole source of information for learning to make recommendation however , the ratings are often very sparse in many applications , causing cf based methods to degrade significantly in their recommendation performance to address this sparsity problem , auxiliary information such as item content information may be utilized collaborative topic regression \( ctr \) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information nevertheless , the latent representation learned by ctr may not be very effective when the auxiliary information is very sparse inspired by recent success demonstrated by deep learning models , we propose in this paper a hierarchical bayesian model called collaborative deep learning \( cdl \) , which tightly couples a bayesian formulation of the stacked denoising autoencoder and probabilistic matrix factorization extensive experiments on real world datasets show that cdl can significantly advance the state of the art"
cs.DS,"we consider the bin packing problem with d different item sizes s i and item multiplicities a i , where all numbers are given in binary encoding this problem formulation is also known as the 1 dimensional cutting stock problem in this work , we provide an algorithm which , for constant d , solves bin packing in polynomial time this was an open problem for all d 3 in fact , for constant d our algorithm solves the following problem in polynomial time given two d dimensional polytopes p and q , find the smallest number of integer points in p whose sum lies in q our approach also applies to high multiplicity scheduling problems in which the number of copies of each job type is given in binary encoding and each type comes with certain parameters such as release dates , processing times and deadlines we show that a variety of high multiplicity scheduling problems can be solved in polynomial time if the number of job types is constant"
cs.AI,we consider qualitative simulation involving a finite set of qualitative relations in presence of complete knowledge about their interrelationship we show how it can be naturally captured by means of constraints expressed in temporal logic and constraint satisfaction problems the constraints relate at each stage the 'past' of a simulation with its 'future' the benefit of this approach is that it readily leads to an implementation based on constraint technology that can be used to generate simulations and to answer queries about them
cs.IT,"in this paper , we propose a non binary belief propagation approach \( nb bp \) for detection of m ary modulation symbols and decoding of q ary ldpc codes in large scale multiuser mimo systems we first propose a message passing based symbol detection algorithm which computes vector messages using a scalar gaussian approximation of interference , which results in a total complexity of just o \( kn sqrt m \) , where k is the number of uplink users and n is the number of base station \( bs \) antennas the proposed nb bp detector does not need to do a matrix inversion , which gives a complexity advantage over mmse detection we then design optimized q ary ldpc codes by matching the exit charts of the proposed detector and the ldpc decoder simulation results show that the proposed nb bp detection decoding approach using the optimized ldpc codes achieve significantly better performance \( by about 1 db to 7 db at 10 5 coded ber for various system loading factors with number of users ranging from 16 to 128 and number of bs antennas fixed at 128 \) compared to using linear detectors \( e g , mmse detector \) and off the shelf q ary irregular ldpc codes also , even with estimated channel knowledge \( e g , with mmse channel estimate \) , the performance of the proposed nb bp detector is better than that of the mmse detector"
cs.IT,"protecting against link failures in communication networks is essential to increase robustness , accessibility , and reliability of data transmission recently , network coding has been proposed as a solution to provide agile and cost efficient network protection against link failures , which does not require data rerouting , or packet retransmission to achieve this , separate paths have to be provisioned to carry encoded packets , hence requiring either the addition of extra links , or reserving some of the resources for this purpose in this paper , we propose network protection codes against a single link failure using network coding , where a separate path using reserved links is not needed in this case portions of the link capacities are used to carry the encoded packets the scheme is extended to protect against multiple link failures and can be implemented at an overlay layer although this leads to reducing the network capacity , the network capacity reduction is asymptotically small in most cases of practical interest we demonstrate that such network protection codes are equivalent to error correcting codes for erasure channels finally , we study the encoding and decoding operations of such codes over the binary field"
cs.IT,"we study the extremality of the bec and the bsc for gallager 's reliability function e 0 evaluated under the uniform input distribution for binary input dmcs from the aspect of channel polarization in particular , we show that amongst all b dmcs of a given e 0 \( rho \) value , for a fixed rho geq 0 , the bec and bsc are extremal in the evolution of e 0 under the one step polarization transformations"
cs.IT,"density evolution for protograph low density parity check \( ldpc \) codes is considered , and it is shown that the message error rate falls double exponentially with iterations whenever the degree 2 subgraph of the protograph is cycle free and noise level is below threshold conditions for stability of protograph density evolution are established and related to the structure of the protograph using large girth graphs , sequences of protograph ldpc codes with block error threshold equal to bit error threshold and block error rate falling near exponentially with blocklength are constructed deterministically small sized protographs are optimized to obtain thresholds near capacity for binary erasure and binary input gaussian channels"
cs.LG,"in many practical applications of learning algorithms , unlabeled data is cheap and abundant whereas labeled data is expensive active learning algorithms developed to achieve better performance with lower cost usually representativeness and informativeness are used in active learning algoirthms advanced recent active learning methods consider both of these criteria despite its vast literature , very few active learning methods consider noisy instances , i e label noisy and outlier instances also , these methods did n't consider accuracy in computing representativeness and informativeness based on the idea that inaccuracy in these measures and not taking noisy instances into consideration are two sides of a coin and are inherently related , a new loss function is proposed this new loss function helps to decrease the effect of noisy instances while at the same time , reduces bias we defined instance complexity as a new notion of complexity for instances of a learning problem it is proved that noisy instances in the data if any , are the ones with maximum instance complexity based on this loss function which has two functions for classifying ordinary and noisy instances , a new classifier , named simple complex classifier is proposed in this classifier there are a simple and a complex function , with the complex function responsible for selecting noisy instances the resulting optimization problem for both learning and active learning is highly non convex and very challenging in order to solve it , a convex relaxation is proposed"
cs.SI,"science is a growing system , exhibiting 4 annual growth in publications and 1 8 annual growth in the number of references per publication combined these trends correspond to a 12 year doubling period in the total supply of references , thereby challenging traditional methods of evaluating scientific production , from researchers to institutions against this background , we analyzed a citation network comprised of 837 million references produced by 32 6 million publications over the period 1965 2012 , allowing for a temporal analysis of the `attention economy' in science unlike previous studies , we analyzed the entire probability distribution of reference ages the time difference between a citing and cited paper thereby capturing previously overlooked trends over this half century period we observe a narrowing range of attention both classic and recent literature are being cited increasingly less , pointing to the important role of socio technical processes to better understand the impact of exponential growth on the underlying knowledge network we develop a network based model , featuring the redirection of scientific attention via publications' reference lists , and validate the model against several empirical benchmarks we then use the model to test the causal impact of real paradigm shifts , thereby providing guidance for science policy analysis in particular , we show how perturbations to the growth rate of scientific output affects the reference age distribution and the functionality of the vast science citation network as an aid for the search retrieval of knowledge in order to account for the inflation of science , our study points to the need for a systemic overhaul of the counting methods used to evaluate citation impact especially in the case of evaluating science careers , which can span several decades and thus several doubling periods"
cs.IT,"we study vector space interference alignment for the mimo interference channel with no time or frequency diversity , and no symbol extensions we prove both necessary and sufficient conditions for alignment in particular , we characterize the feasibility of alignment for the symmetric three user channel where all users transmit along d dimensions , all transmitters have m antennas and all receivers have n antennas , as well as feasibility of alignment for the fully symmetric \( m n \) channel with an arbitrary number of users an implication of our results is that the total degrees of freedom available in a k user interference channel , using only spatial diversity from the multiple antennas , is at most 2 this is in sharp contrast to the k 2 degrees of freedom shown to be possible by cadambe and jafar with arbitrarily large time or frequency diversity moving beyond the question of feasibility , we additionally discuss computation of the number of solutions using schubert calculus in cases where there are a finite number of solutions"
cs.IT,"this paper studies the capacity of the peak and average power limited gaussian channel when its output is quantized using a dithered , infinite level , uniform quantizer of step size delta it is shown that the capacity of this channel tends to that of the unquantized gaussian channel when delta tends to zero , and it tends to zero when delta tends to infinity in the low signal to noise ratio \( snr \) regime , it is shown that , when the peak power constraint is absent , the low snr asymptotic capacity is equal to that of the unquantized channel irrespective of delta furthermore , an expression for the low snr asymptotic capacity for finite peak to average power ratios is given and evaluated in the low and high resolution limit it is demonstrated that , in this case , the low snr asymptotic capacity converges to that of the unquantized channel when delta tends to zero , and it tends to zero when delta tends to infinity comparing these results with achievability results for \( undithered \) 1 bit quantization , it is observed that the dither reduces capacity in the low precision limit , and it reduces the low snr asymptotic capacity unless the peak to average power ratio is unbounded"
cs.IT,"as a basic information theoretic model for fading relay channels , the parallel relay channel is first studied , for which lower and upper bounds on the capacity are derived for the parallel relay channel with degraded subchannels , the capacity is established , and is further demonstrated via the gaussian case , for which the synchronized and asynchronized capacities are obtained the capacity achieving power allocation at the source and relay nodes among the subchannels is characterized the fading relay channel is then studied , for which resource allocations that maximize the achievable rates are obtained for both the full duplex and half duplex cases capacities are established for fading relay channels that satisfy certain conditions"
cs.SI,"we study the time evolution of ranking and spectral properties of the google matrix of english wikipedia hyperlink network during years 2003 2011 the statistical properties of ranking of wikipedia articles via pagerank and cheirank probabilities , as well as the matrix spectrum , are shown to be stabilized for 2007 2011 a special emphasis is done on ranking of wikipedia personalities and universities we show that pagerank selection is dominated by politicians while 2drank , which combines pagerank and cheirank , gives more accent on personalities of arts the wikipedia pagerank of universities recovers 80 percents of top universities of shanghai ranking during the considered time period"
cs.DS,"let g be a directed graph on n vertices given an integer k n , the simple k path problem asks whether there exists a simple k path in g in case g is weighted , the min wt simple k path problem asks for a simple k path in g of minimal weight the fastest currently known deterministic algorithm for min wt simple k path by fomin , lokshtanov and saurabh runs in time o \( 2 851 k cdot n o \( 1 \) cdot log w \) for graphs with integer weights in the range w , w this is also the best currently known deterministic algorithm for simple k path where the running time is the same without the log w factor we define l k \( n \) subseteq n k to be the set of words of length k whose symbols are all distinct we show that an explicit construction of a non deterministic automaton \( nfa \) of size f \( k \) cdot n o \( 1 \) for l k \( n \) implies an algorithm of running time o \( f \( k \) cdot n o \( 1 \) cdot log w \) for min wt simple k path when the weights are non negative or the constructed nfa is acyclic as a directed graph we show that the algorithm of kneis et al and its derandomization by chen et al for simple k path can be used to construct an acylic nfa for l k \( n \) of size o \( 4 k o \( k \) \) we show , on the other hand , that any nfa for l k \( n \) must be size at least 2 k we thus propose closing this gap and determining the smallest nfa for l k \( n \) as an interesting open problem that might lead to faster algorithms for min wt simple k path we use a relation between simple k path and non deterministic xor automata \( nxa \) to give another direction for a deterministic algorithm with running time o \( 2 k \) for simple k path"
cs.SI|stat.ML,"from social science to biology , numerous applications often rely on motifs for intuitive and meaningful characterization of networks at both the global macro level as well as the local micro level while motifs have witnessed a tremendous success and impact in a variety of domains , there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns however , existing methods are not scalable to large networks with millions of nodes and edges , which impedes the application of motifs to new problems that require large scale network analysis to address these problems , we propose a fast , efficient , and parallel algorithm for counting motifs of size k 3 , 4 nodes that take only a fraction of the time to compute when compared with the current methods used the proposed motif counting algorithms leverages a number of proven combinatorial arguments for different motifs for each edge , we count a few motifs , and with these counts along with the combinatorial arguments , we obtain the exact counts of others in constant time on a large collection of 300 networks from a variety of domains , our motif counting strategies are on average 460x faster than current methods this brings new opportunities to investigate the use of motifs on much larger networks and newer applications as we show in our experiments to the best of our knowledge , this paper provides the largest motif computations to date as well as the largest systematic investigation on over 300 networks from a variety of domains"
cs.DM|cs.DS,"in this paper we study the complexity of the firefighter problem and related problems on trees when more than one firefighter is available at each time step , and answer several open questions of finbow and macgillivray 2009 more precisely , when b geq 2 firefighters are allowed at each time step , the problem is np complete for trees of maximum degree b 2 and polynomial time solvable for trees of maximum degree b 2 when the fire breaks out at a vertex of degree at most b 1 moreover we present a polynomial time algorithm for a subclass of trees , namely k caterpillars"
cs.IT,"in this paper , we consider a scenario where one energy harvesting and one battery operated sensor cooperatively transmit a common message to a distant base station the goal is to find the jointly optimal transmission \( power allocation \) policy which maximizes the total throughput for a given deadline first , we address the case in which the storage capacity of the energy harvesting sensor is infinite in this context , we identify the necessary conditions for such optimal transmission policy on their basis , we first show that the problem is convex then we go one step beyond and prove that \( i \) the optimal power allocation for the energy harvesting sensor can be computed independently and \( ii \) it unequivocally determines \( and allows to compute \) that of the battery operated one finally , we generalize the analysis for the case of finite storage capacity performance is assessed by means of computer simulations particular attention is paid to the impact of finite storage capacity and long term battery degradation on the achievable throughput"
cs.DM|cs.DS,"in various research papers , such as 2 , one will find the claim that the minla is optimally solvable on outerplanar graphs , with a reference to 1 however , the problem solved in that publication , which we refer to as the planar minimum linear arrangement problem \( planar minla \) , is different from the minimum linear arrangement problem \( minla \) , as we show in this article"
cs.SI,"many groups with diverse convictions are interacting online interactions in online communities help people to engage each other and enhance understanding across groups online communities include multiple sub communities whose members are similar due to social ties , characteristics , or ideas on a topic in this research , we are interested in understanding the changes in the relative size and activity of these sub communities , their merging or splitting patterns , and the changes in the perspectives of the members of these sub communities due to endogenous dynamics inside the community"
cs.SI,"a stream of unstructured news can be a valuable source of hidden relations between different entities , such as financial institutions , countries , or persons we present an approach to continuously collect online news , recognize relevant entities in them , and extract time varying networks the nodes of the network are the entities , and the links are their co occurrences we present a method to estimate the significance of co occurrences , and a benchmark model against which their robustness is evaluated the approach is applied to a large set of financial news , collected over a period of two years the entities we consider are 50 countries which issue sovereign bonds , and which are insured by credit default swaps \( cds \) in turn we compare the country co occurrence networks to the cds networks constructed from the correlations between the cds the results show relatively small , but significant overlap between the networks extracted from the news and those from the cds correlations"
cs.IT,we improve lower bounds on the k th order nonlinear complexity of pseudorandom sequences over finite fields and we establish a probabilistic result on the behavior of the k th order nonlinear complexity of random sequences over finite fields
cs.DS|cs.LG,"numerous machine learning algorithms contain pairwise statistical problems at their core that is , tasks that require computations over all pairs of input points if implemented naively often , tree structures are used to solve these problems efficiently dual tree algorithms can efficiently solve or approximate many of these problems using cover trees , rigorous worst case runtime guarantees have been proven for some of these algorithms in this paper , we present a problem independent runtime guarantee for any dual tree algorithm using the cover tree , separating out the problem dependent and the problem independent elements this allows us to just plug in bounds for the problem dependent elements to get runtime guarantees for dual tree algorithms for any pairwise statistical problem without re deriving the entire proof we demonstrate this plug and play procedure for nearest neighbor search and approximate kernel density estimation to get improved runtime guarantees under mild assumptions , we also present the first linear runtime guarantee for dual tree based range search"
cs.LG|stat.ML,"recurrent neural networks \( rnns \) are powerful in modeling sequential data such as speech signals and hence are suitable for acoustic modeling in automatic speech recognition \( asr \) although some successful stories have been reported , training rnns remains highly challenging recent research found that a well trained model can be used as a source of `dark knowledge' to train other models , by use of soft targets generated by the teacher model this idea has been employed to train simple models from powerful yet complex models in this paper , we perform an opposite research that uses a simple model to train a complex model such as an rnn we show that the knowledge transfer learning with soft targets leads to a smoother objective function which simplifies complex model training and improves model generalizability additionally , the knowledge transfer learning can be combined with the traditional supervised learning , in a way of either regularization or pre training experiments on an asr task demonstrated that this method can be successfully applied to train rnns , without any tricks on the learning scheme \( such as momentum \) or hessian approximation"
cs.IT,"we derive a formalism of stochastic master equations \( sme \) which describes the decoherence dynamics of a system in spin environments conditioned on the measurement record markovian and non markovian nature of environment can be revealed by a spectroscopy method based on weak quantum measurement \( weak spectroscopy \) on account of that correlated environments can lead to a nonlocal open system which exhibits strong non markovian effects although the local dynamics are markovian , the spectroscopy method can be used to demonstrate that there is correlation between two environments"
cs.AI,"the formalism of quantum theory in hilbert space has been applied with success to the modeling and explanation of several cognitive phenomena , whereas traditional cognitive approaches were problematical however , this 'quantum cognition paradigm' was recently challenged by its proven impossibility to simultaneously model 'question order effects' and 'response replicability' in part i of this paper we describe sequential dichotomic measurements within an operational and realistic framework for human cognition elaborated by ourselves , and represent them in a quantum like 'extended bloch representation' where the born rule of quantum probability does not necessarily hold in part ii we apply this mathematical framework to successfully model question order effects , response replicability and unpacking effects , thus opening the way toward quantum cognition beyond hilbert space"
cs.DM|cs.DS,"we algorithmize the recent structural characterization for claw free graphs by chudnovsky and seymour building on this result , we show that dominating set on claw free graphs is \( i \) fixed parameter tractable and \( ii \) even possesses a polynomial kernel to complement these results , we establish that dominating set is not fixed parameter tractable on the slightly larger class of graphs that exclude k 1 , 4 as an induced subgraph \( k 1 , 4 free graphs \) we show that our algorithmization can also be used to show that the related connected dominating set problem is fixed parameter tractable on claw free graphs to complement that result , we show that connected dominating set has no polynomial kernel on claw free graphs and is not fixed parameter tractable on k 1 , 4 free graphs combined , our results provide a dichotomy for dominating set and connected dominating set on k 1 , l free graphs and show that the problem is fixed parameter tractable if and only if l 3"
cs.IT,"capacity scaling laws are analyzed in an underwater acoustic network with n regularly located nodes on a square a narrow band model is assumed where the carrier frequency is allowed to scale as a function of n in the network , we characterize an attenuation parameter that depends on the frequency scaling as well as the transmission distance a cut set upper bound on the throughput scaling is then derived in extended networks our result indicates that the upper bound is inversely proportional to the attenuation parameter , thus resulting in a highly power limited network interestingly , it is seen that unlike the case of wireless radio networks , our upper bound is intrinsically related to the attenuation parameter but not the spreading factor furthermore , we describe an achievable scheme based on the simple nearest neighbor multi hop \( mh \) transmission it is shown under extended networks that the mh scheme is order optimal as the attenuation parameter scales exponentially with sqrt n \( or faster \) finally , these scaling results are extended to a random network realization"
cs.IT,"we present new constructions of codes for asymmetric channels for both binary and nonbinary alphabets , based on methods of generalized code concatenation for the binary asymmetric channel , our methods construct nonlinear single error correcting codes from ternary outer codes we show that some of the varshamov tenengol'ts constantin rao codes , a class of binary nonlinear codes for this channel , have a nice structure when viewed as ternary codes in many cases , our ternary construction yields even better codes for the nonbinary asymmetric channel , our methods construct linear codes for many lengths and distances which are superior to the linear codes of the same length capable of correcting the same number of symmetric errors in the binary case , varshamov has shown that almost all good linear codes for the asymmetric channel are also good for the symmetric channel our results indicate that varshamov 's argument does not extend to the nonbinary case , i e , one can find better linear codes for asymmetric channels than for symmetric ones"
cs.IT,"we study zero error unicast index coding instances , where each receiver must perfectly decode its requested message set , and the message sets requested by any two receivers do not overlap we show that for all these instances with up to five receivers , linear index codes are optimal although this class contains 9847 non isomorphic instances , by using our recent results and by properly categorizing the instances based on their graphical representations , we need to consider only 13 non trivial instances to solve the entire class this work complements the result by arbabjolfaei et al \( isit 2013 \) , who derived the capacity region of all unicast index coding problems with up to five receivers in the diminishing error setup they employed random coding arguments , which require infinitely long messages we consider the zero error setup our approach uses graph theory and combinatorics , and does not require long messages"
cs.LG,"we propose a multi layer network based on the bayesian framework of the factor graphs in reduced normal form \( fgrn \) applied to a two dimensional lattice the latent variable model \( lvm \) is the basic building block of a quadtree hierarchy built on top of a bottom layer of random variables that represent pixels of an image , a feature map , or more generally a collection of spatially distributed discrete variables the multi layer architecture implements a hierarchical data representation that , via belief propagation , can be used for learning and inference typical uses are pattern completion , correction and classification the fgrn paradigm provides great flexibility and modularity and appears as a promising candidate for building deep networks the system can be easily extended by introducing new and different \( in cardinality and in type \) variables prior knowledge , or supervised information , can be introduced at different scales the fgrn paradigm provides a handy way for building all kinds of architectures by interconnecting only three types of units single input single output \( siso \) blocks , sources and replicators the network is designed like a circuit diagram and the belief messages flow bidirectionally in the whole system the learning algorithms operate only locally within each block the framework is demonstrated in this paper in a three layer structure applied to images extracted from a standard data set"
cs.DS,"in this paper , a contrastive evaluation of massively parallel implementations of suffix tree and suffix array to accelerate genome sequence matching are proposed based on intel core i7 3770k quad core and nvidia geforce gtx680 gpu besides suffix array only held approximately 20 30 of the space relative to suffix tree , the coalesced binary search and tile optimization make suffix array clearly outperform suffix tree using gpu consequently , the experimental results show that multiple genome sequence matching based on suffix array is more than 99 times speedup than that of cpu serial implementation there is no doubt that massively parallel matching algorithm based on suffix array is an efficient approach to high performance bioinformatics applications"
cs.IT,"in this paper , a game theoretic model for studying power control for wireless data networks in frequency selective multipath environments is analyzed the uplink of an impulse radio ultrawideband system is considered the effects of self interference and multiple access interference on the performance of generic rake receivers are investigated for synchronous systems focusing on energy efficiency , a noncooperative game is proposed in which users in the network are allowed to choose their transmit powers to maximize their own utilities , and the nash equilibrium for the proposed game is derived it is shown that , due to the frequency selective multipath , the noncooperative solution is achieved at different signal to interference plus noise ratios , depending on the channel realization and the type of rake receiver employed a large system analysis is performed to derive explicit expressions for the achieved utilities the pareto optimal \( cooperative \) solution is also discussed and compared with the noncooperative approach"
cs.AI|cs.LG|stat.ML,"multi label classification has received considerable interest in recent years multi label classifiers have to address many problems including handling large scale datasets with many instances and a large set of labels , compensating missing label assignments in the training set , considering correlations between labels , as well as exploiting unlabeled data to improve prediction performance to tackle datasets with a large set of labels , embedding based methods have been proposed which seek to represent the label assignments in a low dimensional space many state of the art embedding based methods use a linear dimensionality reduction to represent the label assignments in a low dimensional space however , by doing so , these methods actually neglect the tail labels labels that are infrequently assigned to instances we propose an embedding based method that non linearly embeds the label vectors using an stochastic approach , thereby predicting the tail labels more accurately moreover , the proposed method have excellent mechanisms for handling missing labels , dealing with large scale datasets , as well as exploiting unlabeled data with the best of our knowledge , our proposed method is the first multi label classifier that simultaneously addresses all of the mentioned challenges experiments on real world datasets show that our method outperforms stateof the art multi label classifiers by a large margin , in terms of prediction performance , as well as training time"
cs.SI,"the computer science research community has became increasingly interested in the study of social media due to their pervasiveness in the everyday life of millions of individuals methodological questions and technical challenges abound as more and more data from social platforms become available for analysis this data deluge not only yields the unprecedented opportunity to unravel questions about online individuals' behavior at scale , but also allows to explore the potential perils that the massive adoption of social media brings to our society these communication channels provide plenty of incentives \( both economical and social \) and opportunities for abuse as social media activity became increasingly intertwined with the events in the offline world , individuals and organizations have found ways to exploit these platforms to spread misinformation , to attack and smear others , or to deceive and manipulate during crises , social media have been effectively used for emergency response , but fear mongering actions have also triggered mass hysteria and panic criminal gangs and terrorist organizations like isis adopt social media for propaganda and recruitment synthetic activity and social bots have been used to coordinate orchestrated astroturf campaigns , to manipulate political elections and the stock market the lack of effective content verification systems on many of these platforms , including twitter and facebook , rises concerns when younger users become exposed to cyber bulling , harassment , or hate speech , inducing risks like depression and suicide this article illustrates some of the recent advances facing these issues and discusses what it remains to be done , including the challenges to address in the future to make social media a more useful and accessible , safer and healthier environment for all users"
cs.IT,"unlike in conventional modulation where information bits are conveyed only through symbols from modulation alphabets defined in the complex plane \( e g , quadrature amplitude modulation \( qam \) , phase shift keying \( psk \) \) , in index modulation \( im \) , additional information bits are conveyed through indices of certain transmit entities that get involved in the transmission transmit antennas in multi antenna systems and subcarriers in multi carrier systems are examples of such transmit entities that can be used to convey additional information bits through indexing in this paper , we introduce em generalized space and frequency index modulation , where the indices of active transmit antennas and subcarriers convey information bits we first introduce index modulation in the spatial domain , referred to as generalized spatial index modulation \( gsim \) for gsim , where bits are indexed only in the spatial domain , we derive the expression for achievable rate as well as easy to compute upper and lower bounds on this rate we show that the achievable rate in gsim can be more than that in spatial multiplexing , and analytically establish the condition under which this can happen it is noted that gsim achieves this higher rate using fewer transmit radio frequency \( rf \) chains compared to spatial multiplexing we also propose a gibbs sampling based detection algorithm for gsim and show that gsim can achieve better bit error rate \( ber \) performance than spatial multiplexing for generalized space frequency index modulation \( gsfim \) , where bits are encoded through indexing in both active antennas as well as subcarriers , we derive the achievable rate expression numerical results show that gsfim can achieve higher rates compared to conventional mimo ofdm also , ber results show the potential for gsfim performing better than mimo ofdm"
cs.DM,"we consider optimization of nonlinear objective functions that balance d linear criteria over n element independence systems presented by linear optimization oracles for d 1 , we have previously shown that an r best approximate solution can be found in polynomial time here , using an extended erd h o s ko rado theorem of frankl , we show that for d 2 , finding a rho n best solution requires exponential time"
cs.IT,"in this paper , we propose a signal selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple antenna capability this method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals in particular , the cyclic correlation significance test is used to detect a specific signal of interest by exploiting knowledge of its cyclic frequencies the analytical threshold for achieving constant false alarm rate using this detection method is presented , verified through simulations , and shown to be independent of both the number of samples used and the noise variance , effectively eliminating the dependence on accurate noise estimation the proposed method is also shown , through numerical simulations , to outperform existing multiple antenna cyclostationary based spectrum sensing algorithms under a quasi static rayleigh fading channel , in both spatially correlated and uncorrelated noise environments the algorithm also has significantly lower computational complexity than these other approaches"
cs.IT,"large scale multiple input multiple output \( mimo \) with high spectrum and energy efficiency is a very promising key technology for future 5g wireless communications for large scale mimo systems , accurate channel state information \( csi \) acquisition is a challenging problem , especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas , we propose a spectrum efficient superimposed pilot design for downlink large scale mimo scenarios , where frequency domain pilots of different transmit antennas occupy the completely same subcarriers in the freqency domain meanwhile , spatial temporal common sparsity of large scale mimo channels motivates us to exploit the emerging theory of structured compressive sensing \( cs \) for reliable mimo channel estimation , which is realized by the proposed structured subspace pursuit \( ssp \) algorithm to simultaneously recover multiple channels with low pilot overhead simulation results demonstrate that the proposed scheme performs well and can approach the performance bound"
cs.IT,"distributed source coding schemes are typically based on the use of channels codes as source codes in this paper we propose a new paradigm , termed distributed arithmetic coding , which exploits the fact that arithmetic codes are good source as well as channel codes in particular , we propose a distributed binary arithmetic coder for slepian wolf coding with decoder side information , along with a soft joint decoder the proposed scheme provides several advantages over existing slepian wolf coders , especially its good performance at small block lengths , and the ability to incorporate arbitrary source models in the encoding process , e g context based statistical models we have compared the performance of distributed arithmetic coding with turbo codes and low density parity check codes , and found that the proposed approach has very competitive performance"
cs.IT,"this paper considers the queueing performance of a system that transmits coded data over a time varying erasure channel in our model , the queue length and channel state together form a markov chain that depends on the system parameters this gives a framework that allows a rigorous analysis of the queue as a function of the code rate most prior work in this area either ignores block length \( e g , fluid models \) or assumes error free communication using finite codes this work enables one to determine when such assumptions provide good , or bad , approximations of true behavior moreover , it offers a new approach to optimize parameters and evaluate performance this can be valuable for delay sensitive systems that employ short block lengths"
cs.AI|cs.LG,"we motivate and analyse a new tree search algorithm , gpts , based on recent theoretical advances in the use of gaussian processes for bandit problems we consider tree paths as arms and we assume the target reward function is drawn from a gp distribution the posterior mean and variance , after observing data , are used to define confidence intervals for the function values , and we sequentially play arms with highest upper confidence bounds we give an efficient implementation of gpts and we adapt previous regret bounds by determining the decay rate of the eigenvalues of the kernel matrix on the whole set of tree paths we consider two kernels in the feature space of binary vectors indexed by the nodes of the tree linear and gaussian the regret grows in square root of the number of iterations t , up to a logarithmic factor , with a constant that improves with bigger gaussian kernel widths we focus on practical values of t , smaller than the number of arms finally , we apply gpts to open loop planning in discounted markov decision processes by modelling the reward as a discounted sum of independent gaussian processes we report similar regret bounds to those of the olop algorithm"
cs.IT,"despite the np hardness of acquiring minimum distance d m for linear codes theoretically , in this paper we propose one experimental method of finding minimum weight codewords , the weight of which is equal to d m for ldpc codes one existing syndrome decoding method , called serial belief propagation \( bp \) with ordered statistic decoding \( osd \) , is adapted to serve our purpose we hold the conjecture that among many candidate error patterns in osd reprocessing , modulo 2 addition of the lightest error pattern with one of the left error patterns may generate a light codeword when the decoding syndrome changes to all zero state , the lightest error pattern reduces to all zero , the lightest non zero error pattern is a valid codeword to update lightest codeword list given sufficient codewords sending , the survived lightest codewords are likely to be the target compared with existing techniques , our method demonstrates its efficiency in the simulation of several interested ldpc codes"
cs.IT,"wideband spectrum sensing is becoming increasingly important to cognitive radio \( cr \) systems for exploiting spectral opportunities this paper introduces a novel multi rate sub nyquist spectrum sensing \( ms3 \) system that implements cooperative wideband spectrum sensing in a cr network ms3 can detect the wideband spectrum using partial measurements without reconstructing the full frequency spectrum sub nyquist sampling rates are adopted in sampling channels for wrapping the frequency spectrum onto itself this significantly reduces sensing requirements of cr the effects of sub nyquist sampling are considered , and the performance of multi channel sub nyquist samplings is analyzed to improve its detection performance , sub nyquist sampling rates are chosen to be different such that the numbers of samples are consecutive prime numbers furthermore , when the received signals at crs are faded or shadowed , the performance of ms3 is analytically evaluated numerical results show that the proposed system can significantly enhance the wideband spectrum sensing performance while requiring low computational and implementation complexities"
cs.IT,"this work presents set membership adaptive algorithms based on time varying error bounds for cdma interference suppression we introduce a modified family of set membership adaptive algorithms for parameter estimation with time varying error bounds the algorithms considered include modified versions of the set membership normalized least mean squares \( sm nlms \) , the affine projection \( sm ap \) and the bounding ellipsoidal adaptive constrained \( beacon \) recursive least squares technique the important issue of error bound specification is addressed in a new framework that takes into account parameter estimation dependency , multi access and inter symbol interference for ds cdma communications an algorithm for tracking and estimating the interference power is proposed and analyzed this algorithm is then incorporated into the proposed time varying error bound mechanisms computer simulations show that the proposed algorithms are capable of outperforming previously reported techniques with a significantly lower number of parameter updates and a reduced risk of overbounding or underbounding"
cs.IT,"a harvest use store power splitting \( ps \) relaying strategy with distributed beamforming is proposed for wirelesspowered multi relay cooperative networks in this paper different from the conventional battery free ps relaying strategy , harvested energy is prioritized to power information relaying while the remainder is accumulated and stored for future usage with the help of a battery in the proposed strategy , which supports an efficient utilization of harvested energy however , ps affects throughput at subsequent time slots due to the battery operations including the charging and discharging to this end , ps and battery operations are coupled with distributed beamforming a throughput optimization problem to incorporate these coupled operations is formulated though it is intractable to address the intractability of the optimization , a layered optimization method is proposed to achieve the optimal joint ps and battery operation design with non causal channel state information \( csi \) , in which the ps and the battery operation can be analyzed in a decomposed manner then , a general case with causal csi is considered , where the proposed layered optimization method is extended by utilizing the statistical properties of csi to reach a better tradeoff between performance and complexity , a greedy method that requires no information about subsequent time slots is proposed simulation results reveal the upper and lower bound on performance of the proposed strategy , which are reached by the layered optimization method with non causal csi and the greedy method , respectively moreover , the proposed strategy outperforms the conventional ps based relaying without energy accumulation and time switching based relaying strategy"
cs.DM,"in this paper , we introduce the notion of augmentation for polytopes and use it to show the error in two presumptions that have been key in arriving at over reaching over scoped claims of impossibility in recent extended formulations \( ef \) developments one of these presumptions is that if polytopes p and q are described in the spaces of variables x and y respectively , and there exists a linear map x ay between the feasible sets of p and q , then q is an ef of p the other is \( an augmentation of polytope a projects to polytope b \) \( the external descriptions of a and b are related \) we provide counter examples to these presumptions , and show that in general \( 1 \) if polytopes can always be arbitrarily augmented for the purpose of establishing ef relations , then the notion of ef becomes degenerate meaningless in some cases , and that \( 2 \) the statement \( polytope b is the projection of an augmentation of polytope a \) \( polytope b is the projection of polytope a \) is not true in general \( although , as we show , the converse statement , \( b is the projection of a \) \( b is the projection of every augmentation of a \) , is true in general \) we illustrate some of the ideas using the minimum spanning tree problem , as well as the lower bounds developments in fiorini et al \( 2011 2012 \) , in particular"
cs.IT|stat.ML,"f divergences are a general class of divergences between probability measures which include as special cases many commonly used divergences in probability , mathematical statistics and information theory such as kullback leibler divergence , chi squared divergence , squared hellinger distance , total variation distance etc in this paper , we study the problem of maximizing or minimizing an f divergence between two probability measures subject to a finite number of constraints on other f divergences we show that these infinite dimensional optimization problems can all be reduced to optimization problems over small finite dimensional spaces which are tractable our results lead to a comprehensive and unified treatment of the problem of obtaining sharp inequalities between f divergences we demonstrate that many of the existing results on inequalities between f divergences can be obtained as special cases of our results and we also improve on some existing non sharp inequalities"
cs.DS|cs.LG,"in recent years , spectral clustering has become one of the most popular modern clustering algorithms it is simple to implement , can be solved efficiently by standard linear algebra software , and very often outperforms traditional clustering algorithms such as the k means algorithm on the first glance spectral clustering appears slightly mysterious , and it is not obvious to see why it works at all and what it really does the goal of this tutorial is to give some intuition on those questions we describe different graph laplacians and their basic properties , present the most common spectral clustering algorithms , and derive those algorithms from scratch by several different approaches advantages and disadvantages of the different spectral clustering algorithms are discussed"
cs.IT,"motivated by the broadcast view of the interference channel , the new problem of communication with disturbance constraints is formulated the rate disturbance region is established for the single constraint case and the optimal encoding scheme turns out to be the same as the han kobayashi scheme for the two user pair interference channel this result is extended to the gaussian vector \( mimo \) case for the case of communication with two disturbance constraints , inner and outer bounds on the rate disturbance region for a deterministic model are established the inner bound is achieved by an encoding scheme that involves rate splitting , marton coding , and superposition coding , and is shown to be optimal in several nontrivial cases this encoding scheme can be readily applied to discrete memoryless interference channels and motivates a natural extension of the han kobayashi scheme to more than two user pairs"
cs.IT,"centroidal voronoi tessellations \( cvts \) are voronoi tessellations of a region such that the generating points of the tessellations are also the centroids of the corresponding voronoi regions with respect to a given probability measure cvt is a fundamental notion that has a wide spectrum of applications in computational science and engineering in this paper , an algorithm is given to obtain all the cvts with n generators , for any positive integer n , of any cantor set generated by a pair of self similar mappings given by s 1 \( x \) r 1x and s 2 \( x \) r 2x \( 1 r 2 \) for x in mathbb r , where r 1 , r 2 0 and r 1 r 2 1 , with respect to any probability distribution p such that p p 1 p circ s 1 1 p 2 p circ s 2 1 , where p 1 , p 2 0 and p 1 p 2 1"
cs.DM,"this paper describes a flexible architecture for implementing a new fast computation of the discrete fourier and hartley transforms , which is based on a matrix laurent series the device calculates the transforms based on a single bit selection operator the hardware structure and synthesis are presented , which handled a 16 point fast transform in 65 nsec , with a xilinx spartan 3e device"
cs.IT,a framework to analyze inference performance in densely connected single layer feed forward networks is developed for situations where a given data set is composed of correlated patterns the framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from haar measures this assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross correlation matrix of the pattern matrix links to existing methods for analysis of perceptron learning and gaussian linear vector channels and an application to a simple but nontrivial problem are also shown
cs.IT,"in this paper , we consider the problem of the interference alignment for the k user siso interference channel \( ic \) with blind channel state information \( csi \) at transmitters our achievement in contrast to the traditional k user interference alignment \( ia \) scheme has more practical notions in this case , every receiver is equipped with one reconfigurable antenna which tries to place its desired signal in a subspace which is linearly independent of interference signals we show that if the channel values are known to the receivers only , the sum degrees of freedom \( dof \) of the linear blind ia \( bia \) with reconfigurable antenna is frac kr r 2 r k , where r left lceil frac sqrt 1 4k 1 2 right rceil the result indicates that the optimum sum dof for the k user ic is to achieve the sum dof of lim k rightarrow infty frac kr r 2 r k frac sqrt k 2 for an asymptotically large interference network thus , the dof of the k user ic using reconfigurable antenna grows sublinearly with the number of the users , whereas it grows linearly in the case where transmitters access to the csi in addition , we propose both achievability and converse proof so as to show that this is the sum dof of linear bia with the reconfigurable antenna"
cs.IT,"we consider a coded cooperative data exchange problem with the goal of generating a secret key specifically , we investigate the number of public transmissions required for a set of clients to agree on a secret key with probability one , subject to the constraint that it remains private from an eavesdropper although the problems are closely related , we prove that secret key generation with fewest number of linear transmissions is np hard , while it is known that the analogous problem in traditional cooperative data exchange can be solved in polynomial time in doing this , we completely characterize the best possible performance of linear coding schemes , and also prove that linear codes can be strictly suboptimal finally , we extend the single key results to characterize the minimum number of public transmissions required to generate a desired integer number of statistically independent secret keys"
cs.AI,"in this paper we study lifted inference for the weighted first order model counting problem \( wfomc \) , which counts the assignments that satisfy a given sentence in first order logic \( fol \) it has applications in statistical relational learning \( srl \) and probabilistic databases \( pdb \) we present several results first , we describe a lifted inference algorithm that generalizes prior approaches in srl and pdb second , we provide a novel dichotomy result for a non trivial fragment of fo cnf sentences , showing that for each sentence the wfomc problem is either in ptime or p hard in the size of the input domain we prove that , in the first case our algorithm solves the wfomc problem in ptime , and in the second case it fails third , we present several properties of the algorithm finally , we discuss limitations of lifted inference for symmetric probabilistic databases \( where the weights of ground literals depend only on the relation name , and not on the constants of the domain \) , and prove the impossibility of a dichotomy result for the complexity of probabilistic inference for the entire language fol"
cs.DM,"we consider here on line algorithms for achlioptas processes given a initially empty graph g on n vertices , a random process that at each step selects independently and uniformly at random two edges from the set of non edges is launched we must choose one of the two edges and add it to the graph while discarding the other the goal is to avoid the appearance of a connected component spanning omega \( n \) vertices \( called a giant component \) for as many steps as possible bohman and frieze proved in 2001 that on line achlioptas processes cannot postpone the appearance of the giant for more that roughly n steps whp this upper bound got even lower in 2003 when the two above mentioned authors and wormald proved that each on line achlioptas process creates a giant before step 0 964446n whp the purpose of this work is to determine a new upper bound by using essentially the same methods used by bohman , frieze and wormald in 2003 and some results of spencer and wormald on size algorithms we prove here that achlioptas processes cannot postpone the appearance of the giant for more than 0 9455n steps whp"
cs.LG,"principal component analysis is a multivariate statistical method frequently used in science and engineering to reduce the dimension of a problem or extract the most significant features from a dataset in this paper , using a similar notion to the quantum counting , we show how to apply the amplitude amplification together with the phase estimation algorithm to an operator in order to procure the eigenvectors of the operator associated to the eigenvalues defined in the range left a , b right , where a and b are real and 0 leq a leq b leq 1 this makes possible to obtain a combination of the eigenvectors associated to the largest eigenvalues and so can be used to do principal component analysis on quantum computers"
cs.IT,"in many typical mobile communication receivers the channel is estimated based on pilot symbols to allow for a coherent detection and decoding in a separate processing step currently much work is spent on receivers which break up this separation , e g , by enhancing channel estimation based on reliability information on the data symbols in the present work , we evaluate the possible gain of a joint processing of data and pilot symbols in comparison to the case of a separate processing in the context of stationary rayleigh flat fading channels therefore , we discuss the nature of the possible gain of a joint processing of pilot and data symbols we show that the additional information that can be gained by a joint processing is captured in the temporal correlation of the channel estimation error of the solely pilot based channel estimation , which is not retrieved by the channel decoder in case of separate processing in addition , we derive a new lower bound on the achievable rate for joint processing of pilot and data symbols"
cs.IT,"in this paper , we consider a two way or bidirectional communications system with a relay equipped with multiple antennas we show that when the downlink channel state information is not known at the relay , the benefit of having additional antennas at the relay can only be obtained by using decode and forward \( df \) but not amplify and forward \( af \) the gain becomes significant when we employ transmit diversity together with wireless network coding we also demonstrate how the performance of such system can be improved by performing antenna selection at the relay our results show that if downlink channel state information is known at the relay , network coding may not provide additional gain than simple antenna selection scheme"
cs.AI,"an xml framework for concept description is given , based upon the fact that the tree structure of xml implies the logical structure of concepts as defined by attributional calculus especially , the attribute value representation is implementable in the xml framework since the attribute value representation is an important way to represent knowledge in ai , the framework offers a further and simpler way than the powerful rdf technology"
cs.DS,"modern text retrieval systems often provide a similarity search utility , that allows the user to find efficiently a fixed number k of documents in the data set that are most similar to a given query \( here a query is either a simple sequence of keywords or the identifier of a full document found in previous searches that is considered of interest \) we consider the case of a textual database made of semi structured documents each field , in turns , is modelled with a specific vector space the problem is more complex when we also allow each such vector space to have an associated user defined dynamic weight that influences its contribution to the overall dynamic aggregated and weighted similarity this dynamic problem has been tackled in a recent paper by singitham et al in in vldb 2004 their proposed solution , which we take as baseline , is a variant of the cluster pruning technique that has the potential for scaling to very large corpora of documents , and is far more efficient than the naive exhaustive search we devise an alternative way of embedding weights in the data structure , coupled with a non trivial application of a clustering algorithm based on the furthest point first heuristic for the metric k center problem the validity of our approach is demonstrated experimentally by showing significant performance improvements over the scheme proposed in singitham et al in vldb 2004 we improve significantly tradeoffs between query time and output quality with respect to the baseline method in singitham et al in in vldb 2004 , and also with respect to a novel method by chierichetti et al to appear in acm pods 2007 we also speed up the pre processing time by a factor at least thirty"
cs.IT,"finding the sparse solution of an underdetermined system of linear equations has many applications , especially , it is used in compressed sensing \( cs \) , sparse component analysis \( sca \) , and sparse decomposition of signals on overcomplete dictionaries we have recently proposed a fast algorithm , called smoothed ell 0 \( sl0 \) , for this task contrary to many other sparse recovery algorithms , sl0 is not based on minimizing the ell 1 norm , but it tries to directly minimize the ell 0 norm of the solution the basic idea of sl0 is optimizing a sequence of certain \( continuous \) cost functions approximating the ell 0 norm of a vector however , in previous papers , we did not provide a complete convergence proof for sl0 in this paper , we study the convergence properties of sl0 , and show that under a certain sparsity constraint in terms of asymmetric restricted isometry property \( arip \) , and with a certain choice of parameters , the convergence of sl0 to the sparsest solution is guaranteed moreover , we study the complexity of sl0 , and we show that whenever the dimension of the dictionary grows , the complexity of sl0 increases with the same order as matching pursuit \( mp \) , which is one of the fastest existing sparse recovery methods , while contrary to mp , its convergence to the sparsest solution is guaranteed under certain conditions which are satisfied through the choice of parameters"
cs.IT,"we show how the necessary and sufficient conditions for the nfl to apply can be reduced to the single requirement of the set of objective functions under consideration being closed under permutation , and quantify the extent to which a set of objectives not closed under permutation can give rise to a performance difference between two algorithms then we provide a more refined definition of performance under which we show that revisiting algorithms are always trumped by enumerative ones"
cs.IT,"the so called non uniform fft \( nfft \) is a family of algorithms for efficiently computing the fourier transform of finite length signals , whenever the time or frequency grid is nonuniformly spaced following the usual classification , there exist five nfft types types 1 and 2 make it possible to pass from the time to the frequency domain with nonuniform input and output grids respectively type 3 allows for both input and output nonuniform grids finally , types 4 and 5 are the inverses of types 1 and 2 and are expensive computationally , given that they involve iterative methods in this paper , we solve this last drawback in the one dimensional case by presenting non iterative type 4 and 5 nfft methods that just involve three nffts of types 1 or 2 plus some additional ffts the methods are based on exploiting the structure of the lagrange interpolation formula the paper includes several numerical examples in which the proposed methods are compared with the gaussian elimination \( ge \) and conjugate gradient \( cg \) methods , both in terms of round off error and computational burden"
cs.AI|cs.DS,"knuth \( 1990 \) introduced the class of nested formulas and showed that their satisfiability can be decided in polynomial time we show that , parameterized by the size of a smallest strong backdoor set to the target class of nested formulas , checking the satisfiability of any cnf formula is fixed parameter tractable thus , for any k 0 , the satisfiability problem can be solved in polynomial time for any formula f for which there exists a variable set b of size at most k such that for every truth assignment t to b , the formula f t is nested moreover , the degree of the polynomial is independent of k our algorithm uses the grid minor theorem of robertson and seymour \( 1986 \) to either find that the incidence graph of the formula has bounded treewidth a case that is solved using model checking for monadic second order logic or to find many vertex disjoint obstructions in the incidence graph for the latter case , new combinatorial arguments are used to find a small backdoor set combining both cases leads to an approximation algorithm producing a strong backdoor set whose size is upper bounded by a function of the optimum going through all assignments to this set of variables and using knuth 's algorithm , the satisfiability of the input formula is decided"
cs.IT,"in this paper , we present a new multi antenna modulation scheme , termed as em space time index modulation \( stim \) in stim , information bits are conveyed through antenna indexing in the spatial domain , slot indexing in the time domain , and m ary modulation symbols a time slot in a given frame can be used or unused , and the choice of the slots used for transmission conveys slot index bits in addition , antenna index bits are conveyed in every used time slot by activating one among the available antennas m ary symbols are sent on the active antenna in a used time slot we study stim in a cyclic prefixed single carrier \( cpsc \) system in frequency selective fading channels it is shown that , for the same spectral efficiency , stim can achieve better performance compared to conventional orthogonal frequency division multiplexing \( ofdm \) low complexity iterative algorithms for the detection of large dimensional stim signals are also presented"
cs.SI,"political polarization is traditionally analyzed through the ideological stances of groups and parties , but it also has a behavioral component that manifests in the interactions between individuals we present an empirical analysis of the digital traces of politicians in politnetz ch , a swiss online platform focused on political activity , in which politicians interact by creating support links , comments , and likes we analyze network polarization as the level of intra party cohesion with respect to inter party connectivity , finding that supports show a very strongly polarized structure with respect to party alignment the analysis of this multiplex network shows that each layer of interaction contains relevant information , where comment groups follow topics related to swiss politics our analysis reveals that polarization in the layer of likes evolves in time , increasing close to the federal elections of 2011 furthermore , we analyze the internal social network of each party through metrics related to hierarchical structures , information efficiency , and social resilience our results suggest that the online social structure of a party is related to its ideology , and reveal that the degree of connectivity across two parties increases when they are close in the ideological space of a multi party system"
cs.IT,"orthogonal frequency division multiplexing with offset quadrature amplitude modulation \( ofdm oqam \) is a multicarrier modulation scheme that can be considered as an alternative to the conventional orthogonal frequency division multiplexing \( ofdm \) with cyclic prefix \( cp \) for transmission over multipath fading channels in this paper , we investigate the combination of the ofdm oqam with alamouti system with time reversal \( tr \) technique tr can be viewed as a precoding scheme which can be combined with ofdm oqam and easily carried out in a multiple input single output \( miso \) context such as alamouti system we present the simulation results of the performance of ofdm oqam system in siso case compared with the conventional cp ofdm system and the performance of the combination alamouti ofdm oqam with tr compared to alamouti cp ofdm the performance is derived by computing the bit error rate \( ber \) as a function of the transmit signal to noise ratio \( snr \)"
cs.AI,"we give some lectures on the work on formal logic of jacques herbrand , and sketch his life and his influence on automated theorem proving the intended audience ranges from students interested in logic over historians to logicians besides the well known correction of herbrand 's false lemma by goedel and dreben , we also present the hardly known unpublished correction of heijenoort and its consequences on herbrand 's modus ponens elimination besides herbrand 's fundamental theorem and its relation to the loewenheim skolem theorem , we carefully investigate herbrand 's notion of intuitionism in connection with his notion of falsehood in an infinite domain we sketch herbrand 's two proofs of the consistency of arithmetic and his notion of a recursive function , and last but not least , present the correct original text of his unification algorithm with a new translation"
cs.DS,"in distributed networks , it is often useful for the nodes to be aware of dense subgraphs , e g , such a dense subgraph could reveal dense subtructures in otherwise sparse graphs \( e g the world wide web or social networks \) these might reveal community clusters or dense regions for possibly maintaining good communication infrastructure in this work , we address the problem of self awareness of nodes in a dynamic network with regards to graph density , i e , we give distributed algorithms for maintaining dense subgraphs that the member nodes are aware of the only knowledge that the nodes need is that of the dynamic diameter d , i e , the maximum number of rounds it takes for a message to traverse the dynamic network for our work , we consider a model where the number of nodes are fixed , but a powerful adversary can add or remove a limited number of edges from the network at each time step the communication is by broadcast only and follows the congest model our algorithms are continuously executed on the network , and at any time \( after some initialization \) each node will be aware if it is part \( or not \) of a particular dense subgraph we give algorithms that \( 2 epsilon \) approximate the densest subgraph and \( 3 epsilon \) approximate the at least k densest subgraph \( for a given parameter k \) our algorithms work for a wide range of parameter values and run in o \( d log 1 epsilon n \) time further , a special case of our results also gives the first fully decentralized approximation algorithms for densest and at least k densest subgraph problems for static distributed graphs"
cs.SI,"we introduce a new kind of percolation on finite graphs called jigsaw percolation this model attempts to capture networks of people who innovate by merging ideas and who solve problems by piecing together solutions each person in a social network has a unique piece of a jigsaw puzzle acquainted people with compatible puzzle pieces merge their puzzle pieces more generally , groups of people with merged puzzle pieces merge if the groups know one another and have a pair of compatible puzzle pieces the social network solves the puzzle if it eventually merges all the puzzle pieces for an erd h o s r ' e nyi social network with n vertices and edge probability p n , we define the critical value p c \( n \) for a connected puzzle graph to be the p n for which the chance of solving the puzzle equals 1 2 we prove that for the n cycle \( ring \) puzzle , p c \( n \) theta \( 1 log n \) , and for an arbitrary connected puzzle graph with bounded maximum degree , p c \( n \) o \( 1 log n \) and omega \( 1 n b \) for any b 0 surprisingly , with probability tending to 1 as the network size increases to infinity , social networks with a power law degree distribution cannot solve any bounded degree puzzle this model suggests a mechanism for recent empirical claims that innovation increases with social density , and it might begin to show what social networks stifle creativity and what networks collectively innovate"
cs.IT|cs.LG|stat.ML,"we introduce a simple analysis of the structural complexity of infinite memory processes built from random samples of stationary , ergodic finite memory component processes such processes are familiar from the well known multi arm bandit problem we contrast our analysis with computation theoretic and statistical inference approaches to understanding their complexity the result is an alternative view of the relationship between predictability , complexity , and learning that highlights the distinct ways in which informational and correlational divergences arise in complex ergodic and nonergodic processes we draw out consequences for the resource divergences that delineate the structural hierarchy of ergodic processes and for processes that are themselves hierarchical"
cs.AI|cs.LG,"this paper studies the potential of identifying lexical paraphrases within a single corpus , focusing on the extraction of verb paraphrases most previous approaches detect individual paraphrase instances within a pair \( or set \) of comparable corpora , each of them containing roughly the same information , and rely on the substantial level of correspondence of such corpora we present a novel method that successfully detects isolated paraphrase instances within a single corpus without relying on any a priori structure and information a comparison suggests that an instance based approach may be combined with a vector based approach in order to assess better the paraphrase likelihood for many verb pairs"
cs.DS,"in this paper we describe a new data structure that supports orthogonal range reporting queries on a set of points that move along linear trajectories on a u times u grid the assumption that points lie on a u times u grid enables us to significantly decrease the query time in comparison to the standard kinetic model our data structure answers queries in o \( sqrt log u log log u k \) time , where k denotes the number of points in the answer the above improves over the omega \( log n \) lower bound that is valid in the infinite precision kinetic model the methods used in this paper could be also of independent interest"
cs.DM,"an instance of the valued constraint satisfaction problem \( vcsp \) is given by a finite set of variables , a finite domain of labels , and a sum of functions , each function depending on a subset of the variables each function can take finite values specifying costs of assignments of labels to its variables or the infinite value , which indicates infeasible assignments the goal is to find an assignment of labels to the variables that minimizes the sum we study \( assuming that p ne np \) how the complexity of this very general problem depends on the set of functions allowed in the instances , the so called constraint language the case when all allowed functions take values in 0 , infty corresponds to the csp , where one deals only with the feasibility issue and there is no optimization this case is the subject of the algebraic csp dichotomy conjecture prediciting for which constraint languages csps are tractable and for which np hard the case when all allowed functions take only finite values corresponds to finite valued csp , where the feasibility aspect is trivial and one deals only with the optimization issue the complexity of finite valued csps was fully classified by thapper and v z ivn 'y an algebraic necessary condition for tractability of a general valued csp with a fixed constraint language was recently given by kozik and ochremiak as our main result , we prove that if a constraint language satisfies this algebraic necessary condition , and the feasibility csp corresponding to the vcsp with this language is tractable , then the vcsp is tractable the algorithm is a simple combination of the assumed algorithm for the feasibility csp and the standard lp relaxation as a corollary , we obtain that a dichotomy for csps would imply a dichotomy for general valued csps"
cs.IT,"one way functions are widely used for encrypting the secret in public key cryptography , although they are regarded as plausibly one way but have not been proven so here we discuss the public key cryptosystem based on the system of higher order diophantine equations in this system those diophantine equations are used as public keys for sender and recipient , and sender can recover the secret from the diophantine equation returned from recipient with a trapdoor in general the system of diophantine equations is hard to solve when it is positive dimensional and it implies the diophantine equations in this cryptosystem works as a possible one way function we also discuss some problems on implementation , which are caused from additional complexity necessary for constructing diophantine equations in order to prevent from attacking by tamperers"
cs.IT,"in this paper , we present an extension of the semidefinite programming formulation of the optimal rate code design in single link binary erasure channel \( bec \) proposed by the authors to the binary erasure multiple access channel \( be mac \) with two sources correlation this new way can be easily extended to the multiple access senders simulation results show the efficiency and effectiveness of the new approach in practice"
cs.IT,"pulse position modulation \( ppm \) is a popular signal modulation technique which creates m ary data by means of the position of a pulse within a time interval while ppm and its variations have great advantages in many contexts , this type of modulation is vulnerable to loss of synchronization , potentially causing a severe error floor or throughput penalty even when little or no noise is assumed another disadvantage is that this type of modulation typically offers no error correction mechanism on its own , making them sensitive to intersymbol interference and environmental noise in this paper we propose a coding theoretic variation of ppm that allows for significantly more efficient symbol and frame synchronization as well as strong error correction the proposed scheme can be divided into a synchronization layer and a modulation layer this makes our technique compatible with major existing techniques such as standard ppm , multipluse ppm , and expurgated ppm as well in that the scheme can be realized by adding a simple synchronization layer to one of these standard techniques we also develop a generalization of expurgated ppm suited for the modulation layer of the proposed self synchronizing modulation scheme this generalized ppm can also be used as stand alone error correcting ppm with a larger number of available symbols"
cs.IT,we derive a lower bound on the secrecy capacity of the compound wiretap channel with channel state information at the transmitter which matches the general upper bound on the secrecy capacity of general compound wiretap channels given by liang et al and thus establishing a full coding theorem in this case we achieve this with a quite strong secrecy criterion and with a decoder that is robust against the effect of randomisation in the encoding this relieves us from the need of decoding the randomisation parameter which is in general not possible within this model moreover we prove a lower bound on the secrecy capacity of the compound wiretap channel without channel state information
cs.SI,"given the incessant growth of documents describing the opinions of different people circulating on the web , including web 2 0 has made it possible to give an opinion on any product in the net in this paper , we examine the various opinions expressed in the tweets and classify them positive , negative or neutral by using the emoticons for the bayesian method and adjectives and adverbs for the turney 's method"
cs.DS,"two common objectives for evaluating a schedule are the makespan , or schedule length , and the average completion time this short note gives improved bounds on the existence of schedules that simultaneously optimize both criteria in particular , for any rho 0 , there exists a schedule of makespan at most 1 rho times the minimum , with average completion time at most \( 1 e \) rho times the minimum the proof uses an infininite dimensional linear program to generalize and strengthen a previous analysis by cliff stein and joel wein \( 1997 \)"
cs.IT,"this paper presents a new connection between the generalized marcum q function and the confluent hypergeometric function of two variables , phi3 this result is then applied to the closed form characterization of the bivariate nakagami m distribution and of the distribution of the minimum eigenvalue of correlated non central wishart matrices , both important in communication theory new expressions for the corresponding cumulative distributions are obtained and a number of communication theoretic problems involving them are pointed out"
cs.IT,"a remarkable achievement in algorithmic randomness and algorithmic information theory was the discovery of the notions of k trivial , k low and martin lof random low sets three different definitions turns out to be equivalent for very non trivial reasons this paper , based on the course taught by one of the authors \( l b \) in poncelet laboratory \( cnrs , moscow \) in 2014 , provides an exposition of the proof of this equivalence and some related results we assume that the reader is familiar with basic notions of algorithmic information theory"
cs.IT,"this work focuses on the performance of multi terminal wireless industrial networks , where the transmissions of all terminals are required to be scheduled within a tight deadline the transmissions thus share a fixed amount of resources , i e , symbols , while facing short blocklengths due to the low latency requirement we investigate two distinct relaying strategies , namely best relay selection among the participating terminals and best antenna selection at the access point of the network in both schemes , we incorporate the cost of acquiring instantaneous channel state information \( csi \) at the access point within the transmission deadline an error probability model is developed under the finite blocklength regime to provide accurate performance results as a reference , this model is compared to the corresponding infinite bocklength error model both analytical models are validated by simulation we show that the average packet error rate \( per \) over all terminals is convex in the target error probability at each single link moreover , we find that \( i \) the reliability behavior is different for the two strategies , while the limiting factors are both finite blocklengths and overhead of acquiring csi \( ii \) with the same order of diversity , best antenna selection is more reliable than best relay selection \( iii \) the average per is increasing in the number of participating terminals unless the terminals also act as relay candidates in particular , if each participating terminal is a candidate for best relay selection , the per is convex in the number of terminals"
cs.LG|stat.ML,"online and stochastic learning has emerged as powerful tool in large scale optimization in this work , we generalize the douglas rachford splitting \( drs \) method for minimizing composite functions to online and stochastic settings \( to our best knowledge this is the first time drs been generalized to sequential version \) we first establish an o \( 1 sqrt t \) regret bound for batch drs method then we proved that the online drs splitting method enjoy an o \( 1 \) regret bound and stochastic drs splitting has a convergence rate of o \( 1 sqrt t \) the proof is simple and intuitive , and the results and technique can be served as a initiate for the research on the large scale machine learning employ the drs method numerical experiments of the proposed method demonstrate the effectiveness of the online and stochastic update rule , and further confirm our regret and convergence analysis"
cs.IT,cognitive mimo radio a competitive optimality design based on subspace projections
cs.AI|cs.LG,"typical recommender systems adopt a static view of the recommendation process and treat it as a prediction problem we argue that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and , consequently , that markov decision processes \( mdp \) provide a more appropriate model for recommender systems mdps introduce two benefits they take into account the long term effects of each recommendation , and they take into account the expected value of each recommendation to succeed in practice , an mdp based recommender system must employ a strong initial model and the bulk of this paper is concerned with the generation of such a model in particular , we suggest the use of an n gram predictive model for generating the initial mdp our n gram model induces a markov chain model of user behavior whose predictive accuracy is greater than that of existing predictive models we describe our predictive model in detail and evaluate its performance on real data in addition , we show how the model can be used in an mdp based recommender system"
cs.LG|stat.ML,"recent progress in deep latent variable models has largely been driven by the development of flexible and scalable variational inference methods variational training of this type involves maximizing a lower bound on the log likelihood , using samples from the variational posterior to compute the required gradients recently , burda et al \( 2015 \) have derived a tighter lower bound using a multi sample importance sampling estimate of the likelihood and showed that optimizing it yields models that use more of their capacity and achieve higher likelihoods this development showed the importance of such multi sample objectives and explained the success of several related approaches we extend the multi sample approach to discrete latent variables and analyze the difficulty encountered when estimating the gradients involved we then develop the first unbiased gradient estimator designed for importance sampled objectives and evaluate it at training generative and structured output prediction models the resulting estimator , which is based on low variance per sample learning signals , is both simpler and more effective than the nvil estimator proposed for the single sample variational objective , and is competitive with the currently used biased estimators"
cs.IT,"the low snr capacity of m ary psk transmission over both the additive white gaussian noise \( awgn \) and fading channels is analyzed when hard decision detection is employed at the receiver closed form expressions for the first and second derivatives of the capacity at zero snr are obtained the spectral efficiency bit energy tradeoff in the low snr regime is analyzed by finding the wideband slope and the bit energy required at zero spectral efficiency practical design guidelines are drawn from the information theoretic analysis the fading channel analysis is conducted for both coherent and noncoherent cases , and the performance penalty in the low power regime for not knowing the channel is identified"
cs.IT|stat.ML,"this paper considers the noisy sparse phase retrieval problem recovering a sparse signal x in mathbb r p from noisy quadratic measurements y j \( a j' x \) 2 epsilon j , j 1 , ldots , m , with independent sub exponential noise epsilon j the goals are to understand the effect of the sparsity of x on the estimation precision and to construct a computationally feasible estimator to achieve the optimal rates inspired by the wirtinger flow 12 proposed for noiseless and non sparse phase retrieval , a novel thresholded gradient descent algorithm is proposed and it is shown to adaptively achieve the minimax optimal rates of convergence over a wide range of sparsity levels when the a j 's are independent standard gaussian random vectors , provided that the sample size is sufficiently large compared to the sparsity of x"
cs.DS,"when dealing with modern big data sets , a very common theme is reducing the set through a random process these generally work by making many simple estimates of the full data set , and then judging them as a whole perhaps magically , these many simple estimates can provide a very accurate and small representation of the large data set the key tool in showing how many of these simple estimates are needed for a fixed accuracy trade off is the chernoff hoeffding inequality che52 , hoe63 this document provides a simple form of this bound , and two examples of its use"
cs.IT,"in this paper , we study the upper and the lower bounds on the joint source channel coding error exponent with decoder side information the results in the paper are non trivial extensions of the csiszar 's classical paper 5 unlike the joint source channel coding result in 5 , it is not obvious whether the lower bound and the upper bound are equivalent even if the channel coding error exponent is known for a class of channels , including the symmetric channels , we apply a game theoretic result to establish the existence of a saddle point and hence prove that the lower and upper bounds are the same if the channel coding error exponent is known more interestingly , we show that encoder side information does not increase the error exponents in this case"
cs.IT,"in this paper , delay limited transmission of quasi stationary sources over block fading channels are considered considering distortion outage probability as the performance measure , two source and channel coding schemes with power adaptive transmission are presented the first one is optimized for fixed rate transmission , and hence enjoys simplicity of implementation the second one is a high performance scheme , which also benefits from optimized rate adaptation with respect to source and channel states in high snr regime , the performance scaling laws in terms of outage distortion exponent and asymptotic outage distortion gain are derived , where two schemes with fixed transmission power and adaptive or optimized fixed rates are considered as benchmarks for comparisons various analytical and numerical results are provided which demonstrate a superior performance for source and channel optimized rate and power adaptive scheme it is also observed that from a distortion outage perspective , the fixed rate adaptive power scheme substantially outperforms an adaptive rate fixed power scheme for delay limited transmission of quasi stationary sources over wireless block fading channels the effect of the characteristics of the quasi stationary source on performance , and the implication of the results for transmission of stationary sources are also investigated"
cs.SI,"is it possible to abstract a formal mechanism originating schisms and governing the size evolution of social conversations \? in this work a constructive solution to such problem is proposed an abstract model of a generic n party turn taking conversation the model develops from simple yet realistic assumptions derived from experimental evidence , abstracts from conversation content and semantics while including topological information , and is driven by stochastic dynamics we find that a single mechanism namely the dynamics of conversational party 's individual fitness , as related to conversation size controls the development of the self organized schisming phenomenon potential generalizations of the model including individual traits and preferences , memory effects and more elaborated conversational topologies may find important applications also in other fields of research , where dynamically interacting and networked agents play a fundamental role"
cs.AI,"we examine the class of languages that can be defined entirely in terms of provability in an extension of the sorted type theory \( ty n \) by embedding the logic of phonologies , without introduction of special types for syntactic entities this class is proven to precisely coincide with the class of logically closed languages that may be thought of as functions from expressions to sets of logically equivalent ty n terms for a specific sub class of logically closed languages that are described by finite sets of rules or rule schemata , we find effective procedures for building a compact ty n representation , involving a finite number of axioms or axiom schemata the proposed formalism is characterized by some useful features unavailable in a two component architecture of a language model a further specialization and extension of the formalism with a context type enable effective account of intensional and dynamic semantics"
cs.LG|cs.SI,"we propose a strikingly novel , simple , and effective approach to model online user behavior we extract and analyze digital dna sequences from user online actions and we use twitter as a benchmark to test our proposal we obtain an incisive and compact dna inspired characterization of user actions then , we apply standard dna analysis techniques to discriminate between genuine and spambot accounts on twitter an experimental campaign supports our proposal , showing its effectiveness and viability to the best of our knowledge , we are the first ones to identify and adapt dna inspired techniques to online user behavioral modeling while twitter spambot detection is a specific use case on a specific social media , our proposed methodology is platform and technology agnostic , hence paving the way for diverse behavioral characterization tasks"
cs.AI|cs.SI,"to understand a node 's centrality in a multiplex network , its centrality values in all the layers of the network can be aggregated this requires a normalization of the values , to allow their meaningful comparison and aggregation over networks with different sizes and orders the concrete choices of such preprocessing steps like normalization and aggregation are almost never discussed in network analytic papers in this paper , we show that even sticking to the most simple centrality index \( the degree \) but using different , classic choices of normalization and aggregation strategies , can turn a node from being among the most central to being among the least central we present our results by using an aggregation operator which scales between different , classic aggregation strategies based on three multiplex networks we also introduce a new visualization and characterization of a node 's sensitivity to the choice of a normalization and aggregation strategy in multiplex networks the observed high sensitivity of single nodes to the specific choice of aggregation and normalization strategies is of strong importance , especially for all kinds of intelligence analytic software as it questions the interpretations of the findings"
cs.AI,"in this paper , we show the possibility of using a linear conditional random fields \( crf \) for terminology extraction from a specialized text corpus"
cs.AI,"this paper evaluates the different tasks carried out in the translation of pronominal anaphora in a machine translation \( mt \) system the mt interlingua approach named agir \( anaphora generation with an interlingua representation \) improves upon other proposals presented to date because it is able to translate intersentential anaphors , detect co reference chains , and translate spanish zero pronouns into english issues hardly considered by other systems the paper presents the resolution and evaluation of these anaphora problems in agir with the use of different kinds of knowledge \( lexical , morphological , syntactic , and semantic \) the translation of english and spanish anaphoric third person personal pronouns \( including spanish zero pronouns \) into the target language has been evaluated on unrestricted corpora we have obtained a precision of 80 4 and 84 8 in the translation of spanish and english pronouns , respectively although we have only studied the spanish and english languages , our approach can be easily extended to other languages such as portuguese , italian , or japanese"
cs.AI,"there is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents , both human and machine we characterize the domain independent challenges posed by this problem , and describe how properties of domains influence the challenges and their solutions we will concentrate on dynamic , data rich domains where humans are ultimately responsible for team behavior thus , the automated aid should interactively support effective and timely decision making by the human we present a domain independent categorization of the types of alerts a plan based monitoring system might issue to a user , where each type generally requires different monitoring techniques we describe a monitoring framework for integrating many domain specific and task specific monitoring techniques and then using the concept of value of an alert to avoid operator overload we use this framework to describe an execution monitoring approach we have used to implement execution assistants \( eas \) in two different dynamic , data rich , real world domains to assist a human in monitoring team behavior one domain \( army small unit operations \) has hundreds of mobile , geographically distributed agents , a combination of humans , robots , and vehicles the other domain \( teams of unmanned ground and air vehicles \) has a handful of cooperating robots both domains involve unpredictable adversaries in the vicinity our approach customizes monitoring behavior for each specific task , plan , and situation , as well as for user preferences our eas alert the human controller when reported events threaten plan execution or physically threaten team members alerts were generated in a timely manner without inundating the user with too many alerts \( less than 10 percent of alerts are unwanted , as judged by domain experts \)"
cs.DS,"we give tight cell probe bounds for the time to compute convolution , multiplication and hamming distance in a stream the cell probe model is a particularly strong computational model and subsumes , for example , the popular word ram model we first consider online convolution where the task is to output the inner product between a fixed n dimensional vector and a vector of the n most recent values from a stream one symbol of the stream arrives at a time and the each output must be computed before the next symbols arrives next we show bounds for online multiplication where the stream consists of pairs of digits , one from each of two n digit numbers that are to be multiplied one pair arrives at a time and the task is to output a single new digit from the product before the next pair of digits arrives finally we look at the online hamming distance problem where the hamming distance is outputted instead of the inner product for each of these three problems , we give a lower bound of omega \( frac delta w log n \) time on average per output , where delta is the number of bits needed to represent an input symbol and w is the cell or word size we argue that these bound are in fact tight within the cell probe model"
cs.AI,"a large and diverse set of measurements are regularly collected during a patient 's hospital stay to monitor their health status tools for integrating these measurements into severity scores , that accurately track changes in illness severity , can improve clinicians ability to provide timely interventions existing approaches for creating such scores either 1 \) rely on experts to fully specify the severity score , or 2 \) train a predictive score , using supervised learning , by regressing against a surrogate marker of severity such as the presence of downstream adverse events the first approach does not extend to diseases where an accurate score cannot be elicited from experts the second approach often produces scores that suffer from bias due to treatment related censoring \( paxton , 2013 \) we propose a novel ranking based framework for disease severity score learning \( dssl \) dssl exploits the following key observation while it is challenging for experts to quantify the disease severity at any given time , it is often easy to compare the disease severity at two different times extending existing ranking algorithms , dssl learns a function that maps a vector of patient 's measurements to a scalar severity score such that the resulting score is temporally smooth and consistent with the expert 's ranking of pairs of disease states we apply dssl to the problem of learning a sepsis severity score using a large , real world dataset the learned scores significantly outperform state of the art clinical scores in ranking patient states by severity and in early detection of future adverse events we also show that the learned disease severity trajectories are consistent with clinical expectations of disease evolution further , using simulated datasets , we show that dssl exhibits better generalization performance to changes in treatment patterns compared to the above approaches"
cs.DS,"the mm principle is a device for creating optimization algorithms satisfying the ascent or descent property the current survey emphasizes the role of the mm principle in nonlinear programming for smooth functions , one can construct an adaptive interior point method based on scaled bregmann barriers this algorithm does not follow the central path for convex programming subject to nonsmooth constraints , one can combine an exact penalty method with distance majorization to create versatile algorithms that are effective even in discrete optimization these proximal distance algorithms are highly modular and reduce to set projections and proximal mappings , both very well understood techniques in optimization we illustrate the possibilities in linear programming , binary piecewise linear programming , nonnegative quadratic programming , ell 0 regression , matrix completion , and inverse sparse covariance estimation"
cs.AI,"this paper analyzes the scaling window of a random csp model \( i e model rb \) for which we can identify the threshold points exactly , denoted by r cr or p cr for this model , we establish the scaling window w \( n , delta \) \( r \( n , delta \) , r \( n , delta \) \) such that the probability of a random instance being satisfiable is greater than 1 delta for r r \( n , delta \) and is less than delta for r r \( n , delta \) specifically , we obtain the following result w \( n , delta \) \( r cr theta \( frac 1 n 1 epsilon ln n \) , r cr theta \( frac 1 n ln n \) \) , where 0 leq epsilon 1 is a constant a similar result with respect to the other parameter p is also obtained since the instances generated by model rb have been shown to be hard at the threshold , this is the first attempt , as far as we know , to analyze the scaling window of such a model with hard instances"
cs.DS,"searching for all occurrences of a pattern in a text is a fundamental problem in computer science with applications in many other fields , like natural language processing , information retrieval and computational biology in the last two decades a general trend has appeared trying to exploit the power of the word ram model to speed up the performances of classical string matching algorithms in this model an algorithm operates on words of length w , grouping blocks of characters , and arithmetic and logic operations on the words take one unit of time in this paper we use specialized word size packed string matching instructions , based on the intel streaming simd extensions \( sse \) technology , to design very fast string matching algorithms in the case of short patterns from our experimental results it turns out that , despite their quadratic worst case time complexity , the new presented algorithms become the clear winners on the average for short patterns , when compared against the most effective algorithms known in literature"
cs.DM,"computer based attempts to construct lower bounds for small ramsey numbers are discussed a systematic review of circulant ramsey graphs is attempted many known lower bounds are reproduced new bounds of r \( 4 , 13 \) geq137 , r \( 5 , 9 \) geq133 , r \( 5 , 11 \) geq183 , r \( 6 , 9 \) geq183 are reported"
cs.LG,"most enterprise data is distributed in multiple relational databases with expert designed schema using traditional single table machine learning techniques over such data not only incur a computational penalty for converting to a 'flat' form \( mega join \) , even the human specified semantic information present in the relations is lost in this paper , we present a practical , two phase hierarchical meta classification algorithm for relational databases with a semantic divide and conquer approach we propose a recursive , prediction aggregation technique over heterogeneous classifiers applied on individual database tables the proposed algorithm was evaluated on three diverse datasets , namely tpch , pkdd and uci benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy"
cs.DM,"the k trigonometric functions over the galois field gf \( q \) are introduced and their main properties derived this leads to the definition of the cask \( \) function over gf \( q \) , which in turn leads to a finite field hartley transform the main properties of this new discrete transform are presented and areas for possible applications are mentioned"
cs.DS,"the minimum path cover problem on directed acyclic graphs \( dags \) is a classical problem that provides a clear and simple mathematical formulation for several applications in different areas and that has an efficient algorithmic solution in this paper , we study the computational complexity of two constrained variants of minimum path cover motivated by the recent introduction of next generation sequencing technologies in bioinformatics the first problem \( minpcrp \) , given a dag and a set of pairs of vertices , asks for a minimum cardinality set of paths covering all the vertices such that both vertices of each pair belong to the same path for this problem , we show that , while it is np hard to compute if there exists a solution consisting of at most three paths , it is possible to decide in polynomial time whether a solution consisting of at most two paths exists the second problem \( maxrpsp \) , given a dag and a set of pairs of vertices , asks for a path containing the maximum number of the given pairs of vertices we show its np hardness and also its w 1 hardness when parametrized by the number of covered pairs on the positive side , we give a fixed parameter algorithm when the parameter is the maximum overlapping degree , a natural parameter in the bioinformatics applications of the problem"
cs.IT|stat.ML,"recently , a framework for application oriented optimal experiment design has been introduced in this context , the distance of the estimated system from the true one is measured in terms of a particular end performance metric this treatment leads to superior unknown system estimates to classical experiment designs based on usual pointwise functional distances of the estimated system from the true one the separation of the system estimator from the experiment design is done within this new framework by choosing and fixing the estimation method to either a maximum likelihood \( ml \) approach or a bayesian estimator such as the minimum mean square error \( mmse \) since the mmse estimator delivers a system estimate with lower mean square error \( mse \) than the ml estimator for finite length experiments , it is usually considered the best choice in practice in signal processing and control applications within the application oriented framework a related meaningful question is are there end performance metrics for which the ml estimator outperforms the mmse when the experiment is finite length \? in this paper , we affirmatively answer this question based on a simple linear gaussian regression example"
cs.AI,"the population of elderly people keeps increasing rapidly , which becomes a predominant aspect of our societies as such , solutions both efficacious and cost effective need to be sought ambient assisted living \( aal \) is a new approach which promises to address the needs from elderly people in this paper , we claim that human participation is a key ingredient towards effective aal systems , which not only saves social resources , but also has positive relapses on the psychological health of the elderly people challenges in increasing the human participation in ambient assisted living are discussed in this paper and solutions to meet those challenges are also proposed we use our proposed mutual assistance community , which is built with service oriented approach , as an example to demonstrate how to integrate human tasks in aal systems our preliminary simulation results are presented , which support the effectiveness of human participation"
cs.DM,a calculus of sequences started in 1936 opened the way for future extensions of umbral calculus in its finite operator form because of historically established notation we call it the psi calculus it appears in parts to be almost automatic extension of the standard classical finite operator calculus
cs.DM,"this paper makes two contributions towards determining some well studied optimal constants in fourier analysis newa of boolean functions and high dimensional geometry begin enumerate item it has been known since 1994 cite gl 94 that every linear threshold function has squared fourier mass at least 1 2 on its degree 0 and degree 1 coefficients denote the minimum such fourier mass by w leq 1 ltf , where the minimum is taken over all n variable linear threshold functions and all n ge 0 benjamini , kalai and schramm cite bks 99 have conjectured that the true value of w leq 1 ltf is 2 pi we make progress on this conjecture by proving that w leq 1 ltf geq 1 2 c for some absolute constant c 0 the key ingredient in our proof is a robust version of the well known khintchine inequality in functional analysis , which we believe may be of independent interest item we give an algorithm with the following property given any eta 0 , the algorithm runs in time 2 poly \( 1 eta \) and determines the value of w leq 1 ltf up to an additive error of pm eta we give a similar 2 poly \( 1 eta \) time algorithm to determine emph tomaszewski 's constant to within an additive error of pm eta this is the minimum \( over all origin centered hyperplanes h \) fraction of points in 1 , 1 n that lie within euclidean distance 1 of h tomaszewski 's constant is conjectured to be 1 2 lower bounds on it have been given by holzman and kleitman cite hk92 and independently by ben tal , nemirovski and roos cite bnr02 our algorithms combine tools from anti concentration of sums of independent random variables , fourier analysis , and hermite analysis of linear threshold functions end enumerate"
cs.DM,"this collection of problems and conjectures is based on a subset of the open problems from the seminar series and the problem sessions of the institut mitag leffler programme graphs , hypergraphs , and computing each problem contributor has provided a write up of their proposed problem and the collection has been edited by klas markstr om"
cs.DM,"the concept of rainbow connection was introduced by chartrand et al in 2008 it is fairly interesting and recently quite a lot papers have been published about it in this survey we attempt to bring together most of the results and papers that dealt with it we begin with an introduction , and then try to organize the work into five categories , including \( strong \) rainbow connection number , rainbow k connectivity , k rainbow index , rainbow vertex connection number , algorithms and computational complexity this survey also contains some conjectures , open problems or questions"
cs.DM,total variant of well known graph coloring game is considered we determine exact values of total game chromatic number for some classes of graphs and show show the strategie for first player to win the game we also show relation between total game coloring number and game coloring index
cs.LG,"a fast non linear and non iterative learning and classification algorithm is synthesized and validated this algorithm named the reverse ripple effect \( r r e \) , achieves 100 learning accuracy but is computationally expensive upon classification the r r e is a \( deterministic \) algorithm that super imposes gaussian weighted functions on training points in this work , the r r e algorithm is compared against known learning and classification techniques algorithms such as the perceptron criterion algorithm , linear support vector machines , the linear fisher discriminant and a simple neural network the classification accuracy of the r r e algorithm is evaluated using simulations conducted in matlab the r r e algorithm 's behaviour is analyzed under linearly and non linearly separable data sets for the comparison with the neural network , the classical xor problem is considered"
cs.DM|cs.DS,"approximating the length of the longest increasing sequence \( lis \) of an array is a well studied problem we study this problem in the data stream model , where the algorithm is allowed to make a single left to right pass through the array and the key resource to be minimized is the amount of additional memory used we present an algorithm which , for any delta 0 , given streaming access to an array of length n provides a \( 1 delta \) multiplicative approximation to the emph distance to monotonicity \( n minus the length of the lis \) , and uses only o \( \( log 2 n \) delta \) space the previous best known approximation using polylogarithmic space was a multiplicative 2 factor our algorithm can be used to estimate the length of the lis to within an additive delta n for any delta 0 while previous algorithms could only achieve additive error n \( 1 2 o \( 1 \) \) our algorithm is very simple , being just 3 lines of pseudocode , and has a small update time it is essentially a polylogarithmic space approximate implementation of a classic dynamic program that computes the lis we also give a streaming algorithm for approximating lcs \( x , y \) , the length of the longest common subsequence between strings x and y , each of length n our algorithm works in the asymmetric setting \( inspired by cite ako10 \) , in which we have random access to y and streaming access to x , and runs in small space provided that no single symbol appears very often in y more precisely , it gives an additive delta n approximation to lcs \( x , y \) \( and hence also to e \( x , y \) n lcs \( x , y \) , the edit distance between x and y when insertions and deletions , but not substitutions , are allowed \) , with space complexity o \( k \( log 2 n \) delta \) , where k is the maximum number of times any one symbol appears in y"
cs.LG|stat.ML,"part of speech \( pos \) tagging is a fundamental component for performing natural language tasks such as parsing , information extraction , and question answering when pos taggers are trained in one domain and applied in significantly different domains , their performance can degrade dramatically we present a methodology for rapid adaptation of pos taggers to new domains our technique is unsupervised in that a manually annotated corpus for the new domain is not necessary we use suffix information gathered from large amounts of raw text as well as orthographic information to increase the lexical coverage we present an experiment in the biological domain where our pos tagger achieves results comparable to pos taggers specifically trained to this domain"
cs.LG|stat.ML,"fast approximate nearest neighbor \( nn \) search in large databases is becoming popular several powerful learning based formulations have been proposed recently however , not much attention has been paid to a more fundamental question how difficult is \( approximate \) nearest neighbor search in a given data set \? and which data properties affect the difficulty of nearest neighbor search and how \? this paper introduces the first concrete measure called relative contrast that can be used to evaluate the influence of several crucial data characteristics such as dimensionality , sparsity , and database size simultaneously in arbitrary normed metric spaces moreover , we present a theoretical analysis to prove how the difficulty measure \( relative contrast \) determines affects the complexity of local sensitive hashing , a popular approximate nn search method relative contrast also provides an explanation for a family of heuristic hashing algorithms with good practical performance based on pca finally , we show that most of the previous works in measuring nn search meaningfulness difficulty can be derived as special asymptotic cases for dense vectors of the proposed measure"
cs.LG,"we consider decentralized restless multi armed bandit problems with unknown dynamics and multiple players the reward state of each arm transits according to an unknown markovian rule when it is played and evolves according to an arbitrary unknown random process when it is passive players activating the same arm at the same time collide and suffer from reward loss the objective is to maximize the long term reward by designing a decentralized arm selection policy to address unknown reward models and collisions among players a decentralized policy is constructed that achieves a regret with logarithmic order when an arbitrary nontrivial bound on certain system parameters is known when no knowledge about the system is available , we extend the policy to achieve a regret arbitrarily close to the logarithmic order the result finds applications in communication networks , financial investment , and industrial engineering"
cs.DS,"we consider protocols that serve communication requests arising over time in a wireless network that is subject to interference unlike previous approaches , we take the geometry of the network and power control into account , both allowing to increase the network 's performance significantly we introduce a stochastic and an adversarial model to bound the packet injection although taken as the primary motivation , this approach is not only suitable for models based on the signal to interference plus noise ratio \( sinr \) it also covers virtually all other common interference models , for example the multiple access channel , the radio network model , the protocol model , and distance 2 matching packet routing networks allowing each edge or each node to transmit or receive one packet at a time can be modeled as well starting from algorithms for the respective scheduling problem with static transmission requests , we build distributed stable protocols this is more involved than in previous , similar approaches because the algorithms we consider do not necessarily scale linearly when scaling the input instance we can guarantee a throughput that is as large as the one of the original static algorithm in particular , for sinr models the competitive ratios of the protocol in comparison to optimal ones in the respective model are between constant and o \( log 2 m \) for a network of size m"
cs.LG|stat.ML,this technical report describes an efficient technique for computing the norm of the gradient of the loss function for a neural network with respect to its parameters this gradient norm can be computed efficiently for every example
cs.DS,"in this work , we achieve gap amplification for the small set expansion problem specifically , we show that an instance of the small set expansion problem with completeness epsilon and soundness frac 1 2 is at least as difficult as small set expansion with completeness epsilon and soundness f \( epsilon \) , for any function f \( epsilon \) which grows faster than sqrt epsilon we achieve this amplification via random walks our gadget is the graph with adjacency matrix corresponding to a random walk on the original graph an interesting feature of our reduction is that unlike gap amplification via parallel repetition , the size of the instances \( number of vertices \) produced by the reduction remains the same"
cs.DM,"we derive asymptotic formulas for central extended binomial coefficients , which are generalizations of binomial coefficients to do so , we relate the exact distribution of the sum of independent discrete uniform random variables to the asymptotic distribution , obtained from the central limit theorem and a local limit variant"
cs.DM,"in network modeling of complex systems one is often required to sample random realizations of networks that obey a given set of constraints , often in form of graph measures a much studied class of problems targets uniform sampling of simple graphs with given degree sequence or also with given degree correlations expressed in the form of a joint degree matrix one approach is to use markov chains based on edge switches \( swaps \) that preserve the constraints , are irreducible \( ergodic \) and fast mixing in 1999 , kannan , tetali and vempala \( ktv \) proposed a simple swap markov chain for sampling graphs with given degree sequence and conjectured that it mixes rapidly \( in poly time \) for arbitrary degree sequences while the conjecture is still open for the general case , it was proven for special degree sequences , in particular , for those of undirected and directed regular simple graphs \( cooper , dyer , greenhill , 2007 greenhill , 2011 \) , of half regular bipartite graphs \( mikl 'os , erd h o s , soukup , 2013 \) , and of graphs with certain bounded maximum degrees \( greenhill , 2015 \) here we prove the fast mixing ktv conjecture for novel , exponentially large classes of irregular degree sequences our method is based on a canonical decomposition of degree sequences into split graph degree sequences due to tyshkevich \( 1984 , 2000 \) , a structural theorem for the space of graph realizations by barrus \( 2015 \) and on a factorization theorem for markov chains \( erd h o s , mikl 'os , toroczkai , 2015 \) after introducing bipartite splitted degree sequences , we also generalize tyshkevich 's decomposition for bipartite and directed graphs"
cs.DS,"evolution of large scale networks demand for efficient way of communication in the networks one way to propagate information in the network is to find vertex cover in this paper we describe a variant of vertex cover problem naming it n distance vertex minimal cover \( n mvc \) problem to optimize information propagation throughout the network a minimum subset of vertices of a unweighted and undirected graph g \( v , e \) is called n mvc if for all v in v , v is at distance less than or equal to n from at least one of the the vertices in n mvc in the following paper , this problem is defined , formulated and an approximation algorithm is proposed with discussion on its correctness and upper bound"
cs.DS,"we present a new type of monotone submodular functions emph multi peak submodular functions roughly speaking , given a family of sets cf , we construct a monotone submodular function f with a high value f \( s \) for every set s in cf \( a peak \) , and a low value on every set that does not intersect significantly any set in cf we use this construction to show that a better than \( 1 frac 1 2e \) approximation \( simeq 0 816 \) for welfare maximization in combinatorial auctions with submodular valuations is \( 1 \) impossible in the communication model , \( 2 \) np hard in the computational model where valuations are given explicitly establishing a constant approximation hardness for this problem in the communication model was a long standing open question the valuations we construct for the hardness result in the computational model depend only on a constant number of items , and hence the result holds even if the players can answer arbitrary queries about their valuation , including demand queries we also study two other related problems that received some attention recently max min allocation \( for which we also get hardness of \( 1 frac 1 2e epsilon \) approximation , in both models \) , and combinatorial public projects \( for which we prove hardness of \( 3 4 epsilon \) approximation in the communication model , and hardness of \( 1 frac 1 e epsilon \) approximation in the computational model , using constant size valuations \)"
cs.DM|cs.DS,"deciding the existence of an l times m times n integer threeway table with given line sums is np complete already for fixed l 3 , but is in p with both l , m fixed here we consider em huge tables , where the variable dimension n is encoded in em binary combining recent results on integer cones and graver bases , we show that if the number of em layer types is fixed , then the problem is in p , whereas if it is variable , then the problem is in np intersect conp our treatment goes through the more general class of n fold integer programming problems"
cs.DM|cs.DS,"in this paper we consider several problems concerning packet routing in distributed systems each problem is formulated using terms from graph theory and for each problem we present efficient , novel , algorithmic techniques for computing optimal solutions we address topics like bottleneck paths \( trees \) , optimal paths with non linear costs , optimal paths with multiple optimization objectives , maintaining aggregate connectivity information under a sequence of network link failures , and several others"
cs.LG|stat.ML,"large volume of event data are becoming increasingly available in a wide variety of applications , such as social network analysis , internet traffic monitoring and healthcare analytics event data are observed irregularly in continuous time , and the precise time interval between two events carries a great deal of information about the dynamics of the underlying systems how to detect changes in these systems as quickly as possible based on such event data \? in this paper , we present a novel online detection algorithm for high dimensional event data over networks our method is based on a likelihood ratio test for point processes , and achieve weak signal detection by aggregating local statistics over time and networks we also design an online algorithm for efficiently updating the statistics using an em like algorithm , and derive highly accurate theoretical characterization of the false alarm rate we demonstrate the good performance of our algorithm via numerical examples and real world twitter and memetracker datasets"
cs.SI,"the control of complex systems is an ongoing challenge of complexity research recent advances using concepts of structural control deduce a wide range of control related properties from the network representation of complex systems here , we examine the controllability of complex systems for which the timescale of the dynamics we control and the timescale of changes in the network are comparable we provide both analytical and computational tools to study controllability based on temporal network characteristics we apply these results to investigate the controllable subnetwork using a single input , present analytical results for a generic class of temporal network models , and preform measurements using data collected from a real system depending upon the density of the interactions compared to the timescale of the dynamics , we witness a phase transition describing the sudden emergence of a giant controllable subspace spanning a finite fraction of the network we also study the role of temporal patterns and network topology in real data making use of various randomization procedures , finding that the overall activity and the degree distribution of the underlying network are the main features influencing controllability"
cs.LG,"in this paper we propose a general framework to characterize and solve the stochastic optimization problems with multiple objectives underlying many real world learning applications we first propose a projection based algorithm which attains an o \( t 1 3 \) convergence rate then , by leveraging on the theory of lagrangian in constrained optimization , we devise a novel primal dual stochastic approximation algorithm which attains the optimal convergence rate of o \( t 1 2 \) for general lipschitz continuous objectives"
cs.SI,"an increasing fraction of today social interactions occur using online social media as communication channels recent worldwide events , such as social movements in spain or revolts in the middle east , highlight their capacity to boost people coordination online networks display in general a rich internal structure where users can choose among different types and intensity of interactions despite of this , there are still open questions regarding the social value of online interactions for example , the existence of users with millions of online friends sheds doubts on the relevance of these relations in this work , we focus on twitter , one of the most popular online social networks , and find that the network formed by the basic type of connections is organized in groups the activity of the users conforms to the landscape determined by such groups furthermore , twitter 's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks personal interactions are more likely to occur on internal links to the groups \( the weakness of strong ties \) , events transmitting new information go preferentially through links connecting different groups \( the strength of weak ties \) or even more through links connecting to users belonging to several groups that act as brokers \( the strength of intermediary ties \)"
cs.LG|stat.ML,"we consider the problem of estimating the inverse covariance matrix by maximizing the likelihood function with a penalty added to encourage the sparsity of the resulting matrix we propose a new approach based on the split bregman method to solve the regularized maximum likelihood estimation problem we show that our method is significantly faster than the widely used graphical lasso method , which is based on blockwise coordinate descent , on both artificial and real world data more importantly , different from the graphical lasso , the split bregman based method is much more general , and can be applied to a class of regularization terms other than the ell 1 norm"
cs.LG,"building computers able to answer questions on any subject is a long standing goal of artificial intelligence promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries such approaches can be effective but at the cost of either large amounts of human labeled data or by defining lexicons and grammars tailored by practitioners in this paper , we instead take the radical approach of learning to map questions to vectorial feature representations by mapping answers into the same space one can query any knowledge base independent of its schema , without requiring any grammar or lexicon our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine tuning step using the weak supervision provided by blending automatically and collaboratively generated resources we empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over paralex , the only existing method able to be trained on similar weakly labeled data"
cs.LG,"in this paper , a novel method for representation and recognition of the facial expressions in two dimensional image sequences is presented we apply a variation of two dimensional heteroscedastic linear discriminant analysis \( 2dhlda \) algorithm , as an efficient dimensionality reduction technique , to gabor representation of the input sequence 2dhlda is an extension of the two dimensional linear discriminant analysis \( 2dlda \) approach and it removes the equal within class covariance by applying 2dhlda in two directions , we eliminate the correlations between both image columns and image rows then , we perform a one dimensional lda on the new features this combined method can alleviate the small sample size problem and instability encountered by hlda also , employing both geometric and appearance features and using an ensemble learning scheme based on data fusion , we create a classifier which can efficiently classify the facial expressions the proposed method is robust to illumination changes and it can properly represent temporal information as well as subtle changes in facial muscles we provide experiments on cohn kanade database that show the superiority of the proposed method keywords two dimensional heteroscedastic linear discriminant analysis \( 2dhlda \) , subspace learning , facial expression analysis , gabor wavelets , ensemble learning"
cs.DM,"let x 2k be a set of 2k labeled points in convex position in the plane we consider geometric non intersecting straight line perfect matchings of x 2k two such matchings , m and m' , are disjoint compatible if they do not have common edges , and no edge of m crosses an edge of m' denote by mathrm dcm k the graph whose vertices correspond to such matchings , and two vertices are adjacent if and only if the corresponding matchings are disjoint compatible we show that for each k geq 9 , the connected components of mathrm dcm k form exactly three isomorphism classes namely , there is a certain number of isomorphic small components , a certain number of isomorphic medium components , and one big component the number and the structure of small and medium components is determined precisely"
cs.DM|cs.DS,"we study the problem of transforming one list \( vertex \) coloring of a graph into another list coloring by changing only one vertex color assignment at a time , while at all times maintaining a list coloring , given a list of allowed colors for each vertex this problem is known to be pspace complete for bipartite planar graphs in this paper , we first show that the problem remains pspace complete even for bipartite series parallel graphs , which form a proper subclass of bipartite planar graphs we note that our reduction indeed shows the pspace completeness for graphs with pathwidth two , and it can be extended for threshold graphs in contrast , we give a polynomial time algorithm to solve the problem for graphs with pathwidth one thus , this paper gives precise analyses of the problem with respect to pathwidth"
cs.DM|cs.DS,"we introduce the graph parameter boolean width , related to the number of different unions of neighborhoods across a cut of a graph boolean width is similar to rank width , which is related to the number of gf 2 sums \( 1 1 0 \) of neighborhoods instead of the boolean sums \( 1 1 1 \) used for boolean width we give algorithms for a large class of np hard vertex subset and vertex partitioning problems that are fpt when parameterized by either boolean width , rank width or clique width , with runtime single exponential in either parameter if given the pertinent optimal decomposition to compare boolean width versus rank width or clique width , we first show that for any graph , the square root of its boolean width is never more than its rank width next , we exhibit a class of graphs , the hsu grids , for which we can solve np hard problems in polynomial time , if we use the right parameter an n times frac n 10 hsu grid on 1 10 n 2 vertices has boolean width theta \( log n \) and rank width theta \( n \) moreover , any optimal rank decomposition of such a graph will have boolean width theta \( n \) , i e exponential in the optimal boolean width a main open problem is to approximate the boolean width better than what is given by the algorithm for rank width hlin v e n 'y and oum , 2008"
cs.LG,"we present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification while deep neural networks have reached maturity for image understanding tasks , we are still exploring network topologies and features to handle the richer environment of video clips here , we tackle the problem of multiple velocities in action recognition , and provide state of the art results for gesture recognition , on known and new collected datasets we further provide the training steps for our semi supervised network , suited to learn from huge unlabeled datasets with only a fraction of labeled examples"
cs.DM|cs.LG,"functional decomposition of logic circuits has profound influence on all quality aspects of the cost effective implementation of modern digital systems in this paper , a relational approach to the decomposition of logic circuits is proposed this approach is parallel to the normalization of relational databases , they are governed by the same concepts of functional dependency \( fd \) and multi valued dependency \( mvd \) it is manifest that the functional decomposition of switching function actually exploits the same idea and serves a similar purpose as database normalization partitions play an important role in the decomposition the interdependency of two partitions can be represented by a bipartite graph we demonstrate that both fd and mvd can be represented by bipartite graphs with specific topological properties , which are delineated by partitions of minterms it follows that our algorithms are procedures of constructing those specific bipartite graphs of interest to meet the information lossless criteria of functional decomposition"
cs.LG|stat.ML,"sequential monte carlo \( smc \) , or particle filtering , is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate distributions like other importance sampling based methods , performance is critically dependent on the proposal distribution a bad proposal can lead to arbitrarily inaccurate estimates of the target distribution this paper presents a new method for automatically adapting the proposal using an approximation of the kullback leibler divergence between the true posterior and the proposal distribution the method is very flexible , applicable to any parameterised proposal distribution and it supports online and batch variants we use the new framework to adapt powerful proposal distributions with rich parameterisations based upon neural networks leading to neural adaptive sequential monte carlo \( nasmc \) experiments indicate that nasmc significantly improves inference in a non linear state space model outperforming adaptive proposal methods including the extended kalman and unscented particle filters experiments also indicate that improved inference translates into improved parameter learning when nasmc is used as a subroutine of particle marginal metropolis hastings finally we show that nasmc is able to train a neural network based deep recurrent generative model achieving results that compete with the state of the art for polymorphic music modelling nasmc can be seen as bridging the gap between adaptive smc methods and the recent work in scalable , black box variational inference"
cs.LG|stat.ML,"given a collection of categorical data , we want to find the parameters of a dirichlet distribution which maximizes the likelihood of that data newton 's method is typically used for this purpose but current implementations require reading through the entire dataset on each iteration in this paper , we propose a modification which requires only a single pass through the dataset and substantially decreases running time furthermore we analyze both theoretically and empirically the performance of the proposed algorithm , and provide an open source implementation"
cs.DM,"given a language l and a nondeterministic finite automaton m , we consider whether we can determine efficiently \( in the size of m \) if m accepts at least one word in l , or infinitely many words given that m accepts at least one word in l , we consider how long a shortest word can be the languages l that we examine include the palindromes , the non palindromes , the k powers , the non k powers , the powers , the non powers \( also called primitive words \) , the words matching a general pattern , the bordered words , and the unbordered words"
cs.LG,"intelligence quotient \( iq \) test is a set of standardized questions designed to evaluate human intelligence verbal comprehension questions appear very frequently in iq tests , which measure human 's verbal ability including the understanding of the words with multiple senses , the synonyms and antonyms , and the analogies among words in this work , we explore whether such tests can be solved automatically by artificial intelligence technologies , especially the deep learning technologies that are recently developed and successfully applied in a number of fields however , we found that the task was quite challenging , and simply applying existing technologies \( e g , word embedding \) could not achieve a good performance , mainly due to the multiple senses of words and the complex relations among words to tackle this challenge , we propose a novel framework consisting of three components first , we build a classifier to recognize the specific type of a verbal question \( e g , analogy , classification , synonym , or antonym \) second , we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi sense nature of words and the relational knowledge among words \( or their senses \) contained in dictionaries third , for each specific type of questions , we propose a simple yet effective solver based on the obtained distributed word representations and relation representations according to our experimental results , our proposed framework can not only outperform existing methods for solving verbal comprehension questions but also exceed the average performance of human beings the results are highly encouraging , indicating that with appropriate uses of the deep learning technologies , we could be a further step closer to the true human intelligence"
cs.SI,"since the information available is fundamental for our perceptions and opinions , we are interested in understanding the conditions allowing for a good information to be disseminated this paper explores opinion dynamics by means of multi agent based simulations when agents get informed by different sources of information the scenario implemented includes three main streams of information acquisition , differing in both the contents and the perceived reliability of the messages spread agents' internal opinion is updated either by accessing one of the information sources , namely media and experts , or by exchanging information with one another they are also endowed with cognitive mechanisms to accept , reject or partially consider the acquired information we expect that peer to peer communication and reliable information sources are able both to reduce biased perceptions and to inhibit information cheating , possibly performed by the media as stated by the agenda setting theory in the paper , after having shortly presented both the hypotheses and the model , the simulation design will be specified and results will be discussed with respect to the hypotheses some considerations and ideas for future studies will conclude the paper"
cs.LG,"the ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle 's physical surroundings spatio temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data the persistence and reliability of multi modal registration is therefore the key to the stability of decision support systems ingesting the fused information lidar video systems like on those many driverless cars are a common example of where keeping the lidar and video channels registered to common physical features is important we develop a deep learning method that takes multiple channels of heterogeneous data , to detect the misalignment of the lidar video inputs a number of variations were tested on the ford lidar video driving test data set and will be discussed to the best of our knowledge the use of multi modal deep convolutional neural networks for dynamic real time lidar video registration has not been presented"
cs.DM,"in this paper , we consider the markov chain monte carlo \( mcmc \) approach for random sampling of combinatorial objects the running time of such an algorithm depends on the total mixing time of the underlying markov chain and is unknown in general for some markov chains , upper bounds on this total mixing time exist but are too large to be applicable in practice we try to answer the question , whether the total mixing time is close to its upper bounds , or if there is a significant gap between them in doing so , we present the software library marathon which is designed to support the analysis of mcmc based sampling algorithms the main application of this library is to compute properties of so called state graphs which represent the structure of markov chains we use marathon to investigate the quality of several bounding methods on four well known markov chains for sampling perfect matchings and bipartite graph realizations in a set of experiments , we compute the total mixing time and several of its bounds for a large number of input instances we find that the upper bound gained by the famous canonical path method is several magnitudes larger than the total mixing time and deteriorates with growing input size in contrast , the spectral bound is found to be a precise approximation of the total mixing time"
cs.DS,"we introduce the class of bi arc digraphs , and show they coincide with the class of digraphs that admit a conservative semi lattice polymorphism , i e , a min ordering surprisingly this turns out to be also the class of totally symmetric conservative polymorphisms of all arities we give an obstruction characterization of , and a polynomial time recognition algorithm for , this class of digraphs the existence of a polynomial time algorithm was an open problem due to bagan , durand , filiot , and gauwin we also discuss a generalization to k arc digraphs , which has a similar obstruction characterization and recognition algorithm when restricted to undirected graphs , the class of bi arc digraphs is included in the previously studied class of bi arc graphs in particular , restricted to reflexive graphs , bi arc digraphs coincide precisely with the well known class of interval graphs restricted to reflexive digraphs , they coincide precisely with the class of adjusted interval digraphs , and restricted to bigraphs , they coincide precisely with the class of two directional ray graphs all these classes have been previously investigated as analogues of interval graphs we believe that , in a certain sense , bi arc digraphs are the most general digraph version of interval graphs with nice algorithms and characterizations the second author used these results to give an approximation algorithm for the minimum cost homomorphism problem to a bi arc digraph"
cs.AI|cs.LG,"reinforcement learning \( rl \) is a general and well known method that a robot can use to learn an optimal control policy to solve a particular task we would like to build a versatile robot that can learn multiple tasks , but using rl for each of them would be prohibitively expensive in terms of both time and wear and tear on the robot to remedy this problem , we use the policy gradient efficient lifelong learning algorithm \( pg ella \) , an online multi task rl algorithm that enables the robot to efficiently learn multiple consecutive tasks by sharing knowledge between these tasks to accelerate learning and improve performance we implemented and evaluated three rl methods q learning , policy gradient rl , and pg ella on a ground robot whose task is to find a target object in an environment under different surface conditions in this paper , we discuss our implementations as well as present an empirical analysis of their learning performance"
cs.DM|cs.DS,"this paper presents efficient implementations of several algorithms for solving the minimum cost network flow problem various practical heuristics and other important implementation aspects are also discussed a novel result of this work is the application of goldberg 's recent partial augment relabel method in the cost scaling algorithm the presented implementations are available as part of the lemon open source c optimization library \( url http lemon cs elte hu \) the performance of these codes is compared to well known and efficient minimum cost flow solvers , namely cs2 , relaxiv , mcf , and the corresponding method of the leda library according to thorough experimental analysis , the presented cost scaling and network simplex implementations turned out to be more efficient than leda and mcf furthermore , the cost scaling implementation is competitive with cs2 the relaxiv algorithm is often much slower than the other codes , although it is quite efficient on particular problem instances"
cs.AI|cs.LG|stat.ML,"we introduce scalable deep kernels , which combine the structural properties of deep learning architectures with the non parametric flexibility of kernel methods specifically , we transform the inputs of a spectral mixture base kernel with a deep architecture , using local kernel interpolation , inducing points , and structure exploiting \( kronecker and toeplitz \) algebra for a scalable kernel representation these closed form kernels can be used as drop in replacements for standard kernels , with benefits in expressive power and scalability we jointly learn the properties of these kernels through the marginal likelihood of a gaussian process inference and learning cost o \( n \) for n training points , and predictions cost o \( 1 \) per test point on a large and diverse collection of applications , including a dataset with 2 million examples , we show improved performance over scalable gaussian processes with flexible kernel learning models , and stand alone deep architectures"
cs.LG|stat.ML,"a theoretical study is presented for a simple linear classifier called reference distance estimator \( rde \) , which assigns the weight of each feature j as p \( r j \) p \( r \) , where r is a reference feature relevant to the target class y the analysis shows that if r performs better than random guess in predicting y and is conditionally independent with each feature j , the rde will have the same classification performance as that from p \( y j \) p \( y \) , a classifier trained with the gold standard y since the estimation of p \( r j \) p \( r \) does not require labeled data , under the assumption above , rde trained with a large number of unlabeled examples would be close to that trained with infinite labeled examples for the case the assumption does not hold , we theoretically analyze the factors that influence the closeness of the rde to the perfect one under the assumption , and present an algorithm to select reference features and combine multiple rdes from different reference features using both labeled and unlabeled data the experimental results on 10 text classification tasks show that the semi supervised learning method improves supervised methods using 5 , 000 labeled examples and 13 million unlabeled ones , and in many tasks , its performance is even close to a classifier trained with 13 million labeled examples in addition , the bounds in the theorems provide good estimation of the classification performance and can be useful for new algorithm design"
cs.LG|stat.ML,"this paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data these tools can be used to detect outliers and clusters both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed finite sample behavior , under no distributional assumptions the prediction sets are also informative in that they correspond to the high density region of the underlying process while ordinary conformal prediction has high computational cost for functional data , we use the inductive conformal predictor , together with several novel choices of conformity scores , to simplify the computation our methods are illustrated on some real data examples"
cs.DM|cs.DS,"the paradigm of many choices has influenced significantly the design of efficient data structures and , most notably , hash tables cuckoo hashing is a technique that extends this concept there , we are given a table with n locations , and we assume that each location can hold one item each item to be inserted chooses randomly k 1 locations and has to be placed in any one of them how much load can cuckoo hashing handle before collisions prevent the successful assignment of the available items to the chosen locations \? practical evaluations of this method have shown that one can allocate a number of elements that is a large proportion of the size of the table , being very close to 1 even for small values of k such as 4 or 5 in this paper we show that there is a critical value for this proportion with high probability , when the amount of available items is below this value , then these can be allocated successfully , but when it exceeds this value , the allocation becomes impossible we give explicitly for each k 1 this critical value this answers an open question posed by mitzenmacher \( esa '09 \) and underpins theoretically the experimental results our proofs are based on the translation of the question into a hypergraph setting , and the study of the related typical properties of random k uniform hypergraphs"
cs.DM,"despite the large effort devoted to cybersecurity research over the last decades , cyber intrusions and attacks are still increasing , constantly challenging the design of the current system the increasingly frequent events of bgp \( border gateway protocol \) route hijacking have brought the topic of traffic interception into attention while numerous edits and improvements are proposed for bgp , a theoretical understanding of the fundamental design of this system is missing here we define and analyze an abstract model of information monitoring in routing networks specifically , we study algorithms that measure the potential of groups of dishonest agents to divert traffic through their infrastructure under the constraint that messages must reach their intended destinations we relate two variants of our model based on the allowed kinds of lies , define strategies for colluding agents , and prove optimality in special cases in our main theorem we derive a provably optimal monitoring strategy for subsets of agents in which no two are adjacent , and we extend this strategy to the general case finally , we use our results to analyze the susceptibility of real and synthetic networks to endogenous information monitoring in the autonomous systems \( as \) graph of the united states , we show that compromising only 18 random nodes in the as graph surprisingly captures 10 of all traffic of the network in expectation"
cs.DM|cs.DS,"this paper presents the novel `uniqueness tree' algorithm , as one possible method for determining whether two finite , undirected graphs are isomorphic we prove that the algorithm has polynomial time complexity in the worst case , and that it will always detect the presence of an isomorphism whenever one exists we also propose that the algorithm will equivalently discern the lack of an isomorphism whenever one does not exist , and some initial justifications are given for this proposition , although it cannot yet be rigorously proven finally , we present experimental evidence for both the effectiveness and efficiency of the uniqueness tree method , using data gathered from a practical implementation of the algorithm some consequences and directions for further research are discussed"
cs.AI,"some argue that biologically inspired algorithms are the future of solving difficult problems in computer science others strongly believe that the future lies in the exploration of mathematical foundations of problems at hand the field of computer security tends to accept the latter view as a more appropriate approach due to its more workable validation and verification possibilities the lack of rigorous scientific practices prevalent in biologically inspired security research does not aid in presenting bio inspired security approaches as a viable way of dealing with complex security problems this chapter introduces a biologically inspired algorithm , called the self organising map \( som \) , that was developed by teuvo kohonen in 1981 since the algorithm 's inception it has been scrutinised by the scientific community and analysed in more than 4000 research papers , many of which dealt with various computer security issues , from anomaly detection , analysis of executables all the way to wireless network monitoring in this chapter a review of security related som research undertaken in the past is presented and analysed the algorithm 's biological analogies are detailed and the author 's view on the future possibilities of this successful bio inspired approach are given the som algorithm 's close relation to a number of vital functions of the human brain and the emergence of multi core computer architectures are the two main reasons behind our assumption that the future of the som algorithm and its variations is promising , notably in the field of computer security"
cs.SI,"many algorithms to detect communities in networks typically work without any information on the cluster structure to be found , as one has no a priori knowledge of it , in general not surprisingly , knowing some features of the unknown partition could help its identification , yielding an improvement of the performance of the method here we show that , if the number of clusters were known beforehand , standard methods , like modularity optimization , would considerably gain in accuracy , mitigating the severe resolution bias that undermines the reliability of the results of the original unconstrained version the number of clusters can be inferred from the spectra of the recently introduced non backtracking and flow matrices , even in benchmark graphs with realistic community structure the limit of such two step procedure is the overhead of the computation of the spectra"
cs.DM,"we consider principal pivot transform \( pivot \) on graphs we define a natural variant of this operation , called dual pivot , and show that both the kernel and the set of maximally applicable pivots of a graph are invariant under this operation the result is motivated by and applicable to the theory of gene assembly in ciliates"
cs.DS,"we propose simple heuristics for the assembly line worker assignment and balancing problem this problem typically occurs in assembly lines in sheltered work centers for the disabled different from the classical simple assembly line balancing problem , the task execution times vary according to the assigned worker we develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations we present a number of such rules and compare their performance across three possible uses as a stand alone method , as an initial solution generator for meta heuristics , and as a decoder for a hybrid genetic algorithm our results show that the heuristics are fast , they obtain good results as a stand alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches"
cs.AI,"the immune system is a complex biological system with a highly distributed , adaptive and self organising nature this paper presents an artificial immune system \( ais \) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering \( cf \) natural evolution and in particular the immune system have not been designed for classical optimisation however , for this problem , we are not interested in finding a single optimum rather we intend to identify a sub set of good matches on which recommendations can be based it is our hypothesis that an ais built on two central aspects of the biological immune system will be an ideal candidate to achieve this antigen antibody interaction for matching and idiotypic antibody antibody interaction for diversity computational results are presented in support of this conjecture and compared to those found by other cf techniques"
cs.DM,"the 32 bit hwb \( hwb n32 for short \) problem is from equivalence checking that arises in combining two circuits computing the hidden weighted bit function since 2002 , it remains still unsolvable in every sat competition this paper focuses on solving problems such as hwb n32 generally speaking , modern solvers can detect only xor , and , or and ite gates other non clausal formulas \( propositional logic structures \) cannot be detected to solve the hwb n32 problem , we extract dynamically some special propositional logic structures , and then use a variant of dpll based solvers to solve the subproblem simplified by the extracted structure information using the dynamic extraction technique , we solved efficiently the hwb n32 problem , even some of which were solved within 3000 seconds"
cs.DS,"we show that a simple algorithm for computing a matching on a graph runs in a logarithmic number of phases incurring work linear in the input size the algorithm can be adapted to provide efficient algorithms in several models of computation , such as pram , external memory , mapreduce and distributed memory models our crew pram algorithm is the first o \( log 2 n \) time , linear work algorithm our experimental results indicate the algorithm 's high speed and efficiency combined with good solution quality"
cs.SI,"pagerank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks despite its outstanding popularity and broad use in different areas of science , the relation between the algorithm 's efficacy and properties of the network on which it acts has not yet been fully understood we study here pagerank 's performance on a network model supported by real data , and show that realistic temporal effects make pagerank fail in individuating the most valuable nodes for a broad range of model parameters results on real data are in qualitative agreement with our model based findings this failure of pagerank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems , and suggest that time dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes"
cs.LG|stat.ML,"in this short communication we introduce the quick clustering algorithm \( quist \) , an efficient hierarchical clustering algorithm based on sorting quist is a poly logarithmic divisive clustering algorithm that does not assume the number of clusters , and or the cluster size to be known ahead of time it is also insensitive to the original ordering of the input"
cs.LG|stat.ML,"differential privacy is a cryptographically motivated definition of privacy which has gained significant attention over the past few years differentially private solutions enforce privacy by adding random noise to a function computed over the data , and the challenge in designing such algorithms is to control the added noise in order to optimize the privacy accuracy sample size tradeoff this work studies differentially private statistical estimation , and shows upper and lower bounds on the convergence rates of differentially private approximations to statistical estimators our results reveal a formal connection between differential privacy and the notion of gross error sensitivity \( ges \) in robust statistics , by showing that the convergence rate of any differentially private approximation to an estimator that is accurate over a large class of distributions has to grow with the ges of the estimator we then provide an upper bound on the convergence rate of a differentially private approximation to an estimator with bounded range and bounded ges we show that the bounded range condition is necessary if we wish to ensure a strict form of differential privacy"
cs.LG|stat.ML,"the aim of this paper is to present a new method for visual place recognition our system combines global image characterization and visual words , which allows to use efficient bayesian filtering methods to integrate several images more precisely , we extend the classical hmm model with techniques inspired by the field of natural language processing this paper presents our system and the bayesian filtering algorithm the performance of our system and the influence of the main parameters are evaluated on a standard database the discussion highlights the interest of using such models and proposes improvements"
cs.AI,"we present our approach to the problem of how an agent , within an economic multi agent system , can determine when it should behave strategically \( i e learn and use models of other agents \) , and when it should act as a simple price taker we provide a framework for the incremental implementation of modeling capabilities in agents , and a description of the forms of knowledge required the agents were implemented and different populations simulated in order to learn more about their behavior and the merits of using and learning agent models our results show , among other lessons , how savvy buyers can avoid being ``cheated'' by sellers , how price volatility can be used to quantitatively predict the benefits of deeper models , and how specific types of agent populations influence system behavior"
cs.SI,"risks threatening modern societies form an intricately interconnected network that often underlies crisis situations yet , little is known about how risk materializations in distinct domains in influence each other here we present an approach in which expert assessments of risks likelihoods and influence underlie a quantitative model of the global risk network dynamics the modeled risks range from environmental to economic and technological and include difficult to quantify risks , such as geo political or social using the maximum likelihood estimation , we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others , uncovering full value of the expert collected data we analyze the model dynamics and study its resilience and stability our findings include such risk properties as contagion potential , persistence , roles in cascades of failures and the identity of risks most detrimental to system stability the model provides quantitative means for measuring the adverse effects of risk interdependence and the materialization of risks in the network"
cs.DM|cs.DS,"discrete transforms such as the discrete fourier transform \( dft \) or the discrete hartley transform \( dht \) furnish an indispensable tool in signal processing the successful application of transform techniques relies on the existence of the so called fast transforms in this paper some fast algorithms are derived which meet the lower bound on the multiplicative complexity of a dft dht the approach is based on a decomposition of the dht into layers of hadamard walsh transforms in particular , schemes named turbo fourier transforms for short block lengths such as n 4 , 8 , 12 and 24 are presented"
cs.AI,"several 'edge discovery' applications over graph based data models are known to have worst case quadratic complexity , even if the discovered edges are sparse one example is the generic link discovery problem between two graphs , which has invited research interest in several communities specific versions of this problem include link prediction in social networks , ontology alignment between metadata rich rdf data , approximate joins , and entity resolution between instance rich data as large datasets continue to proliferate , reducing quadratic complexity to make the task practical is an important research problem within the entity resolution community , the problem is commonly referred to as blocking a particular class of learnable blocking schemes is known as disjunctive normal form \( dnf \) blocking schemes , and has emerged as state of the art for homogeneous \( i e same schema \) tabular data despite the promise of these schemes , a formalism or learning framework has not been developed for them when input data instances are generic , attributed graphs possessing both node and edge heterogeneity with such a development , the complexity reducing scope of dnf schemes becomes applicable to a variety of problems , including entity resolution and type alignment between heterogeneous rdf graphs , and link prediction in networks represented as attributed graphs this paper presents a graph theoretic formalism for dnf schemes , and investigates their learnability in an optimization framework experimentally , the dnf schemes learned on pairs of heterogeneous rdf graphs are demonstrated to achieve high complexity reductions \( 98 25 across ten rdf test cases \) at little cost to coverage , and with high reliability \( 2 5 standard deviation \) finally , one extant class of rdf blocking schemes is shown to be a special case of dnf schemes"
cs.AI,"the emerging need for qualitative approaches in context aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage many of the current approaches to context awareness either lack a solid theoretical basis for modeling or ignore important requirements such as modularity , high order uncertainty management and group based context awareness therefore , their real world application and extendability remains limited in this paper , we present f context as a service based context awareness framework , based on language action perspective \( lap \) theory for modeling then we identify some of the complex , informational parts of context which contain high order uncertainties due to differences between members of the group in defining them an agent based perceptual computer architecture is proposed for implementing f context that uses computing with words \( cww \) for handling uncertainty the feasibility of f context is analyzed using a realistic scenario involving a group of mobile users we believe that the proposed approach can open the door to future research on context awareness by offering a theoretical foundation based on human communication , and a service based layered architecture which exploits cww for context aware , group based and platform independent access to information systems"
cs.LG|stat.ML,"the group lasso is a well known tool for joint regularization in machine learning methods while the l 1 , 2 and the l 1 , infty version have been studied in detail and efficient algorithms exist , there are still open questions regarding other l 1 , p variants we characterize conditions for solutions of the l 1 , p group lasso for all p norms with 1 p infty , and we present a unified active set algorithm for all p norms , a highly efficient projected gradient algorithm is presented this new algorithm enables us to compare the prediction performance of many variants of the group lasso in a multi task learning setting , where the aim is to solve many learning problems in parallel which are coupled via the group lasso constraint we conduct large scale experiments on synthetic data and on two real world data sets in accordance with theoretical characterizations of the different norms we observe that the weak coupling norms with p between 1 5 and 2 consistently outperform the strong coupling norms with p 2"
cs.SI,"a large variety of complex systems in ecology , climate science , biomedicine and engineering have been observed to exhibit tipping points , where the internal dynamical state of the system abruptly changes for example , such critical transitions may result in the sudden change of ecological environments and climate conditions data and models suggest that detectable warning signs may precede some of these drastic events this view is also corroborated by abstract mathematical theory for generic bifurcations in stochastic multi scale systems whether the stochastic scaling laws used as warning signs are also present in social networks that anticipate a priori it unknown events in society is an exciting open problem , to which at present only highly speculative answers can be given here , we instead provide a first step towards tackling this formidable question by focusing on a priori it known events and analyzing a social network data set with a focus on classical variance and autocorrelation warning signs our results thus pertain to one absolutely fundamental question can the stochastic warning signs known from other areas also be detected in large scale social network data \? we answer this question affirmatively as we find that several a priori known events are preceded by variance and autocorrelation growth our findings thus clearly establish the necessary starting point to further investigate the relation between abstract mathematical theory and various classes of critical transitions in social networks"
cs.LG,"in recent years , research efforts to extend linear metric learning models to handle nonlinear structures have attracted great interests in this paper , we propose a novel nonlinear solution through the utilization of deformable geometric models to learn spatially varying metrics , and apply the strategy to boost the performance of both knn and svm classifiers thin plate splines \( tps \) are chosen as the geometric model due to their remarkable versatility and representation power in accounting for high order deformations by transforming the input space through tps , we can pull same class neighbors closer while pushing different class points farther away in knn , as well as make the input data points more linearly separable in svms improvements in the performance of knn classification are demonstrated through experiments on synthetic and real world datasets , with comparisons made with several state of the art metric learning solutions our svm based models also achieve significant improvements over traditional linear and kernel svms with the same datasets"
cs.DS,"pipage rounding is a dependent random sampling technique that has several interesting properties and diverse applications one property that has been particularly useful is negative correlation of the resulting vector unfortunately negative correlation has its limitations , and there are some further desirable properties that do not seem to follow from existing techniques in particular , recent concentration results for sums of independent random matrices are not known to extend to a negatively dependent setting we introduce a simple but useful technique called concavity of pessimistic estimators this technique allows us to show concentration of submodular functions and concentration of matrix sums under pipage rounding the former result answers a question of chekuri et al \( 2009 \) to prove the latter result , we derive a new variant of lieb 's celebrated concavity theorem in matrix analysis we provide numerous applications of these results one is to spectrally thin trees , a spectral analog of the thin trees that played a crucial role in the recent breakthrough on the asymmetric traveling salesman problem we show a polynomial time algorithm that , given a graph where every edge has effective conductance at least kappa , returns an o \( kappa 1 cdot log n log log n \) spectrally thin tree there are further applications to rounding of semidefinite programs , to the column subset selection problem , and to a geometric question of extracting a nearly orthonormal basis from an isotropic distribution"
cs.LG,"we propose a strong baseline model for unsupervised feature learning using video data by learning to predict missing frames or extrapolate future frames from an input video sequence , the model discovers both spatial and temporal correlations which are useful to represent complex deformations and motion patterns the models we propose are largely borrowed from the language modeling literature , and adapted to the vision domain by quantizing the space of image patches into a large dictionary we demonstrate the approach on both a filling and a generation task for the first time , we show that , after training on natural videos , such a model can predict non trivial motions over short video sequences"
cs.AI,"bat algorithm \( ba \) is a bio inspired algorithm developed by yang in 2010 and ba has been found to be very efficient as a result , the literature has expanded significantly in the last 3 years this paper provides a timely review of the bat algorithm and its new variants a wide range of diverse applications and case studies are also reviewed and summarized briefly here further research topics are also discussed"
cs.DS,"we present a distributed data structure , which we call the rainbow skip graph to our knowledge , this is the first peer to peer data structure that simultaneously achieves high fault tolerance , constant sized nodes , and fast update and query times for ordered data it is a non trivial adaptation of the skipnet skip graph structures of harvey et al and aspnes and shah , so as to provide fault tolerance as these structures do , but to do so using constant sized nodes , as in the family tree structure of zatloukal and harvey it supports successor queries on a set of n items using o \( log n \) messages with high probability , an improvement over the expected o \( log n \) messages of the family tree"
cs.IT|stat.ML,"iterative reweighted algorithms , as a class of algorithms for sparse signal recovery , have been found to have better performance than their non reweighted counterparts however , for solving the problem of multiple measurement vectors \( mmvs \) , all the existing reweighted algorithms do not account for temporal correlation among source vectors and thus their performance degrades significantly in the presence of correlation in this work we propose an iterative reweighted sparse bayesian learning \( sbl \) algorithm exploiting the temporal correlation , and motivated by it , we propose a strategy to improve existing reweighted ell 2 algorithms for the mmv problem , i e replacing their row norms with mahalanobis distance measure simulations show that the proposed reweighted sbl algorithm has superior performance , and the proposed improvement strategy is effective for existing reweighted ell 2 algorithms"
cs.LG,"many clustering problems in computer vision and other contexts are also classification problems , where each cluster shares a meaningful label subspace clustering algorithms in particular are often applied to problems that fit this description , for example with face images or handwritten digits while it is straightforward to request human input on these datasets , our goal is to reduce this input as much as possible we present an algorithm for active query selection that allows us to leverage the union of subspace structure assumed in subspace clustering the central step of the algorithm is in querying points of minimum margin between estimated subspaces analogous to classifier margin , these lie near the decision boundary this procedure can be used after any subspace clustering algorithm that outputs an affinity matrix and is capable of driving the clustering error down more quickly than other state of the art active query algorithms on datasets with subspace structure we demonstrate the effectiveness of our algorithm on several benchmark datasets , and with a modest number of queries we see significant gains in clustering performance"
cs.LG|stat.ML,"matrix completion and approximation are popular tools to capture a user 's preferences for recommendation and to approximate missing data instead of using low rank factorization we take a drastically different approach , based on the simple insight that an additive model of co clusterings allows one to approximate matrices efficiently this allows us to build a concise model that , per bit of model learned , significantly beats all factorization approaches to matrix approximation even more surprisingly , we find that summing over small co clusterings is more effective in modeling matrices than classic co clustering , which uses just one large partitioning of the matrix following occam 's razor principle suggests that the simple structure induced by our model better captures the latent preferences and decision making processes present in the real world than classic co clustering or matrix factorization we provide an iterative minimization algorithm , a collapsed gibbs sampler , theoretical guarantees for matrix approximation , and excellent empirical evidence for the efficacy of our approach we achieve state of the art results on the netflix problem with a fraction of the model complexity"
cs.LG|stat.ML,"deep neural networks \( dnns \) have been successfully applied to a wide variety of acoustic modeling tasks in recent years these include the applications of dnns either in a discriminative feature extraction or in a hybrid acoustic modeling scenario despite the rapid progress in this area , a number of challenges remain in training dnns this paper presents an effective way of training dnns using a manifold learning based regularization framework in this framework , the parameters of the network are optimized to preserve underlying manifold based relationships between speech feature vectors while minimizing a measure of loss between network outputs and targets this is achieved by incorporating manifold based locality constraints in the objective criterion of dnns empirical evidence is provided to demonstrate that training a network with manifold constraints preserves structural compactness in the hidden layers of the network manifold regularization is applied to train bottleneck dnns for feature extraction in hidden markov model \( hmm \) based speech recognition the experiments in this work are conducted on the aurora 2 spoken digits and the aurora 4 read news large vocabulary continuous speech recognition tasks the performance is measured in terms of word error rate \( wer \) on these tasks it is shown that the manifold regularized dnns result in up to 37 reduction in wer relative to standard dnns"
cs.DM,"a basic statement in graph theory is that every inclusion maximal forest is connected , i e a tree using a definiton for higher dimensional forests by graham and lovasz and the connectivity related notion of tightness for hypergraphs introduced by arocha , bracho and neumann lara in , we provide an example of a saturated , i e inclusion maximal 3 forest that is not tight this resolves an open problem posed by strausz"
cs.LG,"zero sum stochastic games are easy to solve as they can be cast as simple markov decision processes this is however not the case with general sum stochastic games a fairly general optimization problem formulation is available for general sum stochastic games by filar and vrieze 2004 however , the optimization problem there has a non linear objective and non linear constraints with special structure since gradients of both the objective as well as constraints of this optimization problem are well defined , gradient based schemes seem to be a natural choice we discuss a gradient scheme tuned for two player stochastic games we show in simulations that this scheme indeed converges to a nash equilibrium , for a simple terrain exploration problem modelled as a general sum stochastic game however , it turns out that only global minima of the optimization problem correspond to nash equilibria of the underlying general sum stochastic game , while gradient schemes only guarantee convergence to local minima we then provide important necessary conditions for gradient schemes to converge to nash equilibria in general sum stochastic games"
cs.DS,"interdiction problems ask about the worst case impact of a limited change to an underlying optimization problem they are a natural way to measure the robustness of a system , or to identify its weakest spots interdiction problems have been studied for a wide variety of classical combinatorial optimization problems , including maximum s t flows , shortest s t paths , maximum weight matchings , minimum spanning trees , maximum stable sets , and graph connectivity most interdiction problems are np hard , and furthermore , even designing efficient approximation algorithms that allow for estimating the order of magnitude of a worst case impact , has turned out to be very difficult not very surprisingly , the few known approximation algorithms are heavily tailored for specific problems inspired by an approach of burch et al \( 2003 \) , we suggest a general method to obtain pseudoapproximations for many interdiction problems more precisely , for any alpha 0 , our algorithm will return either a \( 1 alpha \) approximation , or a solution that may overrun the interdiction budget by a factor of at most 1 alpha 1 but is also at least as good as the optimal solution that respects the budget furthermore , our approach can handle submodular interdiction costs when the underlying problem is to find a maximum weight independent set in a matroid , as for example the maximum weight forest problem the approach can sometimes be refined by exploiting additional structural properties of the underlying optimization problem to obtain stronger results we demonstrate this by presenting a ptas for interdicting b stable sets in bipartite graphs"
cs.SI,"how to promote the innovative activities is an important problem for modern society in this paper , combining with the evolutionary games and information spreading , we propose a lattice model to investigate dynamics of human innovative behaviors based on benefit driven assumption simulations show several properties in agreement with peoples' daily cognition on innovative behaviors , such as slow diffusion of innovative behaviors , gathering of innovative strategy on innovative centers , and quasi localized dynamics furthermore , our model also emerges rich non poisson properties in the temporal spacial patterns of the innovative status , including the scaling law in the interval time of innovation releases and the bimodal distributions on the spreading range of innovations , which would be universal in human innovative behaviors our model provide a basic framework on the study of the issue relevant to the evolution of human innovative behaviors and the promotion measurement of innovative activities"
cs.DM,"we provide a novel method for constructing asymptotics \( to arbitrary accuracy \) for the number of directed graphs that realize a fixed bidegree sequence d a times b with maximum degree d max o \( s frac 1 2 tau \) for an arbitrarily small positive number tau , where s is the number edges specified by d our approach is based on two key steps , graph partitioning and degree preserving switches the former idea allows us to relate enumeration results for given sequences to those for sequences that are especially easy to handle , while the latter facilitates expansions based on numbers of shared neighbors of pairs of nodes while we focus primarily on directed graphs allowing loops , our results can be extended to other cases , including bipartite graphs , as well as directed and undirected graphs without loops in addition , we can relax the constraint that d max o \( s frac 1 2 tau \) and replace it with a max b max o \( s 1 tau \) where a max and b max are the maximum values for a and b respectively the previous best results , from greenhill et al , only allow for d max o \( s frac 1 3 \) or alternatively a max b max o \( s frac 2 3 \) since in many real world networks , d max scales larger than o \( s frac 1 3 \) , we expect that this work will be helpful for various applications"
cs.AI,"nowadays , there is increasing interest in the development of teamwork skills in the educational context this growing interest is motivated by its pedagogical effectiveness and the fact that , in labour contexts , enterprises organize their employees in teams to carry out complex projects despite its crucial importance in the classroom and industry , there is a lack of support for the team formation process not only do many factors influence team performance , but the problem becomes exponentially costly if teams are to be optimized in this article , we propose a tool whose aim it is to cover such a gap it combines artificial intelligence techniques such as coalition structure generation , bayesian learning , and belbin 's role theory to facilitate the generation of working groups in an educational context this tool improves current state of the art proposals in three ways i \) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self perception questionnaires ii \) it handles uncertainty with regard to each student 's predominant team role iii \) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments we tested the performance of the proposed tool in an experiment involving students that took part in three different team activities the experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction"
cs.AI|cs.LG|stat.ML,"this work presents novel algorithms for learning bayesian network structures with bounded treewidth both exact and approximate methods are developed the exact method combines mixed integer linear programming formulations for structure learning and treewidth computation the approximate method consists in uniformly sampling k trees \( maximal graphs of treewidth k \) , and subsequently selecting , exactly or approximately , the best structure whose moral graph is a subgraph of that k tree some properties of these methods are discussed and proven the approaches are empirically compared to each other and to a state of the art method for learning bounded treewidth structures on a collection of public data sets with up to 100 variables the experiments show that our exact algorithm outperforms the state of the art , and that the approximate approach is fairly accurate"
cs.DS,"the metric traveling salesman problem \( tsp \) is a classical np hard optimization problem the double tree shortcutting method for metric tsp yields an exponentially sized space of tsp tours , each of which approximates the optimal solution within at most a factor of 2 we consider the problem of finding among these tours the one that gives the closest approximation , i e the emph minimum weight double tree shortcutting burkard et al gave an algorithm for this problem , running in time o \( n 3 2 d n 2 \) and memory o \( 2 d n 2 \) , where d is the maximum node degree in the rooted minimum spanning tree we give an improved algorithm for the case of small d \( including planar euclidean tsp , where d leq 4 \) , running in time o \( 4 d n 2 \) and memory o \( 4 d n \) this improvement allows one to solve the problem on much larger instances than previously attempted our computational experiments suggest that in terms of the time quality tradeoff , the minimum weight double tree shortcutting method provides one of the best known tour constructing heuristics"
cs.LG|stat.ML,"metric learning has attracted a lot of interest over the last decade , but the generalization ability of such methods has not been thoroughly studied in this paper , we introduce an adaptation of the notion of algorithmic robustness \( previously introduced by xu and mannor \) that can be used to derive generalization bounds for metric learning we further show that a weak notion of robustness is in fact a necessary and sufficient condition for a metric learning algorithm to generalize to illustrate the applicability of the proposed framework , we derive generalization results for a large family of existing metric learning algorithms , including some sparse formulations that are not covered by previous results"
cs.DM,"we are interested in the asymptotics of random trees built by linear preferential attachment , also known in the literature as barab 'asi albert trees or plane oriented recursive trees we first prove a conjecture of bubeck , mossel r 'acz concerning the influence of the seed graph on the asymptotic behavior of such trees separately we study the geometric structure of nodes of large degrees in a plane version of barab 'asi albert trees via their associated looptrees as the number of nodes grows , we show that these looptrees , appropriately rescaled , converge in the gromov hausdorff sense towards a random compact metric space which we call the brownian looptree the latter is constructed as a quotient space of aldous' brownian continuum random tree and is shown to have almost sure hausdorff dimension 2"
cs.AI,"natural language correction has the potential to help language learners improve their writing skills while approaches with separate classifiers for different error types have high precision , they do not flexibly handle errors such as redundancy or non idiomatic phrasing on the other hand , word and phrase based machine translation methods are not designed to cope with orthographic errors , and have recently been outpaced by neural models motivated by these issues , we present a neural network based approach to language correction the core component of our method is an encoder decoder recurrent neural network with an attention mechanism by operating at the character level , the network avoids the problem of out of vocabulary words we illustrate the flexibility of our approach on dataset of noisy , user generated text collected from an english learner forum when combined with a language model , our method achieves a state of the art f 0 5 score on the conll 2014 shared task we further demonstrate that training the network on additional data with synthesized errors can improve performance"
cs.LG,"in this paper we propose and study a technique to reduce the number of parameters and computation time in fully connected layers of neural network using kronecker product , at a mild cost of the prediction quality the technique proceeds by replacing fully connected layers with so called kronecker fully connected layers , where the weight matrices of the fc layers are approximated by linear combinations of multiple kronecker product of smaller matrices in particular , given a model trained on svhn dataset , we can construct a new kfc model with 73 reduction in total number of parameters , while the error only rises mildly in contrast , using low rank method can only achieve 35 reduction in total number of parameters given similar quality degradation allowance if we only compare the kfc layer with its counterpart fully connected layer , the reduction in number of parameters is more than 99 the amount of computation is also reduced as we replace matrix product of a large matrix with matrix product of a few smaller matrices further experiments on mnist , svhn and some chinese character recognition models also demonstrates effectiveness of our technique"
cs.LG|stat.ML,"for the task of unsupervised spatio temporal forecasting \( e g , learning to predict video data without labels \) , we propose two new nonparametric predictive state algorithms , moonshine and one hundred proof the algorithms are conceptually simple and make few assumptions on the underlying spatio temporal process yet have strong predictive performance and provide predictive distributions over spatio temporal data the latter property allows for likelihood estimation under the models , for classification and other probabilistic inference"
cs.DM,the reconstruction of the history of evolutionary genome wide events among a set of related organisms is of great biological interest a simplified model that captures only content modifying operations was introduced recently it allows the small phylogeny problem to be formulated as an alignment problem in this work we present a branch and cut algorithm for this so called duplication loss alignment problem our method clearly outperforms the existing ilp based method by several orders of magnitude we define classes of valid inequalities and provide algorithms to separate them efficiently and prove the np hardness of the duplication loss alignment problem
cs.AI,"this paper proposes a multimodal biometric system through gaussian mixture model \( gmm \) for face and ear biometrics with belief fusion of the estimated scores characterized by gabor responses and the proposed fusion is accomplished by dempster shafer \( ds \) decision theory face and ear images are convolved with gabor wavelet filters to extracts spatially enhanced gabor facial features and gabor ear features further , gmm is applied to the high dimensional gabor face and gabor ear responses separately for quantitive measurements expectation maximization \( em \) algorithm is used to estimate density parameters in gmm this produces two sets of feature vectors which are then fused using dempster shafer theory experiments are conducted on multimodal database containing face and ear images of 400 individuals it is found that use of gabor wavelet filters along with gmm and ds theory can provide robust and efficient multimodal fusion strategy"
cs.LG|stat.ML,"modeling interactions between features improves the performance of machine learning solutions in many domains \( e g recommender systems or sentiment analysis \) in this paper , we introduce exponential machines \( exm \) , a predictor that models all interactions of every order the key idea is to represent an exponentially large tensor of parameters in a factorized format called tensor train \( tt \) the tensor train format regularizes the model and lets you control the number of underlying parameters to train the model , we develop a stochastic version of riemannian optimization , which allows us to fit tensors with 2 30 entries we show that the model achieves state of the art performance on synthetic data with high order interactions"
cs.DS,"we show that , for any n vertex graph g and integer parameter k , there are at most 3 4k n 4 n 3k maximal independent sets i subset g with i k , and that all such sets can be listed in time o \( 3 4k n 4 n 3k \) these bounds are tight when n 4 k n 3 as a consequence , we show how to compute the exact chromatic number of a graph in time o \( \( 4 3 3 4 3 4 \) n \) 2 4150 n , improving a previous o \( \( 1 3 1 3 \) n \) 2 4422 n algorithm of lawler \( 1976 \)"
cs.LG,"matconvnet is an implementation of convolutional neural networks \( cnns \) for matlab the toolbox is designed with an emphasis on simplicity and flexibility it exposes the building blocks of cnns as easy to use matlab functions , providing routines for computing linear convolutions with filter banks , feature pooling , and many more in this manner , matconvnet allows fast prototyping of new cnn architectures at the same time , it supports efficient computation on cpu and gpu allowing to train complex models on large datasets such as imagenet ilsvrc this document provides an overview of cnns and how they are implemented in matconvnet and gives the technical details of each computational block in the toolbox"
cs.DS|stat.ML,"database theory and database practice are typically the domain of computer scientists who adopt what may be termed an algorithmic perspective on their data this perspective is very different than the more statistical perspective adopted by statisticians , scientific computers , machine learners , and other who work on what may be broadly termed statistical data analysis in this article , i will address fundamental aspects of this algorithmic statistical disconnect , with an eye to bridging the gap between these two very different approaches a concept that lies at the heart of this disconnect is that of statistical regularization , a notion that has to do with how robust is the output of an algorithm to the noise properties of the input data although it is nearly completely absent from computer science , which historically has taken the input data as given and modeled algorithms discretely , regularization in one form or another is central to nearly every application domain that applies algorithms to noisy data by using several case studies , i will illustrate , both theoretically and empirically , the nonobvious fact that approximate computation , in and of itself , can implicitly lead to statistical regularization this and other recent work suggests that , by exploiting in a more principled way the statistical properties implicit in worst case algorithms , one can in many cases satisfy the bicriteria of having algorithms that are scalable to very large scale databases and that also have good inferential or predictive properties"
cs.AI,"several important decision problems on conjunctive queries \( cqs \) are np complete in general but become tractable , and actually highly parallelizable , if restricted to acyclic or nearly acyclic queries examples are the evaluation of boolean cqs and query containment these problems were shown tractable for conjunctive queries of bounded treewidth and of bounded degree of cyclicity the so far most general concept of nearly acyclic queries was the notion of queries of bounded query width introduced by chekuri and rajaraman \( 1997 \) while cqs of bounded query width are tractable , it remained unclear whether such queries are efficiently recognizable chekuri and rajaraman stated as an open problem whether for each constant k it can be determined in polynomial time if a query has query width less than or equal to k we give a negative answer by proving this problem np complete \( specifically , for k 4 \) in order to circumvent this difficulty , we introduce the new concept of hypertree decomposition of a query and the corresponding notion of hypertree width we prove \( a \) for each k , the class of queries with query width bounded by k is properly contained in the class of queries whose hypertree width is bounded by k \( b \) unlike query width , constant hypertree width is efficiently recognizable \( c \) boolean queries of constant hypertree width can be efficiently evaluated"
cs.LG|stat.ML,"standard maximum likelihood estimation cannot be applied to discrete energy based models in the general case because the computation of exact model probabilities is intractable recent research has seen the proposal of several new estimators designed specifically to overcome this intractability , but virtually nothing is known about their theoretical properties in this paper , we present a generalized estimator that unifies many of the classical and recently proposed estimators we use results from the standard asymptotic theory for m estimators to derive a generic expression for the asymptotic covariance matrix of our generalized estimator we apply these results to study the relative statistical efficiency of classical pseudolikelihood and the recently proposed ratio matching estimator"
cs.DM|cs.DS,"in this paper we extend the theory of bidimensionality to two families of graphs that do not exclude fixed minors map graphs and power graphs in both cases we prove a polynomial relation between the treewidth of a graph in the family and the size of the largest grid minor these bounds improve the running times of a broad class of fixed parameter algorithms our novel technique of using approximate max min relations between treewidth and size of grid minors is powerful , and we show how it can also be used , e g , to prove a linear relation between the treewidth of a bounded genus graph and the treewidth of its dual"
cs.DM,"bourgain recently constructed o \( 1 \) monotone bipartite expanders by combining this result with a generalisation of the unraveling method of kannan , we construct 3 monotone bipartite expanders , which is best possible similarly , we construct bipartite expanders that have 3 page book embeddings , 2 queue layouts , and 4 track layouts all these results are best possible"
cs.LG|stat.ML,"motivated by online advertising auctions , we consider repeated vickrey auctions where goods of unknown value are sold sequentially and bidders only learn \( potentially noisy \) information about a good 's value once it is purchased we adopt an online learning approach with bandit feedback to model this problem and derive bidding strategies for two models stochastic and adversarial in the stochastic model , the observed values of the goods are random variables centered around the true value of the good in this case , logarithmic regret is achievable when competing against well behaved adversaries in the adversarial model , the goods need not be identical and we simply compare our performance against that of the best fixed bid in hindsight we show that sublinear regret is also achievable in this case and prove matching minimax lower bounds to our knowledge , this is the first complete set of strategies for bidders participating in auctions of this type"
cs.LG,"greedy restrictive boltzmann machines yield an fairly low 0 72 error rate on the famous mnist database of handwritten digits all that was required to achieve this result was a high number of hidden layers consisting of many neurons , and a graphics card to greatly speed up the rate of learning"
cs.DS|cs.LG|stat.ML,"in sparse recovery we are given a matrix a \( the dictionary \) and a vector of the form a x where x is sparse , and the goal is to recover x this is a central notion in signal processing , statistics and machine learning but in applications such as sparse coding , edge detection , compression and super resolution , the dictionary a is unknown and has to be learned from random examples of the form y ax where x is drawn from an appropriate distribution this is the dictionary learning problem in most settings , a is overcomplete it has more columns than rows this paper presents a polynomial time algorithm for learning overcomplete dictionaries the only previously known algorithm with provable guarantees is the recent work of spielman , wang and wright who gave an algorithm for the full rank case , which is rarely the case in applications our algorithm applies to incoherent dictionaries which have been a central object of study since they were introduced in seminal work of donoho and huo in particular , a dictionary is mu incoherent if each pair of columns has inner product at most mu sqrt n the algorithm makes natural stochastic assumptions about the unknown sparse vector x , which can contain k leq c min \( sqrt n mu log n , m 1 2 eta \) non zero entries \( for any eta 0 \) this is close to the best k allowable by the best sparse recovery algorithms even if one knows the dictionary a exactly moreover , both the running time and sample complexity depend on log 1 epsilon , where epsilon is the target accuracy , and so our algorithms converge very quickly to the true dictionary our algorithm can also tolerate substantial amounts of noise provided it is incoherent with respect to the dictionary \( e g , gaussian \) in the noisy setting , our running time and sample complexity depend polynomially on 1 epsilon , and this is necessary"
cs.DM,"the turan bound is a famous result in graph theory , which relates the independence number of an undirected graph to its edge density also the caro wei inequality , which gives a more refined bound in terms of the vertex degree sequence of a graph , might be regarded today as a classical result we show how these statements can be generalized to directed graphs , thus yielding a bound on directed feedback vertex number in terms of vertex outdegrees and in terms of average outdegree , respectively"
cs.DS|cs.IT,"compressive sensing \( cs \) stipulates that a sparse signal can be recovered from a small number of linear measurements , and that this recovery can be performed efficiently in polynomial time the framework of model based compressive sensing \( model cs \) leverages additional structure in the signal and prescribes new recovery schemes that can reduce the number of measurements even further however , model cs requires an algorithm that solves the model projection problem given a query signal , produce the signal in the model that is also closest to the query signal often , this optimization can be computationally very expensive moreover , an approximation algorithm is not sufficient for this optimization task as a result , the model projection problem poses a fundamental obstacle for extending model cs to many interesting models in this paper , we introduce a new framework that we call approximation tolerant model based compressive sensing this framework includes a range of algorithms for sparse recovery that require only approximate solutions for the model projection problem in essence , our work removes the aforementioned obstacle to model based compressive sensing , thereby extending the applicability of model cs to a much wider class of models we instantiate this new framework for the constrained earth mover distance \( cemd \) model , which is particularly useful for signal ensembles where the positions of the nonzero coefficients do not change significantly as a function of spatial \( or temporal \) location we develop novel approximation algorithms for both the maximization and the minimization versions of the model projection problem via graph optimization techniques leveraging these algorithms into our framework results in a nearly sample optimal sparse recovery scheme for the cemd model"
cs.DM|cs.DS,"certain families of combinatorial objects admit recursive descriptions in terms of generating trees each node of the tree corresponds to an object , and the branch leading to the node encodes the choices made in the construction of the object generating trees lead to a fast computation of enumeration sequences \( sometimes , to explicit formulae as well \) and provide efficient random generation algorithms we investigate the links between the structural properties of the rewriting rules defining such trees and the rationality , algebraicity , or transcendence of the corresponding generating function"
cs.DS|cs.IT,"we study maximum selection and sorting of n numbers using pairwise comparators that output the larger of their two inputs if the inputs are more than a given threshold apart , and output an adversarially chosen input otherwise we consider two adversarial models a non adaptive adversary that decides on the outcomes in advance based solely on the inputs , and an adaptive adversary that can decide on the outcome of each query depending on previous queries and outcomes against the non adaptive adversary , we derive a maximum selection algorithm that uses at most 2n comparisons in expectation , and a sorting algorithm that uses at most 2n ln n comparisons in expectation these numbers are within small constant factors from the best possible against the adaptive adversary , we propose a maximum selection algorithm that uses theta \( n log \( 1 epsilon \) \) comparisons to output a correct answer with probability at least 1 epsilon the existence of this algorithm affirmatively resolves an open problem of ajtai , feldman , hassadim , and nelson our study was motivated by a density estimation problem where , given samples from an unknown underlying distribution , we would like to find a distribution in a known class of n candidate distributions that is close to underlying distribution in ell 1 distance scheffe 's algorithm outputs a distribution at an ell 1 distance at most 9 times the minimum and runs in time theta \( n 2 log n \) using maximum selection , we propose an algorithm with the same approximation guarantee but run time of theta \( n log n \)"
cs.LG,"we present a work in progress snapshot of learning with a 15 billion parameter deep learning network on hpc architectures applied to the largest publicly available natural image and video dataset released to date recent advancements in unsupervised deep neural networks suggest that scaling up such networks in both model and training dataset size can yield significant improvements in the learning of concepts at the highest layers we train our three layer deep neural network on the yahoo ! flickr creative commons 100m dataset the dataset comprises approximately 99 2 million images and 800 , 000 user created videos from yahoo 's flickr image and video sharing platform training of our network takes eight days on 98 gpu nodes at the high performance computing center at lawrence livermore national laboratory encouraging preliminary results and future research directions are presented and discussed"
cs.LG|stat.ML,"the boltzmann machine provides a useful framework to learn highly complex , multimodal and multiscale data distributions that occur in the real world the default method to learn its parameters consists of minimizing the kullback leibler \( kl \) divergence from training samples to the boltzmann model we propose in this work a novel approach for boltzmann training which assumes that a meaningful metric between observations is given this metric can be represented by the wasserstein distance between distributions , for which we derive a gradient with respect to the model parameters minimization of this new wasserstein objective leads to generative models that are better when considering the metric and that have a cluster like structure we demonstrate the practical potential of these models for data completion and denoising , for which the metric between observations plays a crucial role"
cs.AI,"in this paper we name some of the advantages of virtual laboratories and propose that a behaviours virtual laboratory should be useful for both biologists and ai researchers , offering a new perspective for understanding adaptive behaviour we present our development of a behaviours virtual laboratory , which at this stage is focused in action selection , and show some experiments to illustrate the properties of our proposal , which can be accessed via internet"
cs.DM,"concentration inequalities are fundamental tools in probabilistic combinatorics and theoretical computer science for proving that random functions are near their means of particular importance is the case where f \( x \) is a function of independent random variables x \( x 1 , , x n \) here the well known bounded differences inequality \( also called mcdiarmid 's or hoeffding azuma inequality \) establishes sharp concentration if the function f does not depend too much on any of the variables one attractive feature is that it relies on a very simple lipschitz condition \( l \) it suffices to show that f \( x \) f \( x' \) leq c k whenever x , x' differ only in x k while this is easy to check , the main disadvantage is that it considers worst case changes c k , which often makes the resulting bounds too weak to be useful in this paper we prove a variant of the bounded differences inequality which can be used to establish concentration of functions f \( x \) where \( i \) the typical changes are small although \( ii \) the worst case changes might be very large one key aspect of this inequality is that it relies on a simple condition that \( a \) is easy to check and \( b \) coincides with heuristic considerations why concentration should hold indeed , given an event gamma that holds with very high probability , we essentially relax the lipschitz condition \( l \) to situations where gamma occurs the point is that the resulting typical changes c k are often much smaller than the worst case ones to illustrate its application we consider the reverse h free process , where h is 2 balanced we prove that the final number of edges in this process is concentrated , and also determine its likely value up to constant factors this answers a question of bollob 'as and erd h o s"
cs.SI,"variational methods for parameter estimation are an active research area , potentially offering computationally tractable heuristics with theoretical performance bounds we build on recent work that applies such methods to network data , and establish asymptotic normality rates for parameter estimates of stochastic blockmodel data , by either maximum likelihood or variational estimation the result also applies to various sub models of the stochastic blockmodel found in the literature"
cs.AI,"barring swarm robotics , a substantial share of current machine human and machine machine learning and interaction mechanisms are being developed and fed by results of agent based computer simulations , game theoretic models , or robotic experiments based on a dyadic communication pattern yet , in real life , humans no less frequently communicate in groups , and gain knowledge and take decisions basing on information cumulatively gleaned from more than one single source these properties should be taken into consideration in the design of autonomous artificial cognitive systems construed to interact with learn from more than one contact or 'neighbour' to this end , significant practical import can be gleaned from research applying strict science methodology to human and social phenomena , e g to discovery of realistic creativity potential spans , or the 'exposure thresholds' after which new information could be accepted by a cognitive agent the results will be presented of a project analysing the social propagation of neologisms in a microblogging service from local , low level interactions and information flows between agents inventing and imitating discrete lexemes we aim to describe the processes of the emergence of more global systemic order and dynamics , using the latest methods of complexity science whether in order to mimic them , or to 'enhance' them , parameters gleaned from complexity science approaches to humans' social and humanistic behaviour should subsequently be incorporated as points of reference in the field of robotics and human machine interaction"
cs.LG,"we propose a fast and efficient estimation method that is able to accurately recover the parameters of a d dimensional hawkes point process from a set of observations we exploit a mean field approximation that is valid when the fluctuations of the stochastic intensity are small we show that this is notably the case in situations when interactions are sufficiently weak , when the dimension of the system is high or when the fluctuations are self averaging due to the large number of past events they involve in such a regime the estimation of a hawkes process can be mapped on a least squares problem for which we provide an analytic solution though this estimator is biased , we show that its precision can be comparable to the one of the maximum likelihood estimator while its computation speed is shown to be improved considerably we give a theoretical control on the accuracy of our new approach and illustrate its efficiency using synthetic datasets , in order to assess the statistical estimation error of the parameters"
cs.AI|cs.DS,"we study the computational complexity of problems that arise in abstract argumentation in the context of dynamic argumentation , minimal change , and aggregation in particular , we consider the following problems where always an argumentation framework f and a small positive integer k are given the repair problem asks whether a given set of arguments can be modified into an extension by at most k elementary changes \( i e , the extension is of distance k from the given set \) the adjust problem asks whether a given extension can be modified by at most k elementary changes into an extension that contains a specified argument the center problem asks whether , given two extensions of distance k , whether there is a center extension that is a distance at most \( k 1 \) from both given extensions we study these problems in the framework of parameterized complexity , and take the distance k as the parameter our results covers several different semantics , including admissible , complete , preferred , semi stable and stable semantics"
cs.LG|stat.ML,"a new procedure for learning cost sensitive svm \( cs svm \) classifiers is proposed the svm hinge loss is extended to the cost sensitive setting , and the cs svm is derived as the minimizer of the associated risk the extension of the hinge loss draws on recent connections between risk minimization and probability elicitation these connections are generalized to cost sensitive classification , in a manner that guarantees consistency with the cost sensitive bayes risk , and associated bayes decision rule this ensures that optimal decision rules , under the new hinge loss , implement the bayes optimal cost sensitive classification boundary minimization of the new hinge loss is shown to be a generalization of the classic svm optimization problem , and can be solved by identical procedures the dual problem of cs svm is carefully scrutinized by means of regularization theory and sensitivity analysis and the cs svm algorithm is substantiated the proposed algorithm is also extended to cost sensitive learning with example dependent costs the minimum cost sensitive risk is proposed as the performance measure and is connected to roc analysis through vector optimization the resulting algorithm avoids the shortcomings of previous approaches to cost sensitive svm design , and is shown to have superior experimental performance on a large number of cost sensitive and imbalanced datasets"
cs.LG|stat.ML,"tactile information is important for gripping , stable grasp , and in hand manipulation , yet the complexity of tactile data prevents widespread use of such sensors we make use of an unsupervised learning algorithm that transforms the complex tactile data into a compact , latent representation without the need to record ground truth reference data these compact representations can either be used directly in a reinforcement learning based controller or can be used to calibrate the tactile sensor to physical quantities with only a few datapoints we show the quality of our latent representation by predicting important features and with a simple control task"
cs.AI,"previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only this is thought to be a result of intelligent behaviour selection on the part of the idiotypic robot in this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemes to select robot behaviour the aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection , and to try to gain further insight into the processes that govern the idiotypic mechanism trials are carried out using simulated pioneer robots that undertake navigation exercises results show that a scheme that boosts the probability of selecting highly ranked alternative behaviours to 50 during stall conditions comes closest to achieving the properties of the idiotypic system , but remains unable to match it in terms of all round performance"
cs.LG|stat.ML,"we study computational and sample complexity of parameter and structure learning in graphical models our main result shows that the class of factor graphs with bounded factor size and bounded connectivity can be learned in polynomial time and polynomial number of samples , assuming that the data is generated by a network in this class this result covers both parameter estimation for a known network structure and structure learning it implies as a corollary that we can learn factor graphs for both bayesian networks and markov networks of bounded degree , in polynomial time and sample complexity unlike maximum likelihood estimation , our method does not require inference in the underlying network , and so applies to networks where inference is intractable we also show that the error of our learned model degrades gracefully when the generating distribution is not a member of the target class of networks"
cs.AI,"in this work , we propose an abductive framework for biosignal interpretation , based on the concept of temporal abstraction patterns a temporal abstraction pattern defines an abstraction relation between an observation hypothesis and a set of observations constituting its evidence support new observations are generated abductively from any subset of the evidence of a pattern , building an abstraction hierarchy of observations in which higher levels contain those observations with greater interpretative value of the physiological processes underlying a given signal non monotonic reasoning techniques have been applied to this model in order to find the best interpretation of a set of initial observations , permitting even to correct these observations by removing , adding or modifying them in order to make them consistent with the available domain knowledge some preliminary experiments have been conducted to apply this framework to a well known and bounded problem the qrs detection on ecg signals the objective is not to provide a new better qrs detector , but to test the validity of an abductive paradigm these experiments show that a knowledge base comprising just a few very simple rhythm abstraction patterns can enhance the results of a state of the art algorithm by significantly improving its detection f1 score , besides proving the ability of the abductive framework to correct both sensitivity and specificity failures"
cs.LG,"recent machine learning techniques can be modified to produce creative results those results did not exist before it is not a trivial combination of the data which was fed into the machine learning system the obtained results come in multiple forms as images , as text and as audio this paper gives a high level overview of how they are created and gives some examples it is meant to be a summary of the current work and give people who are new to machine learning some starting points"
cs.LG|stat.ML,"latent topic models have been successfully applied as an unsupervised topic discovery technique in large document collections with the proliferation of hypertext document collection such as the internet , there has also been great interest in extending these approaches to hypertext 6 , 9 these approaches typically model links in an analogous fashion to how they model words the document link co occurrence matrix is modeled in the same way that the document word co occurrence matrix is modeled in standard topic models in this paper we present a probabilistic generative model for hypertext document collections that explicitly models the generation of links specifically , links from a word w to a document d depend directly on how frequent the topic of w is in d , in addition to the in degree of d we show how to perform em learning on this model efficiently by not modeling links as analogous to words , we end up using far fewer free parameters and obtain better link prediction results"
cs.LG|stat.ML,"modeling dynamical systems is important in many disciplines , e g , control , robotics , or neurotechnology commonly the state of these systems is not directly observed , but only available through noisy and potentially high dimensional observations in these cases , system identification , i e , finding the measurement mapping and the transition mapping \( system dynamics \) in latent space can be challenging for linear system dynamics and measurement mappings efficient solutions for system identification are available however , in practical applications , the linearity assumptions does not hold , requiring non linear system identification techniques if additionally the observations are high dimensional \( e g , images \) , non linear system identification is inherently hard to address the problem of non linear system identification from high dimensional observations , we combine recent advances in deep learning and system identification in particular , we jointly learn a low dimensional embedding of the observation by means of deep auto encoders and a predictive transition model in this low dimensional space we demonstrate that our model enables learning good predictive models of dynamical systems from pixel information only"
cs.SI,"social scientists have observed that human behavior in society can often be modeled as corresponding to a threshold type policy a new behavior would propagate by a procedure in which an individual adopts the new behavior if the fraction of his neighbors or friends having adopted the new behavior exceeds some threshold in this paper we study the question of whether the emergence of threshold policies may be modeled as a result of some rational process which would describe the behavior of non cooperative rational members of some social network we focus on situations in which individuals take the decision whether to access or not some content , based on the number of views that the content has our analysis aims at understanding not only the behavior of individuals , but also the way in which information about the quality of a given content can be deduced from view counts when only part of the viewers that access the content are informed about its quality in this paper we present a game formulation for the behavior of individuals using a meanfield model the number of individuals is approximated by a continuum of atomless players and for which the wardrop equilibrium is the solution concept we derive conditions on the problem 's parameters that result indeed in the emergence of threshold equilibria policies but we also identify some parameters in which other structures are obtained for the equilibrium behavior of individuals"
cs.SI,"betweenness centrality \( bc \) is an important measure used widely in complex network analysis , such as social network , web page search , etc computing the exact bc values is highly time consuming currently the fastest exact bc determining algorithm is given by brandes , taking o \( nm \) time for unweighted graphs and o \( nm n 2 log n \) time for weighted graphs , where n is the number of vertices and m is the number of edges in the graph due to the extreme difficulty of reducing the time complexity of exact bc determining problem , many researchers have considered the possibility of any satisfactory bc approximation algorithms , especially those based on samplings bader et al give the currently best bc approximation algorithm , with a high probability to successfully estimate the bc of one vertex within a factor of 1 varepsilon using varepsilon t samples , where t is the ratio between n 2 and the bc value of the vertex however , some of the algorithmic parameters in bader 's work are not yet tightly bounded , leaving some space to improve their algorithm in this project , we revisit bader 's algorithm , give more tight bounds on the estimations , and improve the performance of the approximation algorithm , i e , fewer samples and lower factors on the other hand , riondato proposes a non adaptive bc approximation method with samplings on shortest paths we investigate the possibility to give an adaptive algorithm with samplings on vertices with rigorous reasoning , we show that this algorithm can also give bounded approximation factors and number of samplings to evaluate our results , we also conduct extensive experiments using real world datasets , and find that both our algorithms can achieve acceptable performance we verify that our work can improve the current bc approximation algorithm with better parameters , i e , higher successful probabilities , lower factors and fewer samples"
cs.DS,"the subset feedback vertex set problem generalizes the classical feedback vertex set problem and asks , for a given undirected graph g \( v , e \) , a set s subseteq v , and an integer k , whether there exists a set x of at most k vertices such that no cycle in g x contains a vertex of s it was independently shown by cygan et al \( icalp '11 , sidma '13 \) and kawarabayashi and kobayashi \( jctb '12 \) that subset feedback vertex set is fixed parameter tractable for parameter k cygan et al asked whether the problem also admits a polynomial kernelization we answer the question of cygan et al positively by giving a randomized polynomial kernelization for the equivalent version where s is a set of edges in a first step we show that edge subset feedback vertex set has a randomized polynomial kernel parameterized by s k with o \( s 2k \) vertices for this we use the matroid based tools of kratsch and wahlstr om \( focs '12 \) that for example were used to obtain a polynomial kernel for s multiway cut next we present a preprocessing that reduces the given instance \( g , s , k \) to an equivalent instance \( g' , s' , k' \) where the size of s' is bounded by o \( k 4 \) these two results lead to a polynomial kernel for subset feedback vertex set with o \( k 9 \) vertices"
cs.SI,"degree distribution of nodes , especially a power law degree distribution , has been regarded as one of the most significant structural characteristics of social and information networks node degree , however , only discloses the first order structure of a network higher order structures such as the edge embeddedness and the size of communities may play more important roles in many online social networks in this paper , we provide empirical evidence on the existence of rich higherorder structural characteristics in online social networks , develop mathematical models to interpret and model these characteristics , and discuss their various applications in practice in particular , 1 \) we show that the embeddedness distribution of social links in many social networks has interesting and rich behavior that cannot be captured by well known network models we also provide empirical results showing a clear correlation between the embeddedness distribution and the average number of messages communicated between pairs of social network nodes 2 \) we formally prove that random k tree , a recent model for complex networks , has a power law embeddedness distribution , and show empirically that the random k tree model can be used to capture the rich behavior of higherorder structures we observed in real world social networks 3 \) going beyond the embeddedness , we show that a variant of the random k tree model can be used to capture the power law distribution of the size of communities of overlapping cliques discovered recently"
cs.AI,"in a previous paper the authors argued the case for incorporating ideas from innate immunity into articficial immune systems \( aiss \) and presented an outline for a conceptual framework for such systems a number of key general properties observed in the biological innate and adaptive immune systems were hughlighted , and how such properties might be instantiated in artificial systems was discussed in detail the next logical step is to take these ideas and build a software system with which aiss with these properties can be implemented and experimentally evaluated this paper reports on the results of that step the libtissue system"
cs.AI,"recent years witness a growing interest in nonstandard epistemic logics of knowing whether , knowing what , knowing how and so on these logics are usually not normal , i e , the standard axioms and reasoning rules for modal logic may be invalid in this paper , we show that the conditional knowing value logic proposed by wang and fan \( 2013 \) can be viewed as a disguised normal modal logic by treating the negation of kv operator as a special diamond under this perspective , it turns out that the original first order kripke semantics can be greatly simplified by introducing a ternary relation r i c in standard kripke models which associates one world with two i accessible worlds that do not agree on the value of constant c under intuitive constraints , the modal logic based on such kripke models is exactly the one studied by wang and fan \( 2013 , 2014 \) moreover , there is a very natural binary generalizations of the knowing value diamond , which , surprisingly , does not increase the expressive power of the logic the resulting logic with the binary diamond has a transparent normal modal system which sharpens our understanding of the knowing value logic and simplifies some previous hard problems"
cs.LG,"recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium to high throughput analysis tool , particularly for the validation of diagnostic and prognostic biomarkers however , as study size grows , the manual evaluation of these assays becomes a prohibitive limitation it vastly reduces throughput and greatly increases variability and expense we propose an algorithm tissue array co occurrence matrix analysis \( tacoma \) for quantifying cellular phenotypes based on textural regularity summarized by local inter pixel relationships the algorithm can be easily trained for any staining pattern , is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score pathologists' input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type with co training , the error rate of tacoma can be reduced substantially for a very small training sample \( e g , with size 30 \) we give theoretical insights into the success of co training via thinning of the feature set in a high dimensional setting when there is sufficient redundancy among the features tacoma is flexible , transparent and provides a scoring process that can be evaluated with clarity and confidence in a study based on an estrogen receptor \( er \) marker , we show that tacoma is comparable to , or outperforms , pathologists' performance in terms of accuracy and repeatability"
cs.SI,"when a piece of information \( microblog , photograph , video , link , etc \) starts to spread in a social network , an important question arises will it spread to viral proportions where viral is defined as an order of magnitude increase however , several previous studies have established that cascade size and frequency are related through a power law which leads to a severe imbalance in this classification problem in this paper , we devise a suite of measurements based on structural diversity the variety of social contexts \( communities \) in which individuals partaking in a given cascade engage we demonstrate these measures are able to distinguish viral from non viral cascades , despite the severe imbalance of the data for this problem further , we leverage these measurements as features in a classification approach , successfully predicting microblogs that grow from 50 to 500 reposts with precision of 0 69 and recall of 0 52 for the viral class despite this class comprising under 2 of samples this significantly outperforms our baseline approach as well as the current state of the art our work also demonstrates how we can tradeoff between precision and recall"
stat.ML,"most of previous work in knowledge base \( kb \) completion has focused on the problem of relation extraction in this work , we focus on the task of inferring missing entity type instances in a kb , a fundamental task for kb competition yet receives little attention due to the novelty of this task , we construct a large scale dataset and design an automatic evaluation methodology our knowledge base completion method uses information within the existing kb and external information from wikipedia we show that individual methods trained with a global objective that considers unobserved cells from both the entity and the type side gives consistently higher quality predictions compared to baseline methods we also perform manual evaluation on a small subset of the data to verify the effectiveness of our knowledge base completion methods and the correctness of our proposed automatic evaluation method"
cs.AI,"this paper studies single image depth perception in the wild , i e , recovering depth from a single image taken in unconstrained settings we introduce a new dataset depth in the wild consisting of images in the wild annotated with relative depth between pairs of random points we also propose a new algorithm that learns to estimate metric depth using annotations of relative depth compared to the state of the art , our algorithm is simpler and performs better experiments show that our algorithm , combined with existing rgb d data and our new relative depth annotations , significantly improves single image depth perception in the wild"
cs.AI|cs.LG|stat.ML,"multi step ahead forecasting is still an open challenge in time series forecasting several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing this paper aims to fill this gap by reviewing existing strategies for multi step ahead forecasting and comparing them in theoretical and practical terms to attain such an objective , we performed a large scale comparison of these different strategies using a large experimental benchmark \( namely the 111 series from the nn5 forecasting competition \) in addition , we considered the effects of deseasonalization , input variable selection , and forecast combination on these strategies and on multi step ahead forecasting at large the following three findings appear to be consistently supported by the experimental results multiple output strategies are the best performing approaches , deseasonalization leads to uniformly improved forecast accuracy , and input selection is more effective when performed in conjunction with deseasonalization"
cs.DS,"modern applications require processing streams of data for estimating statistical quantities such as quantiles with small amount of memory in many such applications , in fact , one needs to compute such statistical quantities for each of a large number of groups , which additionally restricts the amount of memory available for the stream for any particular group we address this challenge and introduce frugal streaming , that is algorithms that work with tiny typically , sub streaming amount of memory per group we design a frugal algorithm that uses only one unit of memory per group to compute a quantile for each group for stochastic streams where data items are drawn from a distribution independently , we analyze and show that the algorithm finds an approximation to the quantile rapidly and remains stably close to it we also propose an extension of this algorithm that uses two units of memory per group we show with extensive experiments with real world data from http trace and twitter that our frugal algorithms are comparable to existing streaming algorithms for estimating any quantile , but these existing algorithms use far more space per group and are unrealistic in frugal applications further , the two memory frugal algorithm converges significantly faster than the one memory algorithm"
cs.DM|cs.DS,"multi layer graphs consist of several graphs \( layers \) over the same vertex set they are motivated by real world problems where entities \( vertices \) are associated via multiple types of relationships \( edges in different layers \) we chart the border of computational \( in \) tractability for the class of subgraph detection problems on multi layer graphs , including fundamental problems such as maximum matching , finding certain clique relaxations \( motivated by community detection \) , or path problems mostly encountering hardness results , sometimes even for two or three layers , we can also spot some islands of tractability"
cs.AI|cs.LG,"we describe a learning based approach to hand eye coordination for robotic grasping from monocular images to learn hand eye coordination for grasping , we trained a large convolutional neural network to predict the probability that task space motion of the gripper will result in successful grasps , using only monocular camera images and independently of camera calibration or the current robot pose this requires the network to observe the spatial relationship between the gripper and objects in the scene , thus learning hand eye coordination we then use this network to servo the gripper in real time to achieve successful grasps to train our network , we collected over 800 , 000 grasp attempts over the course of two months , using between 6 and 14 robotic manipulators at any given time , with differences in camera placement and hardware our experimental evaluation demonstrates that our method achieves effective real time control , can successfully grasp novel objects , and corrects mistakes by continuous servoing"
cs.AI,"even with impressive advances in automated formal methods , certain problems in system verification and synthesis remain challenging examples include the verification of quantitative properties of software involving constraints on timing and energy consumption , and the automatic synthesis of systems from specifications the major challenges include environment modeling , incompleteness in specifications , and the complexity of underlying decision problems this position paper proposes sciduction , an approach to tackle these challenges by integrating inductive inference , deductive reasoning , and structure hypotheses deductive reasoning , which leads from general rules or concepts to conclusions about specific problem instances , includes techniques such as logical inference and constraint solving inductive inference , which generalizes from specific instances to yield a concept , includes algorithmic learning from examples structure hypotheses are used to define the class of artifacts , such as invariants or program fragments , generated during verification or synthesis sciduction constrains inductive and deductive reasoning using structure hypotheses , and actively combines inductive and deductive reasoning for instance , deductive techniques generate examples for learning , and inductive reasoning is used to guide the deductive engines we illustrate this approach with three applications \( i \) timing analysis of software \( ii \) synthesis of loop free programs , and \( iii \) controller synthesis for hybrid systems some future applications are also discussed"
cs.AI,"bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs there are large number of techniques have been developed to predict the bankruptcy , which helps the decision makers such as investors and financial analysts one of the bankruptcy prediction models is the hybrid model using fuzzy c means clustering and mars , which uses static ratios taken from the bank financial statements for prediction , which has its own theoretical advantages the performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm this dynamic selection can be accomplished by genetic algorithm and it improves the performance of prediction model"
cs.DM,a graph is 1 planar if it can be drawn on the plane so that each edge is crossed by at most one other edge in this note we give examples of class two 1 planar graphs with maximum degree six or seven
cs.LG,"locally weighted regression was created as a nonparametric learning method that is computationally efficient , can learn from very large amounts of data and add data incrementally an interesting feature of locally weighted regression is that it can work with spatially varying length scales , a beneficial property , for instance , in control problems however , it does not provide a generative model for function values and requires training and test data to be generated identically , independently gaussian \( process \) regression , on the other hand , provides a fully generative model without significant formal requirements on the distribution of training data , but has much higher computational cost and usually works with one global scale per input dimension using a localising function basis and approximate inference techniques , we take gaussian \( process \) regression to increasingly localised properties and toward the same computational complexity class as locally weighted regression"
cs.LG,"this paper introduces the encog library for java and c , a scalable , adaptable , multiplatform machine learning framework that was 1st released in 2008 encog allows a variety of machine learning models to be applied to datasets using regression , classification , and clustering various supported machine learning models can be used interchangeably with minimal recoding encog uses efficient multithreaded code to reduce training time by exploiting modern multicore processors the current version of encog can be downloaded from http www encog org"
cs.LG|stat.ML,"a ubiquitous phenomenon is that interesting signals or data concentrate on low dimensional smooth manifolds inside a high dimensional ambient euclidean space random projections are a simple and powerful tool for dimensionality reduction of such signals and data previous , seminal works have studied bounds on how the number of projections needed to preserve the geometry of these manifolds , at a given accuracy , scales with the intrinsic dimensionality , volume and curvature of the manifold however , such works employ definitions of volume and curvature that are inherently difficult to compute therefore such theory cannot be easily tested against numerical simulations to quantitatively understand the tightness of the proven bounds we instead study the typical distortions arising in random projections of an ensemble of smooth gaussian random manifolds in doing so , we find explicitly computable , approximate theoretical bounds on the number of projections required to preserve the geometric structure of these manifolds to a prescribed level of accuracy our bounds , while approximate , can only be violated with a probability that is exponentially small in the ambient dimension , and therefore they hold with high probability in most cases of practical interest moreover , unlike previous work , we test our theoretical bounds against numerical experiments on the actual geometric distortions that typically occur for random projections of random smooth manifolds through this comparison , we find our bounds are tighter than previous results by several orders of magnitude"
cs.SI,"we proposed a deterministic multidimensional growth model for small world networks the model can characterize the distinguishing properties of many real life networks with geometric space structure our results show the model possesses small world effect larger clustering coefficient and smaller characteristic path length we also obtain some accurate results for its properties including degree distribution , clustering coefficient and network diameter and discuss them it is also worth noting that we get an accurate analytical expression for calculating the characteristic path length we verify numerically and experimentally these main features"
cs.DM|cs.IT,"the well known approach of bose , ray chaudhuri and hocquenghem and its generalization by hartmann and tzeng are lower bounds on the minimum distance of simple root cyclic codes we generalize these two bounds to the case of repeated root cyclic codes and present a syndrome based burst error decoding algorithm with guaranteed decoding radius based on an associated folded cyclic code furthermore , we present a third technique for bounding the minimum hamming distance based on the embedding of a given repeated root cyclic code into a repeated root cyclic product code a second quadratic time probabilistic burst error decoding procedure based on the third bound is outlined index terms bound on the minimum distance , burst error , efficient decoding , folded code , repeated root cyclic code , repeated root cyclic product code"
cs.LG|stat.ML,"recommendation systems have been integrated into the majority of large online systems they tailor those systems to individual users by filtering and ranking information according to user profiles this adaptation process influences the way users interact with the system and , as a consequence , increases the difficulty of evaluating a recommendation algorithm with historical data \( via offline evaluation \) this paper analyses this evaluation bias and proposes a simple item weighting solution that reduces its impact the efficiency of the proposed solution is evaluated on real world data extracted from viadeo professional social network"
cs.DM,"zero forcing is an iterative graph coloring process whereby a colored vertex with a single uncolored neighbor forces that neighbor to be colored it is np hard to find a minimum zero forcing set a smallest set of initially colored vertices which forces the entire graph to be colored we show that the problem remains np hard when the initially colored set induces a connected subgraph we also give structural results about the connected zero forcing sets of a graph related to the graph 's density , separating sets , and certain induced subgraphs , and we characterize the cardinality of the minimum connected zero forcing sets of unicyclic graphs and variants of cactus and block graphs finally , we identify several families of graphs whose connected zero forcing sets define greedoids and matroids"
cs.DM,a bowtie is a graph consisting of two triangles with one vertex identified we show that the class of all \( countable \) graphs not containing a bowtie as a subgraph have a ramsey lift \( expansion \) this solves one of the old problems in the area and it is the first non trivial ramsey class with a non trivial algebraic closure
cs.DM,"a circulant is a cayley graph over a cyclic group a well covered graph is a graph in which all maximal stable sets are of the same size , or in other words , they are all maximum a cis graph is a graph in which every maximal stable set and every maximal clique intersect it is not difficult to show that a circulant g is a cis graph if and only if g and its complement are both well covered and the product of the independence and the clique numbers of g is equal to the number of vertices it is also easy to demonstrate that both families , the circulants and the cis graphs , are closed with respect to the operations of taking the complement and lexicographic product we study the structure of the cis circulants it is well known that all p 4 free graphs are cis in this paper , in addition to the simple family of the p 4 free circulants , we construct a non trivial sparse but infinite family of cis circulants we are not aware of any cis circulant that could not be obtained from graphs in this family by the operations of taking the complement and lexicographic product"
cs.LG,"during the past decades , to study high dimensional data in a large variety of problems , researchers have proposed many feature extraction algorithms one of the most effective approaches for optimal feature extraction is based on mutual information \( mi \) however it is not always easy to get an accurate estimation for high dimensional mi in terms of mi , the optimal feature extraction is creating a feature set from the data which jointly have the largest dependency on the target class and minimum redundancy in this paper , a component by component gradient ascent method is proposed for feature extraction which is based on one dimensional mi estimates we will refer to this algorithm as mutual information feature extraction \( mifx \) the performance of this proposed method is evaluated using uci databases the results indicate that mifx provides a robust performance over different data sets which are almost always the best or comparable to the best ones"
cs.DM,"perfect graphs were defined by claude berge in the 1960s they are important objects for graph theory , linear programming and combinatorial optimization claude berge made a conjecture about them , that was proved by chudnovsky , robertson , seymour and thomas in 2002 , and is now called the strong perfect graph theorem this is a survey about perfect graphs , mostly focused on the strong perfect graph theorem"
cs.LG|stat.ML,"in this contribution we describe an approach to evolve composite covariance functions for gaussian processes using genetic programming a critical aspect of gaussian processes and similar kernel based models such as svm is , that the covariance function should be adapted to the modeled data frequently , the squared exponential covariance function is used as a default however , this can lead to a misspecified model , which does not fit the data well in the proposed approach we use a grammar for the composition of covariance functions and genetic programming to search over the space of sentences that can be derived from the grammar we tested the proposed approach on synthetic data from two dimensional test functions , and on the mauna loa co2 time series the results show , that our approach is feasible , finding covariance functions that perform much better than a default covariance function for the co2 data set a composite covariance function is found , that matches the performance of a hand tuned covariance function"
cs.LG|stat.ML,"in real supervised learning scenarios , it is not uncommon that the training and test sample follow different probability distributions , thus rendering the necessity to correct the sampling bias focusing on a particular covariate shift problem , we derive high probability confidence bounds for the kernel mean matching \( kmm \) estimator , whose convergence rate turns out to depend on some regularity measure of the regression function and also on some capacity measure of the kernel by comparing kmm with the natural plug in estimator , we establish the superiority of the former hence provide concrete evidence understanding to the effectiveness of kmm under covariate shift"
cs.LG|stat.ML,"a new approach for functional data description is proposed in this paper it consists of a regression model with a discrete hidden logistic process which is adapted for modeling curves with abrupt or smooth regime changes the model parameters are estimated in a maximum likelihood framework through a dedicated expectation maximization \( em \) algorithm from the proposed generative model , a curve discrimination rule is derived using the maximum a posteriori rule the proposed model is evaluated using simulated curves and real world curves acquired during railway switch operations , by performing comparisons with the piecewise regression approach in terms of curve modeling and classification"
cs.SI,this work analyses the practice of sister city pairing we investigate structural properties of the resulting city and country networks and present rankings of the most central nodes in these networks we identify different country clusters and find that the practice of sister city pairing is not influenced by geographical proximity but results in highly assortative networks
cs.DM,"the paper is devoted to the methods of solving simultaneous recurrences specifically , we discuss transformation of matrix recurrences to regular recurrences and propose a way of solving special matrix recurrences of order three by their decomposition to matrix recurrences of order two"
cs.LG|stat.ML,"model complexity is an important factor to consider when selecting among graphical models when all variables are observed , the complexity of a model can be measured by its standard dimension , i e the number of independent parameters when hidden variables are present , however , standard dimension might no longer be appropriate one should instead use effective dimension \( geiger et al 1996 \) this paper is concerned with the computation of effective dimension first we present an upper bound on the effective dimension of a latent class \( lc \) model this bound is tight and its computation is easy we then consider a generalization of lc models called hierarchical latent class \( hlc \) models \( zhang 2002 \) we show that the effective dimension of an hlc model can be obtained from the effective dimensions of some related lc models we also demonstrate empirically that using effective dimension in place of standard dimension improves the quality of models learned from data"
cs.LG|stat.ML,"radial basis functions neural networks \( rbfnns \) are tools widely used in regression problems one of their principal drawbacks is that the formulation corresponding to the training with the supervision of both the centers and the weights is a highly non convex optimization problem , which leads to some fundamentally difficulties for traditional optimization theory and methods this paper presents a generalized canonical duality theory for solving this challenging problem we demonstrate that by sequential canonical dual transformations , the nonconvex optimization problem of the rbfnn can be reformulated as a canonical dual problem \( without duality gap \) both global optimal solution and local extrema can be classified several applications to one of the most used radial basis functions , the gaussian function , are illustrated our results show that even for one dimensional case , the global minimizer of the nonconvex problem may not be the best solution to the rbfnns , and the canonical dual theory is a promising tool for solving general neural networks training problems"
cs.AI,"the language of probability is used to define several different types of conditional statements there are four principal types subjunctive , material , existential , and feasibility two further types of conditionals are defined using the propositional calculus and boole 's mathematical logic truth functional and boolean feasibility \( which turn out to be special cases of probabilistic conditionals \) each probabilistic conditional is quantified by a fractional parameter between zero and one that says whether it is purely affirmative , purely negative , or intermediate in its sense conditionals can be specialized further by their content to express factuality and counterfactuality , and revised or reformulated to account for exceptions and confounding factors the various conditionals have distinct mathematical representations through intermediate probability expressions and logical formulas , each conditional is eventually translated into a set of polynomial equations and inequalities \( with real coefficients \) the polynomial systems from different types of conditionals exhibit different patterns of behavior , concerning for example opposing conditionals or false antecedents interesting results can be computed from the relevant polynomial systems using well known methods from algebra and computer science among other benefits , the proposed framework of analysis offers paraconsistent procedures for logical deduction that produce such familiar results as modus ponens , transitivity , disjunction introduction , and disjunctive syllogism all while avoiding any explosion of consequences from inconsistent premises several example problems from goodman and adams are analyzed a new perspective called polylogicism is presented mathematical logic that respects the diversity among conditionals in particular and logic problems in general"
cs.SI,"robustness of two coupled networks system has been studied only for dependency coupling \( s buldyrev et al , nature , 2010 \) and only for connectivity coupling \( e a leicht and r m d'souza , arxiv 09070894 \) here we study , using a percolation approach , a more realistic coupled networks system where both interdependent and interconnected links exist we find a rich and unusual phase transition phenomena including hybrid transition of mixed first and second order i e , discontinuities like a first order transition of the giant component followed by a continuous decrease to zero like a second order transition moreover , we find unusual discontinuous changes from second order to first order transition as a function of the dependency coupling between the two networks"
cs.AI|cs.DS,"satisfiability of random boolean expressions built from many clauses with k variables per clause \( random k satisfiability \) is a fundamental problem in combinatorial discrete optimization here we study random k satisfiability for k 3 and k 4 by the backtracking survey propagation algorithm this algorithm is able to find , in a time linear in the problem size , solutions within a region never reached before , very close to sat unsat threshold , and even beyond the freezing threshold for k 3 the algorithmic threshold practically coincides with the sat unsat threshold we also study the whitening process on all the solutions found by the backtracking survey propagation algorithm none contains frozen variables and the whitening procedure is able to remove all variables , following a two steps process , in a time that diverges approaching the algorithmic threshold"
cs.DM,"rotation symmetric boolean functions have important applications in the design of cryptographic algorithms in this paper , the conjecture about rotation symmetric boolean functions \( rsbfs \) of degree 3 proposed by cusik and st u a nic u a is proved as a result , the nonlinearity of such kind of functions is determined"
cs.LG,"we present novel methods for analysing the activation patterns of rnns and identifying the types of linguistic structure they learn as a case study , we use a multi task gated recurrent network model consisting of two parallel pathways with shared word embeddings trained on predicting the representations of the visual scene corresponding to an input sentence , and predicting the next word in the same sentence we show that the image prediction pathway is sensitive to the information structure of the sentence , and pays selective attention to lexical categories and grammatical functions that carry semantic information it also learns to treat the same input token differently depending on its grammatical functions in the sentence the language model is comparatively more sensitive to words with a syntactic function our analysis of the function of individual hidden units shows that each pathway contains specialized units tuned to patterns informative for the task , some of which can carry activations to later time steps to encode long term dependencies"
cs.SI,"we use a cooperative decision making \( cdm \) model to study the effect of committed minorities on group behavior in time of crisis the cdm model has been shown to generate consensus through a phase transition process that at criticality establishes long range correlations among the individuals within a model society in a condition of high consensus , the correlation function vanishes , thereby making the network recover the ordinary locality condition however , this state is not permanent and times of crisis occur when there is an ambiguity concerning a given social issue the correlation function within the cooperative system becomes similarly extended as it is observed at criticality this combination of independence \( free will \) and long range correlation makes it possible for very small but committed minorities to produce substantial changes in social consensus"
cs.IT|cs.LG|stat.ML,"robust tensor cp decomposition involves decomposing a tensor into low rank and sparse components we propose a novel non convex iterative algorithm with guaranteed recovery it alternates between low rank cp decomposition through gradient ascent \( a variant of the tensor power method \) , and hard thresholding of the residual we prove convergence to the globally optimal solution under natural incoherence conditions on the low rank component , and bounded level of sparse perturbations we compare our method with natural baselines which apply robust matrix pca either to the em flattened tensor , or to the matrix slices of the tensor our method can provably handle a far greater level of perturbation when the sparse tensor is block structured this naturally occurs in many applications such as the activity detection task in videos our experiments validate these findings thus , we establish that tensor methods can tolerate a higher level of gross corruptions compared to matrix methods"
cs.SI,"the detection of overlapping communities is a challenging problem which is gaining increasing interest in recent years because of the natural attitude of individuals , observed in real world networks , to participate in multiple groups at the same time this review gives a description of the main proposals in the field besides the methods designed for static networks , some new approaches that deal with the detection of overlapping communities in networks that change over time , are described methods are classified with respect to the underlying principles guiding them to obtain a network division in groups sharing part of their nodes for each of them we also report , when available , computational complexity and web site address from which it is possible to download the software implementing the method"
cs.LG,"quality control at each stage of production in textile industry has become a key factor to retaining the existence in the highly competitive global market problems of manual fabric defect inspection are lack of accuracy and high time consumption , where early and accurate fabric defect detection is a significant phase of quality control computer vision based , i e automated fabric defect inspection systems are thought by many researchers of different countries to be very useful to resolve these problems there are two major challenges to be resolved to attain a successful automated fabric defect inspection system they are defect detection and defect classification in this work , we discuss different techniques used for automated fabric defect classification , then show a survey of classifiers used in automated fabric defect inspection systems , and finally , compare these classifiers by using performance metrics this work is expected to be very useful for the researchers in the area of automated fabric defect inspection to understand and evaluate the many potential options in this field"
cs.DM|cs.DS,"this paper presents a variable neighborhood search \( vns \) algorithm for solving bandwidth coloring problem \( bcp \) and bandwidth multicoloring problem \( bmcp \) bcp and bmcp are generalizations of the well known vertex coloring problem and they are of a great interest from both theoretical and practical points of view presented vns combines a shaking procedure which perturbs the colors for an increasing number of vertices and a specific variable neighborhood descent \( vnd \) procedure , based on the specially designed arrangement of the vertices which are the subject of re coloring by this approach , local search is split in a series of disjoint procedures , enabling better choice of the vertices which are addressed to re color the experiments show that proposed method is highly competitive with the state of the art algorithms and improves 2 out of 33 previous best known solutions for bmcp"
cs.SI,"microbloging is an extremely prevalent broadcast medium amidst the internet fraternity these days people share their opinions and sentiments about variety of subjects like products , news , institutions , etc , every day on microbloging websites sentiment analysis plays a key role in prediction systems , opinion mining systems , etc twitter , one of the microbloging platforms allows a limit of 140 characters to its users this restriction stimulates users to be very concise about their opinion and twitter an ocean of sentiments to analyze twitter also provides developer friendly streaming api for data retrieval purpose allowing the analyst to search real time tweets from various users in this paper , we discuss the state of art of the works which are focused on twitter , the online social network platform , for sentiment analysis we survey various lexical , machine learning and hybrid approaches for sentiment analysis on twitter"
cs.LG,"this is an overview paper written in style of research proposal in recent years we introduced a general framework for large scale unconstrained optimization sequential subspace optimization \( sesop \) and demonstrated its usefulness for sparsity based signal image denoising , deconvolution , compressive sensing , computed tomography , diffraction imaging , support vector machines we explored its combination with parallel coordinate descent and separable surrogate function methods , obtaining state of the art results in above mentioned areas there are several methods , that are faster than plain sesop under specific conditions trust region newton method for problems with easily invertible hessian matrix truncated newton method when fast multiplication by hessian is available stochastic optimization methods for problems with large stochastic type data multigrid methods for problems with nested multilevel structure each of these methods can be further improved by merge with sesop one can also accelerate augmented lagrangian method for constrained optimization problems and alternating direction method of multipliers for problems with separable objective function and non separable constraints"
cs.LG|stat.ML,"the smart grid vision entails advanced information technology and data analytics to enhance the efficiency , sustainability , and economics of the power grid infrastructure aligned to this end , modern statistical learning tools are leveraged here for electricity market inference day ahead price forecasting is cast as a low rank kernel learning problem uniquely exploiting the market clearing process , congestion patterns are modeled as rank one components in the matrix of spatio temporally varying prices through a novel nuclear norm based regularization , kernels across pricing nodes and hours can be systematically selected even though market wide forecasting is beneficial from a learning perspective , it involves processing high dimensional market data the latter becomes possible after devising a block coordinate descent algorithm for solving the non convex optimization problem involved the algorithm utilizes results from block sparse vector recovery and is guaranteed to converge to a stationary point numerical tests on real data from the midwest iso \( miso \) market corroborate the prediction accuracy , computational efficiency , and the interpretative merits of the developed approach over existing alternatives"
cs.DS,"an octilinear drawing of a planar graph is one in which each edge is drawn as a sequence of horizontal , vertical and diagonal at 45 degrees line segments for such drawings to be readable , special care is needed in order to keep the number of bends small as the problem of finding planar octilinear drawings of minimum number of bends is np hard , in this paper we focus on upper and lower bounds from a recent result of keszegh et al on the slope number of planar graphs , we can derive an upper bound of 4n 10 bends for 8 planar graphs with n vertices we considerably improve this general bound and corresponding previous ones for triconnected 4 , 5 and 6 planar graphs we also derive non trivial lower bounds for these three classes of graphs by a technique inspired by the network flow formulation of tamassia"
cs.LG|cs.SI,"the study of social networks is a burgeoning research area however , most existing work deals with networks that simply encode whether relationships exist or not in contrast , relationships in signed networks can be positive \( like , trust \) or negative \( dislike , distrust \) the theory of social balance shows that signed networks tend to conform to some local patterns that , in turn , induce certain global characteristics in this paper , we exploit both local as well as global aspects of social balance theory for two fundamental problems in the analysis of signed networks sign prediction and clustering motivated by local patterns of social balance , we first propose two families of sign prediction methods measures of social imbalance \( mois \) , and supervised learning using high order cycles \( hocs \) these methods predict signs of edges based on triangles and ell cycles for relatively small values of ell interestingly , by examining measures of social imbalance , we show that the classic katz measure , which is used widely in unsigned link prediction , actually has a balance theoretic interpretation when applied to signed networks furthermore , motivated by the global structure of balanced networks , we propose an effective low rank modeling approach for both sign prediction and clustering for the low rank modeling approach , we provide theoretical performance guarantees via convex relaxations , scale it up to large problem sizes using a matrix factorization based algorithm , and provide extensive experimental validation including comparisons with local approaches our experimental results indicate that , by adopting a more global viewpoint of balance structure , we get significant performance and computational gains in prediction and clustering tasks on signed networks our work therefore highlights the usefulness of the global aspect of balance theory for the analysis of signed networks"
cs.DM|cs.DS,"short cycles connectivity is a generalization of ordinary connectivity instead by a path \( sequence of edges \) , two vertices have to be connected by a sequence of short cycles , in which two adjacent cycles have at least one common vertex if all adjacent cycles in the sequence share at least one edge , we talk about edge short cycles connectivity it is shown that the short cycles connectivity is an equivalence relation on the set of vertices , while the edge short cycles connectivity components determine an equivalence relation on the set of edges efficient algorithms for determining equivalence classes are presented short cycles connectivity can be extended to directed graphs \( cyclic and transitive connectivity \) for further generalization we can also consider connectivity by small cliques or other families of graphs"
cs.AI,"we present mantis , a new framework that automatically predicts program performance with high accuracy mantis integrates techniques from programming language and machine learning for performance modeling , and is a radical departure from traditional approaches mantis extracts program features , which are information about program execution runs , through program instrumentation it uses machine learning techniques to select features relevant to performance and creates prediction models as a function of the selected features through program analysis , it then generates compact code slices that compute these feature values for prediction our evaluation shows that mantis can achieve more than 93 accuracy with less than 10 training data set , which is a significant improvement over models that are oblivious to program features the system generates code slices that are cheap to compute feature values"
cs.LG|stat.ML,"the cumulative distribution network \( cdn \) is a recently developed class of probabilistic graphical models \( pgms \) permitting a copula factorization , in which the cdf , rather than the density , is factored despite there being much recent interest within the machine learning community about copula representations , there has been scarce research into the cdn , its amalgamation with copula theory , and no evaluation of its performance algorithms for inference , sampling , and learning in these models are underdeveloped compared those of other pgms , hindering widerspread use one advantage of the cdn is that it allows the factors to be parameterized as copulae , combining the benefits of graphical models with those of copula theory in brief , the use of a copula parameterization enables greater modelling flexibility by separating representation of the marginals from the dependence structure , permitting more efficient and robust learning another advantage is that the cdn permits the representation of implicit latent variables , whose parameterization and connectivity are not required to be specified unfortunately , that the model can encode only latent relationships between variables severely limits its utility in this thesis , we present inference , learning , and sampling for cdns , and further the state of the art first , we explain the basics of copula theory and the representation of copula cdns then , we discuss inference in the models , and develop the first sampling algorithm we explain standard learning methods , propose an algorithm for learning from data missing completely at random \( mcar \) , and develop a novel algorithm for learning models of arbitrary treewidth and size properties of the models and algorithms are investigated through monte carlo simulations we conclude with further discussion of the advantages and limitations of cdns , and suggest future work"
cs.LG|stat.ML,"efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems whereas distributed estimation of sample mean statistics has been the subject of a good deal of attention , computation of u statistics , relying on more expensive averaging over pairs of observations , is a less investigated area yet , such data functionals are essential to describe global properties of a statistical population , with important examples including area under the curve , empirical variance , gini mean difference and within cluster point scatter this paper proposes new synchronous and asynchronous randomized gossip algorithms which simultaneously propagate data across the network and maintain local estimates of the u statistic of interest we establish convergence rate bounds of o \( 1 t \) and o \( log t t \) for the synchronous and asynchronous cases respectively , where t is the number of iterations , with explicit data and network dependent terms beyond favorable comparisons in terms of rate analysis , numerical experiments provide empirical evidence the proposed algorithms surpasses the previously introduced approach"
cs.DM|cs.DS,"this note provides very simple , efficient algorithms for computing the number of distinct longest common subsequences of two input strings and for computing the number of lcs embeddings"
cs.LG|stat.ML,"sparse learning has recently received increasing attention in many areas including machine learning , statistics , and applied mathematics the mixed norm regularization based on the l1q norm with q 1 is attractive in many applications of regression and classification in that it facilitates group sparsity in the model the resulting optimization problem is , however , challenging to solve due to the inherent structure of the mixed norm regularization existing work deals with special cases with q 1 , 2 , infinity , and they cannot be easily extended to the general case in this paper , we propose an efficient algorithm based on the accelerated gradient method for solving the general l1q regularized problem one key building block of the proposed algorithm is the l1q regularized euclidean projection \( ep 1q \) our theoretical analysis reveals the key properties of ep 1q and illustrates why ep 1q for the general q is significantly more challenging to solve than the special cases based on our theoretical analysis , we develop an efficient algorithm for ep 1q by solving two zero finding problems to further improve the efficiency of solving large dimensional mixed norm regularized problems , we propose a screening method which is able to quickly identify the inactive groups , i e , groups that have 0 components in the solution this may lead to substantial reduction in the number of groups to be entered to the optimization an appealing feature of our screening method is that the data set needs to be scanned only once to run the screening compared to that of solving the mixed norm regularized problems , the computational cost of our screening test is negligible the key of the proposed screening method is an accurate sensitivity analysis of the dual optimal solution when the regularization parameter varies experimental results demonstrate the efficiency of the proposed algorithm"
cs.DS|cs.SI,"the maximum clique problem is a well known np hard problem with applications in data mining , network analysis , information retrieval and many other areas related to the world wide web there exist several algorithms for the problem with acceptable runtimes for certain classes of graphs , but many of them are infeasible for massive graphs we present a new exact algorithm that employs novel pruning techniques and is able to find maximum cliques in very large , sparse graphs quickly extensive experiments on different kinds of synthetic and real world graphs show that our new algorithm can be orders of magnitude faster than existing algorithms we also present a heuristic that runs orders of magnitude faster than the exact algorithm while providing optimal or near optimal solutions we illustrate a simple application of the algorithms in developing methods for detection of overlapping communities in networks"
cs.DS,"finding the coordinate wise maxima and the convex hull of a planar point set are probably the most classic problems in computational geometry we consider these problems in the self improving setting here , we have n distributions mathcal d 1 , ldots , mathcal d n of planar points an input point set \( p 1 , ldots , p n \) is generated by taking an independent sample p i from each mathcal d i , so the input is distributed according to the product mathcal d prod i mathcal d i a self improving algorithm repeatedly gets inputs from the distribution mathcal d \( which is a priori unknown \) , and it tries to optimize its running time for mathcal d the algorithm uses the first few inputs to learn salient features of the distribution mathcal d , before it becomes fine tuned to mathcal d let text optmax mathcal d \( resp text optch mathcal d \) be the expected depth of an emph optimal linear comparison tree computing the maxima \( resp convex hull \) for mathcal d our maxima algorithm eventually achieves expected running time o \( text optmax mathcal d n \) furthermore , we give a self improving algorithm for convex hulls with expected running time o \( text optch mathcal d n log log n \) our results require new tools for understanding linear comparison trees in particular , we convert a general linear comparison tree to a restricted version that can then be related to the running time of our algorithms another interesting feature is an interleaved search procedure to determine the likeliest point to be extremal with minimal computation this allows our algorithms to be competitive with the optimal algorithm for mathcal d"
cs.AI|cs.LG,"we use optimism to introduce generic asymptotically optimal reinforcement learning agents they achieve , with an arbitrary finite or compact class of environments , asymptotically optimal behavior furthermore , in the finite deterministic case we provide finite error bounds"
cs.AI,"we consider the discrete time infinite horizon optimal control problem formalized by markov decision processes we revisit the work of bertsekas and ioffe , that introduced lambda policy iteration , a family of algorithms parameterized by lambda that generalizes the standard algorithms value iteration and policy iteration , and has some deep connections with the temporal differences algorithm td \( lambda \) described by sutton and barto we deepen the original theory developped by the authors by providing convergence rate bounds which generalize standard bounds for value iteration described for instance by puterman then , the main contribution of this paper is to develop the theory of this algorithm when it is used in an approximate form and show that this is sound doing so , we extend and unify the separate analyses developped by munos for approximate value iteration and approximate policy iteration eventually , we revisit the use of this algorithm in the training of a tetris playing controller as originally done by bertsekas and ioffe we provide an original performance bound that can be applied to such an undiscounted control problem our empirical results are different from those of bertsekas and ioffe \( which were originally qualified as paradoxical and intriguing \) , and much more conform to what one would expect from a learning experiment we discuss the possible reason for such a difference"
cs.DM,"the critical exponent of an infinite word is defined to be the supremum of the exponent of each of its factors for k automatic sequences , we show that this critical exponent is always either a rational number or infinite , and its value is computable our results also apply to variants of the critical exponent , such as the initial critical exponent of berthe , holton , and zamboni and the diophantine exponent of adamczewski and bugeaud our work generalizes or recovers previous results of krieger and others , and is applicable to other situations e g , the computation of the optimal recurrence constant for a linearly recurrent k automatic sequence"
cs.SI,"anomalies in online social networks can signify irregular , and often illegal behaviour anomalies in online social networks can signify irregular , and often illegal behaviour detection of such anomalies has been used to identify malicious individuals , including spammers , sexual predators , and online fraudsters in this paper we survey existing computational techniques for detecting anomalies in online social networks we characterise anomalies as being either static or dynamic , and as being labelled or unlabelled , and survey methods for detecting these different types of anomalies we suggest that the detection of anomalies in online social networks is composed of two sub processes the selection and calculation of network features , and the classification of observations from this feature space in addition , this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas of future research"
cs.AI,"we observe a trend regarding restart strategies used in sat solvers a few years ago , most state of the art solvers restarted on average after a few thousands of backtracks currently , restarting after a dozen backtracks results in much better performance the main reason for this trend is that heuristics and data structures have become more restart friendly we expect further continuation of this trend , so future sat solvers will restart even more rapidly additionally , we present experimental results to support our observations"
cs.AI|stat.ML,"understanding how images of objects and scenes behave in response to specific ego motions is a crucial aspect of proper visual development , yet existing visual learning methods are conspicuously disconnected from the physical source of their images we propose to exploit proprioceptive motor signals to provide unsupervised regularization in convolutional neural networks to learn visual representations from egocentric video specifically , we enforce that our learned features exhibit equivariance i e they respond systematically to transformations associated with distinct ego motions with three datasets , we show that our unsupervised feature learning system significantly outperforms previous approaches on visual recognition and next best view prediction tasks in the most challenging test , we show that features learned from video captured on an autonomous driving platform improve large scale scene recognition in a disjoint domain"
stat.ML,"we address the problem of retrieving relevant experiments given a query experiment , motivated by the public databases of datasets in molecular biology and other experimental sciences , and the need of scientists to relate to earlier work on the level of actual measurement data since experiments are inherently noisy and databases ever accumulating , we argue that a retrieval engine should possess two particular characteristics first , it should compare models learnt from the experiments rather than the raw measurements themselves this allows incorporating experiment specific prior knowledge to suppress noise effects and focus on what is important second , it should be updated sequentially from newly published experiments , without explicitly storing either the measurements or the models , which is critical for saving storage space and protecting data privacy this promotes life long learning we formulate the retrieval as a ``supermodelling'' problem , of sequentially learning a model of the set of posterior distributions , represented as sets of mcmc samples , and suggest the use of particle learning based sequential dirichlet process mixture \( dpm \) for this purpose the relevance measure for retrieval is derived from the supermodel through the mixture representation we demonstrate the performance of the proposed retrieval method on simulated data and molecular biological experiments"
cs.IT|cs.LG|stat.ML,"in subspace clustering , a group of data points belonging to a union of subspaces are assigned membership to their respective subspaces this paper presents a new approach dubbed innovation pursuit \( ipursuit \) to the problem of subspace clustering using a new geometrical idea whereby each subspace is identified based on its novelty with respect to the other subspaces the proposed approach finds the subspaces consecutively by solving a series of simple linear optimization problems , each searching for some direction in the span of the data that is potentially orthogonal to all subspaces except for the one to be identified in one step of the algorithm a detailed mathematical analysis is provided establishing sufficient conditions for the proposed approach to correctly cluster the data points remarkably , the proposed approach can provably yield exact clustering even when the subspaces have significant intersections under mild conditions on the distribution of the data points in the subspaces moreover , it is shown that the complexity of ipursuit is almost independent of the dimension of the data the numerical simulations demonstrate that ipursuit can often outperform the state of the art subspace clustering algorithms , more so for subspaces with significant intersections"
cs.DM,"in the k edge connected spanning subgraph problem we are given a graph \( v , e \) and costs for each edge , and want to find a minimum cost subset f of e such that \( v , f \) is k edge connected we show there is a constant eps 0 so that for all k 1 , finding a \( 1 eps \) approximation for k ecss is np hard , establishing a gap between the unit cost and general cost versions next , we consider the multi subgraph cousin of k ecss , in which we purchase a multi subset f of e , with unlimited parallel copies available at the same cost as the original edge we conjecture that a \( 1 theta \( 1 k \) \) approximation algorithm exists , and we describe an approach based on graph decompositions applied to its natural linear programming \( lp \) relaxation the lp is essentially equivalent to the held karp lp for tsp and the undirected lp for steiner tree we give a family of extreme points for the lp which are more complex than those previously known"
cs.LG,"the use of small cell deployments in heterogeneous network \( hetnet \) environments is expected to be a key feature of 4g networks and beyond , and essential for providing higher user throughput and cell edge coverage however , due to different coverage sizes of macro and pico base stations \( bss \) , such a paradigm shift introduces additional requirements and challenges in dense networks among these challenges is the handover performance of user equipment \( ues \) , which will be impacted especially when high velocity ues traverse picocells in this paper , we propose a coordination based and context aware mobility management \( mm \) procedure for small cell networks using tools from reinforcement learning here , macro and pico bss jointly learn their long term traffic loads and optimal cell range expansion , and schedule their ues based on their velocities and historical rates \( exchanged among tiers \) the proposed approach is shown to not only outperform the classical mm in terms of ue throughput , but also to enable better fairness in average , a gain of up to 80 is achieved for ue throughput , while the handover failure probability is reduced up to a factor of three by the proposed learning based mm approaches"
cs.LG|stat.ML,"we consider the stochastic multi armed bandit problem with a prior distribution on the reward distributions we are interested in studying prior free and prior dependent regret bounds , very much in the same spirit as the usual distribution free and distribution dependent bounds for the non bayesian stochastic bandit building on the techniques of audibert and bubeck 2009 and russo and roy 2013 we first show that thompson sampling attains an optimal prior free bound in the sense that for any prior distribution its bayesian regret is bounded from above by 14 sqrt n k this result is unimprovable in the sense that there exists a prior distribution such that any algorithm has a bayesian regret bounded from below by frac 1 20 sqrt n k we also study the case of priors for the setting of bubeck et al 2013 \( where the optimal mean is known as well as a lower bound on the smallest gap \) and we show that in this case the regret of thompson sampling is in fact uniformly bounded over time , thus showing that thompson sampling can greatly take advantage of the nice properties of these priors"
cs.AI|cs.DS,"temporal logic model checking is a verification method in which we describe a system , the model , and then we verify whether some properties , expressed in a temporal logic formula , hold in the system it has many industrial applications in order to improve performance , some tools allow preprocessing of the model , verifying on line a set of properties reusing the same compiled model we prove that the complexity of the model checking problem , without any preprocessing or preprocessing the model or the formula in a polynomial data structure , is the same as a result preprocessing does not always exponentially improve performance symbolic model checking algorithms work by manipulating sets of states , and these sets are often represented by bdds it has been observed that the size of bdds may grow exponentially as the model and formula increase in size as a side result , we formally prove that a superpolynomial increase of the size of these bdds is unavoidable in the worst case while this exponential growth has been empirically observed , to the best of our knowledge it has never been proved so far in general terms this result not only holds for all types of bdds regardless of the variable ordering , but also for more powerful data structures , such as beds , rbcs , mtbdds , and adds"
cs.AI,"based on cumulative number of confirmed human cases of avian influenza \( h5n1 \) reported to world health organization \( who \) in the 2011 from 15 countries , indonesia has the largest number death because avian influenza which 146 deaths in this research , the researcher built a web mapping and dempster shafer theory as early warning system of avian influenza early warning is the provision of timely and effective information , through identified institutions , that allows individuals exposed to a hazard to take action to avoid or reduce their risk and prepare for effective response in this paper as example we use five symptoms as major symptoms which include depression , combs , wattle , bluish face region , swollen face region , narrowness of eyes , and balance disorders research location is in the lampung province , south sumatera the researcher reason to choose lampung province in south sumatera on the basis that has a high poultry population geographically , lampung province is located at 103040' to 105050' east longitude and 6045' 3045' south latitude , confined with south sumatera and bengkulu on north side , sunda strait on the side , java sea on the east side , indonesia ocean on the west side our approach uses dempster shafer theory to combine beliefs in certain hypotheses under conditions of uncertainty and ignorance , and allows quantitative measurement of the belief and plausibility in our identification result web mapping is also used for displaying maps on a screen to visualize the result of the identification process the result reveal that avian influenza warning system has successfully identified the existence of avian influenza and the maps can be displayed as the visualization"
cs.AI|cs.LG,"in this paper , we consider the problem of learning a visual representation from the raw spatiotemporal signals in videos for use in action recognition our representation is learned without supervision from semantic labels we formulate it as an unsupervised sequential verification task , i e , we determine whether a sequence of frames from a video is in the correct temporal order with this simple task and no semantic labels , we learn a powerful unsupervised representation using a convolutional neural network \( cnn \) the representation contains complementary information to that learned from supervised image datasets like imagenet qualitative results show that our method captures information that is temporally varying , such as human pose when used as pre training for action recognition , our method gives significant gains over learning without external data on benchmark datasets like ucf101 and hmdb51 our method can also be combined with supervised representations to provide an additional boost in accuracy for action recognition finally , to quantify its sensitivity to human pose , we show results for human pose estimation on the flic dataset that are competitive with approaches using significantly more supervised training data"
cs.DS|cs.LG,"given a directed acyclic graph g , and a set of values y on the vertices , the isotonic regression of y is a vector x that respects the partial order described by g , and minimizes x y , for a specified norm this paper gives improved algorithms for computing the isotonic regression for all weighted ell p norms with rigorous performance guarantees our algorithms are quite practical , and their variants can be implemented to run fast in practice"
cs.DM|cs.IT,"in this paper we define critical graphs as minimal graphs that support a given set of rates for the index coding problem , and study them for both the one shot and asymptotic setups for the case of equal rates , we find the critical graph with minimum number of edges for both one shot and asymptotic cases for the general case of possibly distinct rates , we show that for one shot and asymptotic linear index coding , as well as asymptotic non linear index coding , each critical graph is a union of disjoint strongly connected subgraphs \( uscs \) on the other hand , we identify a non uscs critical graph for a one shot non linear index coding problem next , we identify a few graph structures that are critical we also generalize some of our results to the groupcast problem in addition , we show that the capacity region of the index coding is additive for union of disjoint graphs"
cs.SI,"the stochastic block model \( sbm \) with two communities , or equivalently the planted bisection model , is a popular model of random graph exhibiting a cluster behaviour in the symmetric case , the graph has two equally sized clusters and vertices connect with probability p within clusters and q across clusters in the past two decades , a large body of literature in statistics and computer science has focused on providing lower bounds on the scaling of p q to ensure exact recovery in this paper , we identify a sharp threshold phenomenon for exact recovery if alpha pn log \( n \) and beta qn log \( n \) are constant \( with alpha beta \) , recovering the communities with high probability is possible if frac alpha beta 2 sqrt alpha beta 1 and impossible if frac alpha beta 2 sqrt alpha beta 1 in particular , this improves the existing bounds this also sets a new line of sight for efficient clustering algorithms while maximum likelihood \( ml \) achieves the optimal threshold \( by definition \) , it is in the worst case np hard this paper proposes an efficient algorithm based on a semidefinite programming relaxation of ml , which is proved to succeed in recovering the communities close to the threshold , while numerical experiments suggest it may achieve the threshold an efficient algorithm which succeeds all the way down to the threshold is also obtained using a partial recovery algorithm combined with a local improvement procedure"
cs.LG,"in order to achieve high efficiency of classification in intrusion detection , a compressed model is proposed in this paper which combines horizontal compression with vertical compression oner is utilized as horizontal com pression for attribute reduction , and affinity propagation is employed as vertical compression to select small representative exemplars from large training data as to be able to computationally compress the larger volume of training data with scalability , mapreduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned , on which common but efficient classification methods can be directly used experimental application study on two publicly available datasets of intrusion detection , kdd99 and cmdc2012 , demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times , most importantly at the cost of a minimal accuracy difference with less than 1 on average"
cs.AI|cs.LG,"dynamic probabilistic networks are a compact representation of complex stochastic processes in this paper we examine how to learn the structure of a dpn from data we extend structure scoring rules for standard probabilistic networks to the dynamic case , and show how to search for structure when some of the variables are hidden finally , we examine two applications where such a technology might be useful predicting and classifying dynamic behaviors , and learning causal orderings in biological processes we provide empirical results that demonstrate the applicability of our methods in both domains"
cs.DS,"association rules is a very important part of data mining it is used to find the interesting patterns from transaction databases apriori algorithm is one of the most classical algorithms of association rules , but it has the bottleneck in efficiency in this article , we proposed a prefixed itemset based data structure for candidate itemset generation , with the help of the structure we managed to improve the efficiency of the classical apriori algorithm"
cs.DM,"we give an excluded minor and a structural characterization of digraphs d that have the property that for every subdigraph h of d , the maximum number of disjoint circuits in h is equal to the minimum cardinality of a subset t of v \( h \) such that h t is acyclic"
cs.SI,"embers is an anticipatory intelligence system forecasting population level events in multiple countries of latin america a deployed system from 2012 , embers has been generating alerts 24x7 by ingesting a broad range of data sources including news , blogs , tweets , machine coded events , currency rates , and food prices in this paper , we describe our experiences operating embers continuously for nearly 4 years , with specific attention to the discoveries it has enabled , correct as well as missed forecasts , and lessons learnt from participating in a forecasting tournament including our perspectives on the limits of forecasting and ethical considerations"
cs.SI,"modularity introduced by newman and girvan phys rev e 69 , 026113 \( 2004 \) is a quality function for community detection numerous methods for modularity maximization have been developed so far in 2007 , barber phys rev e 76 , 066102 \( 2007 \) introduced a variant of modularity called bipartite modularity which is appropriate for bipartite networks although maximizing the standard modularity is known to be np hard , the computational complexity of maximizing bipartite modularity has yet to be revealed in this study , we prove that maximizing bipartite modularity is also np hard more specifically , we show the np completeness of its decision version by constructing a reduction from a classical partitioning problem"
cs.LG|stat.ML,"multi task feature learning aims to identity the shared features among tasks to improve generalization it has been shown that by minimizing non convex learning models , a better solution than the convex alternatives can be obtained therefore , a non convex model based on the capped ell 1 , ell 1 regularization was proposed in cite gong2013 , and a corresponding efficient multi stage multi task feature learning algorithm \( msmtfl \) was presented however , this algorithm harnesses a prescribed fixed threshold in the definition of the capped ell 1 , ell 1 regularization and the lack of adaptivity might result in suboptimal performance in this paper we propose to employ an adaptive threshold in the capped ell 1 , ell 1 regularized formulation , where the corresponding variant of msmtfl will incorporate an additional component to adaptively determine the threshold value this variant is expected to achieve a better feature selection performance over the original msmtfl algorithm in particular , the embedded adaptive threshold component comes from our previously proposed iterative support detection \( isd \) method cite wang2010 empirical studies on both synthetic and real world data sets demonstrate the effectiveness of this new variant over the original msmtfl"
cs.AI|cs.LG,"this paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi supervised learning subproblems which can be solved in quadratic time using label propagation based on k nearest neighbor graphs considering that this time cost is proportional to the number of all possible pairwise constraints , our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset the resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering other than the traditional constraint propagation on single source data , our approach is also extended to more challenging constraint propagation on multi source data where each pairwise constraint is defined over a pair of data points from different sources this multi source constraint propagation has an important application to cross modal multimedia retrieval extensive results have shown the superior performance of our approach"
cs.DM,"we show that there is a straightforward algorithm to determine if the polyhedron guaranteed to exist by alexandrov 's gluing theorem is a degenerate flat polyhedron , and to reconstruct it from the gluing instructions the algorithm runs in o \( n 3 \) time for polygons whose gluings are specified by n labels"
cs.SI,"human behaviors are often driven by human interests despite intense recent efforts in exploring the dynamics of human behaviors , little is known about human interest dynamics , partly due to the extreme difficulty in accessing the human mind from observations however , the availability of large scale data , such as those from e commerce and smart phone communications , makes it possible to probe into and quantify the dynamics of human interest using three prototypical big data sets , we investigate the scaling behaviors associated with human interest dynamics in particular , from the data sets we uncover power law scaling associated with the three basic quantities \( 1 \) the length of continuous interest , \( 2 \) the return time of visiting certain interest , and \( 3 \) interest ranking and transition we argue that there are three basic ingredients underlying human interest dynamics preferential return to previously visited interests , inertial effect , and exploration of new interests we develop a biased random walk model , incorporating the three ingredients , to account for the observed power law scaling relations our study represents the first attempt to understand the dynamical processes underlying human interest , which has significant applications in science and engineering , commerce , as well as defense , in terms of specific tasks such as recommendation and human behavior prediction"
cs.LG|stat.ML,"in our work , we propose a novel formulation for supervised dimensionality reduction based on a nonlinear dependency criterion called statistical distance correlation , szekely et al \( 2007 \) we propose an objective which is free of distributional assumptions on regression variables and regression model assumptions our proposed formulation is based on learning a low dimensional feature representation mathbf z , which maximizes the squared sum of distance correlations between low dimensional features mathbf z and response y , and also between features mathbf z and covariates mathbf x we propose a novel algorithm to optimize our proposed objective using the generalized minimization maximizaiton method of parizi et al \( 2015 \) we show superior empirical results on multiple datasets proving the effectiveness of our proposed approach over several relevant state of the art supervised dimensionality reduction methods"
cs.LG|stat.ML,"gaussian processes are widely used for regression tasks a known limitation in the application of gaussian processes to regression tasks is that the computation of the solution requires performing a matrix inversion the solution also requires the storage of a large matrix in memory these factors restrict the application of gaussian process regression to small and moderate size data sets we present an algorithm based on empirically determined subset selection that works well on both real world and synthetic datasets on the synthetic and real world datasets used in this study , the algorithm demonstrated sub linear time and space complexity the correctness proof for the algorithm is also presented"
cs.DS,"given an arbitrary , non negatively weighted , directed graph g \( v , e \) we present an algorithm that computes all pairs shortest paths in time mathcal o \( m n m lg n nt psi \( m , n \) \) , where m is the number of different edges contained in shortest paths and t psi \( m , n \) is a running time of an algorithm to solve a single source shortest path problem \( sssp \) this is a substantial improvement over a trivial n times application of psi that runs in mathcal o \( nt psi \( m , n \) \) in our algorithm we use psi as a black box and hence any improvement on psi results also in improvement of our algorithm furthermore , a combination of our method , johnson 's reweighting technique and topological sorting results in an mathcal o \( m n m lg n \) all pairs shortest path algorithm for arbitrarily weighted directed acyclic graphs in addition , we also point out a connection between the complexity of a certain sorting problem defined on shortest paths and sssp"
cs.LG|stat.ML,"we carefully study how well minimizing convex surrogate loss functions , corresponds to minimizing the misclassification error rate for the problem of binary classification with linear predictors in particular , we show that amongst all convex surrogate losses , the hinge loss gives essentially the best possible bound , of all convex loss functions , for the misclassification error rate of the resulting linear predictor in terms of the best possible margin error rate we also provide lower bounds for specific convex surrogates that show how different commonly used losses qualitatively differ from each other"
cs.AI,"most causal inference algorithms in the literature \( e g , pearl \( 2000 \) , spirtes et al \( 2000 \) , heckerman et al \( 1999 \) \) exploit an assumption usually referred to as the causal faithfulness or stability condition in this paper , we highlight two components of the condition used in constraint based algorithms , which we call adjacency faithfulness and orientation faithfulness we point out that assuming adjacency faithfulness is true , it is in principle possible to test the validity of orientation faithfulness based on this observation , we explore the consequence of making only the adjacency faithfulness assumption we show that the familiar pc algorithm has to be modified to be \( asymptotically \) correct under the weaker , adjacency faithfulness assumption roughly the modified algorithm , called conservative pc \( cpc \) , checks whether orientation faithfulness holds in the orientation phase , and if not , avoids drawing certain causal conclusions the pc algorithm would draw however , if the stronger , standard causal faithfulness condition actually obtains , the cpc algorithm is shown to output the same pattern as the pc algorithm does in the large sample limit we also present a simulation study showing that the cpc algorithm runs almost as fast as the pc algorithm , and outputs significantly fewer false causal arrowheads than the pc algorithm does on realistic sample sizes we end our paper by discussing how score based algorithms such as ges perform when the adjacency faithfulness but not the standard causal faithfulness condition holds , and how to extend our work to the fci algorithm , which allows for the possibility of latent variables"
cs.LG|stat.ML,"deep architecture such as hierarchical semi markov models is an important class of models for nested sequential data current exact inference schemes either cost cubic time in sequence length , or exponential time in model depth these costs are prohibitive for large scale problems with arbitrary length and depth in this contribution , we propose a new approximation technique that may have the potential to achieve sub cubic time complexity in length and linear time depth , at the cost of some loss of quality the idea is based on two well known methods gibbs sampling and rao blackwellisation we provide some simulation based evaluation of the quality of the rgbs with respect to run time and sequence length"
cs.DS,"we introduce an algorithm that iteratively produces a sequence of natural numbers k i and functions b i the number k \( i 1 \) arises as the first point of discontinuity of b i above k i we derive a set of properties of both sequences , suggesting that \( 1 \) the algorithm produces square free numbers k i , \( 2 \) all the square free numbers are generated as the output of the algorithm , and \( 3 \) the value of the moebius function mu \( k i \) can be evaluated as b i \( k \( i 1 \) \) b i \( k i \) the logical equivalence of these properties is rigorously proved the question remains open if one of these properties can be derived from the definition of the algorithm numerical evidence , limited to 5x10 6 , seems to support this conjecture , and shows a total running time linear or quadratic , depending on the implementation"
cs.SI,"the burst in the use of online social networks over the last decade has provided evidence that current rumor spreading models miss some fundamental ingredients in order to reproduce how information is disseminated in particular , recent literature has revealed that these models fail to reproduce the fact that some nodes in a network have an influential role when it comes to spread a piece of information in this work , we introduce two mechanisms with the aim of filling the gap between theoretical and experimental results the first model introduces the assumption that spreaders are not always active whereas the second model considers the possibility that an ignorant is not interested in spreading the rumor in both cases , results from numerical simulations show a higher adhesion to real data than classical rumor spreading models our results shed some light on the mechanisms underlying the spreading of information and ideas in large social systems and pave the way for more realistic diffusion models"
cs.DM|cs.DS,"we consider drawings of graphs that contain dense subgraphs we introduce intersection link representations for such graphs , in which each vertex u is represented by a geometric object r \( u \) and in which each edge \( u , v \) is represented by the intersection between r \( u \) and r \( v \) if it belongs to a dense subgraph or by a curve connecting the boundaries of r \( u \) and r \( v \) otherwise we study a notion of planarity , called clique planarity , for intersection link representations of graphs in which the dense subgraphs are cliques"
cs.LG|stat.ML,"bi linear feature learning models , like the gated autoencoder , were proposed as a way to model relationships between frames in a video by minimizing reconstruction error of one frame , given the previous frame , these models learn mapping units that encode the transformations inherent in a sequence , and thereby learn to encode motion in this work we extend bi linear models by introducing higher order mapping units that allow us to encode transformations between frames and transformations between transformations we show that this makes it possible to encode temporal structure that is more complex and longer range than the structure captured within standard bi linear models we also show that a natural way to train the model is by replacing the commonly used reconstruction objective with a prediction objective which forces the model to correctly predict the evolution of the input multiple steps into the future learning can be achieved by back propagating the multi step prediction through time we test the model on various temporal prediction tasks , and show that higher order mappings and predictive training both yield a significant improvement over bi linear models in terms of prediction accuracy"
cs.DS,"we present a parallel algorithm for the undirected s , t mincut problem with floating point valued weights our overarching algorithm uses an iteratively reweighted least squares framework this generates a sequence of laplacian linear systems , which we solve using parallel matrix algorithms our overall implementation is up to 30 times faster than a serial solver when using 128 cores"
cs.DS,this paper introduces a new algorithm for solving a sub class of quantified constraint satisfaction problems \( qcsp \) where existential quantifiers precede universally quantified inequalities on continuous domains this class of qcsps has numerous applications in engineering and design we propose here a new generic branch and prune algorithm for solving such continuous qcsps standard pruning operators and solution identification operators are specialized for universally quantified inequalities special rules are also proposed for handling the parameters of the constraints first experimentation show that our algorithm outperforms the state of the art methods
cs.AI|cs.IT,"we represent agents as sets of strings each string encodes a potential interaction with another agent or environment we represent the total set of dynamics between two agents as the intersection of their respective strings , we prove complexity properties of player interactions using algorithmic information theory we show how the proposed construction is compatible with universal artificial intelligence , in that the aixi model can be seen as universal with respect to interaction"
cs.AI,"this paper presents a novel approach for automatic recognition of group activities for video surveillance applications we propose to use a group representative to handle the recognition with a varying number of group members , and use an asynchronous hidden markov model \( ahmm \) to model the relationship between people furthermore , we propose a group activity detection algorithm which can handle both symmetric and asymmetric group activities , and demonstrate that this approach enables the detection of hierarchical interactions between people experimental results show the effectiveness of our approach"
cs.LG|stat.ML,"the significance of the study of the theoretical and practical properties of adaboost is unquestionable , given its simplicity , wide practical use , and effectiveness on real world datasets here we present a few open problems regarding the behavior of optimal adaboost , a term coined by rudin , daubechies , and schapire in 2004 to label the simple version of the standard adaboost algorithm in which the weak learner that adaboost uses always outputs the weak classifier with lowest weighted error among the respective hypothesis class of weak classifiers implicit in the weak learner we concentrate on the standard , vanilla version of optimal adaboost for binary classification that results from using an exponential loss upper bound on the misclassification training error we present two types of open problems one deals with general weak hypotheses the other deals with the particular case of decision stumps , as often and commonly used in practice answers to the open problems can have immediate significant impact to \( 1 \) cementing previously established results on asymptotic convergence properties of optimal adaboost , for finite datasets , which in turn can be the start to any convergence rate analysis \( 2 \) understanding the weak hypotheses class of effective decision stumps generated from data , which we have empirically observed to be significantly smaller than the typically obtained class , as well as the effect on the weak learner 's running time and previously established improved bounds on the generalization performance of optimal adaboost classifiers and \( 3 \) shedding some light on the self control that adaboost tends to exhibit in practice"
cs.AI|cs.LG,"control applications often feature tasks with similar , but not identical , dynamics we introduce the hidden parameter markov decision process \( hip mdp \) , a framework that parametrizes a family of related dynamical systems with a low dimensional set of latent factors , and introduce a semiparametric regression approach for learning its structure from data in the control setting , we show that a learned hip mdp rapidly identifies the dynamics of a new task instance , allowing an agent to flexibly adapt to task variations"
cs.AI|cs.LG|stat.ML,"approaches to learning bayesian networks from data typically combine a scoring function with a heuristic search procedure given a bayesian network structure , many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs when using such a scoring function , it is appropriate for the heuristic search algorithm to search over equivalence classes of bayesian networks as opposed to individual structures we present the general formulation of a search space for which the states of the search correspond to equivalence classes of structures using this space , any one of a number of heuristic search algorithms can easily be applied we compare greedy search performance in the proposed search space to greedy search performance in a search space for which the states correspond to individual bayesian network structures"
cs.DS,"given a model of a system and an objective , the model checking question asks whether the model satisfies the objective we study polynomial time problems in two classical models , graphs and markov decision processes \( mdps \) , with respect to several fundamental omega regular objectives , e g , rabin and streett objectives for many of these problems the best known upper bounds are quadratic or cubic , yet no super linear lower bounds are known in this work our contributions are two fold first , we present several improved algorithms , and second , we present the first conditional super linear lower bounds based on widely believed assumptions about the complexity of cnf sat and combinatorial boolean matrix multiplication a separation result for two models with respect to an objective means a conditional lower bound for one model that is strictly higher than the existing upper bound for the other model , and similarly for two objectives with respect to a model our results establish the following separation results \( 1 \) a separation of models \( graphs and mdps \) for disjunctive queries of reachability and b uchi objectives \( 2 \) two kinds of separations of objectives , both for graphs and mdps , namely , \( 2a \) the separation of dual objectives such as reachability safety \( for disjunctive questions \) and streett rabin objectives , and \( 2b \) the separation of conjunction and disjunction of multiple objectives of the same type such as safety , b uchi , and cob uchi in summary , our results establish the first model and objective separation results for graphs and mdps for various classical omega regular objectives quite strikingly , we establish conditional lower bounds for the disjunction of objectives that are strictly higher than the existing upper bounds for the conjunction of the same objectives"
cs.AI|cs.LG,"in evolutionary algorithms a critical parameter that must be tuned is that of selection pressure if it is set too low then the rate of convergence towards the optimum is likely to be slow alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population the recent fitness uniform selection scheme \( fuss \) is a conceptually simple but somewhat radical approach to addressing this problem rather than biasing the selection towards higher fitness , fuss biases selection towards sparsely populated fitness levels in this paper we compare the relative performance of fuss with the well known tournament selection scheme on a range of problems"
cs.DM,"addressing a question of gowers , we determine the order of the tower height for the partition size in a version of szemer 'edi 's regularity lemma"
cs.LG|stat.ML,"determinantal point processes \( dpps \) are well suited for modeling repulsion and have proven useful in many applications where diversity is desired while dpps have many appealing properties , such as efficient sampling , learning the parameters of a dpp is still considered a difficult problem due to the non convex nature of the likelihood function in this paper , we propose using bayesian methods to learn the dpp kernel parameters these methods are applicable in large scale and continuous dpp settings even when the exact form of the eigendecomposition is unknown we demonstrate the utility of our dpp learning methods in studying the progression of diabetic neuropathy based on spatial distribution of nerve fibers , and in studying human perception of diversity in images"
cs.DS,"the best known algorithm to compute the jacobi symbol of two n bit integers runs in time o \( m \( n \) log n \) , using sch onhage 's fast continued fraction algorithm combined with an identity due to gauss we give a different o \( m \( n \) log n \) algorithm based on the binary recursive gcd algorithm of stehl 'e and zimmermann our implementation which to our knowledge is the first to run in time o \( m \( n \) log n \) is faster than gmp 's quadratic implementation for inputs larger than about 10000 decimal digits"
cs.DM|cs.SI,"in the context of influence propagation in a social graph , we can identify three orthogonal dimensions the number of seed nodes activated at the beginning \( known as budget \) , the expected number of activated nodes at the end of the propagation \( known as expected spread or coverage \) , and the time taken for the propagation we can constrain one or two of these and try to optimize the third in their seminal paper , kempe et al constrained the budget , left time unconstrained , and maximized the coverage this problem is known as influence maximization in this paper , we study alternative optimization problems which are naturally motivated by resource and time constraints on viral marketing campaigns in the first problem , termed minimum target set selection \( or mintss for short \) , a coverage threshold n is given and the task is to find the minimum size seed set such that by activating it , at least n nodes are eventually activated in the expected sense in the second problem , termed mintime , a coverage threshold n and a budget threshold k are given , and the task is to find a seed set of size at most k such that by activating it , at least n nodes are activated , in the minimum possible time both these problems are np hard , which motivates our interest in their approximation for mintss , we develop a simple greedy algorithm and show that it provides a bicriteria approximation we also establish a generic hardness result suggesting that improving it is likely to be hard for mintime , we show that even bicriteria and tricriteria approximations are hard under several conditions however , if we allow the budget to be boosted by a logarithmic factor and allow the coverage to fall short , then the problem can be solved exactly in ptime finally , we show the value of the approximation algorithms , by comparing them against various heuristics"
cs.AI,"the methods used to establish pspace bounds for modal logics can roughly be grouped into two classes syntax driven methods establish that exhaustive proof search can be performed in polynomial space whereas semantic approaches directly construct shallow models in this paper , we follow the latter approach and establish generic pspace bounds for a large and heterogeneous class of modal logics in a coalgebraic framework in particular , no complete axiomatisation of the logic under scrutiny is needed this does not only complement our earlier , syntactic , approach conceptually , but also covers a wide variety of new examples which are difficult to harness by purely syntactic means apart from re proving known complexity bounds for a large variety of structurally different logics , we apply our method to obtain previously unknown pspace bounds for elgesem 's logic of agency and for graded modal logic over reflexive frames"
cs.AI,"belief fusion is the principle of combining separate beliefs or bodies of evidence originating from different sources depending on the situation to be modelled , different belief fusion methods can be applied cumulative and averaging belief fusion is defined for fusing opinions in subjective logic , and for fusing belief functions in general the principle of fission is the opposite of fusion , namely to eliminate the contribution of a specific belief from an already fused belief , with the purpose of deriving the remaining belief this paper describes fission of cumulative belief as well as fission of averaging belief in subjective logic these operators can for example be applied to belief revision in bayesian belief networks , where the belief contribution of a given evidence source can be determined as a function of a given fused belief and its other contributing beliefs"
cs.AI,"in answer set programming \( asp \) , a problem at hand is solved by \( i \) writing a logic program whose answer sets correspond to the solutions of the problem , and by \( ii \) computing the answer sets of the program using an answer set solver as a search engine typically , a programmer creates a series of gradually improving logic programs for a particular problem when optimizing program length and execution time on a particular solver this leads the programmer to a meta level problem of ensuring that the programs are equivalent , i e , they give rise to the same answer sets to ease answer set programming at methodological level , we propose a translation based method for verifying the equivalence of logic programs the basic idea is to translate logic programs p and q under consideration into a single logic program eqt \( p , q \) whose answer sets \( if such exist \) yield counter examples to the equivalence of p and q the method is developed here in a slightly more general setting by taking the visibility of atoms properly into account when comparing answer sets the translation based approach presented in the paper has been implemented as a translator called lpeq that enables the verification of weak equivalence within the smodels system using the same search engine as for the search of models our experiments with lpeq and smodels suggest that establishing the equivalence of logic programs in this way is in certain cases much faster than naive cross checking of answer sets"
cs.LG,"we study the tail bound of the emperical covariance of multivariate normal distribution following the work of \( gittens tropp , 2011 \) , we provide a tail bound with a small constant"
cs.AI|cs.SI,"we propose an analysis of the codified law of france as a structured system fifty two legal codes are selected on the basis of explicit legal criteria and considered as vertices with their mutual quotations forming the edges in a network which properties are analyzed relying on graph theory we find that a group of 10 codes are simultaneously the most citing and the most cited by other codes , and are also strongly connected together so forming a rich club sub graph three other code communities are also found that somewhat partition the legal field is distinct thematic sub domains the legal interpretation of this partition is opening new untraditional lines of research we also conjecture that many legal systems are forming such new kind of networks that share some properties in common with small worlds but are far denser we propose to call concentrated world"
cs.SI,"massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media examples include rumors , hoaxes , fake news , and conspiracy theories at the same time , several journalistic organizations devote significant efforts to high quality fact checking of online claims the resulting information cascades contain instances of both accurate and inaccurate information , unfold over multiple time scales , and often reach audiences of considerable size all these factors pose challenges for the study of the social dynamics of online news sharing here we introduce hoaxy , a platform for the collection , detection , and analysis of online misinformation and its related fact checking efforts we discuss the design of the platform and present a preliminary analysis of a sample of public tweets containing both fake news and fact checking we find that , in the aggregate , the sharing of fact checking content typically lags that of misinformation by 10 20 hours moreover , fake news are dominated by very active users , while fact checking is a more grass roots activity with the increasing risks connected to massive online misinformation , social news observatories have the potential to help researchers , journalists , and the general public understand the dynamics of real and fake news sharing"
cs.LG|stat.ML,"collaborative filtering \( cf \) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products we consider in this paper the problem of online and interactive cf given the current ratings associated with a user , what queries \( new ratings \) would most improve the quality of the recommendations made \? we cast this terms of expected value of information \( evoi \) but the online computational cost of computing optimal queries is prohibitive we show how offline prototyping and computation of bounds on evoi can be used to dramatically reduce the required online computation the framework we develop is general , but we focus on derivations and empirical study in the specific case of the multiple cause vector quantization model"
cs.DM,"a recurrent formula is presented , for the enumeration of the compositions of positive integers as sums over multisets of positive integers , that closely resembles euler 's recurrence based on the pentagonal numbers , but where the coefficients result from a discrete integration of euler 's coefficients both a bijective proof and one based on generating functions show the equivalence of the subject recurrences"
cs.SI,"recently , there is a surge of social recommendation , which leverages social relations among users to improve recommendation performance however , in many applications , social relations are absent or very sparse meanwhile , the attribute information of users or items may be rich it is a big challenge to exploit these attribute information for the improvement of recommendation performance in this paper , we organize objects and relations in recommendation system as a heterogeneous information network , and introduce meta path based similarity measure to evaluate the similarity of users or items furthermore , a matrix factorization based dual regularization framework simmf is proposed to flexibly integrate different types of information through adopting the similarity of users and items as regularization on latent factors of users and items extensive experiments not only validate the effectiveness of simmf but also reveal some interesting findings we find that attribute information of users and items can significantly improve recommendation accuracy , and their contribution seems more important than that of social relations the experiments also reveal that different regularization models have obviously different impact on users and items"
cs.AI|cs.LG,"in this study , a spectral graph theoretic grouping strategy for weakly supervised classification is introduced , where a limited number of labelled samples and a larger set of unlabelled samples are used to construct a larger annotated training set composed of strongly labelled and weakly labelled samples the inherent relationship between the set of strongly labelled samples and the set of unlabelled samples is established via spectral grouping , with the unlabelled samples subsequently weakly annotated based on the strongly labelled samples within the associated spectral groups a number of similarity graph models for spectral grouping , including two new similarity graph models introduced in this study , are explored to investigate their performance in the context of weakly supervised classification in handling different types of data experimental results using benchmark datasets as well as real emg datasets demonstrate that the proposed approach to weakly supervised classification can provide noticeable improvements in classification performance , and that the proposed similarity graph models can lead to ultimate learning results that are either better than or on a par with existing similarity graph models in the context of spectral grouping for weakly supervised classification"
cs.DM|cs.LG,"monotone boolean functions , and the monotone boolean circuits that compute them , have been intensively studied in complexity theory in this paper we study the structure of boolean functions in terms of the minimum number of negations in any circuit computing them , a complexity measure that interpolates between monotone functions and the class of all functions we study this generalization of monotonicity from the vantage point of learning theory , giving near matching upper and lower bounds on the uniform distribution learnability of circuits in terms of the number of negations they contain our upper bounds are based on a new structural characterization of negation limited circuits that extends a classical result of a a markov our lower bounds , which employ fourier analytic tools from hardness amplification , give new results even for circuits with no negations \( i e monotone functions \)"
cs.SI,"popular internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses using crowd sourcing systems however , crowd sourcing systems can also pose a real challenge to existing security mechanisms deployed to protect internet services many of these techniques make the assumption that malicious activity is generated automatically by machines , and perform poorly or fail if users can be organized to perform malicious tasks using crowd sourcing systems through measurements , we have found surprising evidence showing that not only do malicious crowd sourcing systems exist , but they are rapidly growing in both user base and total revenue in this paper , we describe a significant effort to study and understand these crowdturfing systems in today 's internet we use detailed crawls to extract data about the size and operational structure of these crowdturfing systems we analyze details of campaigns offered and performed in these sites , and evaluate their end to end effectiveness by running active , non malicious campaigns of our own finally , we study and compare the source of workers on crowdturfing sites in different countries our results suggest that campaigns on these systems are highly effective at reaching users , and their continuing growth poses a concrete threat to online communities such as social networks , both in the us and elsewhere"
cs.AI|cs.IT|cs.LG,"recent works have validated the possibility of improving energy efficiency in radio access networks \( rans \) , achieved by dynamically turning on off some base stations \( bss \) in this paper , we extend the research over bs switching operations , which should match up with traffic load variations instead of depending on the dynamic traffic loads which are still quite challenging to precisely forecast , we firstly formulate the traffic variations as a markov decision process afterwards , in order to foresightedly minimize the energy consumption of rans , we design a reinforcement learning framework based bs switching operation scheme furthermore , to avoid the underlying curse of dimensionality in reinforcement learning , a transfer actor critic algorithm \( tact \) , which utilizes the transferred learning expertise in historical periods or neighboring regions , is proposed and provably converges in the end , we evaluate our proposed scheme by extensive simulations under various practical configurations and show that the proposed tact algorithm contributes to a performance jumpstart and demonstrates the feasibility of significant energy efficiency improvement at the expense of tolerable delay performance"
cs.LG|stat.ML,"this paper proposes a generative model , the latent dirichlet hidden markov models \( ldhmm \) , for characterizing a database of sequential behaviors \( sequences \) ldhmms posit that each sequence is generated by an underlying markov chain process , which are controlled by the corresponding parameters \( i e , the initial state vector , transition matrix and the emission matrix \) these sequence level latent parameters for each sequence are modeled as latent dirichlet random variables and parameterized by a set of deterministic database level hyper parameters through this way , we expect to model the sequence in two levels the database level by deterministic hyper parameters and the sequence level by latent parameters to learn the deterministic hyper parameters and approximate posteriors of parameters in ldhmms , we propose an iterative algorithm under the variational em framework , which consists of e and m steps we examine two different schemes , the fully factorized and partially factorized forms , for the framework , based on different assumptions we present empirical results of behavior modeling and sequence classification on three real world data sets , and compare them to other related models the experimental results prove that the proposed ldhmms produce better generalization performance in terms of log likelihood and deliver competitive results on the sequence classification problem"
cs.LG,"traditional convolutional neural networks \( cnn \) are stationary and feedforward they neither change their parameters during evaluation nor use feedback from higher to lower layers real brains , however , do so does our deep attention selective network \( dasnet \) architecture dasnets feedback structure can dynamically alter its convolutional filter sensitivities during classification it harnesses the power of sequential processing to improve classification performance , by allowing the network to iteratively focus its internal attention on some of its convolutional filters feedback is trained through direct policy search in a huge million dimensional parameter space , through scalable natural evolution strategies \( snes \) on the cifar 10 and cifar 100 datasets , dasnet outperforms the previous state of the art model"
cs.SI,"in the last decade , the advertisement market spread significantly in the web and mobile app system its effectiveness is also due thanks to the possibility to target the advertisement on the specific interests of the actual user , other than on the content of the website hosting the advertisement in this scenario , became of great value services that collect and hence can provide information about the browsing user , like facebook and google in this paper , we show how to maliciously exploit the google targeted advertising system to infer personal information in google user profiles in particular , the attack we consider is external from google and relies on combining data from google adwords with other data collected from a website of the google display network we validate the effectiveness of our proposed attack , also discussing possible application scenarios the result of our research shows a significant practical privacy issue behind such type of targeted advertising service , and call for further investigation and the design of more privacy aware solutions , possibly without impeding the current business model involved in online advertisement"
cs.LG,"in this paper , we propose training very deep neural networks \( dnns \) for supervised learning of hash codes existing methods in this context train relatively shallow networks limited by the issues arising in back propagation \( eg vanishing gradients \) as well as computational efficiency we propose a novel and efficient training algorithm inspired by alternating direction method of multipliers \( admm \) that overcomes some of these limitations our method decomposes the training process into independent layer wise local updates through auxiliary variables empirically we observe that our training algorithm always converges and its computational complexity is linearly proportional to the number of edges in the networks empirically we manage to train dnns with 64 hidden layers and 1024 nodes per layer for supervised hashing in about 3 hours using a single gpu our proposed very deep supervised hashing \( vdsh \) method significantly outperforms the state of the art on several benchmark datasets"
cs.SI,"recent years have witnessed the explosion of online social networks \( osns \) they provide powerful it innovations for online social activities such as organizing contacts , publishing contents , and sharing interests between friends who may never meet before as more and more people become the active users of online social networks , one may ponder questions such as \( 1 \) do osns indeed improve our sociability \? \( 2 \) to what extent can we expand our offline social spectrum in osns \? \( 3 \) can we identify some interesting user behaviors in osns \? our work in this paper just aims to answer these interesting questions to this end , we pay a revisit to the well known dunbar 's number in online social networks our main research contributions are as follows first , to our best knowledge , our work is the first one that systematically validates the existence of the online dunbar 's number in the range of 200 , 300 to reach this , we combine using local structure analysis and user interaction analysis for extensive real world osns second , we divide osns users into two categories rational and aggressive , and find that rational users intend to develop close and reciprocated relationships , whereas aggressive users have no consistent behaviors third , we build a simple model to capture the constraints of time and cognition that affect the evolution of online social networks finally , we show the potential use of our findings in viral marketing and privacy management in online social networks"
cs.LG|stat.ML,"we present a novel architecture , the stacked what where auto encoders \( swwae \) , which integrates discriminative and generative pathways and provides an unified approach to supervised , semi supervised and unsupervised learning without requiring sampling an instantiation of swwae is essentially a convolutional net \( convnet \) coupled with a deconvolutional net \( deconvnet \) the objective function includes reconstruction terms that penalizes the hidden states in the deconvnet for being different from the hidden state of the convnet each pooling layer is seen producing two sets of variables the what which arefed to the next layer , and the where \( the max pooling switch positions \) that arefed to the corresponding layer in the generative decoder"
cs.SI,"the naming games \( ng \) are agent based models for agreement dynamics , peer pressure and herding in social networks , and protocol selection in autonomous ad hoc sensor networks by introducing a small noise term to the ng , the resulting markov chain model called noisy naming games \( nng \) are ergodic , in which all partial consensus states are recurrent by using gibbs markov equivalence we show how to get the nng 's stationary distribution in terms of the local specification of a related markov random field \( mrf \) by ordering the partially synchronized states according to their gibbs energy , taken here to be a good measure of social tension , this method offers an enhanced method for community detection in social interaction data we show how the lowest gibbs energy multi name states separate and display the hidden community structures within a social network"
cs.DS,"we prove a number of results around kernelization of problems parameterized by the size of a given vertex cover of the input graph we provide three sets of simple general conditions characterizing problems admitting kernels of polynomial size our characterizations not only give generic explanations for the existence of many known polynomial kernels for problems like q coloring , odd cycle transversal , chordal deletion , eta transversal , or long path , parameterized by the size of a vertex cover , but also imply new polynomial kernels for problems like f minor free deletion , which is to delete at most k vertices to obtain a graph with no minor from a fixed finite set f while our characterization captures many interesting problems , the kernelization complexity landscape of parameterizations by vertex cover is much more involved we demonstrate this by several results about induced subgraph and minor containment testing , which we find surprising while it was known that testing for an induced complete subgraph has no polynomial kernel unless np is in conp poly , we show that the problem of testing if a graph contains a complete graph on t vertices as a minor admits a polynomial kernel on the other hand , it was known that testing for a path on t vertices as a minor admits a polynomial kernel , but we show that testing for containment of an induced path on t vertices is unlikely to admit a polynomial kernel"
cs.SI,"we present work in jointly inferring the unique individuals as well as their social rank within a collection of letters from an old assyrian trade colony in k ultepe , turkey , settled by merchants from the ancient city of assur for approximately 200 years between 1950 1750 bce , the height of the middle bronze age using a probabilistic latent variable model , we leverage pairwise social differences between names in cuneiform tablets to infer a single underlying social order that best explains the data we observe evaluating our output with published judgments by domain experts suggests that our method may be used for building informed hypotheses that are driven by data , and that may offer promising avenues for directed research by assyriologists"
cs.SI,"social dynamics determined by voting in a stochastic environment is analyzed for a society composed of two cohesive groups of similar size within the model of random walks determined by voting , explicit formulas are derived for the capital increments of the groups against the parameters of the environment and claim thresholds of the groups the unanimous acceptance and unanimous rejection group rules are considered as the voting procedures claim thresholds are evaluated that are most beneficial to the participants of the groups and to the society as a whole"
cs.AI|cs.LG|stat.ML,"annotation errors can significantly hurt classifier performance , yet datasets are only growing noisier with the increased use of amazon mechanical turk and techniques like distant supervision that automatically generate labels in this paper , we present a robust extension of logistic regression that incorporates the possibility of mislabelling directly into the objective our model can be trained through nearly the same means as logistic regression , and retains its efficiency on high dimensional datasets through named entity recognition experiments , we demonstrate that our approach can provide a significant improvement over the standard model when annotation errors are present"
cs.DM|cs.DS,"we consider the problem of clustering the nodes of directed acyclic graphs when the replication of logic is not allowed we show that the problem is bf np hard even when the vertices of the dag are unweighted and the cluster capacity is 2 moreover , when the vertices of the dag are weighted , the problem does not admit a \( 2 epsilon \) approximation algorithm for each epsilon 0 , unless bf p np on the positive side , we show that in case the vertices of the dag are unweighted and m 2 , the problem admits a 2 approximation algorithm finally , we present some cases when the problem can be solved in polynomial time"
cs.DM,"a set of integers is s recognizable in an abstract numeration system s if the language made up of the representations of its elements is accepted by a finite automaton for abstract numeration systems built over bounded languages with at least three letters , we show that multiplication by an integer lambda ge2 does not preserve s recognizability , meaning that there always exists a s recognizable set x such that lambda x is not s recognizable the main tool is a bijection between the representation of an integer over a bounded language and its decomposition as a sum of binomial coefficients with certain properties , the so called combinatorial numeration system"
cs.LG,"we present an efficient learning algorithm for the problem of training neural networks with discrete synapses , a well known hard \( np complete \) discrete optimization problem the algorithm is a variant of the so called max sum \( ms \) algorithm in particular , we show how , for bounded integer weights with q distinct states and independent concave a priori distribution \( e g l 1 regularization \) , the algorithm 's time complexity can be made to scale as o left \( n log n right \) per node update , thus putting it on par with alternative schemes , such as belief propagation \( bp \) , without resorting to approximations two special cases are of particular interest binary synapses w in 1 , 1 and ternary synapses w in 1 , 0 , 1 with l 0 regularization the algorithm we present performs as well as bp on binary perceptron learning problems , and may be better suited to address the problem on fully connected two layer networks , since inherent symmetries in two layer networks are naturally broken using the ms approach"
cs.DM,"we prove that every simple bridgeless cubic graph with n 8 vertices has a travelling salesman tour of length at most 1 3n 2 , which can be constructed in polynomial time the algorithm provides the best polynomial time approximation of tsp on simple cubic graphs known at present"
cs.LG,"this paper introduces latent relational analysis \( lra \) , a method for measuring relational similarity lra has potential applications in many areas , including information extraction , word sense disambiguation , machine translation , and information retrieval relational similarity is correspondence between relations , in contrast with attributional similarity , which is correspondence between attributes when two words have a high degree of attributional similarity , we call them synonyms when two pairs of words have a high degree of relational similarity , we say that their relations are analogous for example , the word pair mason stone is analogous to the pair carpenter wood past work on semantic similarity measures has mainly been concerned with attributional similarity recently the vector space model \( vsm \) of information retrieval has been adapted to the task of measuring relational similarity , achieving a score of 47 on a collection of 374 college level multiple choice word analogy questions in the vsm approach , the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus lra extends the vsm approach in three ways \( 1 \) the patterns are derived automatically from the corpus \( they are not predefined \) , \( 2 \) the singular value decomposition \( svd \) is used to smooth the frequency data \( it is also used this way in latent semantic analysis \) , and \( 3 \) automatically generated synonyms are used to explore reformulations of the word pairs lra achieves 56 on the 374 analogy questions , statistically equivalent to the average human score of 57 on the related problem of classifying noun modifier relations , lra achieves similar gains over the vsm , while using a smaller corpus"
cs.SI|stat.ML,"multiplex networks , a special type of multilayer networks , are increasingly applied in many domains ranging from social media analytics to biology a common task in these applications concerns the detection of community structures many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers in this article we propose a community detection algorithm , lart \( locally adaptive random transitions \) , for the detection of communities that are shared by either some or all the layers in the multiplex the algorithm is based on a random walk on the multiplex , and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers based on this random walk , a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion we present experimental results using networks simulated under various scenarios to showcase the performance of lart in comparison to related community detection algorithms"
cs.LG|stat.ML,"we consider a variant of the stochastic multi armed bandit problem , where multiple players simultaneously choose from the same set of arms and may collide , receiving no reward this setting has been motivated by problems arising in cognitive radio networks , and is especially challenging under the realistic assumption that communication between players is limited we provide a communication free algorithm \( musical chairs \) which attains constant regret with high probability , as well as a sublinear regret , communication free algorithm \( dynamic musical chairs \) for the more difficult setting of players dynamically entering and leaving throughout the game moreover , both algorithms do not require prior knowledge of the number of players to the best of our knowledge , these are the first communication free algorithms with these types of formal guarantees we also rigorously compare our algorithms to previous works , and complement our theoretical findings with experiments"
cs.DS,"bidimensionality theory was introduced by demaine et al in 2005 as a tool to obtain subexponential time parameterized algorithms on h minor free graphs demaine and hajiaghayi extended the theory to obtain polynomial time approximation schemes \( ptass \) for bidimen sional problems in this work , we establish a third meta algorithmic direction for bidimensionality theory by relating it to the existence of linear kernels for parameterized problems in particular , we prove that every minor \( respectively contraction \) bidimensional problem that satisfies a separation property and is expressible in countable monadic second order logic \( cmso \) , admits a linear kernel for classes of graphs that exclude a fixed graph \( respectively an apex graph \) h as a minor our results imply that a multitude of bidimensional problems admit linear kernels on the corresponding graph classes for most of these problems no polynomial kernels on h minor free graphs were known prior to our work"
cs.SI,"in a recent work proc natl acad sci usa 108 , 3838 \( 2011 \) , schneider et al proposed a new measure for network robustness and investigated optimal networks with respect to this quantity for networks with a power law degree distribution , the optimized networks have an onion structure high degree vertices forming a core with radially decreasing degrees and an over representation of edges within the same radial layer in this paper we relate the onion structure to graphs with good expander properties \( another characterization of robust network \) and argue that networks of skewed degree distributions with large spectral gaps \( and thus good expander properties \) are typically onion structured furthermore , we propose a generative algorithm producing synthetic scale free networks with onion structure , circumventing the optimization procedure of schneider et al we validate the robustness of our generated networks against malicious attacks and random removals"
cs.DM|cs.DS,"in the travelling salesman problem \( tsp \) , we are given a complete graph k n together with an integer weighting w on the edges of k n , and we are asked to find a hamilton cycle of k n of minimum weight let h \( w \) denote the average weight of a hamilton cycle of k n for the weighting w vizing \( 1973 \) asked whether there is a polynomial time algorithm which always finds a hamilton cycle of weight at most h \( w \) he answered this question in the affirmative and subsequently rublineckii \( 1973 \) and others described several other tsp heuristics satisfying this property in this paper , we prove a considerable generalisation of vizing 's result for each fixed k , we give an algorithm that decides whether , for any input edge weighting w of k n , there is a hamilton cycle of k n of weight at most h \( w \) k \( and constructs such a cycle if it exists \) for k fixed , the running time of the algorithm is polynomial in n , where the degree of the polynomial does not depend on k \( i e , the generalised vizing problem is fixed parameter tractable with respect to the parameter k \)"
cs.LG|stat.ML,"tensor factorizations have become increasingly popular approaches for various learning tasks on structured data in this work , we extend the rescal tensor factorization , which has shown state of the art results for multi relational learning , to account for the binary nature of adjacency tensors we study the improvements that can be gained via this approach on various benchmark datasets and show that the logistic extension can improve the prediction results significantly"
cs.DS,"we design algorithms for computing approximately revenue maximizing em sequential posted pricing mechanisms \( spm \) in k unit auctions , in a standard bayesian model a seller has k copies of an item to sell , and there are n buyers , each interested in only one copy , who have some value for the item the seller must post a price for each buyer , the buyers arrive in a sequence enforced by the seller , and a buyer buys the item if its value exceeds the price posted to it the seller does not know the values of the buyers , but have bayesian information about them an spm specifies the ordering of buyers and the posted prices , and may be em adaptive or em non adaptive in its behavior the goal is to design spm in polynomial time to maximize expected revenue we compare against the expected revenue of optimal spm , and provide a polynomial time approximation scheme \( ptas \) for both non adaptive and adaptive spms this is achieved by two algorithms an efficient algorithm that gives a \( 1 frac 1 sqrt 2 pi k \) approximation \( and hence a ptas for sufficiently large k \) , and another that is a ptas for constant k the first algorithm yields a non adaptive spm that yields its approximation guarantees against an optimal adaptive spm this implies that the em adaptivity gap in spms vanishes as k becomes larger"
cs.SI,"identifying factors that affect human decision making and quantifying their influence remain essential and challenging tasks for the design and implementation of social and technological communication systems we report results of a behavioral experiment involving decision making in the face of an impending natural disaster in a controlled laboratory setting , we characterize individual and group evacuation decision making influenced by several key factors , including the likelihood of the disaster , available shelter capacity , group size , and group decision protocol our results show that success in individual decision making is not a strong predictor of group performance we use an artificial neural network trained on the collective behavior of subjects to predict individual and group outcomes overall model accuracy increases with the inclusion of a subject specific performance parameter based on laboratory trials that captures individual differences in parallel , we demonstrate that the social media activity of individual subjects , specifically their facebook use , can be used to generate an alternative individual personality profile that leads to comparable model accuracy quantitative characterization and prediction of collective decision making is crucial for the development of effective policies to guide the action of populations in the face of threat or uncertainty"
stat.ML,"we propose a new stochastic coordinate descent method for minimizing the sum of convex functions each of which depends on a small number of coordinates only our method \( approx \) is simultaneously accelerated , parallel and proximal this is the first time such a method is proposed in the special case when the number of processors is equal to the number of coordinates , the method converges at the rate 2 bar omega bar l r 2 \( k 1 \) 2 , where k is the iteration counter , bar omega is an average degree of separability of the loss function , bar l is the average of lipschitz constants associated with the coordinates and individual functions in the sum , and r is the distance of the initial point from the minimizer we show that the method can be implemented without the need to perform full dimensional vector operations , which is the major bottleneck of existing accelerated coordinate descent methods the fact that the method depends on the average degree of separability , and not on the maximum degree of separability , can be attributed to the use of new safe large stepsizes , leading to improved expected separable overapproximation \( eso \) these are of independent interest and can be utilized in all existing parallel stochastic coordinate descent algorithms based on the concept of eso"
cs.DS,"this article aims to explain the nested benders algorithm for the solution of large scale stochastic programming problems in a way that is intelligible to someone coming to it for the first time in doing so it gives an explanation of benders decomposition and of its application to two stage stochastic programming problems \( also known in this context as the l shaped method \) , then extends this to multi stage problems as the nested benders algorithm the article is aimed at readers with some knowledge of linear and possibly stochastic programming but aims to develop most concepts from simple principles in an understandable way the focus is on intuitive understanding rather than rigorous proofs"
cs.DM,"properties of systems with majority voting rules have been exhaustingly studied in this work we focus on the randomized case where the system is initialized by randomized initial set of seeds our main aim is to give an asymptotic estimate for sampling probability , such that the initial set of seeds is \( is not \) a dynamic monopoly almost surely after presenting some trivial examples , we present exhaustive results for toroidal mesh and random 4 regular graph under simple majority scenario"
cs.AI|stat.ML,"during the past decade , several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data driven models in the area of dialogue systems , the trend is less obvious , and most practical systems are still built through significant engineering and expert knowledge nevertheless , several recent results suggest that data driven approaches are feasible and quite promising to facilitate research in this area , we have carried out a wide survey of publicly available datasets suitable for data driven learning of dialogue systems we discuss important characteristics of these datasets and how they can be used to learn diverse dialogue strategies we also describe other potential uses of these datasets , such as methods for transfer learning between datasets and the use of external knowledge , and discuss appropriate choice of evaluation metrics for the learning objective"
cs.DM,"in this paper , we are interested in the combinatorial analysis of the whole genome duplication random loss model of genome rearrangement initiated in a paper of chaudhuri , chen , mihaescu , and rao in soda 2006 and continued by bouvel and rossin in 2007 in this model , genomes composed of n genes are modeled by permutations of the set of integers 1 n , that can evolve through duplication loss steps it was previously shown that the class of permutations obtained in this model after a given number p of steps is a class of pattern avoiding permutations of finite basis the excluded patterns were described as the minimal permutations with d 2 p descents , minimal being intended in the sense of the pattern involvement relation on permutations here , we give a local and simpler characterization of the set b d of minimal permutations with d descents we also provide a more detailed analysis characterization , bijection and enumeration of two particular subsets of b d , namely the patterns in b d of size d 2 and 2d"
cs.SI,"technology development produces terabytes of data generated by hu man activity in space and time this enormous amount of data often called big data becomes crucial for delivering new insights to decision makers it contains behavioral information on different types of human activity influenced by many external factors such as geographic infor mation and weather forecast early recognition and prediction of those human behaviors are of great importance in many societal applications like health care , risk management and urban planning , etc in this pa per , we investigate relevant geographical areas based on their categories of human activities \( i e , working and shopping \) which identified from ge ographic information \( i e , openstreetmap \) we use spectral clustering followed by k means clustering algorithm based on tf idf cosine simi larity metric we evaluate the quality of those observed clusters with the use of silhouette coefficients which are estimated based on the similari ties of the mobile communication activity temporal patterns the area clusters are further used to explain typical or exceptional communication activities we demonstrate the study using a real dataset containing 1 million call detailed records this type of analysis and its application are important for analyzing the dependency of human behaviors from the external factors and hidden relationships and unknown correlations and other useful information that can support decision making"
cs.LG|stat.ML,"this paper introduces a general multi class approach to weakly supervised classification inferring the labels and learning the parameters of the model is usually done jointly through a block coordinate descent algorithm such as expectation maximization \( em \) , which may lead to local minima to avoid this problem , we propose a cost function based on a convex relaxation of the soft max loss we then propose an algorithm specifically designed to efficiently solve the corresponding semidefinite program \( sdp \) empirically , our method compares favorably to standard ones on different datasets for multiple instance learning and semi supervised learning as well as on clustering tasks"
cs.IT|cs.LG,"the problem of joint universal source coding and modeling , addressed by rissanen in the context of lossless codes , is generalized to fixed rate lossy coding of continuous alphabet memoryless sources we show that , for bounded distortion measures , any compactly parametrized family of i i d real vector sources with absolutely continuous marginals \( satisfying appropriate smoothness and vapnik chervonenkis learnability conditions \) admits a joint scheme for universal lossy block coding and parameter estimation , and give nonasymptotic estimates of convergence rates for distortion redundancies and variational distances between the active source and the estimated source we also present explicit examples of parametric sources admitting such joint universal compression and modeling schemes"
cs.AI,"in this paper we sum up our works on multiscale programs , mainly simulations we \? rst start with describing what multiscaling is about , how it helps perceiving signal from a background noise in a \? ow of data for example , for a direct perception by a user or for a further use by another program we then give three examples of multiscale techniques we used in the past , maintaining a summary , using an environmental marker introducing an history in the data and \? nally using a knowledge on the behavior of the di \? erent scales to really handle them at the same time"
cs.DM,"based on a non rigorous formalism called the cavity method , physicists have put forward intriguing predictions on phase transitions in discrete structures one of the most remarkable ones is that in problems such as random k sat or random graph k coloring , very shortly before the threshold for the existence of solutions there occurs another phase transition called condensation krzakala et al , pnas 2007 the existence of this phase transition appears to be intimately related to the difficulty of proving precise results on , e g , the k colorability threshold as well as to the performance of message passing algorithms in random graph k coloring , there is a precise conjecture as to the location of the condensation phase transition in terms of a distributional fixed point problem in this paper we prove this conjecture for k exceeding a certain constant k 0"
cs.DM,"butman , hermelin , lewenstein , and rawitz proved that clique in t interval graphs is np hard for t 3 we strengthen this result to show that clique in 3 track interval graphs is apx hard"
cs.DM|cs.SI,"in this article , we extend several algebraic graph analysis methods to bipartite networks in various areas of science , engineering and commerce , many types of information can be represented as networks , and thus the discipline of network analysis plays an important role in these domains a powerful and widespread class of network analysis methods is based on algebraic graph theory , i e , representing graphs as square adjacency matrices however , many networks are of a very specific form that clashes with that representation they are bipartite that is , they consist of two node types , with each edge connecting a node of one type with a node of the other type examples of bipartite networks \( also called emph two mode networks \) are persons and the social groups they belong to , musical artists and the musical genres they play , and text documents and the words they contain in fact , any type of feature that can be represented by a categorical variable can be interpreted as a bipartite network although bipartite networks are widespread , most literature in the area of network analysis focuses on unipartite networks , i e , those networks with only a single type of node the purpose of this article is to extend a selection of important algebraic network analysis methods to bipartite networks , showing that many methods from algebraic graph theory can be applied to bipartite networks with only minor modifications we show methods for clustering , visualization and link prediction additionally , we introduce new algebraic methods for measuring the bipartivity in near bipartite graphs"
cs.IT|stat.ML,"we consider the change point detection problem of deciding , based on noisy measurements , whether an unknown signal over a given graph is constant or is instead piecewise constant over two connected induced subgraphs of relatively low cut size we analyze the corresponding generalized likelihood ratio \( glr \) statistics and relate it to the problem of finding a sparsest cut in a graph we develop a tractable relaxation of the glr statistic based on the combinatorial laplacian of the graph , which we call the spectral scan statistic , and analyze its properties we show how its performance as a testing procedure depends directly on the spectrum of the graph , and use this result to explicitly derive its asymptotic properties on few significant graph topologies finally , we demonstrate both theoretically and by simulations that the spectral scan statistic can outperform naive testing procedures based on edge thresholding and chi 2 testing"
cs.AI|cs.DM,"this paper first analyzes the resolution complexity of two random csp models \( i e model rb rd \) for which we can establish the existence of phase transitions and identify the threshold points exactly by encoding csps into cnf formulas , it is proved that almost all instances of model rb rd have no tree like resolution proofs of less than exponential size thus , we not only introduce new families of cnf formulas hard for resolution , which is a central task of proof complexity theory , but also propose models with both many hard instances and exact phase transitions then , the implications of such models are addressed it is shown both theoretically and experimentally that an application of model rb rd might be in the generation of hard satisfiable instances , which is not only of practical importance but also related to some open problems in cryptography such as generating one way functions subsequently , a further theoretical support for the generation method is shown by establishing exponential lower bounds on the complexity of solving random satisfiable and forced satisfiable instances of rb rd near the threshold finally , conclusions are presented , as well as a detailed comparison of model rb rd with the hamiltonian cycle problem and random 3 sat , which , respectively , exhibit three different kinds of phase transition behavior in np complete problems"
cs.AI,"the subject of this paper is the elucidation of effects of actions from causal assumptions represented as a directed graph , and statistical knowledge given as a probability distribution in particular , we are interested in predicting conditional distributions resulting from performing an action on a set of variables and , subsequently , taking measurements of another set we provide a necessary and sufficient graphical condition for the cases where such distributions can be uniquely computed from the available information , as well as an algorithm which performs this computation whenever the condition holds furthermore , we use our results to prove completeness of do calculus pearl , 1995 for the same identification problem"
cs.AI|cs.LG,"as data science continues to grow in popularity , there will be an increasing need to make data science tools more scalable , flexible , and accessible in particular , automated machine learning \( automl \) systems seek to automate the process of designing and optimizing machine learning pipelines in this chapter , we present a genetic programming based automl system called tpot that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem further , we analyze a large database of pipelines that were previously used to solve various supervised classification problems and identify 100 short series of machine learning operations that appear the most frequently , which we call the building blocks of machine learning pipelines we harness these building blocks to initialize tpot with promising solutions , and find that this sensible initialization method significantly improves tpot 's performance on one benchmark at no cost of significantly degrading performance on the others thus , sensible initialization with machine learning pipeline building blocks shows promise for gp based automl systems , and should be further refined in future work"
cs.DM|cs.DS,"we prove explicit approximation hardness results for the graphic tsp on cubic and subcubic graphs as well as the new inapproximability bounds for the corresponding instances of the \( 1 , 2 \) tsp the proof technique uses new modular constructions of simulating gadgets for the restricted cubic and subcubic instances the modular constructions used in the paper could be also of independent interest"
cs.DS,"to obtain sorting algorithms that scale to the largest available machines , conventional parallel sorting algorithms cannot be used since they either have prohibitive communication volume or prohibitive critical path length for computation we outline ideas how to combine a number of basic algorithmic techniques which overcome these bottlenecks"
cs.IT|cs.LG|stat.ML,"the normalized maximized likelihood \( nml \) provides the minimax regret solution in universal data compression , gambling , and prediction , and it plays an essential role in the minimum description length \( mdl \) method of statistical modeling and estimation here we show that the normalized maximum likelihood has a bayes like representation as a mixture of the component models , even in finite samples , though the weights of linear combination may be both positive and negative this representation addresses in part the relationship between mdl and bayes modeling this representation has the advantage of speeding the calculation of marginals and conditionals required for coding and prediction applications"
cs.DM,"an irreversible k threshold process \( also a k neighbor bootstrap percolation \) is a dynamic process on a graph where vertices change color from white to black if they have at least k black neighbors an irreversible k conversion set of a graph g is a subset s of vertices of g such that the irreversible k threshold process starting with s black eventually changes all vertices of g to black we show that deciding the existence of an irreversible 2 conversion set of a given size is np complete , even for graphs of maximum degree 4 , which answers a question of dreyer and roberts conversely , we show that for graphs of maximum degree 3 , the minimum size of an irreversible 2 conversion set can be computed in polynomial time moreover , we find an optimal irreversible 3 conversion set for the toroidal grid , simplifying constructions of pike and zou"
cs.AI|cs.IT|cs.LG,"approximation of the optimal two part mdl code for given data , through successive monotonically length decreasing two part mdl codes , has the following properties \( i \) computation of each step may take arbitrarily long \( ii \) we may not know when we reach the optimum , or whether we will reach the optimum at all \( iii \) the sequence of models generated may not monotonically improve the goodness of fit but \( iv \) the model associated with the optimum has \( almost \) the best goodness of fit to express the practically interesting goodness of fit of individual models for individual data sets we have to rely on kolmogorov complexity"
cs.AI|cs.LG|stat.ML,"we present a novel hybrid algorithm for bayesian network structure learning , called h2pc it first reconstructs the skeleton of a bayesian network and then performs a bayesian scoring greedy hill climbing search to orient the edges the algorithm is based on divide and conquer constraint based subroutines to learn the local structure around a target variable we conduct two series of experimental comparisons of h2pc against max min hill climbing \( mmhc \) , which is currently the most powerful state of the art algorithm for bayesian network structure learning first , we use eight well known bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms our extensive experiments show that h2pc outperforms mmhc in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data second , we investigate h2pc 's ability to solve the multi label learning problem we provide theoretical results to characterize and identify graphically the so called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition the multi label learning problem is then decomposed into a series of multi class classification problems , where each multi class variable encodes a label powerset h2pc is shown to compare favorably to mmhc in terms of global classification accuracy over ten multi label data sets covering different application domains overall , our experiments support the conclusions that local structural learning with h2pc in the form of local neighborhood induction is a theoretically well motivated and empirically effective learning framework that is well suited to multi label learning the source code \( in r \) of h2pc as well as all data sets used for the empirical tests are publicly available"
cs.DS,"consider the following toy problem there are m rectangles and n points on the plane each rectangle r is a consumer with budget b r , who is interested in purchasing the cheapest item \( point \) inside r , given that she has enough budget our job is to price the items to maximize the revenue this problem can also be defined on higher dimensions we call this problem the geometric pricing problem in this paper , we study a new class of problems arising from a geometric aspect of the pricing problem it intuitively captures typical real world assumptions that have been widely studied in marketing research , healthcare economics , etc it also helps classify other well known pricing problems , such as the highway pricing problem and the graph vertex pricing problem on planar and bipartite graphs moreover , this problem turns out to have close connections to other natural geometric problems such as the geometric versions of the unique coverage and maximum feasible subsystem problems we show that the low dimensionality arising in this pricing problem does lead to improved approximation ratios , by presenting sublinear approximation algorithms for two central versions of the problem unit demand uniform budget min buying and single minded pricing problems our algorithm is obtained by combining algorithmic pricing and geometric techniques these results suggest that considering geometric aspect might be a promising research direction in obtaining improved approximation algorithms for such pricing problems to the best of our knowledge , this is one of very few problems in the intersection between geometry and algorithmic pricing areas thus its study may lead to new algorithmic techniques that could benefit both areas"
stat.ML,"in many classification systems , sensing modalities have different acquisition costs it is often it unnecessary to use every modality to classify a majority of examples we study a multi stage system in a prediction time cost reduction setting , where the full data is available for training , but for a test example , measurements in a new modality can be acquired at each stage for an additional cost we seek decision rules to reduce the average measurement acquisition cost we formulate an empirical risk minimization problem \( erm \) for a multi stage reject classifier , wherein the stage k classifier either classifies a sample using only the measurements acquired so far or rejects it to the next stage where more attributes can be acquired for a cost to solve the erm problem , we show that the optimal reject classifier at each stage is a combination of two binary classifiers , one biased towards positive examples and the other biased towards negative examples we use this parameterization to construct stage by stage global surrogate risk , develop an iterative algorithm in the boosting framework and present convergence and generalization results we test our work on synthetic , medical and explosives detection datasets our results demonstrate that substantial cost reduction without a significant sacrifice in accuracy is achievable"
cs.SI,"the theory of community structure is a powerful tool for real networks , which can simplify their topological and functional analysis considerably however , since community detection methods have random factors and real social networks obtained from complex systems always contain error edges , evaluating the robustness of community structure is an urgent and important task in this letter , we employ the critical threshold of resolution parameter in hamiltonian function , gamma c , to measure the robustness of a network according to spectral theory , a rigorous proof shows that the index we proposed is inversely proportional to robustness of community structure furthermore , by utilizing the co evolution model , we provides a new efficient method for computing the value of gamma c the research can be applied to broad clustering problems in network analysis and data mining due to its solid mathematical basis and experimental effects"
cs.DM,"considered is the distribution of the crosscorrelation between m sequences of length 2 m 1 , where m 2k , and m sequences of shorter length 2 k 1 new pairs of m sequences with three valued crosscorrelation are found and the complete correlation distribution is determined finally , we conjecture that there are no more cases with a three valued crosscorrelation apart from the ones proven here"
cs.DM,"we propose a new simple construction of an aperiodic tile set based on self referential \( fixed point \) argument people often say about some discovery that it appeared ahead of time , meaning that it could be fully understood only in the context of ideas developed later for the topic of this note , the construction of an aperiodic tile set based on the fixed point \( self referential \) approach , the situation is exactly the opposite it should have been found in 1960s when the question about aperiodic tile sets was first asked all the tools were quite standard and widely used at that time however , the history had chosen a different path and many nice geometric ad hoc constructions were developed instead \( by berger , robinson , penrose , ammann and many others in this note we try to correct this error and present a construction that should have been discovered first but seemed to be unnoticed for more that forty years"
cs.DS,"we survey incremental methods for minimizing a sum sum i 1 mf i \( x \) consisting of a large number of convex component functions f i our methods consist of iterations applied to single components , and have proved very effective in practice we introduce a unified algorithmic framework for a variety of such methods , some involving gradient and subgradient iterations , which are known , and some involving combinations of subgradient and proximal methods , which are new and offer greater flexibility in exploiting the special structure of f i we provide an analysis of the convergence and rate of convergence properties of these methods , including the advantages offered by randomization in the selection of components we also survey applications in inference machine learning , signal processing , and large scale and distributed optimization"
cs.DM,"given an undirected graph , one can assign directions to each of the edges of the graph , thus orienting the graph to be as egalitarian as possible , one may wish to find an orientation such that no vertex is unfairly hit with too many arcs directed into it we discuss how this objective arises in problems resulting from telecommunications we give optimal , polynomial time algorithms for finding an orientation that minimizes the lexicographic order of the indegrees and finding a strongly connected orientation that minimizes the maximum indegree we show that minimizing the lexicographic order of the indegrees is np hard when the resulting orientation is required to be acyclic"
cs.DM,"given an undirected n vertex graph g \( v , e \) and an integer k , let t k \( g \) denote the random vertex induced subgraph of g generated by ordering v according to a random permutation pi and including in t k \( g \) those vertices with at most k 1 of their neighbors preceding them in this order the distribution of subgraphs sampled in this manner is called the emph layers model with parameter k the layers model has found applications in studying ell degenerate subgraphs , the design of algorithms for the maximum independent set problem , and in bootstrap percolation in the current work we expand the study of structural properties of the layers model we prove that there are 3 regular graphs g for which with high probability t 3 \( g \) has a connected component of size omega \( n \) moreover , this connected component has treewidth omega \( n \) this lower bound on the treewidth extends to many other random graph models in contrast , t 2 \( g \) is known to be a forest \( hence of treewidth 1 \) , and we establish that if g is of bounded degree then with high probability the largest connected component in t 2 \( g \) is of size o \( log n \) we also consider the infinite two dimensional grid , for which we prove that the first four layers contain a unique infinite connected component with probability 1"
cs.DM|cs.DS,"we present a new exact algorithm for the steiner tree problem in edge weighted graphs our algorithm improves the classical dynamic programming approach by dreyfus and wagner we achieve a significantly better practical performance via pruning and future costs , a generalization of a well known concept to speed up shortest path computations our algorithm matches the best known worst case run time and has a fast , often superior , practical performance on some large instances originating from vlsi design , previous best run times are improved upon by orders of magnitudes we are also able to solve larger instances of the d dimensional rectilinear steiner tree problem for d in 3 , 4 , 5 , whose hanan grids contain up to several millions of edges"
cs.DM,"a wide family of nonlinear sequence generators , the so called clock controlled shrinking generators , has been analyzed and identified with a subset of linear cellular automata the algorithm that converts the given generator into a linear model based on automata is very simple and can be applied in a range of practical interest due to the linearity of these automata as well as the characteristics of this class of generators , a cryptanalytic approach can be proposed linear cellular structures easily model keystream generators with application in stream cipher cryptography"
cs.AI|cs.LG|stat.ML,"recent approaches based on artificial neural networks \( anns \) have shown promising results for short text classification however , many short texts occur in sequences \( e g , sentences in a document or utterances in a dialog \) , and most existing ann based systems do not leverage the preceding short texts when classifying a subsequent one in this work , we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts our model achieves state of the art results on three different datasets for dialog act prediction"
cs.LG,"learning in restricted boltzmann machine is typically hard due to the computation of gradients of log likelihood function to describe the network state statistics of the restricted boltzmann machine , we develop an advanced mean field theory based on the bethe approximation our theory provides an efficient message passing based method that evaluates not only the partition function \( free energy \) but also its gradients without requiring statistical sampling the results are compared with those obtained by the computationally expensive sampling based method"
cs.LG|stat.ML,"in this article we discuss some of the consequences of the mixed membership perspective on time series analysis in its most abstract form , a mixed membership model aims to associate an individual entity with some set of attributes based on a collection of observed data although much of the literature on mixed membership models considers the setting in which exchangeable collections of data are associated with each member of a set of entities , it is equally natural to consider problems in which an entire time series is viewed as an entity and the goal is to characterize the time series in terms of a set of underlying dynamic attributes or dynamic regimes indeed , this perspective is already present in the classical hidden markov model , where the dynamic regimes are referred to as states , and the collection of states realized in a sample path of the underlying process can be viewed as a mixed membership characterization of the observed time series our goal here is to review some of the richer modeling possibilities for time series that are provided by recent developments in the mixed membership framework"
cs.LG|stat.ML,"exponential family harmoniums \( efhs \) , which extend restricted boltzmann machines \( rbms \) from bernoulli random variables to other exponential families \( welling et al , 2005 \) , are generative models that can be trained with unsupervised learning techniques , like contrastive divergence \( hinton et al 2006 hinton , 2002 \) , as density estimators for static data methods for extending rbms and likewise efhs to data with temporal dependencies have been proposed previously \( sutskever and hinton , 2007 sutskever et al , 2009 \) , the learning procedure being validated by qualitative assessment of the generative model here we propose and justify , from a very different perspective , an alternative training procedure , proving sufficient conditions for optimal inference under that procedure the resulting algorithm can be learned with only forward passes through the data backprop through time is not required , as in previous approaches the proof exploits a recent result about information retention in density estimators \( makin and sabes , 2015 \) , and applies it to a recurrent efh \( refh \) by induction finally , we demonstrate optimality by simulation , testing the refh \( 1 \) as a filter on training data generated with a linear dynamical system , the position of which is noisily reported by a population of neurons with poisson distributed spike counts and \( 2 \) with the qualitative experiments proposed by sutskever et al \( 2009 \)"
cs.LG,"in the framework of on line learning , a learning machine might move around a teacher due to the differences in structures or output functions between the teacher and the learning machine in this paper we analyze the generalization performance of a new student supervised by a moving machine a model composed of a fixed true teacher , a moving teacher , and a student is treated theoretically using statistical mechanics , where the true teacher is a nonmonotonic perceptron and the others are simple perceptrons calculating the generalization errors numerically , we show that the generalization errors of a student can temporarily become smaller than that of a moving teacher , even if the student only uses examples from the moving teacher however , the generalization error of the student eventually becomes the same value with that of the moving teacher this behavior is qualitatively different from that of a linear model"
cs.LG|stat.ML,"given genetic variations and various phenotypical traits , such as magnetic resonance imaging \( mri \) features , we consider two important and related tasks in biomedical research i \) to select genetic and phenotypical markers for disease diagnosis and ii \) to identify associations between genetic and phenotypical data these two tasks are tightly coupled because underlying associations between genetic variations and phenotypical features contain the biological basis for a disease while a variety of sparse models have been applied for disease diagnosis and canonical correlation analysis and its extensions have bee widely used in association studies \( e g , eqtl analysis \) , these two tasks have been treated separately to unify these two tasks , we present a new sparse bayesian approach for joint association study and disease diagnosis in this approach , common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels based on gaussian process ordinal regression in return , the disease status is used to guide the discovery of relationships between the data sources the sparse projection matrices not only reveal interactions between data sources but also select groups of biomarkers related to the disease to learn the model from data , we develop an efficient variational expectation maximization algorithm simulation results demonstrate that our approach achieves higher accuracy in both predicting ordinal labels and discovering associations between data sources than alternative methods we apply our approach to an imaging genetics dataset for the study of alzheimer 's disease \( ad \) our method identifies biologically meaningful relationships between genetic variations , mri features , and ad status , and achieves significantly higher accuracy for predicting ordinal ad stages than the competing methods"
cs.DM,"motivated by the problem of routing reliably and scalably in a graph , we introduce the notion of a splicer , the union of spanning trees of a graph we prove that for any bounded degree n vertex graph , the union of two random spanning trees approximates the expansion of every cut of the graph to within a factor of o \( log n \) for the random graph g n , p , for p c log n n , two spanning trees give an expander this is suggested by the case of the complete graph , where we prove that two random spanning trees give an expander the construction of the splicer is elementary each spanning tree can be produced independently using an algorithm by aldous and broder a random walk in the graph with edges leading to previously unvisited vertices included in the tree a second important application of splicers is to graph sparsification where the goal is to approximate every cut \( and more generally the quadratic form of the laplacian \) using only a small subgraph of the original graph benczur karger as well as spielman srivastava have shown sparsifiers with o \( n log n eps 2 \) edges that achieve approximation within factors 1 eps and 1 eps their methods , based on independent sampling of edges , need omega \( n log n \) edges to get any approximation \( else the subgraph could be disconnected \) and leave open the question of linear size sparsifiers splicers address this question for random graphs by providing sparsifiers of size o \( n \) that approximate every cut to within a factor of o \( log n \)"
cs.LG|stat.ML,"this paper provides new insight into maximizing f1 scores in the context of binary classification and also in the context of multilabel classification the harmonic mean of precision and recall , f1 score is widely used to measure the success of a binary classifier when one class is rare micro average , macro average , and per instance average f1 scores are used in multilabel classification for any classifier that produces a real valued output , we derive the relationship between the best achievable f1 score and the decision making threshold that achieves this optimum as a special case , if the classifier outputs are well calibrated conditional probabilities , then the optimal threshold is half the optimal f1 score as another special case , if the classifier is completely uninformative , then the optimal behavior is to classify all examples as positive since the actual prevalence of positive examples typically is low , this behavior can be considered undesirable as a case study , we discuss the results , which can be surprising , of applying this procedure when predicting 26 , 853 labels for medline documents"
cs.DS,"parameterized runtime analysis seeks to understand the influence of problem structure on algorithmic runtime in this paper , we contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the euclidean traveling salesperson problem \( euclidean tsp \) we investigate the structural properties in tsp instances that influence the optimization process of evolutionary algorithms and use this information to bound the runtime of simple evolutionary algorithms our analysis studies the runtime in dependence of the number of inner points k and shows that \( mu lambda \) evolutionary algorithms solve the euclidean tsp in expected time o \( \( mu lambda \) cdot n 3 gamma \( epsilon \) n gamma \( epsilon \) \( mu lambda \) cdot n 4k \( 2k 1 \) ! \) where gamma is a function of the minimum angle epsilon between any three points finally , our analysis provides insights into designing a mutation operator that improves the upper bound on expected runtime we show that a mixed mutation strategy that incorporates both 2 opt moves and permutation jumps results in an upper bound of o \( \( mu lambda \) cdot n 3 gamma \( epsilon \) n gamma \( epsilon \) \( mu lambda \) cdot n 2k \( k 1 \) ! \) for the \( mu lambda \) ea"
cs.DS|cs.LG,"the question of polynomial learnability of probability distributions , particularly gaussian mixture distributions , has recently received significant attention in theoretical computer science and machine learning however , despite major progress , the general question of polynomial learnability of gaussian mixture distributions still remained open the current work resolves the question of polynomial learnability for gaussian mixtures in high dimension with an arbitrary fixed number of components the result on learning gaussian mixtures relies on an analysis of distributions belonging to what we call polynomial families in low dimension these families are characterized by their moments being polynomial in parameters and include almost all common probability distributions as well as their mixtures and products using tools from real algebraic geometry , we show that parameters of any distribution belonging to such a family can be learned in polynomial time and using a polynomial number of sample points the result on learning polynomial families is quite general and is of independent interest to estimate parameters of a gaussian mixture distribution in high dimensions , we provide a deterministic algorithm for dimensionality reduction this allows us to reduce learning a high dimensional mixture to a polynomial number of parameter estimations in low dimension combining this reduction with the results on polynomial families yields our result on learning arbitrary gaussian mixtures in high dimensions"
cs.DS,"while algebrisation constitutes a powerful technique in the design and analysis of centralised algorithms , to date there have been hardly any applications of algebraic techniques in the context of distributed graph algorithms this work is a case study that demonstrates the potential of algebrisation in the distributed context we will focus on distributed graph algorithms in the congested clique model the graph problems that we will consider include , e g , the triangle detection problem and the all pairs shortest path problem \( apsp \) there is plenty of prior work on combinatorial algorithms in the congested clique model for example , dolev et al \( disc 2012 \) gave an algorithm for triangle detection with a running time of tilde o \( n 1 3 \) , and nanongkai \( stoc 2014 \) gave an approximation algorithm for apsp with a running time of tilde o \( n 1 2 \) in this work , we will use algebraic techniques in particular , algorithms based on fast matrix multiplication to solve both triangle detection and the unweighted apsp in time o \( n 0 15715 \) for weighted apsp , we give a \( 1 o \( 1 \) \) approximation with this running time , as well as an exact tilde o \( n 1 3 \) solution"
cs.AI,"more users and companies make use of cloud services every day they all expect a perfect performance and any issue to remain transparent to them this last statement is very challenging to perform a user 's activities in our cloud can affect the overall performance of our servers , having an impact on other resources we can consider these kind of activities as fraudulent they can be either illegal activities , such as launching a ddos attack or just activities which are undesired by the cloud provider , such as bitcoin mining , which uses substantial power , reduces the life of the hardware and can possibly slow down other user 's activities this article discusses a method to detect such activities by using non intrusive , privacy friendly data billing data we use openstack as an example with data provided by telemetry , the component in charge of measuring resource usage for billing purposes results will be shown proving the efficiency of this method and ways to improve it will be provided as well as its advantages and disadvantages"
cs.LG|stat.ML,"for a certain class of distributions , we prove that the linear programming relaxation of k medoids clustering a variant of k means clustering where means are replaced by exemplars from within the dataset distinguishes points drawn from nonoverlapping balls with high probability once the number of points drawn and the separation distance between any two balls are sufficiently large our results hold in the nontrivial regime where the separation distance is small enough that points drawn from different balls may be closer to each other than points drawn from the same ball in this case , clustering by thresholding pairwise distances between points can fail we also exhibit numerical evidence of high probability recovery in a substantially more permissive regime"
cs.SI|stat.ML,"an increasing number of people are using online social networking services \( snss \) , and a significant amount of information related to experiences in consumption is shared in this new media form text mining is an emerging technique for mining useful information from the web we aim at discovering in particular tweets semantic patterns in consumers' discussions on social media specifically , the purposes of this study are twofold 1 \) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities , two forms of positive vs negative opinions and 2 \) driving actual content from the textual data that has a semantic trend the considered tweets include consumers opinions on us retail companies \( e g , amazon , walmart \) cosine similarity and k means clustering methods are used to achieve the former goal , and latent dirichlet allocation \( lda \) , a popular topic modeling algorithm , is used for the latter purpose this is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis in addition to major findings , we apply lda \( latent dirichlet allocations \) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media"
cs.AI,"alarm correlation plays an important role in improving the service and reliability in modern telecommunications networks most previous research of alarm correlation did n't consider the effect of noise data in database this paper focuses on the method of discovering alarm correlation rules from database containing noise data we firstly define two parameters win freq and win add as the measure of noise data and then present the robust search algorithm to solve the problem at different size of win freq and win add , experiments with alarm data containing noise data show that the robust search algorithm can discover the more rules with the bigger size of win add we also experimentally compare two different interestingness measures of confidence and correlation"
cs.LG|stat.ML,"we propose a hierarchy for approximate inference based on the dobrushin , lanford , ruelle \( dlr \) equations this hierarchy includes existing algorithms , such as belief propagation , and also motivates novel algorithms such as factorized neighbors \( fn \) algorithms and variants of mean field \( mf \) algorithms in particular , we show that extrema of the bethe free energy correspond to approximate solutions of the dlr equations in addition , we demonstrate a close connection between these approximate algorithms and gibbs sampling finally , we compare and contrast various of the algorithms in the dlr hierarchy on spin glass problems the experiments show that algorithms higher up in the hierarchy give more accurate results when they converge but tend to be less stable"
cs.DM,"we study methods for transforming valued constraint satisfaction problems \( vcsps \) to binary vcsps first , we show that the standard dual encoding preserves many aspects of the algebraic properties that capture the computational complexity of vcsps second , we extend the reduction of csps to binary csps described by bulin et al lmcs'15 to vcsps this reduction establishes that vcsps over a fixed valued constraint language are polynomial time equivalent to minimum cost homomorphism problems over a fixed digraph"
cs.LG,"this paper describes the evolution of controllers for racing a simulated radio controlled car around a track , modelled on a real physical track five different controller architectures were compared , based on neural networks , force fields and action sequences the controllers use either egocentric \( first person \) , newtonian \( third person \) or no information about the state of the car \( open loop controller \) the only controller that was able to evolve good racing behaviour was based on a neural network acting on egocentric inputs"
cs.SI,"a formal but intuitive framework is introduced to bridge the gap between data obtained from empirical studies and that generated by agent based models this is based on three key tenets firstly , a simulation can be given multiple formal descriptions corresponding to static and dynamic properties at different levels of observation these can be easily mapped to empirically observed phenomena and data obtained from them secondly , an agent based model generates a set of closed systems , and computational simulation is the means by which we sample from this set thirdly , properties at different levels and statistical relationships between them can be used to classify simulations as those that instantiate a more sophisticated set of constraints these can be validated with models obtained from statistical models of empirical data \( for example , structural equation or multi level models \) and hence provide more stringent criteria for validating the agent based model itself"
cs.SI,"agglomerative clustering is a well established strategy for identifying communities in networks communities are successively merged into larger communities , coarsening a network of actors into a more manageable network of communities the order in which merges should occur is not in general clear , necessitating heuristics for selecting pairs of communities to merge we describe a hierarchical clustering algorithm based on a local optimality property for each edge in the network , we associate the modularity change for merging the communities it links for each community vertex , we call the preferred edge that edge for which the modularity change is maximal when an edge is preferred by both vertices that it links , it appears to be the optimal choice from the local viewpoint we use the locally optimal edges to define the algorithm simultaneously merge all pairs of communities that are connected by locally optimal edges that would increase the modularity , redetermining the locally optimal edges after each step and continuing so long as the modularity can be further increased we apply the algorithm to model and empirical networks , demonstrating that it can efficiently produce high quality community solutions we relate the performance and implementation details to the structure of the resulting community hierarchies we additionally consider a complementary local clustering algorithm , describing how to identify overlapping communities based on the local optimality condition"
cs.DM,"we study a natural process for allocating m balls into n bins that are organized as the vertices of an undirected graph g balls arrive one at a time when a ball arrives , it first chooses a vertex u in g uniformly at random then the ball performs a local search in g starting from u until it reaches a vertex with local minimum load , where the ball is finally placed on then the next ball arrives and this procedure is repeated for the case m n , we give an upper bound for the maximum load on graphs with bounded degrees we also propose the study of the cover time of this process , which is defined as the smallest m so that every bin has at least one ball allocated to it we establish an upper bound for the cover time on graphs with bounded degrees our bounds for the maximum load and the cover time are tight when the graph is transitive or sufficiently homogeneous we also give upper bounds for the maximum load when m n"
cs.AI,"gesture recognition is mainly apprehensive on analyzing the functionality of human wits the main goal of gesture recognition is to create a system which can recognize specific human gestures and use them to convey information or for device control hand gestures provide a separate complementary modality to speech for expressing ones ideas information associated with hand gestures in a conversation is degree , discourse structure , spatial and temporal structure the approaches present can be mainly divided into data glove based and vision based approaches an important face feature point is the nose tip since nose is the highest protruding point from the face besides that , it is not affected by facial expressions another important function of the nose is that it is able to indicate the head pose knowledge of the nose location will enable us to align an unknown 3d face with those in a face database eye detection is divided into eye position detection and eye contour detection existing works in eye detection can be classified into two major categories traditional image based passive approaches and the active ir based approaches the former uses intensity and shape of eyes for detection and the latter works on the assumption that eyes have a reflection under near ir illumination and produce bright dark pupil effect the traditional methods can be broadly classified into three categories template based methods , appearance based methods and feature based methods the purpose of this paper is to compare various human gesture recognition systems for interfacing machines directly to human wits without any corporeal media in an ambient environment"
cs.DM|cs.DS,"given a property of boolean functions , what is the minimum number of queries required to determine with high probability if an input function satisfies this property or is far from satisfying it \? this is a fundamental question in property testing , where traditionally the testing algorithm is allowed to pick its queries among the entire set of inputs balcan , blais , blum and yang have recently suggested to restrict the tester to take its queries from a smaller random subset of polynomial size of the inputs this model is called active testing , and in the extreme case when the size of the set we can query from is exactly the number of queries performed it is known as passive testing we prove that passive or active testing of k linear functions \( that is , sums of k variables among n over z 2 \) requires theta \( k log n \) queries , assuming k is not too large this extends the case k 1 , \( that is , dictator functions \) , analyzed by balcan et al we also consider other classes of functions including low degree polynomials , juntas , and partially symmetric functions our methods combine algebraic , combinatorial , and probabilistic techniques , including the talagrand concentration inequality and the erdos rado theorem on delta systems"
cs.SI,"it has been argued that the reservation system in india , which has existed since the time of indian independence \( 1947 \) , has caused more havoc and degradation than progress this being a popular public opinion , these notions have not been based on any rigorous scientific study or research in this paper , we revisit the cultural divide among the indian population from a purely social networks based approach we study the reservation system in detail , starting from its past and observing its effect on the people through a survey , we analyze the variation in behavioural characteristics exhibited towards members of the same caste group versus members from another caste group we study the distinct cluster formation that takes place in the indian community , and find that this is largely due to the effect of caste based homophily to study the social capital associated with each individual in the backward class , we define a new parameter called social distance we study the changes that take place with regard to the average social distance of a cluster as well as network formation when a new link is established between the clusters which in its essence , is what the reservation system is accomplishing our extensive study calls for the change in the mindset of people in india although the animosity towards the reservation system could be rooted due to historical influence , hero worship and herd mentality , our results make it clear that the system has had a considerable impact in the country 's overall development by bridging the gap between the conflicting social groups"
cs.IT|cs.LG,"in many real world problems , control decisions have to be made with limited information the controller may have no a priori \( or even posteriori \) data on the nonlinear system , except from a limited number of points that are obtained over time this is either due to high cost of observation or the highly non stationary nature of the system the resulting conflict between information collection \( identification , exploration \) and control \( optimization , exploitation \) necessitates an active learning approach for iteratively selecting the control actions which concurrently provide the data points for system identification this paper presents a dual control approach where the information acquired at each control step is quantified using the entropy measure from information theory and serves as the training input to a state of the art gaussian process regression \( bayesian learning \) method the explicit quantification of the information obtained from each data point allows for iterative optimization of both identification and control objectives the approach developed is illustrated with two examples control of logistic map as a chaotic system and position control of a cart with inverted pendulum"
cs.LG,"pretraining is widely used in deep neutral network and one of the most famous pretraining models is deep belief network \( dbn \) the optimization formulas are different during the pretraining process for different pretraining models in this paper , we pretrained deep neutral network by different pretraining models and hence investigated the difference between dbn and stacked denoising autoencoder \( sda \) when used as pretraining model the experimental results show that dbn get a better initial model however the model converges to a relatively worse model after the finetuning process yet after pretrained by sda for the second time the model converges to a better model if finetuned"
cs.DM|cs.DS,"in the em capacitated survivable network design problem \( cap sndp \) , we are given an undirected multi graph where each edge has a capacity and a cost the goal is to find a minimum cost subset of edges that satisfies a given set of pairwise minimum cut requirements unlike its classical special case of sndp when all capacities are unit , the approximability of cap sndp is not well understood even in very restricted settings no known algorithm achieves a o \( m \) approximation , where m is the number of edges in the graph in this paper , we obtain several new results and insights into the approximability of cap sndp"
cs.LG,"we propose neural transformation machine \( ntram \) , a novel architecture for sequence to sequence learning , which performs the task through a series of nonlinear transformations from the representation of the input sequence \( e g , a chinese sentence \) to the final output sequence \( e g , translation to english \) inspired by the recent neural turing machines 8 , we store the intermediate representations in stacked layers of memories , and use read write operations on the memories to realize the nonlinear transformations of those representations those transformations are designed in advance but the parameters are learned from data through layer by layer transformations , ntram can model complicated relations necessary for applications such as machine translation between distant languages the architecture can be trained with normal back propagation on parallel texts , and the learning can be easily scaled up to a large corpus ntram is broad enough to subsume the state of the art neural translation model in 2 as its special case , while significantly improves upon the model with its deeper architecture remarkably , ntram , being purely neural network based , can achieve performance comparable to the traditional phrase based machine translation system \( moses \) with a small vocabulary and a modest parameter size"
cs.SI,"in this work , water distribution systems are regarded as large sparse planar graphs with complex network characteristics and the relationship between important topological features of the network \( i e structural robustness and loop redundancy \) and system resilience , viewed as the antonym to structural vulnerability , are assessed deterministic techniques from complex networks and spectral graph theory are utilized to quantify well connectedness and estimate loop redundancy in the studied benchmark networks by using graph connectivity and expansion properties , system robustness against node link failures and isolation of the demand nodes from the source \( s \) are assessed and network tolerance against random failures and targeted attacks on their bridges and cut sets are analyzed among other measurements , two metrics of meshed ness and algebraic connectivity are proposed as candidates for quantification of redundancy and robustness , respectively , in optimization design models a brief discussion on the scope and limitations of the provided measurements in the analysis of operational reliability of water distribution systems is presented"
cs.LG,"topic models extract representative word sets called topics from word counts in documents without requiring any semantic annotations topics are not guaranteed to be well interpretable , therefore , coherence measures have been proposed to distinguish between good and bad topics studies of topic coherence so far are limited to measures that score pairs of individual words for the first time , we include coherence measures from scientific philosophy that score pairs of more complex word subsets and apply them to topic scoring"
cs.AI,"cooperation between individuals is crucial for a population based meta heuristic search method to be successful this paper presents a new population based search method , namely negatively correlated search \( ncs \) , which essentially maintains multiple individual search processes in parallel in ncs , the search behaviors of individual search processes are modeled as probability distributions negatively correlated search behaviors are explicitly promoted by encouraging difference among the probability distributions \( search behaviors \) by this means , individual search processes share information and cooperate with each other to search diverse regions of a search space , and makes ncs a promising method for multimodal problems the cooperation scheme of ncs could also be regarded as a novel diversity preservation scheme that , different from other existing schemes , directly promotes diversity at the level of search behaviors rather than merely trying to maintain diversity among candidate solutions empirical studies on a set of multimodal continuous optimization problems showed that ncs is competitive to 6 well established meta heuristic search methods in the sense that ncs achieved better solutions on majority of the tested problems"
cs.AI,"lp relaxation based message passing algorithms provide an effective tool for map inference over probabilistic graphical models however , different lp relaxations often have different objective functions and variables of differing dimensions , which presents a barrier to effective comparison and analysis in addition , the computational complexity of lp relaxation based methods grows quickly with the number of constraints reducing the number of constraints without sacrificing the quality of the solutions is thus desirable we propose a unified formulation under which existing map lp relaxations may be compared and analysed furthermore , we propose a new tool called marginal polytope diagrams some properties of marginal polytope diagrams are exploited such as node redundancy and edge equivalence we show that using marginal polytope diagrams allows the number of constraints to be reduced without loosening the lp relaxations then , using marginal polytope diagrams and constraint reduction , we develop three novel message passing algorithms , and demonstrate that two of these show a significant improvement in speed over state of art algorithms while delivering a competitive , and sometimes higher , quality of solution"
cs.AI|cs.LG,"the recursive least squares \( rls \) algorithm is one of the most well known algorithms used in adaptive filtering , system identification and adaptive control its popularity is mainly due to its fast convergence speed , which is considered to be optimal in practice in this paper , rls methods are used to solve reinforcement learning problems , where two new reinforcement learning algorithms using linear value function approximators are proposed and analyzed the two algorithms are called rls td \( lambda \) and fast ahc \( fast adaptive heuristic critic \) , respectively rls td \( lambda \) can be viewed as the extension of rls td \( 0 \) from lambda 0 to general lambda within interval 0 , 1 , so it is a multi step temporal difference \( td \) learning algorithm using rls methods the convergence with probability one and the limit of convergence of rls td \( lambda \) are proved for ergodic markov chains compared to the existing ls td \( lambda \) algorithm , rls td \( lambda \) has advantages in computation and is more suitable for online learning the effectiveness of rls td \( lambda \) is analyzed and verified by learning prediction experiments of markov chains with a wide range of parameter settings the fast ahc algorithm is derived by applying the proposed rls td \( lambda \) algorithm in the critic network of the adaptive heuristic critic method unlike conventional ahc algorithm , fast ahc makes use of rls methods to improve the learning prediction efficiency in the critic learning control experiments of the cart pole balancing and the acrobot swing up problems are conducted to compare the data efficiency of fast ahc with conventional ahc from the experimental results , it is shown that the data efficiency of learning control can also be improved by using rls methods in the learning prediction process of the critic the performance of fast ahc is also compared with that of the ahc method using ls td \( lambda \) furthermore , it is demonstrated in the experiments that different initial values of the variance matrix in rls td \( lambda \) are required to get better performance not only in learning prediction but also in learning control the experimental results are analyzed based on the existing theoretical work on the transient phase of forgetting factor rls methods"
cs.AI,"we study and solve some variations of the random k satisfiability problem balanced k sat and biased random k sat on a regular tree , using techniques we have developed earlier \( arxiv 1110 2065 \) in both these problems , as well as variations of these that we have looked at , we find that the sat unsat transition obtained on the bethe lattice matches the exact threshold for the same model on a random graph for k 2 and is very close to the numerical value obtained for k 3 for higher k it deviates from the numerical estimates of the solvability threshold on random graphs , but is very close to the dynamical 1 rsb threshold as obtained from the first non trivial fixed point of the survey propagation algorithm"
cs.SI,"groups social communities are important components of entire societies , analysed by means of the social network concept their immanent feature is continuous evolution over time if we know how groups in the social network has evolved we can use this information and try to predict the next step in the given group evolution in the paper , a new aproach for group evolution prediction is presented and examined experimental studies on four evolving social networks revealed that \( i \) the prediction based on the simple input features may be very accurate , \( ii \) some classifiers are more precise than the others and \( iii \) parameters of the group evolution extracion method significantly influence the prediction quality"
cs.LG|stat.ML,"recently , prediction markets have shown considerable promise for developing flexible mechanisms for machine learning in this paper , agents with isoelastic utilities are considered it is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices corresponding to alpha mixtures , with a particular form of mixing component relating to each agent 's wealth we also demonstrate that wealth accumulation for logarithmic and other isoelastic agents \( through payoffs on prediction of training targets \) can implement both bayesian model updates and mixture weight updates by imposing different market payoff structures an iterative algorithm is given for market equilibrium computation we demonstrate that inhomogeneous markets of agents with isoelastic utilities outperform state of the art aggregate classifiers such as random forests , as well as single classifiers \( neural networks , decision trees \) on a number of machine learning benchmarks , and show that isoelastic combination methods are generally better than their logarithmic counterparts"
cs.AI,"dynamics of a chaotic spiking neuron model are being studied mathematically and experimentally the nonlinear dynamic state neuron \( nds \) is analysed to further understand the model and improve it chaos has many interesting properties such as sensitivity to initial conditions , space filling , control and synchronization as suggested by biologists , these properties may be exploited and play vital role in carrying out computational tasks in human brain the nds model has some limitations in thus paper the model is investigated to overcome some of these limitations in order to enhance the model therefore , the models parameters are tuned and the resulted dynamics are studied also , the discretization method of the model is considered moreover , a mathematical analysis is carried out to reveal the underlying dynamics of the model after tuning of its parameters the results of the aforementioned methods revealed some facts regarding the nds attractor and suggest the stabilization of a large number of unstable periodic orbits \( upos \) which might correspond to memories in phase space"
cs.DM,"given a set x , a collection mathcal f subseteq mathcal p \( x \) is said to be k sperner if it does not contain a chain of length k 1 under set inclusion and it is saturated if it is maximal with respect to this property gerbner et al conjectured that , if x is sufficiently large with respect to k , then the minimum size of a saturated k sperner system mathcal f subseteq mathcal p \( x \) is 2 k 1 we disprove this conjecture by showing that there exists varepsilon 0 such that for every k and x geq n 0 \( k \) there exists a saturated k sperner system mathcal f subseteq mathcal p \( x \) with cardinality at most 2 \( 1 varepsilon \) k a collection mathcal f subseteq mathcal p \( x \) is said to be an oversaturated k sperner system if , for every s in mathcal p \( x \) setminus mathcal f , mathcal f cup s contains more chains of length k 1 than mathcal f gerbner et al proved that , if x geq k , then the smallest such collection contains between 2 k 2 1 and o left \( frac log k k 2 k right \) elements we show that if x geq k 2 k , then the lower bound is best possible , up to a polynomial factor"
cs.LG|stat.ML,"despite the success of the popular kernelized support vector machines , they have two major limitations they are restricted to positive semi definite \( psd \) kernels , and their training complexity scales at least quadratically with the size of the data many natural measures of similarity between pairs of samples are not psd e g invariant kernels , and those that are implicitly or explicitly defined by latent variable models in this paper , we investigate scalable approaches for using indefinite similarity measures in large margin frameworks in particular we show that a normalization of similarity to a subset of the data points constitutes a representation suitable for linear classifiers the result is a classifier which is competitive to kernelized svm in terms of accuracy , despite having better training and test time complexities experimental results demonstrate that on cifar 10 dataset , the model equipped with similarity measures invariant to rigid and non rigid deformations , can be made more than 5 times sparser while being more accurate than kernelized svm using rbf kernels"
cs.SI,"the rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics , but also from the decisions of people who are close to them in their networks one obstacle to consider is that there may be several different measures for closeness that are appropriate , either through different types of friendships , or different functions of distance on one kind of friendship , where only a subset of these networks may actually be relevant another is that these decisions are often binary and more difficult to model with conventional approaches , both conceptually and computationally to address these issues , we present a hierarchical model for individual binary outcomes that uses and extends the machinery of the auto probit method for binary data we demonstrate the behavior of the parameters estimated by the multiple network regime auto probit model \( m nap \) under various sensitivity conditions , such as the impact of the prior distribution and the nature of the structure of the network , and demonstrate on several examples of correlated binary data in networks of interest to information systems , including the adoption of caller ring back tones , whose use is governed by direct connection but explained by additional network topologies"
cs.AI,"relational representation of knowledge makes it possible to perform all the computations and decision making in a uniform relational way by means of special relational compositions called triangle and square products in this paper some applications in manufacturing related to cost analysis are described testing fuzzy relational structures for various relational properties allows us to discover dependencies , hierarchies , similarities , and equivalences of the attributes characterizing technological processes and manufactured artifacts in their relationship to costs and performance a brief overview of mathematical aspects of bk relational products is given in appendix 1 together with further references in the literature"
cs.LG,"visual representation is crucial for a visual tracking method 's performances conventionally , visual representations adopted in visual tracking rely on hand crafted computer vision descriptors these descriptors were developed generically without considering tracking specific information in this paper , we propose to learn complex valued invariant representations from tracked sequential image patches , via strong temporal slowness constraint and stacked convolutional autoencoders the deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker the proposed observational model retains old training samples to alleviate drift , and collect negative samples which are coherent with target 's motion pattern for better discriminative tracking with the learned representation and online training samples , a logistic regression classifier is adopted to distinguish target from background , and retrained online to adapt to appearance changes subsequently , the observational model is integrated into a particle filter framework to peform visual tracking experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favourably against several state of the art trackers"
cs.LG|stat.ML,"a popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary while this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing , there have only been a few theoretical arguments supporting these evidences in particular , sparse coding , or sparse dictionary learning , relies on a non convex procedure whose local minima have not been fully analyzed yet in this paper , we consider a probabilistic model of sparse signals , and show that , with high probability , sparse coding admits a local minimum around the reference dictionary generating the signals our study takes into account the case of over complete dictionaries and noisy signals , thus extending previous work limited to noiseless settings and or under complete dictionaries the analysis we conduct is non asymptotic and makes it possible to understand how the key quantities of the problem , such as the coherence or the level of noise , can scale with respect to the dimension of the signals , the number of atoms , the sparsity and the number of observations"
cs.SI,"much of the past work in network analysis has focused on analyzing discrete graphs , where binary edges represent the presence or absence of a relationship since traditional network measures \( e g , betweenness centrality \) utilize a discrete link structure , complex systems must be transformed to this representation in order to investigate network properties however , in many domains there may be uncertainty about the relationship structure and any uncertainty information would be lost in translation to a discrete representation uncertainty may arise in domains where there is moderating link information that cannot be easily observed , i e , links become inactive over time but may not be dropped or observed links may not always corresponds to a valid relationship in order to represent and reason with these types of uncertainty , we move beyond the discrete graph framework and develop social network measures based on a probabilistic graph representation more specifically , we develop measures of path length , betweenness centrality , and clustering coefficient one set based on sampling and one based on probabilistic paths we evaluate our methods on three real world networks from enron , facebook , and dblp , showing that our proposed methods more accurately capture salient effects without being susceptible to local noise , and that the resulting analysis produces a better understanding of the graph structure and the uncertainty resulting from its change over time"
cs.DM,"the bend number b \( g \) of a graph g is the minimum k such that g may be represented as the edge intersection graph of a set of grid paths with at most k bends we confirm a conjecture of biedl and stern showing that the maximum bend number of outerplanar graphs is 2 moreover we improve the formerly known lower and upper bound for the maximum bend number of planar graphs from 2 and 5 to 3 and 4 , respectively"
cs.AI|cs.LG,"hol \( y \) hammer is an online ai atp service for formal \( computer understandable \) mathematics encoded in the hol light system the service allows its users to upload and automatically process an arbitrary formal development \( project \) based on hol light , and to attack arbitrary conjectures that use the concepts defined in some of the uploaded projects for that , the service uses several automated reasoning systems combined with several premise selection methods trained on all the project proofs the projects that are readily available on the server for such query answering include the recent versions of the flyspeck , multivariate analysis and complex analysis libraries the service runs on a 48 cpu server , currently employing in parallel for each task 7 ai atp combinations and 4 decision procedures that contribute to its overall performance the system is also available for local installation by interested users , who can customize it for their own proof development an emacs interface allowing parallel asynchronous queries to the service is also provided the overall structure of the service is outlined , problems that arise and their solutions are discussed , and an initial account of using the system is given"
cs.DM|cs.DS,"a boolean function f 0 , 1 n mapsto 0 , 1 is said to be eps far from monotone if f needs to be modified in at least eps fraction of the points to make it monotone we design a randomized tester that is given oracle access to f and an input parameter eps 0 , and has the following guarantee it outputs sf yes if the function is monotonically non decreasing , and outputs sf no with probability 2 3 , if the function is eps far from monotone this non adaptive , one sided tester makes o \( n 7 8 eps 3 2 ln \( 1 eps \) \) queries to the oracle"
cs.SI,"in this work , we propose a regression method to predict the popularity of an online video based on temporal and visual cues our method uses support vector regression with gaussian radial basis functions we show that modelling popularity patterns with this approach provides higher and more stable prediction results , mainly thanks to the non linearity character of the proposed method as well as its resistance against overfitting we compare our method with the state of the art on datasets containing over 14 , 000 videos from youtube and facebook furthermore , we show that results obtained relying only on the early distribution patterns , can be improved by adding social and visual metadata"
cs.DM|cs.DS,"two problems in the search of metric characteristics on weighted undirected graphs with non negative edge weights are being considered the first problem a weighted undirected graph with non negative edge weight is given the radius , diameter and at least one center and one pair of peripheral vertices of the graph are to be found in the second problem we have additionally calculated the distances matrix for the problems being considered , we proposed fast search algorithms which use only small fraction of graph 's vertices for the search of the metric characteristics the proposed algorithms have been compared to other popular methods of solving problems considered on various inputs"
cs.LG|stat.ML,"structure learning of gaussian graphical models is an extensively studied problem in the classical multivariate setting where the sample size n is larger than the number of random variables p , as well as in the more challenging setting when p n however , analogous approaches for learning the structure of graphical models with mixed discrete and continuous variables when p n remain largely unexplored here we describe a statistical learning procedure for this problem based on limited order correlations and assess its performance with synthetic and real data"
cs.DM,"trotignon and vuskovic completely characterized graphs that do not contain cycles with exactly one chord in particular , they show that such a graph g has chromatic number at most max \( 3 , w \( g \) \) we generalize this result to the class of graphs that do not contain cycles with exactly two chords and the class of graphs that do not contain cycles with exactly three chords more precisely we prove that graphs with no cycle with exactly two chords have chromatic number at most 6 and a graph g with no cycle with exactly three chords have chromatic number at most max \( 96 , w \( g \) 1 \)"
cs.AI,"in this paper , we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models we demonstrate the applications of this representation in information extraction , semantic search , and education we argue that the ontology can be a core of future integration of math aware data sets in the web of data and , therefore , provide mappings onto relevant datasets , such as dbpedia and sciencewise"
cs.DM|cs.DS,"hierarchical data representations in the context of classi cation and data clustering were put forward during the fties recently , hierarchical image representations have gained renewed interest for segmentation purposes in this paper , we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology constrained connectivity and ultrametric watersheds constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed the framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering , in particular when such a hierarchy is constrained the suitability of this framework for solving practical problems is illustrated with applications in remote sensing"
cs.AI|cs.LG,"in this theoretical paper we are concerned with the problem of learning a value function by a smooth general function approximator , to solve a deterministic episodic control problem in a large continuous state space it is shown that learning the gradient of the value function at every point along a trajectory generated by a greedy policy is a sufficient condition for the trajectory to be locally extremal , and often locally optimal , and we argue that this brings greater efficiency to value function learning this contrasts to traditional value function learning in which the value function must be learnt over the whole of state space it is also proven that policy gradient learning applied to a greedy policy on a value function produces a weight update equivalent to a value gradient weight update , which provides a surprising connection between these two alternative paradigms of reinforcement learning , and a convergence proof for control problems with a value function represented by a general smooth function approximator"
cs.LG|stat.ML,"we developed a new approach for the analysis of physiological time series an iterative convolution filter is used to decompose the time series into various components statistics of these components are extracted as features to characterize the mechanisms underlying the time series motivated by the studies that show many normal physiological systems involve irregularity while the decrease of irregularity usually implies the abnormality , the statistics for outliers in the components are used as features measuring irregularity support vector machines are used to select the most relevant features that are able to differentiate the time series from normal and abnormal systems this new approach is successfully used in the study of congestive heart failure by heart beat interval time series"
cs.DM|cs.DS,"given an input graph g and an integer k , the parameterized k 4 minor cover problem asks whether there is a set s of at most k vertices whose deletion results in a k 4 minor free graph , or equivalently in a graph of treewidth at most 2 this problem is inspired by two well studied parameterized vertex deletion problems , vertex cover and feedback vertex set , which can also be expressed as treewidth t vertex deletion problems t 0 for vertex cover and t 1 for feedback vertex set while a single exponential fpt algorithm has been known for a long time for textsc vertex cover , such an algorithm for feedback vertex set was devised comparatively recently while it is known to be unlikely that treewidth t vertex deletion can be solved in time c o \( k \) n o \( 1 \) , it was open whether the k 4 minor cover problem could be solved in single exponential fpt time , i e in c k n o \( 1 \) time this paper answers this question in the affirmative"
cs.LG,"the past few years have seen a tremendous growth in the popularity of smartphones as newer features continue to be added to smartphones to increase their utility , their significance will only increase in future combining machine learning with mobile computing can enable smartphones to become 'intelligent' devices , a feature which is hitherto unseen in them also , the combination of machine learning and context aware computing can enable smartphones to gauge user 's requirements proactively , depending upon their environment and context accordingly , necessary services can be provided to users in this paper , we have explored the methods and applications of integrating machine learning and context aware computing on the android platform , to provide higher utility to the users to achieve this , we define a machine learning \( ml \) module which is incorporated in the basic android architecture firstly , we have outlined two major functionalities that the ml module should provide then , we have presented three architectures , each of which incorporates the ml module at a different level in the android architecture the advantages and shortcomings of each of these architectures have been evaluated lastly , we have explained a few applications in which our proposed system can be incorporated such that their functionality is improved"
cs.DM,"the notion of travel groupoids was introduced by l nebesk 'y in 2006 in connection with a study on geodetic graphs a travel groupoid is a pair of a set v and a binary operation on v satisfying two axioms for a travel groupoid , we can associate a graph we say that a graph g has a travel groupoid if the graph associated with the travel groupoid is equal to g nebesk 'y gave a characterization for finite graphs to have a travel groupoid in this paper , we introduce the notion of t partition systems on a graph and give a characterization of travel groupoids on a graph in terms of t partition systems"
cs.LG|stat.ML,"this paper proposes a new estimation algorithm for the parameters of an hmm as to best account for the observed data in this model , in addition to the observation sequence , we have emph partial and emph noisy access to the hidden state sequence as side information this access can be seen as partial labeling of the hidden states furthermore , we model possible mislabeling in the side information in a joint framework and derive the corresponding em updates accordingly in our simulations , we observe that using this side information , we considerably improve the state recognition performance , up to 70 , with respect to the achievable margin defined by the baseline algorithms moreover , our algorithm is shown to be robust to the training conditions"
cs.AI,"we present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart of many nlp components this is accomplished by partitioning the features into a sequence of templates which are ordered such that high confidence can often be reached using only a small fraction of all features parameter estimation is arranged to maximize accuracy and early confidence in this sequence we present experiments in left to right part of speech tagging on wsj , demonstrating that we can preserve accuracy above 97 with over a five fold reduction in run time"
cs.DM,"we consider the problem of aggregating votes cast by a society on a fixed set of issues , where each member of the society may vote for one of several positions on each issue , but the combination of votes on the various issues is restricted to a feasible set of voting patterns our main result asserts that , in such a set up , non dictatorial aggregation of votes in a society of arbitrary size is possible if and only non dictatorial aggregation is possible in a society with just three members"
cs.SI,"in this work , we study how twitter can be used as a sensor to detect frequent and diverse social and physical events in real time we devise efficient data collection and event recognition solutions that work despite various limits on free access to twitter data we describe a web service implementation of our solution and report our experience with the 2010 2011 us national football league \( nfl \) games the service was able to recognize nfl game events within 40 seconds and with accuracy up to 90 this capability will be very useful for not only real time electronic program guide for live broadcast programs but also refined auction of advertisement slots more importantly , it demonstrates for the first time the feasibility of using twitter for real time social and physical event detection for ubiquitous computing"
cs.IT|cs.LG,"we measure the influence of individual observations on the sequence of the hidden states of the hidden markov model \( hmm \) by means of the kullback leibler distance \( kld \) namely , we consider the kld between the conditional distribution of the hidden states' chain given the complete sequence of observations and the conditional distribution of the hidden chain given all the observations but the one under consideration we introduce a linear complexity algorithm for computing the influence of all the observations as an illustration , we investigate the application of our algorithm to the problem of detecting outliers in hmm data series"
cs.DM,the bisimulation problem for equational graphs of finite out degree is shown to be decidable we reduce this problem to the bisimulation problem for deterministic rational \( vectors of \) boolean series on the alphabet of a dpda m we then exhibit a complete formal system for deducing equivalent pairs of such vectors
cs.AI,"we consider the one way vehicle sharing systems where customers can pick a car at one station and drop it off at another \( e g , zipcar , car2go \) we aim to optimize the distribution of cars , and quality of service , by pricing rentals appropriately however , with highly uncertain demands and other uncertain parameters \( e g , pick up and drop off location , time , duration \) , pricing each individual rental becomes prohibitively difficult as a first step towards overcoming this difficulty , we propose a bidding approach inspired from auctions , and reminiscent of priceline or hotwire in contrast to current car sharing systems , the operator does not set prices instead , customers submit bids and the operator decides to rent or not the operator can even accept negative bids to motivate drivers to rebalance available cars in unpopular routes we model the operator 's sequential decision problem as a emph constrained markov decision problem \( cmdp \) , whose exact solution can be found by solving a sequence of stochastic shortest path problems in real time furthermore , we propose an online approximate algorithm using the emph actor critic method of reinforcement learning , for which this algorithm has a fast convergence rate and small variance in generalization error we also show that its solution converges to the stationary \( locally optimal \) policy"
cs.LG,"we propose a novel method for speeding up stochastic optimization algorithms via sketching methods , which recently became a powerful tool for accelerating algorithms for numerical linear algebra we revisit the method of conditioning for accelerating first order methods and suggest the use of sketching methods for constructing a cheap conditioner that attains a significant speedup with respect to the stochastic gradient descent \( sgd \) algorithm while our theoretical guarantees assume convexity , we discuss the applicability of our method to deep neural networks , and experimentally demonstrate its merits"
cs.AI,"coalitional games are mathematical models suited to analyze scenarios where players can collaborate by forming coalitions in order to obtain higher worths than by acting in isolation a fundamental problem for coalitional games is to single out the most desirable outcomes in terms of appropriate notions of worth distributions , which are usually called solution concepts motivated by the fact that decisions taken by realistic players cannot involve unbounded resources , recent computer science literature reconsidered the definition of such concepts by advocating the relevance of assessing the amount of resources needed for their computation in terms of their computational complexity by following this avenue of research , the paper provides a complete picture of the complexity issues arising with three prominent solution concepts for coalitional games with transferable utility , namely , the core , the kernel , and the bargaining set , whenever the game worth function is represented in some reasonable compact form \( otherwise , if the worths of all coalitions are explicitly listed , the input sizes are so large that complexity problems are artificially trivial \) the starting investigation point is the setting of graph games , about which various open questions were stated in the literature the paper gives an answer to these questions , and in addition provides new insights on the setting , by characterizing the computational complexity of the three concepts in some relevant generalizations and specializations"
cs.DS|cs.IT|cs.LG,"a basic problem in the design of privacy preserving algorithms is the private maximization problem the goal is to pick an item from a universe that \( approximately \) maximizes a data dependent function , all under the constraint of differential privacy this problem has been used as a sub routine in many privacy preserving algorithms for statistics and machine learning previous algorithms for this problem are either range dependent i e , their utility diminishes with the size of the universe or only apply to very restricted function classes this work provides the first general purpose , range independent algorithm for private maximization that guarantees approximate differential privacy its applicability is demonstrated on two fundamental tasks in data mining and machine learning"
cs.DM,"let g be a bipartite graph , and let h be a bipartite graph with a fixed bipartition \( b h , w h \) we consider three different , natural ways of forbidding h as an induced subgraph in g first , g is h free if it does not contain h as an induced subgraph second , g is strongly h free if g is h free or else has no bipartition \( b g , w g \) with b h subseteq b g and w h subseteq w g third , g is weakly h free if g is h free or else has at least one bipartition \( b g , w g \) with b h not subseteq b g or w h not subseteq w g lozin and volz characterized all bipartite graphs h for which the class of strongly h free bipartite graphs has bounded clique width we extend their result by giving complete classifications for the other two variants of h freeness"
cs.SI,"we introduce a new conception of community structure , which we refer to as hidden community structure hidden community structure refers to a specific type of overlapping community structure , in which the detection of weak , but meaningful , communities is hindered by the presence of stronger communities we present hidden community detection hicode , an algorithm template that identifies both the strong , dominant community structure as well as the weaker , hidden community structure in networks hicode begins by first applying an existing community detection algorithm to a network , and then removing the structure of the detected communities from the network in this way , the structure of the weaker communities becomes visible through application of hicode , we demonstrate that a wide variety of real networks from different domains contain many communities that , though meaningful , are not detected by any of the popular community detection algorithms that we consider additionally , on both real and synthetic networks containing a hidden ground truth community structure , hicode uncovers this structure better than any baseline algorithms that we compared against for example , on a real network of undergraduate students that can be partitioned either by `dorm' \( residence hall \) or `year' , we see that hicode uncovers the weaker `year' communities with a jcrecall score \( a recall based metric that we define in the text \) of over 0 7 , while the baseline algorithms achieve scores below 0 2"
cs.LG|stat.ML,"yes , apparently they do previous research demonstrated that shallow feed forward nets sometimes can learn the complex functions previously learned by deep nets while using a similar number of parameters as the deep models they mimic in this paper we investigate if shallow models can learn to mimic the functions learned by deep convolutional models we experiment with shallow models and models with a varying number of convolutional layers , all trained to mimic a state of the art ensemble of cifar 10 models we demonstrate that we are unable to train shallow models to be of comparable accuracy to deep convolutional models although the student models do not have to be as deep as the teacher models they mimic , the student models apparently need multiple convolutional layers to learn functions of comparable accuracy"
cs.AI,"the islamic state of iraq and al sham \( isis \) is a dominant insurgent group operating in iraq and syria that rose to prominence when it took over mosul in june , 2014 in this paper , we present a data driven approach to analyzing this group using a dataset consisting of 2200 incidents of military activity surrounding isis and the forces that oppose it \( including iraqi , syrian , and the american led coalition \) we combine ideas from logic programming and causal reasoning to mine for association rules for which we present evidence of causality we present relationships that link isis vehicle bourne improvised explosive device \( vbied \) activity in syria with military operations in iraq , coalition air strikes , and isis ied activity , as well as rules that may serve as indicators of spikes in indirect fire , suicide attacks , and arrests"
cs.AI,"in order to overcome difficult dynamic optimization and environment extrema tracking problems , we propose a self regulated swarm \( srs \) algorithm which hybridizes the advantageous characteristics of swarm intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape \( properly balancing the exploration exploitation nature of our dynamic search strategy \) , with a simple evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self regulate the above exploratory swarm population , speeding it up globally in order to test his adaptive response and robustness , we have recurred to different dynamic multimodal complex functions as well as to dynamic optimization control problems , measuring reaction speeds and performance final comparisons were made with standard genetic algorithms \( gas \) , bacterial foraging strategies \( bfoa \) , as well as with recent co evolutionary approaches srs 's were able to demonstrate quick adaptive responses , while outperforming the results obtained by the other approaches additionally , some successful behaviors were found one of the most interesting illustrate that the present srs collective swarm of bio inspired ant like agents is able to track about 65 of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system"
cs.LG|stat.ML,"this letter extends the concept of graph frequency to graph signals that evolve with time our goal is to generalize and , in fact , unify the familiar concepts from time and graph frequency analysis to this end , we study a joint temporal and graph fourier transform \( jft \) and demonstrate its attractive properties we build on our results to create filters which act on the joint \( temporal and graph \) frequency domain , and show how these can be used to perform interference cancellation the proposed algorithms are distributed , have linear complexity , and can approximate any desired joint filtering objective"
cs.LG|stat.ML,"a central unsolved problem in machine learning involves modeling complex data sets using highly flexible families of probability distributions in which learning , sampling , inference , and evaluation are still analytically or computationally tractable previous approaches to this problem are subject to tradeoffs between flexibility and tractability we develop a promising approach that simultaneously achieves both the essential idea , inspired by non equilibrium statistical physics , is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process we then learn a reverse diffusion process that restores structure in data , yielding a highly flexible and tractable generative model of the data this approach allows us to rapidly learn , sample from , and evaluate probabilities in deep generative models with thousands of layers or time steps"
cs.LG,"we show that boolean functions expressible as monotone disjunctive normal forms are pac evolvable under a uniform distribution on the boolean cube if the hypothesis size is allowed to remain fixed we further show that this result is insufficient to prove the pac learnability of monotone boolean functions , thereby demonstrating a counter example to a recent claim to the contrary we further discuss scenarios wherein evolvability and learnability will coincide as well as scenarios under which they differ the implications of the latter case on the prospects of learning in complex hypothesis spaces is briefly examined"
cs.AI|stat.ML,"this work proposes a way to align statistical modeling with decision making we provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost , where operational cost is the amount spent by the practitioner in solving the problem the method allows us to explore the range of operational costs associated with the set of reasonable statistical models , so as to provide a useful way for practitioners to understand uncertainty to do this , the operational cost is cast as a regularization term in a learning algorithm 's objective function , allowing either an optimistic or pessimistic view of possible costs , depending on the regularization parameter from another perspective , if we have prior knowledge about the operational cost , for instance that it should be low , this knowledge can help to restrict the hypothesis space , and can help with generalization we provide a theoretical generalization bound for this scenario we also show that learning with operational costs is related to robust optimization"
cs.LG|stat.ML,"we present a global optimization algorithm for clustering data given the ratio of likelihoods that each pair of data points is in the same cluster or in different clusters to define a clustering solution in terms of pairwise relationships , a necessary and sufficient condition is that belonging to the same cluster satisfies transitivity we define a global objective function based on pairwise likelihood ratios and a transitivity constraint over all triples , assigning an equal prior probability to all clustering solutions we maximize the objective function by implementing max sum message passing on the corresponding factor graph to arrive at an o \( n 3 \) algorithm lastly , we demonstrate an application inspired by mutational sequencing for decoding random binary words transmitted through a noisy channel"
cs.DM,"internet is a complex network composed by several networks the autonomous systems , each one designed to transport information efficiently routing protocols aim to find paths between nodes whenever it is possible \( i e , the network is not partitioned \) , or to find paths verifying specific constraints \( e g , a certain qos is required \) as connectivity is a measure related to both of them \( partitions and selected paths \) this work provides a formal lower bound to it based on core decomposition , under certain conditions , and low complexity algorithms to find it we apply them to analyze maps obtained from the prominent internet mapping projects , using the lanet vi open source software for its visualization"
cs.SI,"modularity is widely used to effectively measure the strength of the community structure found by community detection algorithms however , modularity maximization suffers from two opposite yet coexisting problems in some cases , it tends to favor small communities over large ones while in others , large communities over small ones the latter tendency is known in the literature as the resolution limit problem to address them , we propose to modify modularity by subtracting from it the fraction of edges connecting nodes of different communities and by including community density into modularity we refer to the modified metric as modularity density and we demonstrate that it indeed resolves both problems mentioned above we describe the motivation for introducing this metric by using intuitively clear and simple examples we also prove that this new metric solves the resolution limit problem finally , we discuss the results of applying this metric , modularity , and several other popular community quality metrics to two real dynamic networks the results imply that modularity density is consistent with all the community quality measurements but not modularity , which suggests that modularity density is an improved measurement of the community quality compared to modularity"
cs.AI,"we present a modification of the superposition calculus that is meant to generate consequences of sets of first order axioms this approach is proven to be sound and deductive complete in the presence of redundancy elimination rules , provided the considered consequences are built on a given finite set of ground terms , represented by constant symbols in contrast to other approaches , most existing results about the termination of the superposition calculus can be carried over to our procedure this ensures in particular that the calculus is terminating for many theories of interest to the smt community"
cs.LG|stat.ML,"standard gaussian process \( gp \) regression , a powerful machine learning tool , is computationally expensive when it is applied to large datasets , and potentially inaccurate when data points are sparsely distributed in a high dimensional feature space to address these challenges , a new multiscale , sparsified gp algorithm is formulated , with the goal of application to large scientific computing datasets in this approach , the data is partitioned into clusters and the cluster centers are used to define a reduced training set , resulting in an improvement over standard gps in terms of training and evaluation costs further , a hierarchical technique is used to adaptively map the local covariance representation to the underlying sparsity of the feature space , leading to improved prediction accuracy when the data distribution is highly non uniform a theoretical investigation of the computational complexity of the algorithm is presented the efficacy of this method is then demonstrated on simple analytical functions and on data from a direct numerical simulation of turbulent combustion"
cs.DS|cs.LG,"the k means algorithm is one of the most widely used clustering heuristics despite its simplicity , analyzing its running time and quality of approximation is surprisingly difficult and can lead to deep insights that can be used to improve the algorithm in this paper we survey the recent results in this direction as well as several extension of the basic k means method"
cs.LG,"learning dictionaries suitable for sparse coding instead of using engineered bases has proven effective in a variety of image processing tasks this paper studies the optimization of dictionaries on image data where the representation is enforced to be explicitly sparse with respect to a smooth , normalized sparseness measure this involves the computation of euclidean projections onto level sets of the sparseness measure while previous algorithms for this optimization problem had at least quasi linear time complexity , here the first algorithm with linear time complexity and constant space complexity is proposed the key for this is the mathematically rigorous derivation of a characterization of the projection 's result based on a soft shrinkage function this theory is applied in an original algorithm called easy dictionary learning \( ezdl \) , which learns dictionaries with a simple and fast to compute hebbian like learning rule the new algorithm is efficient , expressive and particularly simple to implement it is demonstrated that despite its simplicity , the proposed learning algorithm is able to generate a rich variety of dictionaries , in particular a topographic organization of atoms or separable atoms further , the dictionaries are as expressive as those of benchmark learning algorithms in terms of the reproduction quality on entire images , and result in an equivalent denoising performance ezdl learns approximately 30 faster than the already very efficient online dictionary learning algorithm , and is therefore eligible for rapid data set analysis and problems with vast quantities of learning samples"
cs.SI,"the availability of an overwhelmingly large amount of bibliographic information including citation and co authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably an effective method could be to have one 's co authorship network arranged into a set of circles , which has been a recent practice for organizing relationships \( e g , friendship \) in many online social networks in this paper , we propose an unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers our model is an unsupervised method which combines a variety of node features and node similarity measures the model is built from a rich co authorship network data of more than 8 hundred thousand authors in the first level of evaluation , our model achieves 13 33 improvement in terms of overlapping modularity compared to the best among four state of the art community detection methods further , we conduct a task based evaluation two basic frameworks for collaboration prediction are considered with the circle information \( obtained from our model \) included in the feature set experimental results show that including the circle information detected by our model improves the prediction performance by 9 87 and 15 25 on average in terms of auc \( area under the roc \) and p rec 20 \( precision at top 20 \) respectively compared to the case , where the circle information is not present"
cs.LG,"we develop a general framework for margin based multicategory classification in metric spaces the basic work horse is a margin regularized version of the nearest neighbor classifier we prove generalization bounds that match the state of the art in sample size n and significantly improve the dependence on the number of classes k our point of departure is a nearly bayes optimal finite sample risk bound independent of k although k free , this bound is unregularized and non adaptive , which motivates our main result rademacher and scale sensitive margin bounds with a logarithmic dependence on k as the best previous risk estimates in this setting were of order sqrt k , our bound is exponentially sharper from the algorithmic standpoint , in doubling metric spaces our classifier may be trained on n examples in o \( n 2 log n \) time and evaluated on new points in o \( log n \) time"
cs.SI,"the voluntary process of wikipedia edition provides an environment where the outcome is clearly a collective product of interactions involving a large number of people we propose a simple agent based model , developed from real data , to reproduce the collaborative process of wikipedia edition with a small number of simple ingredients , our model mimics several interesting features of real human behaviour , namely in the context of edit wars we show that the level of conflict is determined by a tolerance parameter , which measures the editors' capability to accept different opinions and to change their own opinion we propose to measure conflict with a parameter based on mutual reverts , which increases only in contentious situations using this parameter , we find a distribution for the inter peace periods that is heavy tailed the effects of wiki robots in the conflict levels and in the edition patterns are also studied our findings are compared with previous parameters used to measure conflicts in edit wars"
cs.AI|cs.LG|stat.ML,"we propose a new reinforcement learning algorithm for partially observable markov decision processes \( pomdp \) based on spectral decomposition methods while spectral methods have been previously employed for consistent learning of \( passive \) latent variable models such as hidden markov models , pomdps are more challenging since the learner interacts with the environment and possibly changes the future observations in the process we devise a learning algorithm running through episodes , in each episode we employ spectral techniques to learn the pomdp parameters from a trajectory generated by a fixed policy at the end of the episode , an optimization oracle returns the optimal memoryless planning policy which maximizes the expected reward based on the estimated pomdp model we prove an order optimal regret bound w r t the optimal memoryless policy and efficient scaling with respect to the dimensionality of observation and action spaces"
cs.AI,"this bachelor 's thesis , written in russian , is devoted to a relatively new direction in the field of machine learning and artificial intelligence , namely probabilistic programming the thesis gives a brief overview to the already existing probabilistic programming languages church , venture , and anglican it also describes the results of the first experiments on the automatic induction of probabilistic programs the thesis was submitted , in june 2014 , in partial fulfilment of the requirements for the degree of bachelor of science in mathematics in the department of mathematics and computer science , siberian federal university , krasnoyarsk , russia the work , which is described in this thesis , has been performing in 2012 2014 in the massachusetts institute of technology and in the university of oxford by the colleagues of the author and by himself"
cs.DS,"in this paper , we present near optimal space bounds for lp samplers given a stream of updates \( additions and subtraction \) to the coordinates of an underlying vector x in r n , a perfect lp sampler outputs the i th coordinate with probability x i p x p p in soda 2010 , monemizadeh and woodruff showed polylog space upper bounds for approximate lp samplers and demonstrated various applications of them very recently , andoni , krauthgamer and onak improved the upper bounds and gave a o \( epsilon p log 3 n \) space epsilon relative error and constant failure rate lp sampler for p in 1 , 2 in this work , we give another such algorithm requiring only o \( epsilon p log 2 n \) space for p in \( 1 , 2 \) for p in \( 0 , 1 \) , our space bound is o \( epsilon 1 log 2 n \) , while for the p 1 case we have an o \( log \( 1 epsilon \) epsilon 1 log 2 n \) space algorithm we also give a o \( log 2 n \) bits zero relative error l0 sampler , improving the o \( log 3 n \) bits algorithm due to frahling , indyk and sohler as an application of our samplers , we give better upper bounds for the problem of finding duplicates in data streams in case the length of the stream is longer than the alphabet size , l1 sampling gives us an o \( log 2 n \) space algorithm , thus improving the previous o \( log 3 n \) bound due to gopalan and radhakrishnan in the second part of our work , we prove an omega \( log 2 n \) lower bound for sampling from 0 , pm 1 vectors \( in this special case , the parameter p is not relevant for lp sampling \) this matches the space of our sampling algorithms for constant epsilon 0 we also prove tight space lower bounds for the finding duplicates and heavy hitters problems we obtain these lower bounds using reductions from the communication complexity problem augmented indexing"
cs.DM,"in this paper we study the parameterized complexity of two well known permutation group problems which are np complete 1 given a permutation group g s , subgroup of s n , and a parameter k , find a permutation pi in g such that i in n mid pi \( i \) ne i is at least k this generalizes the well known np complete problem of finding a fixed point free permutation in g \( this is the case when k n \) we show that this problem with parameter k is fixed parameter tractable in the process , we give a simple deterministic polynomial time algorithm for finding a fixed point free element in a transitive permutation group , answering an open question of cameron 2 next we consider the problem of computing a base for a permutation group g s a base for g is a subset b of n such that the subgroup of g that fixes b pointwise is trivial this problem is known to be np complete we show that it is fixed parameter tractable for the case of cyclic permutation groups and for permutation groups of constant orbit size for more general classes of permutation groups we do not know whether the problem is in fpt or is w 1 hard"
cs.DM|cs.DS,"we address the classical knapsack problem and a variant in which an upper bound is imposed on the number of items that can be selected we show that appropriate combinations of rounding techniques yield novel and powerful ways of rounding as an application of these techniques , we present a linear storage polynomial time approximation scheme \( ptas \) and a fully polynomial time approximation scheme \( fptas \) that compute an approximate solution , of any fixed accuracy , in linear time this linear complexity bound gives a substantial improvement of the best previously known polynomial bounds"
cs.LG,"this paper introduces a greedy parser based on neural networks , which leverages a new compositional sub tree representation the greedy parser and the compositional procedure are jointly trained , and tightly depends on each other the composition procedure outputs a vector representation which summarizes syntactically \( parsing tags \) and semantically \( words \) sub trees composition and tagging is achieved over continuous \( word or tag \) representations , and recurrent neural networks we reach f1 performance on par with well known existing parsers , while having the advantage of speed , thanks to the greedy nature of the parser we provide a fully functional implementation of the method described in this paper"
cs.AI,"this volume contains the proceedings of the thirteenth international workshop on the acl2 theorem prover and its applications , acl2 2015 , a two day workshop held in austin , texas , usa , on october 1 2 , 2015 acl2 workshops occur at approximately 18 month intervals and provide a major technical forum for researchers to present and discuss improvements and extensions to the theorem prover , comparisons of acl2 with other systems , and applications of acl2 in formal verification acl2 is a state of the art automated reasoning system that has been successfully applied in academia , government , and industry for specification and verification of computing systems and in teaching computer science courses in 2005 , boyer , kaufmann , and moore were awarded the 2005 acm software system award for their work on acl2 and the other theorem provers in the boyer moore family"
cs.IT|stat.ML,"in this paper , we consider the joint design of data compression and 802 15 4 based medium access control \( mac \) protocol for smartgrids with renewable energy we study the setting where a number of nodes , each of which comprises electricity load and or renewable sources , report periodically their injected powers to a data concentrator our design exploits the correlation of the reported data in both time and space to efficiently design the data compression using the compressed sensing \( cs \) technique and themac protocol so that the reported data can be recovered reliably within minimum reporting time specifically , we perform the following design tasks i \) we employ the two dimensional \( 2d \) cs technique to compress the reported data in the distributed manner ii \) we propose to adapt the 802 15 4 mac protocol frame structure to enable efficient data transmission and reliable data reconstruction and iii \) we develop an analytical model based on which we can obtain efficient mac parameter configuration to minimize the reporting delay finally , numerical results are presented to demonstrate the effectiveness of our proposed framework compared to existing solutions"
cs.SI,"cyberbullying has emerged as an important and growing social problem , wherein people use online social networks and mobile phones to bully victims with offensive text , images , audio and video on a 247 basis this paper studies negative user behavior in the ask fm social network , a popular new site that has led to many cases of cyberbullying , some leading to suicidal behavior we examine the occurrence of negative words in ask fms question answer profiles along with the social network of likes of questions answers we also examine properties of users with cutting behavior in this social network"
cs.SI,"geometric approaches to network analysis combine simply defined models with great descriptive power in this work we provide a method for embedding directed acyclic graphs into minkowski spacetime using multidimensional scaling \( mds \) first we generalise the classical mds algorithm , defined only for metrics with a euclidean signature , to manifolds of any metric signature we then use this general method to develop an algorithm to be used on networks which have causal structure allowing them to be embedded in lorentzian manifolds the method is demonstrated by calculating embeddings for both causal sets and citation networks in minkowski spacetime we finally suggest a number of applications in citation analysis such as paper recommendation , identifying missing citations and fitting citation models to data using this geometric approach"
cs.DM|cs.DS,"in this paper we develop algorithms for approximating matrix multiplication with respect to the spectral norm let a in rr n times m and b in rr n times p be two matrices and eps 0 we approximate the product a top b using two down sampled sketches , tilde a in rr t times m and tilde b in rr t times p , where t ll n such that norm tilde a top tilde b a top b leq eps norm a norm b with high probability we use two different sampling procedures for constructing tilde a and tilde b one of them is done by i i d non uniform sampling rows from a and b and the other is done by taking random linear combinations of their rows we prove bounds that depend only on the intrinsic dimensionality of a and b , that is their rank and their stable rank namely the squared ratio between their frobenius and operator norm for achieving bounds that depend on rank we employ standard tools from high dimensional geometry such as concentration of measure arguments combined with elaborate eps net constructions for bounds that depend on the smaller parameter of stable rank this technology itself seems weak however , we show that in combination with a simple truncation argument is amenable to provide such bounds to handle similar bounds for row sampling , we develop a novel matrix valued chernoff bound inequality which we call low rank matrix valued chernoff bound thanks to this inequality , we are able to give bounds that depend only on the stable rank of the input matrices"
cs.SI,"the theoretical study of social learning by observation typically assumes that each agent 's action only affects her own payoff in this paper , i present a model in which agents' actions directly affect one another 's payoff on a discrete time line , there is a community of finitely many agents in each period each community receives a private signal about the underlying state of the world and may observe some past actions in previous communities agents in the same community then simultaneously take an action , and each agent 's payoff is higher if her action matches the state , and also higher if more agents take that same action i analyze both the case where observation is exogenous and the one where observation can be strategically chosen by paying a cost i show that in both cases network externalities in payoff enhance social learning , in the sense that the highest probability of agents taking the correct action in equilibrium is significantly higher with large communities than with small communities in particular , when the community size is sufficiently large , this probability reaches one \( asymptotic learning \) when private beliefs are unbounded , and can get arbitrarily close to one when private beliefs are unbounded i then discuss the issue of multiple equilibria and use risk dominance as a criterion for equilibrium selection i find that in the selected equilibria , the community size has no effect on learning when observation is exogenous , facilitates learning when observation is endogenous and private beliefs are bounded , and may either help or hinder learning when observation is endogenous and private beliefs are bounded"
cs.DM,"we extend the notion of canonical ordering \( initially developed for planar triangulations and 3 connected planar maps \) to cylindric \( essentially simple \) triangulations and more generally to cylindric \( essentially internally \) 3 connected maps this allows us to extend the incremental straight line drawing algorithm of de fraysseix , pach and pollack \( in the triangulated case \) and of kant \( in the 3 connected case \) to this setting precisely , for any cylindric essentially internally 3 connected map g with n vertices , we can obtain in linear time a periodic \( in x \) straight line drawing of g that is crossing free and internally \( weakly \) convex , on a regular grid mathbb z w mathbb z times 0 h , with w leq 2n and h leq n \( 2d 1 \) , where d is the face distance between the two boundaries this also yields an efficient periodic drawing algorithm for graphs on the torus precisely , for any essentially 3 connected map g on the torus \( i e , 3 connected in the periodic representation \) with n vertices , we can compute in linear time a periodic straight line drawing of g that is crossing free and \( weakly \) convex , on a periodic regular grid mathbb z w mathbb z times mathbb z h mathbb z , with w leq 2n and h leq 1 2n \( c 1 \) , where c is the face width of g since c leq sqrt 2n , the grid area is o \( n 5 2 \)"
cs.DS,"we study competitive equilibria in the classic shapley shubik assignment model with indivisible goods and unit demand buyers , with budget constraints buyers can specify a maximum price they are willing to pay for each item , beyond which they cannot afford the item this single discontinuity introduced by the budget constraint fundamentally changes the properties of equilibria in the assignment model without budget constraints , a competitive equilibrium always exists , and corresponds exactly to a stable matching with budgets , a competitive equilibrium need not always exist in addition , there are now two distinct notions of stability , depending on whether both or only one of the buyer and seller can strictly benefit in a blocking pair , that no longer coincide due to the budget induced discontinuity we define weak and strong stability for the assignment model with transferable utilities , and show that competitive equilibria correspond exactly to strongly stable matchings we consider the algorithmic question of efficiently computing competitive equilibria in an extension of the assignment model with budgets , where each buyer specifies his preferences over items using utility functions u ij , where u ij \( p j \) is the utility of buyer i for item j when its price is p j our main result is a strongly polynomial time algorithm that decides whether or not a competitive equilibrium exists and if yes , computes a minimum one , for a general class of utility functions u ij this class of utility functions includes the standard quasi linear utility model with a budget constraint , and in addition , allows modeling marketplaces where , for example , buyers only have a preference ranking amongst items subject to a maximum payment limit for each item , or where buyers want to optimize return on investment \( roi \) instead of a quasi linear utility and only know items' relative values"
cs.AI|cs.LG|stat.ML,"in recent years there has been a flurry of works on learning bayesian networks from data one of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data that is , in the presence of missing values or hidden variables in a recent paper , i introduced an algorithm called structural em that combines the standard expectation maximization \( em \) algorithm , which optimizes parameters , with structure search for model selection that algorithm learns networks based on penalized likelihood scores , which include the bic mdl score and various approximations to the bayesian score in this paper , i extend structural em to deal directly with bayesian model selection i prove the convergence of the resulting algorithm and show how to apply it for learning a large class of probabilistic models , including bayesian networks and some variants thereof"
cs.AI|stat.ML,"identifying the type of font \( e g , roman , blackletter \) used in historical documents can help optical character recognition \( ocr \) systems produce more accurate text transcriptions towards this end , we present an active learning strategy that can significantly reduce the number of labeled samples needed to train a font classifier our approach extracts image based features that exploit geometric differences between fonts at the word level , and combines them into a bag of word representation for each page in a document we evaluate six sampling strategies based on uncertainty , dissimilarity and diversity criteria , and test them on a database containing over 3 , 000 historical documents with blackletter , roman and mixed fonts our results show that a combination of uncertainty and diversity achieves the highest predictive accuracy \( 89 of test cases correctly classified \) while requiring only a small fraction of the data \( 17 \) to be labeled we discuss the implications of this result for mass digitization projects of historical documents"
cs.DM,"given a set of n balls each colored with a color , a ball is said to be majority , k majority , plurality if its color class has size larger than half of the number of balls , has size at least k , has size larger than any other color class respectively we address the problem of finding the minimum number of queries \( a comparison of a pair of balls if they have the same color or not \) that is needed to decide whether a majority , k majority or plurality ball exists and if so then show one such ball we consider both adaptive and non adaptive strategies and in certain cases , we also address weighted versions of the problems"
cs.DM,"a simple random walk on a graph is a sequence of movements from one vertex to another where at each step an edge is chosen uniformly at random from the set of edges incident on the current vertex , and then transitioned to next vertex central to this thesis is the cover time of the walk , that is , the expectation of the number of steps required to visit every vertex , maximised over all starting vertices in our first contribution , we establish a relation between the cover times of a pair of graphs , and the cover time of their cartesian product this extends previous work on special cases of the cartesian product , in particular , the square of a graph we show that when one of the factors is in some sense larger than the other , its cover time dominates , and can become within a logarithmic factor of the cover time of the product as a whole our main theorem effectively gives conditions for when this holds the techniques and lemmas we introduce may be of independent interest in our second contribution , we determine the precise asymptotic value of the cover time of a random graph with given degree sequence this is a graph picked uniformly at random from all simple graphs with that degree sequence we also show that with high probability , a structural property of the graph called conductance , is bounded below by a constant this is of independent interest finally , we explore random walks with weighted random edge choices we present a weighting scheme that has a smaller worst case cover time than a simple random walk we give an upper bound for a random graph of given degree sequence weighted according to our scheme we demonstrate that the speed up \( that is , the ratio of cover times \) over a simple random walk can be unbounded"
cs.DM,"it is well known that the behavior of many dynamical processes running on networks is intimately related to the eigenvalue spectrum of the network in this paper , we address the problem of inferring global information regarding the eigenvalue spectrum of a network from a set of local samples of its structure in particular , we find explicit relationships between the so called spectral moments of a graph and the presence of certain small subgraphs , also called motifs , in the network since the eigenvalues of the network have a direct influence on the network dynamical behavior , our result builds a bridge between local network measurements \( i e , the presence of small subgraphs \) and global dynamical behavior \( via the spectral moments \) furthermore , based on our result , we propose a novel decentralized scheme to compute the spectral moments of a network by aggregating local measurements of the network topology our final objective is to understand the relationships between the behavior of dynamical processes taking place in a large scale complex network and its local topological properties"
cs.DS|cs.IT,"consider two remote nodes having binary sequences x and y , respectively y is an edited version of x , where the editing involves random deletions , insertions , and substitutions , possibly in bursts the goal is for the node with y to reconstruct x with minimal exchange of information over a noiseless link the communication is measured in terms of both the total number of bits exchanged and the number of interactive rounds of communication this paper focuses on the setting where the number of edits is o \( tfrac n log n \) , where n is the length of x we first consider the case where the edits are a mixture of insertions and deletions \( indels \) , and propose an interactive synchronization algorithm with near optimal communication rate and average computational complexity of o \( n \) arithmetic operations the algorithm uses interaction to efficiently split the source sequence into substrings containing exactly one deletion or insertion each of these substrings is then synchronized using an optimal one way synchronization code based on the single deletion correcting channel codes of varshamov and tenengolts \( vt codes \) we then build on this synchronization algorithm in three different ways first , it is modified to work with a single round of interaction the reduction in the number of rounds comes at the expense of higher communication , which is quantified next , we present an extension to the practically important case where the insertions and deletions may occur in \( potentially large \) bursts finally , we show how to synchronize the sources to within a target hamming distance this feature can be used to differentiate between substitution and indel edits in addition to theoretical performance bounds , we provide several validating simulation results for the proposed algorithms"
cs.LG|stat.ML,"poisson likelihood models have been prevalently used in imaging , social networks , and time series analysis we propose fast , simple , theoretically grounded , and versatile , optimization algorithms for poisson likelihood modeling the poisson log likelihood is concave but not lipschitz continuous since almost all gradient based optimization algorithms rely on lipschitz continuity , optimizing poisson likelihood models with a guarantee of convergence can be challenging , especially for large scale problems we present a new perspective allowing to efficiently optimize a wide range of penalized poisson likelihood objectives we show that an appropriate saddle point reformulation enjoys a favorable geometry and a smooth structure therefore , we can design a new gradient based optimization algorithm with o \( 1 t \) convergence rate , in contrast to the usual o \( 1 sqrt t \) rate of non smooth minimization alternatives furthermore , in order to tackle problems with large samples , we also develop a randomized block decomposition variant that enjoys the same convergence rate yet more efficient iteration cost experimental results on several point process applications including social network estimation and temporal recommendation show that the proposed algorithm and its randomized block variant outperform existing methods both on synthetic and real world datasets"
cs.SI,"bipartite networks are currently regarded as providing a major insight into the organization of real world systems , unveiling the mechanisms shaping the interactions occurring between distinct groups of nodes one of the major problems encountered when dealing with bipartite networks is obtaining a \( monopartite \) projection over the layer of interest which preserves as much as possible the information encoded into the original bipartite structure in the present paper we propose an algorithm to obtain statistically validated projections of bipartite networks the criterion adopted to quantify the similarity of nodes rests upon the similarity of their neighborhoods in order for any two nodes to be linked , a significantly large number of neighbors must be shared naturally , assessing the statistical significance of nodes similarity requires the definition of a proper statistical benchmark here we consider two recently proposed null models for bipartite networks , opportunely defined through the exponential random graph formalism the output of our algorithm thus consists of a matrix of p values , from which a validated projection can be straightforwardly obtained , upon running a multiple hypothesis testing and linking only the nodes which pass it we apply our algorithm to social and economic bipartite networks when projecting the network of countries and exported goods on the countries layer , our method is able to cluster countries with similar a industrialisation we also analysed a social network of users and positively rated movies on the films layer in this case our approach divides movies in clusters of similar genres or audience"
cs.AI|cs.LG,"given a point set s and an unknown metric d on s , we study the problem of efficiently partitioning s into k clusters while querying few distances between the points in our model we assume that we have access to one versus all queries that given a point s 2 s return the distances between s and all other points we show that given a natural assumption about the structure of the instance , we can efficiently find an accurate clustering using only o \( k \) distance queries we use our algorithm to cluster proteins by sequence similarity this setting nicely fits our model because we can use a fast sequence database search program to query a sequence against an entire dataset we conduct an empirical study that shows that even though we query a small fraction of the distances between the points , we produce clusterings that are close to a desired clustering given by manual classification"
cs.AI,"while belief functions may be seen formally as a generalization of probabilistic distributions , the question of the interactions between belief functions and probability is still an issue in practice this question is difficult , since the contexts of use of these theory are notably different and the semantics behind these theories are not exactly the same a prominent issue is increasingly regarded by the community , that is the management of the conflicting information recent works have introduced new rules for handling the conflict redistribution while combining belief functions the notion of conflict , or its cancellation by an hypothesis of open world , seems by itself to prevent a direct interpretation of belief function in a probabilistic framework this paper addresses the question of a probabilistic interpretation of belief functions it first introduces and implements a theoretically grounded rule , which is in essence an adaptive conjunctive rule it is shown , how this rule is derived from a logical interpretation of the belief functions by means of a probabilistic multimodal logic in addition , a concept of source independence is introduced , based on a principle of entropy maximization"
cs.DS|cs.SI,"given a social network , which of its nodes are more central \? this question has been asked many times in sociology , psychology and computer science , and a whole plethora of centrality measures \( a k a centrality indices , or rankings \) were proposed to account for the importance of the nodes of a network in this paper , we approach the problem of computing geometric centralities , such as closeness and harmonic centrality , on very large graphs traditionally this task requires an all pairs shortest path computation in the exact case , or a number of breadth first traversals for approximated computations , but these techniques yield very weak statistical guarantees on highly disconnected graphs we rather assume that the graph is accessed in a semi streaming fashion , that is , that adjacency lists are scanned almost sequentially , and that a very small amount of memory \( in the order of a dozen bytes \) per node is available in core memory we leverage the newly discovered algorithms based on hyperloglog counters , making it possible to approximate a number of geometric centralities at a very high speed and with high accuracy while the application of similar algorithms for the approximation of closeness was attempted in the mapreduce framework , our exploitation of hyperloglog counters reduces exponentially the memory footprint , paving the way for in core processing of networks with a hundred billion nodes using just 2tib of ram moreover , the computations we describe are inherently parallelizable , and scale linearly with the number of available cores"
cs.LG,"in this paper , we propose a novel method for fast face recognition called l1 2 regularized sparse representation using hierarchical feature selection \( hsr \) by employing hierarchical feature selection , we can compress the scale and dimension of global dictionary , which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in it consists of gabor wavelets and extreme learning machine auto encoder \( elm ae \) hierarchically for gabor wavelets part , local features can be extracted at multiple scales and orientations to form gabor feature based image , which in turn improves the recognition rate besides , in the presence of occluded face image , the scale of gabor feature based global dictionary can be compressed accordingly because redundancies exist in gabor feature based occlusion dictionary for elm ae part , the dimension of gabor feature based global dictionary can be compressed because high dimensional face images can be rapidly represented by low dimensional feature by introducing l1 2 regularization , our approach can produce sparser and more robust representation compared to regularized sparse representation based classification \( src \) , which also contributes to the decrease of the computational cost in sparse representation in comparison with related work such as src and gabor feature based src \( gsrc \) , experimental results on a variety of face databases demonstrate the great advantage of our method for computational cost moreover , we also achieve approximate or even better recognition rate"
cs.AI,"a pattern based approach to the presentation , codification and reuse of property specifications for finite state verification was proposed by dwyer and his collegues the patterns enable non experts to read and write formal specifications for realistic systems and facilitate easy conversion of specifications between formalisms , such as ltl , ctl , qre in this paper , we extend the pattern system with events changes of values of variables in the context of ltl"
cs.LG,"in this paper , we address the task of learning novel visual concepts , and their interactions with other concepts , from a few images with sentence descriptions using linguistic context and visual features , our method is able to efficiently hypothesize the semantic meaning of new words and add them to its word dictionary so that they can be used to describe images which contain these novel concepts our method has an image captioning module based on m rnn with several improvements in particular , we propose a transposed weight sharing scheme , which not only improves performance on image captioning , but also makes the model more suitable for the novel concept learning task we propose methods to prevent overfitting the new concepts in addition , three novel concept datasets are constructed for this new task in the experiments , we show that our method effectively learns novel visual concepts from a few examples without disturbing the previously learned concepts"
cs.DS|cs.LG|stat.ML,"we consider two problems that arise in machine learning applications the problem of recovering a planted sparse vector in a random linear subspace and the problem of decomposing a random low rank overcomplete 3 tensor for both problems , the best known guarantees are based on the sum of squares method we develop new algorithms inspired by analyses of the sum of squares method our algorithms achieve the same or similar guarantees as sum of squares for these problems but the running time is significantly faster for the planted sparse vector problem , we give an algorithm with running time nearly linear in the input size that approximately recovers a planted sparse vector with up to constant relative sparsity in a random subspace of mathbb r n of dimension up to tilde omega \( sqrt n \) these recovery guarantees match the best known ones of barak , kelner , and steurer \( stoc 2014 \) up to logarithmic factors for tensor decomposition , we give an algorithm with running time subquadratic in the input size \( with exponent approx 1 086 \) that approximately recovers a component of a random 3 tensor over mathbb r n of rank up to tilde omega \( n 4 3 \) the best previous algorithm for this problem due to ge and ma \( random 2015 \) works up to rank tilde omega \( n 3 2 \) but requires quasipolynomial time"
cs.DM|cs.IT,"a new approach for estimating the decoding error probability \( dep \) of lt codes with dense rows is derived by using the conditional kovalenko 's rank distribution the estimate by the proposed approach is very close to the dep approximated by gaussian elimination , and is significantly less complex as a key application , we utilize the estimates for obtaining optimal lt codes with dense rows , whose dep is very close to the kovalenko 's full rank limit within a desired error bound experimental evidences which show the viability of the estimates are also provided"
cs.AI,"the hardness of finite domain constraint satisfaction problems \( csps \) is a very important research area in constraint programming \( cp \) community however , this problem has not yet attracted much attention from the researchers in the association rule mining community as a popular data mining technique , association rule mining has an extremely wide application area and it has already been successfully applied to many interdisciplines in this paper , we study the association rule mining techniques and propose a cascaded approach to extract the interesting patterns of the hard csps as far as we know , this problem is investigated with the data mining techniques for the first time specifically , we generate the random csps and collect their characteristics by solving all the csp instances , and then apply the data mining techniques on the data set and further to discover the interesting patterns of the hardness of the randomly generated csps"
cs.IT|cs.LG|stat.ML,"disagreement between two classifiers regarding the class membership of an observation in pattern recognition can be indicative of an anomaly and its nuance as in general classifiers base their decision on class aposteriori probabilities , the most natural approach to detecting classifier incongruence is to use divergence however , existing divergences are not particularly suitable to gauge classifier incongruence in this paper , we postulate the properties that a divergence measure should satisfy and propose a novel divergence measure , referred to as delta divergence in contrast to existing measures , it is decision cognizant the focus in delta divergence on the dominant hypotheses has a clutter reducing property , the significance of which grows with increasing number of classes the proposed measure satisfies other important properties such as symmetry , and independence of classifier confidence the relationship of the proposed divergence to some baseline measures is demonstrated experimentally , showing its superiority"
cs.DS|cs.IT,"compressed counting \( cc \) , based on maximally skewed stable random projections , was recently proposed for estimating the p th frequency moments of data streams the case p 1 is extremely useful for estimating shannon entropy of data streams in this study , we provide a very simple algorithm based on the sample minimum estimator and prove a much improved sample complexity bound , compared to prior results"
cs.SI,"in this paper we study a novel model of opinion dynamics in social networks , which has two main features first , agents asynchronously interact in pairs , and these pairs are chosen according to a random process we refer to this communication model as gossiping second , agents are not completely open minded , but instead take into account their initial opinions , which may be thought of as their prejudices in the literature , such agents are often called stubborn we show that the opinions of the agents fail to converge , but persistently undergo ergodic oscillations , which asymptotically concentrate around a mean distribution of opinions this mean value is exactly the limit of the synchronous dynamics of the expected opinions"
cs.LG,"multi instance multi label \( miml \) learning is a challenging problem in many aspects such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification in this study subset of digipath dataset \( whole slide digital breast cancer histopathology images \) are used for training and evaluation of six state of the art miml methods at the end , performance comparison of these approaches are given by means of effective evaluation metrics it is shown that miml knn achieve the best performance that is 65 3 average precision , where most of other methods attain acceptable results as well"
cs.LG|stat.ML,"hypergraphs allow one to encode higher order relationships in data and are thus a very flexible modeling tool current learning methods are either based on approximations of the hypergraphs via graphs or on tensor methods which are only applicable under special conditions in this paper , we present a new learning framework on hypergraphs which fully uses the hypergraph structure the key element is a family of regularization functionals based on the total variation on hypergraphs"
cs.AI,"real world problems always have different multiple solutions for instance , optical engineers need to tune the recording parameters to get as many optimal solutions as possible for multiple trials in the varied line spacing holographic grating design problem unfortunately , most traditional optimization techniques focus on solving for a single optimal solution they need to be applied several times yet all solutions are not guaranteed to be found thus the multimodal optimization problem was proposed in that problem , we are interested in not only a single optimal point , but also the others with strong parallel search capability , evolutionary algorithms are shown to be particularly effective in solving this type of problem in particular , the evolutionary algorithms for multimodal optimization usually not only locate multiple optima in a single run , but also preserve their population diversity throughout a run , resulting in their global optimization ability on multimodal functions in addition , the techniques for multimodal optimization are borrowed as diversity maintenance techniques to other problems in this chapter , we describe and review the state of the arts evolutionary algorithms for multimodal optimization in terms of methodology , benchmarking , and application"
cs.DS,"new algorithms are proposed for the tucker approximation of a 3 tensor , that access it using only the tensor by vector by vector multiplication subroutine in the matrix case , krylov methods are methods of choice to approximate the dominant column and row subspaces of a sparse or structured matrix given through the matrix by vector multiplication subroutine using the wedderburn rank reduction formula , we propose an algorithm of matrix approximation that computes krylov subspaces and allows generalization to the tensor case several variants of proposed tensor algorithms differ by pivoting strategies , overall cost and quality of approximation by convincing numerical experiments we show that the proposed methods are faster and more accurate than the minimal krylov recursion , proposed recently by elden and savas"
cs.LG|stat.ML,"in the era of deep learning several unsupervised models have been developed to capture the key features in unlabeled handwritten data popular among them is the restricted boltzmann machines rbm however , due to the novelty in handwritten multidialect data , the rbm may fail to generate an efficient representation in this paper we propose a generative model , the mode synthesizing machine msm for on line representation of real life handwritten multidialect language data the msm takes advantage of the hierarchical representation of the modes of a data distribution using a two point error update to learn a sequence of representative multidialects in a generative way experiments were performed to evaluate the performance of the msm over the rbm with the former attaining much lower error values than the latter on both independent and mixed data set"
cs.SI,"this paper considers a model for cascades on random networks in which the cascade propagation at any node depends on the load at the failed neighbor , the degree of the neighbor as well as the load at that node each node in the network bears an initial load that is below the capacity of the node the trigger for the cascade emanates at a single node or a small fraction of the nodes from some external shock upon failure , the load at the failed node gets divided randomly and added to the existing load at those neighboring nodes that have not yet failed subsequently , a neighboring node fails if its accumulated load exceeds its capacity the failed node then plays no further part in the process the cascade process stops as soon as the accumulated load at all nodes that have not yet failed is below their respective capacities the model is shown to operate in two regimes , one in which the cascade terminates with only a finite number of node failures in the other regime there is a positive probability that the cascade continues indefinitely bounds are obtained on the critical parameter where the phase transition occurs"
cs.AI,"luhmann \( 1984 \) defined society as a communication system which is structurally coupled to , but not an aggregate of , human action systems the communication system is then considered as self organizing \( autopoietic \) , as are human actors communication systems can be studied by using shannon 's \( 1948 \) mathematical theory of communication the update of a network by action at one of the local nodes is then a well known problem in artificial intelligence \( pearl 1988 \) by combining these various theories , a general algorithm for probabilistic structure action contingency can be derived the consequences of this contingency for each system , its consequences for their further histories , and the stabilization on each side by counterbalancing mechanisms are discussed , in both mathematical and theoretical terms an empirical example is elaborated"
cs.DM|cs.DS,"frick and grohe j acm 48 \( 2006 \) , 1184 1206 introduced a notion of graph classes with locally bounded tree width and established that every first order logic property can be decided in almost linear time in such a graph class here , we introduce an analogous notion for matroids \( locally bounded branch width \) and show the existence of a fixed parameter algorithm for first order logic properties in classes of regular matroids with locally bounded branch width to obtain this result , we show that the problem of deciding the existence of a circuit of length at most k containing two given elements is fixed parameter tractable for regular matroids"
cs.AI,"identifying effects of actions \( treatments \) on outcome variables from observational data and causal assumptions is a fundamental problem in causal inference this identification is made difficult by the presence of confounders which can be related to both treatment and outcome variables confounders are often handled , both in theory and in practice , by adjusting for covariates , in other words considering outcomes conditioned on treatment and covariate values , weighed by probability of observing those covariate values in this paper , we give a complete graphical criterion for covariate adjustment , which we term the adjustment criterion , and derive some interesting corollaries of the completeness of this criterion"
cs.DS|cs.LG,"we extend the langevin monte carlo \( lmc \) algorithm to compactly supported measures via a projection step , akin to projected stochastic gradient descent \( sgd \) we show that \( projected \) lmc allows to sample in polynomial time from a log concave distribution with smooth potential this gives a new markov chain to sample from a log concave distribution our main result shows in particular that when the target distribution is uniform , lmc mixes in tilde o \( n 7 \) steps \( where n is the dimension \) we also provide preliminary experimental evidence that lmc performs at least as well as hit and run , for which a better mixing time of tilde o \( n 4 \) was proved by lov 'a sz and vempala"
cs.DM,"a median graph is a connected graph , such that for any three vertices u , v , w there is exactly one vertex x that lies simultaneously on a shortest \( u , v \) path , a shortest \( v , w \) path and a shortest \( w , u \) path examples of median graphs are trees and hypercubes we introduce and study a generalisation of tree decompositions , to be called median decompositions , where instead of decomposing a graph g in a treelike fashion , we use general median graphs as the underlying graph of the decomposition we show that the corresponding width parameter text mw \( g \) , the medianwidth of g , is equal to the clique number of the graph , while a suitable variation of it is equal to the chromatic number of g we study in detail the i medianwidth text mw i \( g \) of a graph , where we do not allow the underlying median graph of a decomposition to contain i 1 dimensional hypercubes as induced subgraphs , for i geq 1 , the parameters text mw i constitute a hierarchy starting from treewidth and converging to the clique number we characterize the i medianwidth of a graph to be , roughly said , the largest intersection of the best choice of i many tree decompositions of the graph lastly , we extend the concept of tree and median decompositions and propose a general framework of how to decompose a graph g in any fixed graphlike fashion"
cs.LG,"people often use a web search engine to find information about events of interest , for example , sport competitions , political elections , festivals and entertainment news in this paper , we study a problem of detecting event related queries , which is the first step before selecting a suitable time aware retrieval model in general , event related information needs can be observed in query streams through various temporal patterns of user search behavior , e g , spiky peaks for popular events , and periodicities for repetitive events however , it is also common that users search for non popular events , which may not exhibit temporal variations in query streams , e g , past events recently occurred , historical events triggered by anniversaries or similar events , and future events anticipated to happen to address the challenge of detecting dynamic classes of events , we propose a novel deep learning model to classify a given query into a predetermined set of multiple event types our proposed model , a stacked multilayer perceptron \( s mlp \) network , consists of multilayer perceptron used as a basic learning unit we assemble stacked units to further learn complex relationships between neutrons in successive layers to evaluate our proposed model , we conduct experiments using real world queries and a set of manually created ground truth preliminary results have shown that our proposed deep learning model outperforms the state of the art classification models significantly"
cs.LG,"in this paper , we defined the viseme \( visual speech element \) and described about the method of extracting visual feature vector we defined the 10 visemes based on vowel by analyzing of korean utterance and proposed the method of extracting the 20 dimensional visual feature vector , combination of static features and dynamic features lastly , we took an experiment in recognizing words based on 3 viseme hmm and evaluated the efficiency"
cs.LG|stat.ML,"bayesian methods suffer from the problem of how to specify prior beliefs one interesting idea is to consider worst case priors this requires solving a stochastic zero sum game in this paper , we extend well known results from bandit theory in order to discover minimax bayes policies and discuss when they are practical"
cs.IT|stat.ML,"representing patterns by complex relational structures , such as labeled graphs , is becoming an increasingly common practice in the broad field of computational intelligence accordingly , a wide repertoire of pattern recognition tools , such as classifiers and knowledge discovery procedures , are nowadays available and tested for various labeled graph data types however , the design of effective learning and mining procedures operating in the space of labeled graphs is still a challenging problem , especially from the computational complexity viewpoint in this paper , we present a major improvement of a general purpose graph classification system , which is conceived on an interplay among dissimilarity representation , clustering , information theoretic techniques , and evolutionary optimization the improvement focuses on a specific key subroutine of the system that performs the compression of the input data we prove different theorems which are fundamental to the setting of such a compression operation we demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well known datasets of labeled graphs , considering as distinct performance indicators the classification accuracy , the computing time , and the parsimony in terms of structural complexity of the synthesized classification model overall , the results show state of the art standards in terms of test set accuracy , while achieving considerable reductions for what concerns the effective computing time and classification model complexity"
cs.DS,we analyze a distributed system where n nodes called boxes store a large set of videos and collaborate to serve simultaneously n videos or less we explore under which conditions such a system can be scalable while serving any sequence of demands we model this problem through a combination of two algorithms a video allocation algorithm and a connection scheduling algorithm the latter plays against an adversary that incrementally proposes video requests
cs.LG|stat.ML,"face verification remains a challenging problem in very complex conditions with large variations such as pose , illumination , expression , and occlusions this problem is exacerbated when we rely unrealistically on a single training data source , which is often insufficient to cover the intrinsically complex face variations this paper proposes a principled multi task learning approach based on discriminative gaussian process latent variable model , named gaussianface , to enrich the diversity of training data in comparison to existing methods , our model exploits additional data from multiple source domains to improve the generalization performance of face verification in an unknown target domain importantly , our model can adapt automatically to complex data distributions , and therefore can well capture complex face variations inherent in multiple sources extensive experiments demonstrate the effectiveness of the proposed model in learning from diverse data sources and generalize to unseen domain specifically , the accuracy of our algorithm achieves an impressive accuracy rate of 98 52 on the well known and challenging labeled faces in the wild \( lfw \) benchmark for the first time , the human level performance in face verification \( 97 53 \) on lfw is surpassed"
cs.DM|cs.DS,"we study a long standing conjecture on the necessary and sufficient conditions for the compatibility of multi state characters there exists a function f \( r \) such that , for any set c of r state characters , c is compatible if and only if every subset of f \( r \) characters of c is compatible we show that for every r ge 2 , there exists an incompatible set c of lfloor frac r 2 rfloor cdot lceil frac r 2 rceil 1 r state characters such that every proper subset of c is compatible thus , f \( r \) ge lfloor frac r 2 rfloor cdot lceil frac r 2 rceil 1 for every r ge 2 this improves the previous lower bound of f \( r \) ge r given by meacham \( 1983 \) , and generalizes the construction showing that f \( 4 \) ge 5 given by habib and to \( 2011 \) we prove our result via a result on quartet compatibility that may be of independent interest for every integer n ge 4 , there exists an incompatible set q of lfloor frac n 2 2 rfloor cdot lceil frac n 2 2 rceil 1 quartets over n labels such that every proper subset of q is compatible we contrast this with a result on the compatibility of triplets for every n ge 3 , if r is an incompatible set of more than n 1 triplets over n labels , then some proper subset of r is incompatible we show this upper bound is tight by exhibiting , for every n ge 3 , a set of n 1 triplets over n taxa such that r is incompatible , but every proper subset of r is compatible"
cs.AI,"a natural and established way to restrict the constraint satisfaction problem is to fix the relations that can be used to pose constraints such a family of relations is called a constraint language in this article , we study arc consistency , a heavily investigated inference method , and three extensions thereof from the perspective of constraint languages we conduct a comparison of the studied methods on the basis of which constraint languages they solve , and we present new polynomial time tractability results for singleton arc consistency , the most powerful method studied"
cs.AI,"answer set programming \( asp \) is an increasingly popular framework for declarative programming that admits the description of problems by means of rules and constraints that form a disjunctive logic program in particular , many ai problems such as reasoning in a nonmonotonic setting can be directly formulated in asp although the main problems of asp are of high computational complexity , located at the second level of the polynomial hierarchy , several restrictions of asp have been identified in the literature , under which asp problems become tractable in this paper we use the concept of backdoors to identify new restrictions that make asp problems tractable small backdoors are sets of atoms that represent clever reasoning shortcuts through the search space and represent a hidden structure in the problem input the concept of backdoors is widely used in the areas of propositional satisfiability and constraint satisfaction we show that it can be fruitfully adapted to asp we demonstrate how backdoors can serve as a unifying framework that accommodates several tractable restrictions of asp known from the literature furthermore , we show how backdoors allow us to deploy recent algorithmic results from parameterized complexity theory to the domain of answer set programming"
cs.AI,a new learning algorithm for evolving cascade neural networks \( ecnns \) is described an ecnn starts to learn with one input node and then adding new inputs as well as new hidden neurons evolves it the trained ecnn has a nearly minimal number of input and hidden neurons as well as connections the algorithm was successfully applied to classify artifacts and normal segments in clinical electroencephalograms \( eegs \) the eeg segments were visually labeled by eeg viewer the trained ecnn has correctly classified 96 69 of the testing segments it is slightly better than a standard fully connected neural network
cs.AI|cs.LG,"recurrent neural networks are often used for learning time series data based on a few assumptions we model this learning task as a minimization problem of a nonlinear least squares cost function the special structure of the cost function allows us to build a connection to reinforcement learning we exploit this connection and derive a convergent , policy iteration based algorithm furthermore , we argue that rnn training can be fit naturally into the reinforcement learning framework"
cs.LG|stat.ML,"we consider the problem of learning decision rules for prediction with feature budget constraint in particular , we are interested in pruning an ensemble of decision trees to reduce expected feature cost while maintaining high prediction accuracy for any test example we propose a novel 0 1 integer program formulation for ensemble pruning our pruning formulation is general it takes any ensemble of decision trees as input by explicitly accounting for feature sharing across trees together with accuracy cost trade off , our method is able to significantly reduce feature cost by pruning subtrees that introduce more loss in terms of feature cost than benefit in terms of prediction accuracy gain theoretically , we prove that a linear programming relaxation produces the exact solution of the original integer program this allows us to use efficient convex optimization tools to obtain an optimally pruned ensemble for any given budget empirically , we see that our pruning algorithm significantly improves the performance of the state of the art ensemble method budgetrf"
cs.DS,"since the advent of wireless communication , the need for mobile ad hoc networks has been growing exponentially this has opened up a pandoras box of algorithms for dealing with mobile ad hoc networks , or manets , as they are generally referred to most attempts made at evaluating these algorithms so far have focused on parameters such as throughput , packet delivery ratio , overhead etc an analysis of the convergence times of these algorithms is still an open issue the work carried out fills this gap by evaluating the algorithms on the basis of convergence time algorithms for manets can be classified into three categories reactive , proactive , and hybrid protocols in this project , we compare the convergence times of representative algorithms in each category , namely ad hoc on demand distance vector \( aodv \) reactive , destination sequence distance vector protocol \( dsdv \) proactive , and temporally ordered routing algorithm \( tora \) hybrid the algorithm performances are compared by simulating them in ns2 tcl is used to conduct the simulations , while perl is used to extract data from the simulation output and calculate convergence time the design of the experiments carried on is documented using unified modeling language also , a user interface is created using perl , which enables the user to either run a desired simulation and measure convergence time , or measure the convergence time of a simulation that has been run earlier"
cs.DS|stat.ML,"the problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible in this paper we approximate the optimal biclustering by applying one way clustering algorithms independently on the rows and on the columns of the input matrix we show that such a solution yields a worst case approximation ratio of 1 sqrt \( 2 \) under l1 norm for 0 1 valued matrices , and of 2 under l2 norm for real valued matrices"
cs.LG,"predicting trends in stock market prices has been an area of interest for researchers for many years due to its complex and dynamic nature intrinsic volatility in stock market across the globe makes the task of prediction challenging forecasting and diffusion modeling , although effective ca n't be the panacea to the diverse range of problems encountered in prediction , short term or otherwise market risk , strongly correlated with forecasting errors , needs to be minimized to ensure minimal risk in investment the authors propose to minimize forecasting error by treating the forecasting problem as a classification problem , a popular suite of algorithms in machine learning in this paper , we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning some of the technical indicators such as relative strength index \( rsi \) , stochastic oscillator etc are used as inputs to train our model the learning model used is an ensemble of multiple decision trees the algorithm is shown to outperform existing algo rithms found in the literature out of bag \( oob \) error estimates have been found to be encouraging key words random forest classifier , stock price forecasting , exponential smoothing , feature extraction , oob error and convergence"
cs.LG|stat.ML,"probabilistic k nearest neighbour \( pknn \) classification has been introduced to improve the performance of original k nearest neighbour \( knn \) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector however , an issue common to both knn and pknn is to select the optimal number of neighbours , k the contribution of this paper is to incorporate the uncertainty in k into the decision making , and in so doing use bayesian model averaging to provide improved classification indeed the problem of assessing the uncertainty in k can be viewed as one of statistical model selection which is one of the most important technical issues in the statistics and machine learning domain in this paper , a new functional approximation algorithm is proposed to reconstruct the density of the model \( order \) without relying on time consuming monte carlo simulations in addition , this algorithm avoids cross validation by adopting bayesian framework the performance of this algorithm yielded very good performance on several real experimental datasets"
cs.SI,"trust has been explored by many researchers in the past as a successful solution for assisting recommender systems even though the approach of using a web of trust scheme for assisting the recommendation production is well adopted , issues like the sparsity problem have not been explored adequately so far with regard to this in this work we are proposing and testing a scheme that uses the existing ratings of users to calculate the hypothetical trust that might exist between them the purpose is to demonstrate how some basic social networking when applied to an existing system can help in alleviating problems of traditional recommender system schemes interestingly , such schemes are also alleviating the cold start problem from which mainly new users are suffering in order to show how good the system is in that respect , we measure the performance at various times as the system evolves and we also contrast the solution with existing approaches finally , we present the results which justify that such schemes undoubtedly work better than a system that makes no use of trust at all"
stat.ML,"the homology groups of a manifold are important topological invariants that provide an algebraic summary of the manifold these groups contain rich topological information , for instance , about the connected components , holes , tunnels and sometimes the dimension of the manifold in earlier work , we have considered the statistical problem of estimating the homology of a manifold from noiseless samples and from noisy samples under several different noise models we derived upper and lower bounds on the minimax risk for this problem in this note we revisit the noiseless case in previous work we used le cam 's lemma to establish a lower bound that differed from the upper bound of niyogi , smale and weinberger by a polynomial factor in the condition number in this note we use a different construction based on the direct analysis of the likelihood ratio test to show that the upper bound of niyogi , smale and weinberger is in fact tight , thus establishing rate optimal asymptotic minimax bounds for the problem the techniques we use here extend in a straightforward way to the noisy settings considered in our earlier work"
cs.SI,"we propose an opinion dynamics model that combines processes of vanity and opinion propagation the interactions take place between randomly chosen pairs during an interaction , the agents propagate their opinions about themselves and about other people they know moreover , each individual is subject to vanity if her interlocutor seems to value her highly , then she increases her opinion about this interlocutor on the contrary she tends to decrease her opinion about those who seem to undervalue her the combination of these dynamics with the hypothesis that the opinion propagation is more efficient when coming from highly valued individuals , leads to different patterns when varying the parameters for instance , for some parameters the positive opinion links between individuals generate a small world network in one of the patterns , absolute dominance of one agent alternates with a state of generalised distrust , where all agents have a very low opinion of all the others \( including themselves \) we provide some explanations of the mechanisms behind these emergent behaviors and finally propose a discussion about their interest"
cs.AI,"hex programs are an extension of the answer set programming \( asp \) paradigm incorporating external means of computation into the declarative programming language through so called external atoms their semantics is defined in terms of minimal models of the faber leone pfeifer \( flp \) reduct developing native solvers for hex programs based on an appropriate notion of unfounded sets has been subject to recent research for reasons of efficiency although this has lead to an improvement over naive minimality checking using the flp reduct , testing for foundedness remains a computationally expensive task in this work we improve on hex program evaluation in this respect by identifying a syntactic class of programs , that can be efficiently recognized and allows to entirely skip the foundedness check moreover , we develop criteria for decomposing a program into components , such that the search for unfounded sets can be restricted observing that our results apply to many hex program applications provides analytic evidence for the significance and effectiveness of our approach , which is complemented by a brief discussion of preliminary experimental validation"
cs.AI|stat.ML,"much of scientific data is collected as randomized experiments intervening on some and observing other variables of interest quite often , a given phenomenon is investigated in several studies , and different sets of variables are involved in each study in this article we consider the problem of integrating such knowledge , inferring as much as possible concerning the underlying causal structure with respect to the union of observed variables from such experimental or passive observational overlapping data sets we do not assume acyclicity or joint causal sufficiency of the underlying data generating model , but we do restrict the causal relationships to be linear and use only second order statistics of the data we derive conditions for full model identifiability in the most generic case , and provide novel techniques for incorporating an assumption of faithfulness to aid in inference in each case we seek to establish what is and what is not determined by the data at hand"
cs.SI,"the roll calls of the italian parliament in the xvi legislature are studied by employing multidimensional scaling , hierarchical clustering , and network analysis in order to detect changes in voting behavior , the roll calls have been divided in seven periods of six months each all the methods employed pointed out an increasing fragmentation of the political parties endorsing the previous government that culminated in its downfall by using the concept of modularity at different resolution levels , we identify the community structure of parliament and its evolution in each of the considered time periods the analysis performed revealed as a valuable tool in detecting trends and drifts of parliamentarians it showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness , without having at disposal any knowledge about political membership of representatives"
cs.AI,"in order to fulfill the test requirement of kstar \( korea superconducting tokamak advanced research \) superconducting magnet system , a large scale superconducting magnet and conductor test facility , sstf \( samsung superconductor test facility \) , has been constructed at samsung advanced institute of technology the computer system for sstf dac \( data acquisition and control \) is based on unix system and vxworks is used for the real time os of the vme system epics \( experimental physics and industrial control system \) is used for the communication between ioc server and client a database program has been developed for the efficient management of measured data and a linux workstation with pentium 4 cpu is used for the database server in this paper , the current status of sstf dac system , the database management system and recent test results are presented"
cs.SI,"online social networks \( osn \) during last years acquired a huge and increasing popularity as one of the most important emerging web phenomena , deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the web in this preliminary work paper , our purpose is to analyze facebook , considering a significant sample of data reflecting relationships among subscribed users our goal is to extract , from this platform , relevant information about the distribution of these relations and exploit tools and algorithms provided by the social network analysis \( sna \) to discover and , possibly , understand underlying similarities between the developing of osn and real life social networks"
cs.DM,"an interval t coloring of a graph g is a proper edge coloring of g with colors 1 , 2 , , t such that at least one edge of g is colored by i , i 1 , 2 , , t , and the edges incident to each vertex v in v \( g \) are colored by d g \( v \) consecutive colors , where d g \( v \) is the degree of the vertex v in g in this paper interval edge colorings of various graph products are investigated"
cs.AI,"knowledge bases of real world facts about entities and their relationships are useful resources for a variety of natural language processing tasks however , because knowledge bases are typically incomplete , it is useful to be able to perform link prediction , i e , predict whether a relationship not in the knowledge base is likely to be true this paper combines insights from several previous link prediction models into a new embedding model stranse that represents each entity as a low dimensional vector , and each relation by two matrices and a translation vector stranse is a simple combination of the se and transe models , but it obtains better link prediction performance on two benchmark datasets than previous embedding models thus , stranse can serve as a new baseline for the more complex models in the link prediction task"
cs.DM,"this paper introduces a new approach to represent logic functions in the form of sierpinski gaskets the structure of the gasket allows to manipulate with the corresponding logic expression using recursive essence of fractals thus , the sierpinski gasket 's pattern has myriad useful properties which can enhance practical features of other graphic representations like decision diagrams we have covered possible applications of sierpinski gaskets in logic design and justified our assumptions in logic function minimization \( both boolean and multiple valued cases \) the experimental results on benchmarks with advances in the novel structure are considered as well"
cs.DS,"the interval discrete logarithm problem is defined as follows given some g , h in a group g , and some n in mathbb n such that g z h for some z where 0 leq z n , find z at the moment , kangaroo methods are the best low memory algorithm to solve the interval discrete logarithm problem the fastest non parallelised kangaroo methods to solve this problem are the three kangaroo method , and the four kangaroo method these respectively have expected average running times of big \( 1 818 o \( 1 \) big \) sqrt n , and big \( 1 714 o \( 1 \) big \) sqrt n group operations it is currently an open question as to whether it is possible to improve kangaroo methods by using more than four kangaroos before this dissertation , the fastest kangaroo method that used more than four kangaroos required at least 2 sqrt n group operations to solve the interval discrete logarithm problem in this thesis , i improve the running time of methods that use more than four kangaroos significantly , and almost beat the fastest kangaroo algorithm , by presenting a seven kangaroo method with an expected average running time of big \( 1 7195 o \( 1 \) big \) sqrt n pm o \( 1 \) group operations the question , 'are five kangaroos worse than three \? ' is also answered in this thesis , as i propose a five kangaroo algorithm that requires on average big \( 1 737 o \( 1 \) big \) sqrt n group operations to solve the interval discrete logarithm problem"
cs.AI|cs.LG,"pattern learning in an important problem in natural language processing \( nlp \) some exhaustive pattern learning \( epl \) methods \( bod , 1992 \) were proved to be flawed \( johnson , 2002 \) , while similar algorithms \( och and ney , 2004 \) showed great advantages on other tasks , such as machine translation in this article , we first formalize epl , and then show that the probability given by an epl model is constant factor approximation of the probability given by an ensemble method that integrates exponential number of models obtained with various segmentations of the training data this work for the first time provides theoretical justification for the widely used epl algorithm in nlp , which was previously viewed as a flawed heuristic method better understanding of epl may lead to improved pattern learning algorithms in future"
cs.DS,"this paper investigates and bounds the expected solution quality of combinatorial optimization problems when feasible solutions are chosen at random loose general bounds are discovered , as well as families of combinatorial optimization problems for which random feasible solutions are expected to be a constant factor of optimal one implication of this result is that , for graphical problems , if the average edge weight in a feasible solution is sufficiently small , then any randomly chosen feasible solution to the problem will be a constant factor of optimal for example , under certain well defined circumstances , the expected constant of approximation of a randomly chosen feasible solution to the steiner network problem is bounded above by 3 empirical analysis supports these bounds and actually suggest that they might be tightened"
cs.AI,"while looking for abductive explanations of a given set of manifestations , an ordering between possible solutions is often assumed the complexity of finding verifying optimal solutions is already known in this paper we consider the computational complexity of finding second best solutions we consider different orderings , and consider also different possible definitions of what a second best solution is"
cs.SI,"with the advent of online networks , societies are substantially more connected with individual members able to easily modify and maintain their own social links here , we show that active network maintenance exposes agents to confirmation bias , the tendency to confirm one 's beliefs , and we explore how this affects collective opinion formation we introduce a model of binary opinion dynamics on a complex network with fast , stochastic rewiring and show that confirmation bias induces a segregation of individuals with different opinions we use the dynamics of global opinion to generally categorize opinion update rules and find that confirmation bias always stabilizes the consensus state finally , we show that the time to reach consensus has a non monotonic dependence on the magnitude of the bias , suggesting a novel avenue for large scale opinion engineering"
cs.AI,"security games employ game theoretical tools to derive resource allocation strategies in security domains recent works considered the presence of alarm systems , even suffering various forms of uncertainty , and showed that disregarding alarm signals may lead to arbitrarily bad strategies the central problem with an alarm system , unexplored in other security games , is finding the best strategy to respond to alarm signals for each mobile defensive resource the literature provides results for the basic single resource case , showing that even in that case the problem is computationally hard in this paper , we focus on the challenging problem of designing algorithms scaling with multiple resources first , we focus on finding the minimum number of resources assuring non null protection to every target then , we deal with the computation of multi resource strategies with different degrees of coordination among resources for each considered problem , we provide a computational analysis and propose algorithmic methods"
cs.SI,"e communities , social groups interacting online , have recently become an object of interdisciplinary research as with face to face meetings , internet exchanges may not only include factual information but also emotional information how participants feel about the subject discussed or other group members emotions are known to be important in affecting interaction partners in offline communication in many ways could emotions in internet exchanges affect others and systematically influence quantitative and qualitative aspects of the trajectory of e communities \? the development of automatic sentiment analysis has made large scale emotion detection and analysis possible using text messages collected from the web it is not clear if emotions in e communities primarily derive from individual group members' personalities or if they result from intra group interactions , and whether they influence group activities we show the collective character of affective phenomena on a large scale as observed in 4 million posts downloaded from blogs , digg and bbc forums to test whether the emotions of a community member may influence the emotions of others , posts were grouped into clusters of messages with similar emotional valences the frequency of long clusters was much higher than it would be if emotions occurred at random distributions for cluster lengths can be explained by preferential processes because conditional probabilities for consecutive messages grow as a power law with cluster length for bbc forum threads , average discussion lengths were higher for larger values of absolute average emotional valence in the first ten comments and the average amount of emotion in messages fell during discussions our results prove that collective emotional states can be created and modulated via internet communication and that emotional expressiveness is the fuel that sustains some e communities"
cs.DS,"some randomized algorithms , used to obtain a random n 2 times n 2 sudoku matrix , where n is a natural number , is reviewed in this study below is described the set pi n of all \( 2n \) times n matrices , consisting of elements of the set mathbb z n 1 , 2 , ldots , n , such that every row is a permutation it is proved that such matrices would be particularly useful in developing efficient algorithms in generating sudoku matrices an algorithm to obtain random pi n matrices is presented in this paper the algorithms are evaluated according to two criteria probability evaluation , and time evaluation this type of criteria is interesting from both theoretical and practical point of view because they are particularly useful in the analysis of computer programs"
cs.DM,"multitildes are regular operators that were introduced by caron et al in order to increase the number of glushkov automata in this paper , we study the family of the multitilde operators from an algebraic point of view using the notion of operad this leads to a combinatorial description of already known results as well as new results on compositions , actions and enumerations"
cs.SI,"taking a pragmatic approach to the processes involved in the phenomena of collective opinion formation , we investigate two specific modifications to the co evolving network voter model of opinion formation , studied by holme and newman 1 first , we replace the rewiring probability parameter by a distribution of probability of accepting or rejecting opinions between individuals , accounting for the asymmetric influences in relationships among individuals in a social group second , we modify the rewiring step by a path length based preference for rewiring that reinforces local clustering we have investigated the influences of these modifications on the outcomes of the simulations of this model we found that varying the shape of the distribution of probability of accepting or rejecting opinions can lead to the emergence of two qualitatively distinct final states , one having several isolated connected components each in internal consensus leading to the existence of diverse set of opinions and the other having one single dominant connected component with each node within it having the same opinion furthermore , and more importantly , we found that the initial clustering in network can also induce similar transitions our investigation also brings forward that these transitions are governed by a weak and complex dependence on system size we found that the networks in the final states of the model have rich structural properties including the small world property for some parameter regimes 1 p holme and m newman , phys rev e 74 , 056108 \( 2006 \)"
cs.SI,"the evolution of communication technology and the proliferation of electronic devices have rendered adversaries powerful means for targeted attacks via all sorts of accessible resources in particular , owing to the intrinsic interdependency and ubiquitous connectivity of modern communication systems , adversaries can devise malware that propagates through intermediate hosts to approach the target , which we refer to as transmissive attacks inspired by biology , the transmission pattern of such an attack in the digital space much resembles the spread of an epidemic in real life this paper elaborates transmissive attacks , summarizes the utility of epidemic models in communication systems , and draws connections between transmissive attacks and epidemic models simulations , experiments , and ongoing research challenges on transmissive attacks are also addressed"
cs.LG|stat.ML,"in this paper we consider a problem of searching a space of predictive models for a given training data set we propose an iterative procedure for deriving a sequence of improving models and a corresponding sequence of sets of non linear features on the original input space after a finite number of iterations n , the non linear features become 2 n degree polynomials on the original space we show that in a limit of an infinite number of iterations derived non linear features must form an associative algebra a product of two features is equal to a linear combination of features from the same feature space for any given input point because each iteration consists of solving a series of convex problems that contain all previous solutions , the likelihood of the models in the sequence is increasing with each iteration while the dimension of the model parameter space is set to a limited controlled value"
cs.DS,"in this paper , we study movements of simple polygonal chains in 3d we say that an open , simple polygonal chain can be straightened if it can be continuously reconfigured to a straight sequence of segments in such a manner that both the length of each link and the simplicity of the chain are maintained throughout the movement the analogous concept for closed chains is convexification reconfiguration to a planar convex polygon chains that cannot be straightened or convexified are called locked while there are open chains in 3d that are locked , we show that if an open chain has a simple orthogonal projection onto some plane , it can be straightened for closed chains , we show that there are unknotted but locked closed chains , and we provide an algorithm for convexifying a planar simple polygon in 3d with a polynomial number of moves"
cs.DS,"we introduce a hierarchy of problems between the textsc dominating set problem and the textsc power dominating set \( pds \) problem called the ell round power dominating set \( ell round pds , for short \) problem for ell 1 , this is the textsc dominating set problem , and for ell geq n 1 , this is the pds problem here n denotes the number of nodes in the input graph in pds the goal is to find a minimum size set of nodes s that power dominates all the nodes , where a node v is power dominated if \( 1 \) v is in s or it has a neighbor in s , or \( 2 \) v has a neighbor u such that u and all of its neighbors except v are power dominated note that rule \( 1 \) is the same as for the textsc dominating set problem , and that rule \( 2 \) is a type of propagation rule that applies iteratively the ell round pds problem has the same set of rules as pds , except we apply rule \( 2 \) in ``parallel'' in at most ell 1 rounds we prove that ell round pds cannot be approximated better than 2 log 1 epsilon n even for ell 4 in general graphs we provide a dynamic programming algorithm to solve ell round pds optimally in polynomial time on graphs of bounded tree width we present a ptas \( polynomial time approximation scheme \) for ell round pds on planar graphs for ell o \( tfrac log n log log n \) finally , we give integer programming formulations for ell round pds"
cs.DS,"we propose a framework for the exact probabilistic analysis of window based pattern matching algorithms , such as boyer moore , horspool , backward dawg matching , backward oracle matching , and more in particular , we show how to efficiently obtain the distribution of such an algorithm 's running time cost for any given pattern in a random text model , which can be quite general , from simple uniform models to higher order markov models or hidden markov models \( hmms \) furthermore , we provide a technique to compute the exact distribution of emph differences in running time cost of two algorithms in contrast to previous work , our approach is neither limited to simple text models , nor to asymptotic statements , nor to moment computations such as expectation and variance methodically , we use extensions of finite automata which we call deterministic arithmetic automata \( daas \) and probabilistic arithmetic automata \( paas \) 13 to our knowledge , this is the first time that substring or suffix based pattern matching algorithms are analyzed exactly experimentally , we compare horspool 's algorithm , backward dawg matching , and backward oracle matching on prototypical patterns of short length and provide statistics on the size of minimal daas for these computations"
cs.SI,"we establish a relationship between decay centrality and two widely used and computationally cheaper measures of centrality , namely degree and closeness centrality we show that for low values of the decay parameter the nodes with maximum decay centrality also have maximum degree , whereas for high values of the decay parameter they also maximize closeness for intermediate values of the decay parameter , we perform an extensive set of simulations on random networks and find that maximum degree or closeness are good proxies for maximum decay centrality in particular , in the vast majority of simulated networks , the nodes with maximum decay centrality are characterized by a threshold on the decay parameter below which they belong to the set of nodes with maximum degree and above which they belong to the set of nodes with maximum closeness the threshold values vary with the characteristics of the network moreover , nodes with maximum degree or closeness are highly ranked in terms of decay centrality even when they are not maximizing it the latter analysis allows us to propose a simple rule of thumb that ensures a nearly optimal choice with very high probability"
cs.DM,"for each d 0 , we find all the smallest fullerenes for which the least distance between two pentagons is d we also show that for each d there is an h d such that fullerenes with pentagons at least distance d apart and any number of hexagons greater than or equal to h d exist we also determine the number of fullerenes where the minimum distance between any two pentagons is at least d , for 1 le d le 5 , up to 400 vertices"
cs.LG,"ca has grown as potential classifier for addressing major problems in bioinformatics lot of bioinformatics problems like predicting the protein coding region , finding the promoter region , predicting the structure of protein and many other problems in bioinformatics can be addressed through cellular automata even though there are some prediction techniques addressing these problems , the approximate accuracy level is very less an automated procedure was proposed with maca \( multiple attractor cellular automata \) which can address all these problems the genetic algorithm is also used to find rules with good fitness values extensive experiments are conducted for reporting the accuracy of the proposed tool the average accuracy of maca when tested with encode , bg570 , hmr195 , fickett and tongue , asp67 datasets is 78"
cs.LG|stat.ML,"we propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel conditional ordinal random fields \( kcorf \) we extend this framework to account for heteroscedasticity on the output labels \( i e , pain intensity scores \) and introduce a novel dynamic features , dynamic ranks , that impose temporal ordinal constraints on the static ranks \( i e , intensity scores \) our experimental results show that the proposed approach outperforms state of the art methods for sequence classification with ordinal data and other ordinal regression models the approach performs significantly better than other models in terms of intra class correlation measure , which is the most accepted evaluation measure in the tasks of facial behaviour intensity estimation"
cs.DS|cs.LG,"we consider a multi round auction setting motivated by pay per click auctions for internet advertising in each round the auctioneer selects an advertiser and shows her ad , which is then either clicked or not an advertiser derives value from clicks the value of a click is her private information initially , neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements the auctioneer 's goal is to design a \( dominant strategies \) truthful mechanism that \( approximately \) maximizes the social welfare if the advertisers bid their true private values , our problem is equivalent to the multi armed bandit problem , and thus can be viewed as a strategic version of the latter in particular , for both problems the quality of an algorithm can be characterized by regret , the difference in social welfare between the algorithm and the benchmark which always selects the same best advertisement we investigate how the design of multi armed bandit algorithms is affected by the restriction that the resulting mechanism must be truthful we find that truthful mechanisms have certain strong structural properties essentially , they must separate exploration from exploitation and they incur much higher regret than the optimal multi armed bandit algorithms moreover , we provide a truthful mechanism which \( essentially \) matches our lower bound on regret"
cs.LG,"neural machine translation \( nmt \) offers a novel alternative formulation of translation that is potentially simpler than statistical approaches however to reach competitive performance , nmt models need to be exceedingly large in this paper we consider applying knowledge distillation approaches \( bucila et al , 2006 hinton et al , 2015 \) that have proven successful for reducing the size of neural models in other domains to the problem of nmt we demonstrate that standard knowledge distillation applied to word level prediction can be effective for nmt , and also introduce two novel sequence level versions of knowledge distillation that further improve performance , and somewhat surprisingly , seem to eliminate the need for beam search \( even when applied on the original teacher model \) our best student model runs 10 times faster than its state of the art teacher with only a decrease of 0 2 bleu it is also significantly better than a baseline model trained without knowledge distillation by 4 2 1 7 bleu with greedy decoding beam search"
cs.LG,"in this paper , we study the impact of design choices for recommender systems on one choice tasks where users want to select one item out of a variety of options instead of focusing on only user factors or recommendation quality , we consider how an interface design that provides the user with digital short term memory impacts both user behavior and recommendation quality in particular , we focus on improving recommendations and user experience through the use of shortlists a shortlist is a temporary list of candidates that the user is currently considering , e g , a list of a few movies the user is currently considering for viewing users can off load the items they want to keep in memory to the shortlist thereby decreasing their cognitive load since shortlists are designed to support the user 's task , they provide more implicit feedback naturally as part of the workflow in making decisions rather than artificially soliciting explicit feedback this feedback provides additional data for training recommendation systems which in turn improves how well the recommendation models predict a user 's preferences we perform a user study with a movie recommendation setup to compare interfaces that offer shortlist support with those that do not from the user studies we conclude \( i \) users make better decisions with a shortlist \( ii \) users prefer an interface with shortlist support and \( iii \) the additional implicit feedback from sessions with a shortlist improves the quality of recommendations by nearly a factor of two"
cs.LG,"clustering is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process it is used for the exploration of inter relationships among a collection of patterns , by organizing them into homogeneous clusters clustering has been dynamically applied to a variety of tasks in the field of information retrieval \( ir \) clustering has become one of the most active area of research and the development clustering attempts to discover the set of consequential groups where those within each group are more closely related to one another than the others assigned to different groups the resultant clusters can provide a structure for organizing large bodies of text for efficient browsing and searching there exists a wide variety of clustering algorithms that has been intensively studied in the clustering problem among the algorithms that remain the most common and effectual , the iterative optimization clustering algorithms have been demonstrated reasonable performance for clustering , e g the expectation maximization \( em \) algorithm and its variants , and the well known k means algorithm this paper presents an analysis on how partition method clustering techniques em , k means and k means algorithm work on heartspect dataset with below mentioned features purity , entropy , cpu time , cluster wise analysis , mean value analysis and inter cluster distance thus the paper finally provides the experimental results of datasets for five clusters to strengthen the results that the quality of the behavior in clusters in em algorithm is far better than k means algorithm and k means algorithm"
cs.AI,"in this paper we describe a web search agent , called global search agent \( hereafter gsa for short \) gsa integrates and enhances several search techniques in order to achieve significant improvements in the user perceived quality of delivered information as compared to usual web search engines gsa features intelligent merging of relevant documents from different search engines , anticipated selective exploration and evaluation of links from the current result set , automated derivation of refined queries based on user relevance feedback system architecture as well as experimental accounts are also illustrated"
cs.SI,"weblog is the fourth way of network exchange after email , bbs and msn most bloggers begin to write blogs with great interest , and then their interests gradually achieve a balance with the passage of time in order to describe the phenomenon that people 's interest in something gradually decreases until it reaches a balance , we first propose the model that describes the attenuation of interest and reflects the fact that people 's interest becomes more stable after a long time we give a rigorous analysis on this model by non homogeneous poisson processes our analysis indicates that the interval distribution of arrival time is a mixed distribution with exponential and power law feature , that is , it is a power law with an exponential cutoff second , we collect blogs in sciencenet cn and carry on empirical studies on the interarrival time distribution the empirical results agree well with the analytical result , obeying a special power law with the exponential cutoff , that is , a special kind of gamma distribution these empirical results verify the model , providing an evidence for a new class of phenomena in human dynamics in human dynamics there are other distributions , besides power law distributions these findings demonstrate the variety of human behavior dynamics"
cs.LG,"convolutional neural networks \( cnn \) have shown great performance as a global representation for object recognition however , for multi label images that contain multiple objects from different categories , scales and locations , single cnn features might not be be optimal to enhance the robustness and discriminative power of cnn features for multi label object recognition problem , we propose a multi view multi instance framework this framework transforms the multi label classification problem into a multi class multi instance learning problem by extracting object proposals from images a multi view pipeline is then applied to generate a two view representation of each proposal by exploiting two levels of labels in multi label recognition problem the proposed framework not only has the flexibility of utilizing both weak and strong labels or just weak labels , but also holds the generalization ability to boost the performance of unseen categories by available strong labels our framework is extensively compared with state of the art hand crafted feature based and cnn based methods on two multi label benchmark datasets the experimental results validate the discriminative power and generalization ability of the proposed framework when combined with a very deep network , we can achieve state of the art results in both datasets"
stat.ML,"we introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources , e g , an occluder in the case of a visual recording within this framework , we propose i \) a novel algorithm to efficiently separate , i e , detect and localize , possible corruptions from a given suspicious data instance and ii \) a maximum a posteriori \( map \) estimator to impute the corrupted data as a generalization to euclidean distance , we also propose a novel distance measure , which is based on the ranked deviations among the data attributes and empirically shown to be superior in separating the corruptions our algorithm first splits the suspicious instance into parts through a binary partitioning tree in the space of data attributes and iteratively tests those parts to detect local anomalies using the nominal statistics extracted from an uncorrupted \( clean \) reference data set once each part is labeled as anomalous vs normal , the corresponding binary patterns over this tree that characterize corruptions are identified and the affected attributes are imputed under a certain conditional independency structure assumed for the binary patterns , we analytically show that the false alarm rate of the introduced algorithm in detecting the corruptions is independent of the data and can be directly set without any parameter tuning the proposed framework is tested over several well known machine learning data sets with synthetically generated corruptions and experimentally shown to produce remarkable improvements in terms of classification purposes with strong corruption separation capabilities our experiments also indicate that the proposed algorithms outperform the typical approaches and are robust to varying training phase conditions"
cs.DS|cs.SI,"the importance of a node in a directed graph can be measured by its pagerank the pagerank of a node is used in a number of application contexts including ranking websites and can be interpreted as the average portion of time spent at the node by an infinite random walk we consider the problem of maximizing the pagerank of a node by selecting some of the edges from a set of edges that are under our control by applying results from markov decision theory , we show that an optimal solution to this problem can be found in polynomial time our core solution results in a linear programming formulation , but we also provide an alternative greedy algorithm , a variant of policy iteration , which runs in polynomial time , as well finally , we show that , under the slight modification for which we are given mutually exclusive pairs of edges , the problem of pagerank optimization becomes np hard"
cs.DM,"we study a min max relation conjectured by saks and west for any two posets p and q the size of a maximum semiantichain and the size of a minimum unichain covering in the product p times q are equal for positive we state conditions on p and q that imply the min max relation based on these conditions we identify some new families of posets where the conjecture holds and get easy proofs for several instances where the conjecture had been verified before however , we also have examples showing that in general the min max relation is false , i e , we disprove the saks west conjecture"
cs.LG,"the present phase of machine learning is characterized by supervised learning algorithms relying on large sets of labeled examples \( n to infty \) the next phase is likely to focus on algorithms capable of learning from very few labeled examples \( n to 1 \) , like humans seem able to do we propose an approach to this problem and describe the underlying theory , based on the unsupervised , automatic learning of a ``good'' representation for supervised learning , characterized by small sample complexity \( n \) we consider the case of visual object recognition though the theory applies to other domains the starting point is the conjecture , proved in specific cases , that image representations which are invariant to translations , scaling and other transformations can considerably reduce the sample complexity of learning we prove that an invariant and unique \( discriminative \) signature can be computed for each image patch , i , in terms of empirical distributions of the dot products between i and a set of templates stored during unsupervised learning a module performing filtering and pooling , like the simple and complex cells described by hubel and wiesel , can compute such estimates hierarchical architectures consisting of this basic hubel wiesel moduli inherit its properties of invariance , stability , and discriminability while capturing the compositional organization of the visual world in terms of wholes and parts the theory extends existing deep learning convolutional architectures for image and speech recognition it also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects images which is invariant to transformations , stable , and discriminative for recognition and that this representation may be continuously learned in an unsupervised way during development and visual experience"
cs.SI,"the emerging domain of data enabled science necessitates development of algorithms and tools to provide for knowledge discovery human interaction with data through well constructed graphical representation can take special advantage of our visual processing system 's ability to identify patterns we develop a data visualization framework , called bifold , for exploratory analysis of binary relationships between two groups of objects typical data examples would include voting records , organizational memberships , and pairwise associations , or other binary datasets bifold visualization provides a low dimensional embedding of the data in a way that captures similarity by visual nearness , analogous to multidimensional scaling \( mds \) the unique and new feature of bifold is its ability to simultaneously capture both the within group and between group relationships among the objects , significantly enhancing visual knowledge discovery we benchmark bifold using the classic southern women dataset , where previously identified social groups are now visually evident we then construct bifold plots for two us voting datasets for the presidential election outcomes since 1976 , bifold plot illustrates the evolving geopolitical structures that underlie these election results for senate congressional voting , bifold identifies a partisan coordinate which separates the senators into two parties \( as expected \) while simultaneously visualizing a bipartisan coalition coordinate which captures the ultimate fate of the bills \( pass fail \) finally , we consider a global cuisine dataset of the association between recipes and food ingredients bifold plot allows us to visually compare and contrast cuisines while also allowing identification of signature ingredients of individual cuisines"
cs.DS,"it is known that every proper minor closed class of graphs has bounded stack number \( a k a book thickness and page number \) while this includes notable graph families such as planar graphs and graphs of bounded genus , many other graph families are not closed under taking minors for fixed g and k , we show that every n vertex graph that can be embedded on a surface of genus g with at most k crossings per edge has stack number mathcal o \( log n \) this includes k planar graphs the previously best known bound for the stack number of these families was mathcal o \( sqrt n \) , except in the case of 1 planar graphs analogous results are proved for map graphs that can be embedded on a surface of fixed genus none of these families is closed under taking minors the main ingredient in the proof of these results is a construction proving that n vertex graphs that admit constant layered separators have mathcal o \( log n \) stack number"
cs.DS,"the notions of hypertree width and generalized hypertree width were introduced by gottlob , leone , and scarcello in order to extend the concept of hypergraph acyclicity these notions were further generalized by grohe and marx , who introduced the fractional hypertree width of a hypergraph all these width parameters on hypergraphs are useful for extending tractability of many problems in database theory and artificial intelligence in this paper , we study the approximability of \( generalized , fractional \) hyper treewidth of sparse hypergraphs where the criterion of sparsity reflects the sparsity of their incidence graphs our first step is to prove that the \( generalized , fractional \) hypertree width of a hypergraph h is constant factor sandwiched by the treewidth of its incidence graph , when the incidence graph belongs to some apex minor free graph class this determines the combinatorial borderline above which the notion of \( generalized , fractional \) hypertree width becomes essentially more general than treewidth , justifying that way its functionality as a hypergraph acyclicity measure while for more general sparse families of hypergraphs treewidth of incidence graphs and all hypertree width parameters may differ arbitrarily , there are sparse families where a constant factor approximation algorithm is possible in particular , we give a constant factor approximation polynomial time algorithm for \( generalized , fractional \) hypertree width on hypergraphs whose incidence graphs belong to some h minor free graph class"
cs.LG|stat.ML,"this paper highlights new opportunities for designing large scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application level practitioners to stay for the most part oblivious to the details of the underlying hardware level implementations the hardware software co design methodology advocated here hinges on the deployment of compute intensive machine learning kernels onto compute platforms that trade off determinism in the computation for improvement in speed and or energy efficiency to achieve this , we revisit digital stochastic circuits for approximating matrix computations that are ubiquitous in machine learning algorithms theoretical and empirical evaluation is undertaken to assess the impact of the hardware induced computational noise on algorithm performance as a proof of concept , a stochastic hardware simulator is employed for training deep neural networks for image recognition problems"
cs.SI,"one interesting phenomenon that emerges from the typical structure of social networks is the friendship paradox it states that your friends have on average more friends than you do recent efforts have explored variations of it , with numerous implications for the dynamics of social networks however , the friendship paradox and its variations consider only the topological structure of the networks and neglect many other characteristics that are correlated with node degree in this article , we take the case of scientific collaborations to investigate whether a similar paradox also arises in terms of a researcher 's scientific productivity as measured by her h index the h index is a widely used metric in academia to capture both the quality and the quantity of a researcher 's scientific output it is likely that a researcher may use her coauthors' h indexes as a way to infer whether her own h index is adequate in her research area nevertheless , in this article , we show that the average h index of a researcher 's coauthors is usually higher than her own h index we present empirical evidence of this paradox and discuss some of its potential consequences"
cs.LG,"low rank matrix factorization \( mf \) is an important technique in data science the key idea of mf is that there exists latent structures in the data , by uncovering which we could obtain a compressed representation of the data by factorizing an original matrix to low rank matrices , mf provides a unified method for dimesion reduction , clustering , and matrix completion in this article we review several important variants of mf , including basic mf , non negative mf , orthogonal non negative mf as can be seen from their names , non negative mf and orthogonal non negative mf are variants of basic mf with non negativity and or orthogonality constraints such constraints are useful in specific senarios in the first part of this article , we introduce , for each of these models , the application scenarios , the distinctive properties , and the optimizing method by properly adapting mf , we can go beyond the problem of clustering and matrix completion in the second part of this article , we will extend mf to sparse matrix compeletion , enhance matrix compeletion using various regularization methods , and make use of mf for \( semi \) supervised learning by introducing latent space reinforcement and transformation we will see that mf is not only a useful model but also as a flexible framework that is applicable for various prediction problems"
cs.SI,"we study the diffusion of epidemics on networks that are partitioned into local communities the gross structure of hierarchical networks of this kind can be described by a quotient graph the rationale of this approach is that individuals infect fellows with higher probability than individuals in other communities , so that small communities may approximately reach equilibrium at a time when the network is still evolving as a whole in community models the nodal infection probability is thus expected to depend mainly on the interaction of a few , large interconnected clusters in this work , we describe the epidemic process as a continuous time susceptible infected susceptible \( sis \) model upon performing a first order mean field approximation it is possible to describe the nodal infection probability by a lower dimensional dynamical system associated with the evolution of quotient graph our main result is that the spectral radius of this much smaller quotient graph \( which only captures the macroscopic structure of the community network \) is all we need to know in order to decide whether the overall healthy state defines a stable or an unstable equilibrium our investigations are based on the graph theoretical notion of equitable partition and of its recent and rather flexible generalization , that of quasi equitable partition"
cs.LG|stat.ML,the paper describes an application of aggregating algorithm to the problem of regression it generalizes earlier results concerned with plain linear regression to kernel techniques and presents an on line algorithm which performs nearly as well as any oblivious kernel predictor the paper contains the derivation of an estimate on the performance of this algorithm the estimate is then used to derive an application of the complexity approximation principle to kernel methods
cs.AI,"the discovery , representation and reconstruction of \( technical \) integration networks from network mining \( nm \) raw data is a difficult problem for enterprises this is due to large and complex it landscapes within and across enterprise boundaries , heterogeneous technology stacks , and fragmented data to remain competitive , visibility into the enterprise and partner it networks on different , interrelated abstraction levels is desirable we present an approach to represent and reconstruct the integration networks from nm raw data using logic programming based on first order logic the raw data expressed as integration network model is represented as facts , on which rules are applied to reconstruct the network we have built a system that is used to apply this approach to real world enterprise landscapes and we report on our experience with this system"
cs.DM,"building an infinite square free word by appending one letter at a time while simultaneously avoiding the creation of squares is most likely to fail when the alphabet has two letters this approach is impossible when the alphabet has three or more letters , one will most probably create a word in which the addition of any letter invariably creates a square when one restricts the set of undesired squares to a finite one , this can be possible we study the constraints on the alphabet and the set of squares which permit this approach to work"
cs.AI,"this paper focuses on finding spatial and temporal criminal hotspots it analyses two different real world crimes datasets for denver , co and los angeles , ca and provides a comparison between the two datasets through a statistical analysis supported by several graphs then , it clarifies how we conducted apriori algorithm to produce interesting frequent patterns for criminal hotspots in addition , the paper shows how we used decision tree classifier and naive bayesian classifier in order to predict potential crime types to further analyse crimes datasets , the paper introduces an analysis study by combining our findings of denver crimes dataset with its demographics information in order to capture the factors that might affect the safety of neighborhoods the results of this solution could be used to raise awareness regarding the dangerous locations and to help agencies to predict future crimes in a specific location within a particular time"
cs.DM,a synchronizing word of a deterministic automaton is a word in the alphabet of colors of its edges that maps the automaton to a single state a coloring of edges of a directed graph is synchronizing if the coloring turns the graph into a deterministic finite automaton possessing a synchronizing word the road coloring problem is the problem of synchronizing coloring of a directed finite strongly connected graph with constant outdegree of all its vertices if the greatest common divisor of the lengths of all its cycles is one a polynomial time algorithm of the road coloring has been based on recent positive solution of this old famous problem one can use our new visualization program for demonstration of the algorithm as well as for visualization of the transition graph of any finite automaton the visual image presents some structure properties of the transition graph this help tool is linear in the size of the automaton
cs.LG|stat.ML,"metric learning makes it plausible to learn distances for complex distributions of data from labeled data however , to date , most metric learning methods are based on a single mahalanobis metric , which cannot handle heterogeneous data well those that learn multiple metrics throughout the space have demonstrated superior accuracy , but at the cost of computational efficiency here , we take a new angle to the metric learning problem and learn a single metric that is able to implicitly adapt its distance function throughout the feature space this metric adaptation is accomplished by using a random forest based classifier to underpin the distance function and incorporate both absolute pairwise position and standard relative position into the representation we have implemented and tested our method against state of the art global and multi metric methods on a variety of data sets overall , the proposed method outperforms both types of methods in terms of accuracy \( consistently ranked first \) and is an order of magnitude faster than state of the art multi metric methods \( 16x faster in the worst case \)"
cs.DS|cs.LG,"we give a greedy learning algorithm for reconstructing an evolutionary tree based on a certain harmonic average on triplets of terminal taxa after the pairwise distances between terminal taxa are estimated from sequence data , the algorithm runs in o \( n 2 \) time using o \( n \) work space , where n is the number of terminal taxa these time and space complexities are optimal in the sense that the size of an input distance matrix is n 2 and the size of an output tree is n moreover , in the jukes cantor model of evolution , the algorithm recovers the correct tree topology with high probability using sample sequences of length polynomial in \( 1 \) n , \( 2 \) the logarithm of the error probability , and \( 3 \) the inverses of two small parameters"
cs.AI,"community sensing , fusing information from populations of privately held sensors , presents a great opportunity to create efficient and cost effective sensing applications yet , reasonable privacy concerns often limit the access to such data streams how should systems valuate and negotiate access to private information , for example in return for monetary incentives \? how should they optimally choose the participants from a large population of strategic users with privacy concerns , and compensate them for information shared \? in this paper , we address these questions and present a novel mechanism , seqtgreedy , for budgeted recruitment of participants in community sensing we first show that privacy tradeoffs in community sensing can be cast as an adaptive submodular optimization problem we then design a budget feasible , incentive compatible \( truthful \) mechanism for adaptive submodular maximization , which achieves near optimal utility for a large class of sensing applications this mechanism is general , and of independent interest we demonstrate the effectiveness of our approach in a case study of air quality monitoring , using data collected from the mechanical turk platform compared to the state of the art , our approach achieves up to 30 reduction in cost in order to achieve a desired level of utility"
cs.DM|cs.DS|cs.SI,this dissertation contributes to mathematical and algorithmic problems that arise in the analysis of network and biological data
cs.AI,"a data integration system provides transparent access to different data sources by suitably combining their data , and providing the user with a unified view of them , called global schema however , source data are generally not under the control of the data integration process , thus integrated data may violate global integrity constraints even in presence of locally consistent data sources in this scenario , it may be anyway interesting to retrieve as much consistent information as possible the process of answering user queries under global constraint violations is called consistent query answering \( cqa \) several notions of cqa have been proposed , e g , depending on whether integrated information is assumed to be sound , complete , exact or a variant of them this paper provides a contribution in this setting it uniforms solutions coming from different perspectives under a common asp based core , and provides query driven optimizations designed for isolating and eliminating inefficiencies of the general approach for computing consistent answers moreover , the paper introduces some new theoretical results enriching existing knowledge on decidability and complexity of the considered problems the effectiveness of the approach is evidenced by experimental results to appear in theory and practice of logic programming \( tplp \)"
cs.SI,"identifying influential nodes in the complex networks is of theoretical and practical significance there are many methods are proposed to identify the influential nodes in the complex networks in this paper , a local structure entropy which is based on the degree centrality and the statistical mechanics is proposed to identifying the influential nodes in the complex network in the definition of the local structure entropy , each node has a local network , the local structure entropy of each node is equal to the structure entropy of the local network the main idea in the local structure entropy is try to use the influence of the local network to replace the node 's influence on the whole network the influential nodes which are identified by the local structure entropy are the intermediate nodes in the network the intermediate nodes which connect those nodes with a big value of degree we use the susceptible infective \( si \) model to evaluate the performance of the influential nodes which are identified by the local structure entropy in the si model the nodes use as the source of infection according to the si model , the bigger the percentage of the infective nodes in the network the important the node to the whole networks the simulation on four real networks show that the proposed method is efficacious and rationality to identify the influential nodes in the complex networks"
cs.DS,"we define the min min expectation selection problem \( resp max min expectation selection problem \) to be that of selecting k out of n given discrete probability distributions , to minimize \( resp maximize \) the expectation of the minimum value resulting when independent random variables are drawn from the selected distributions we assume each distribution has finitely many atoms let d be the number of distinct values in the support of the distributions we show that if d is a constant greater than 2 , the min min expectation problem is np complete but admits a fully polynomial time approximation scheme for d an arbitrary integer , it is np hard to approximate the min min expectation problem with any constant approximation factor the max min expectation problem is polynomially solvable for constant d we leave open its complexity for variable d we also show similar results for binary selection problems in which we must choose one distribution from each of n pairs of distributions"
cs.AI|cs.LG,"in the process of training support vector machines \( svms \) by decomposition methods , working set selection is an important technique , and some exciting schemes were employed into this field to improve working set selection , we propose a new model for working set selection in sequential minimal optimization \( smo \) decomposition methods in this model , it selects b as working set without reselection some properties are given by simple proof , and experiments demonstrate that the proposed method is in general faster than existing methods"
cs.LG|stat.ML,"in adaptive data analysis , the user makes a sequence of queries on the data , where at each step the choice of query may depend on the results in previous steps the releases are often randomized in order to reduce overfitting for such adaptively chosen queries in this paper , we propose a minimax framework for adaptive data analysis assuming gaussianity of queries , we establish the first sharp minimax lower bound on the squared error in the order of o \( frac sqrt k sigma 2 n \) , where k is the number of queries asked , and sigma 2 n is the ordinary signal to noise ratio for a single query our lower bound is based on the construction of an approximately least favorable adversary who picks a sequence of queries that are most likely to be affected by overfitting this approximately least favorable adversary uses only one level of adaptivity , suggesting that the minimax risk for 1 step adaptivity with k 1 initial releases and that for k step adaptivity are on the same order the key technical component of the lower bound proof is a reduction to finding the convoluting distribution that optimally obfuscates the sign of a gaussian signal our lower bound construction also reveals a transparent and elementary proof of the matching upper bound as an alternative approach to russo and zou \( 2015 \) , who used information theoretic tools to provide the same upper bound we believe that the proposed framework opens up opportunities to obtain theoretical insights for many other settings of adaptive data analysis , which would extend the idea to more practical realms"
cs.LG|stat.ML,"we present a novel approach for learning an hmm whose outputs are distributed according to a parametric family this is done by em decoupling the learning task into two steps first estimating the output parameters , and then estimating the hidden states transition probabilities the first step is accomplished by fitting a mixture model to the output stationary distribution given the parameters of this mixture model , the second step is formulated as the solution of an easily solvable convex quadratic program we provide an error analysis for the estimated transition probabilities and show they are robust to small perturbations in the estimates of the mixture parameters finally , we support our analysis with some encouraging empirical results"
cs.DS,"secondary spectrum auction is widely applied in wireless networks for mitigating the spectrum scarcity in a realistic spectrum trading market , the requests from secondary users often specify the usage of a fixed spectrum frequency band in a certain geographical region and require a duration time in a fixed available time interval considering the selfish behaviors of secondary users , it is imperative to design a truthful auction which matches the available spectrums and requests of secondary users optimally unfortunately , existing designs either do not consider spectrum heterogeneity or ignore the differences of required time among secondary users in this paper , we address this problem by investigating how to use auction mechanisms to allocate and price spectrum resources so that the social efficiency can be maximized we begin by classifying the spectrums and requests from secondary users into different local markets which ensures there is no interference between local markets , and then we can focus on the auction in a single local market we first design an optimal auction based on the vickrey clarke groves \( vcg \) mechanism to maximize the social efficiency while enforcing truthfulness to reduce the computational complexity , we further propose a truthful sub optimal auction with polynomial time complexity , which yields an approximation factor 6 4 surd2 our extensive simulation results using real spectrum availability data show that the social efficiency ratio of the sub optimal auction is always above 70 compared with the optimal auction"
cs.DS,"starting with a monodisperse configuration with n size 1 particles , an additive marcus lushnikov process evolves until it reaches its final state \( a unique particle with mass n \) at each of the n 1 steps of its evolution , a merging cost is incurred , that depends on the sizes of the two particles involved , and on an independent random factor this paper deals with the asymptotic behaviour of the cumulated costs up to the k th clustering , under various regimes for \( n , k \) , with applications to the study of union find algorithms"
cs.SI,"this article examines the relationship between acquaintanceship and coauthorship patterns in a multi disciplinary , multi institutional , geographically distributed research center two social networks are constructed and compared a network of coauthorship , representing how researchers write articles with one another , and a network of acquaintanceship , representing how those researchers know each other on a personal level , based on their responses to an online survey statistical analyses of the topology and community structure of these networks point to the importance of small scale , local , personal networks predicated upon acquaintanceship for accomplishing collaborative work in scientific communities"
cs.DM,"a finite word u is said to be bordered if u has a proper prefix which is also a suffix of u , and unbordered otherwise ehrenfeucht and silberger proved that an infinite word is purely periodic if and only if it contains only finitely many unbordered factors we are interested in abelian and weak abelian analogues of this result namely , we investigate the following question \( s \) let w be an infinite word such that all sufficiently long factors are \( weakly \) abelian bordered is w \( weakly \) abelian periodic \? in the process we answer a question of avgustinovich et al concerning the abelian critical factorization theorem"
cs.DM,we show that the space of polygonizations of a fixed planar point set s of n points is connected by o \( n 2 \) ``moves'' between simple polygons each move is composed of a sequence of atomic moves called ``stretches'' and ``twangs'' these atomic moves walk between weakly simple ``polygonal wraps'' of s these moves show promise to serve as a basis for generating random polygons
cs.AI,"the paper describes combinatorial synthesis approach with interval multset estimates of system elements for modeling , analysis , design , and improvement of a modular telemetry system morphological \( modular \) system design and improvement are considered as composition of the telemetry system elements \( components \) configuration the solving process is based on hierarchical morphological multicriteria design \( hmmd \) \( i \) multicriteria selection of alternatives for system components , \( ii \) synthesis of the selected alternatives into a resultant combination \( while taking into account quality of the alternatives above and their compatibility \) interval multiset estimates are used for assessment of design alternatives for telemetry system elements two additional systems problems are examined \( a \) improvement of the obtained solutions , \( b \) aggregation of the obtained solutions into a resultant system configuration the improvement and aggregation processes are based on multiple choice problem with interval multiset estimates numerical examples for an on board telemetry subsystem illustrate the design and improvement processes"
cs.AI,"in this paper , we study the impact of selection methods in the context of on line on board distributed evolutionary algorithms we propose a variant of the medea algorithm in which we add a selection operator , and we apply it in a taskdriven scenario we evaluate four selection methods that induce different intensity of selection pressure in a multi robot navigation with obstacle avoidance task and a collective foraging task experiments show that a small intensity of selection pressure is sufficient to rapidly obtain good performances on the tasks at hand we introduce different measures to compare the selection methods , and show that the higher the selection pressure , the better the performances obtained , especially for the more challenging food foraging task"
cs.DS,"given an undirected graph g \( v g , e g \) and a fixed pattern graph h \( v h , e h \) with k vertices , we consider the h transversal and h packing problems the former asks to find the smallest s subseteq v g such that the subgraph induced by v g setminus s does not have h as a subgraph , and the latter asks to find the maximum number of pairwise disjoint k subsets s 1 , , s m subseteq v g such that the subgraph induced by each s i has h as a subgraph we prove that if h is 2 connected , h transversal and h packing are almost as hard to approximate as general k hypergraph vertex cover and k set packing , so it is np hard to approximate them within a factor of omega \( k \) and widetilde omega \( k \) respectively we also show that there is a 1 connected h where h transversal admits an o \( log k \) approximation algorithm , so that the connectivity requirement cannot be relaxed from 2 to 1 for a special case of h transversal where h is a \( family of \) cycles , we mention the implication of our result to the related feedback vertex set problem , and give a different hardness proof for directed graphs"
cs.AI,"causality has been recently introduced in databases , to model , characterize and possibly compute causes for query results \( answers \) connections between queryanswer causality , consistency based diagnosis , database repairs \( wrt integrity constraint violations \) , abductive diagnosis and the view update problem have been established in this work we further investigate connections between query answer causality and abductive diagnosis and the view update problem in this context , we also define and investigate the notion of query answer causality in the presence of integrity constraints"
cs.SI,"this is a report , where preliminary work regarding the topic of voting intention inference from social media such as twitter is presented our case study is the uk 2010 general election and we are focusing on predicting the percentages of voting intention polls \( conducted by yougov \) for the three major political parties conservatives , labours and liberal democrats during a 5 month period before the election date \( may 6 , 2010 \) we form three methodologies for extracting positive or negative sentiment from tweets , which build on each other , and then propose two supervised models for turning sentiment into voting intention percentages interestingly , when the content of tweets is enriched by attaching synonymous words , a significant improvement on inference performance is achieved reaching a mean absolute error of 4 34 2 13 in that case , the predictions are also shown to be statistically significant the presented methods should be considered as work in progress limitations and suggestions for future work appear in the final section of this script"
cs.DS,"we provide a framework for the design and analysis of dynamic programming algorithms for surface embedded graphs on n vertices and branchwidth at most k our technique applies to general families of problems where standard dynamic programming runs in 2 o \( k log k \) n steps our approach combines tools from topological graph theory and analytic combinatorics in particular , we introduce a new type of branch decomposition called surface cut decomposition , generalizing sphere cut decompositions of planar graphs introduced by seymour and thomas , which has nice combinatorial properties namely , the number of partial solutions that can be arranged on a surface cut decomposition can be upper bounded by the number of non crossing partitions on surfaces with boundary it follows that partial solutions can be represented by a single exponential \( in the branchwidth k \) number of configurations this proves that , when applied on surface cut decompositions , dynamic programming runs in 2 o \( k \) n steps that way , we considerably extend the class of problems that can be solved in running times with a single exponential dependence on branchwidth and unify improve most previous results in this direction"
cs.IT|cs.LG,"in pomdps , information about the hidden state , delivered through observations , is both valuable to the agent , allowing it to base its actions on better informed internal states , and a curse , exploding the size and diversity of the internal state space one attempt to deal with this is to focus on reactive policies , that only base their actions on the most recent observation however , even reactive policies can be demanding on resources , and agents need to pay selective attention to only some of the information available to them in observations in this report we present the minimum information principle for selective attention in reactive agents we further motivate this approach by reducing the general problem of optimal control in pomdps , to reactive control with complex observations lastly , we explore a newly discovered phenomenon of this optimization process period doubling bifurcations this necessitates periodic policies , and raises many more questions regarding stability , periodicity and chaos in optimal control"
cs.SI,"since 1950 , information flows have been in the centre of scientific research up until internet penetration in the late 90s , these studies were based over traditional offline social networks several observations in offline information flows studies , such as two step flow of communication and the importance of weak ties , were verified in several online studies , showing that the diffused information flows from one online social network \( osn \) to several others within that flow , information is shared to and reproduced by the users of each network furthermore , the original content is enhanced or weakened according to its topic , the dynamic and exposure of each osns in such a concept , each osn is considered a layer of information flows that interacts with each other in this paper , we examine such flows in several social networks , as well as their diffusion and lifespan across multiple osns , in terms of user generated content our results verify the perception of content and information connection in various osns"
cs.LG,"we consider the arithmetic complexity of index sets of uniformly computably enumerable families learnable under different learning criteria we determine the exact complexity of these sets for the standard notions of finite learning , learning in the limit , behaviorally correct learning and anomalous learning in the limit in proving the sigma 5 0 completeness result for behaviorally correct learning we prove a result of independent interest if a uniformly computably enumerable family is not learnable , then for any computable learner there is a delta 2 0 enumeration witnessing failure"
cs.DS,"in the mixed chinese postman problem \( mcpp \) , given a weighted mixed graph g \( g may have both edges and arcs \) , our aim is to find a minimum weight closed walk traversing each edge and arc at least once the mcpp parameterized by the number of edges in g or the number of arcs in g is fixed parameter tractable as proved by van bevern et al \( in press \) and gutin , jones and sheng \( esa 2014 \) , respectively solving an open question of van bevern et al \( in press \) , we show that unexpectedly the mcpp parameterized by the treewidth of g is w 1 hard in fact , we prove that even the mcpp parameterized by the pathwidth of g is w 1 hard"
cs.LG|stat.ML,"top performing deep architectures are trained on massive amounts of labeled data in the absence of labeled data for a certain task , domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain \( e g synthetic images \) are available here , we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain \( no labeled target domain data is necessary \) as the training progresses , the approach promotes the emergence of deep features that are \( i \) discriminative for the main learning task on the source domain and \( ii \) invariant with respect to the shift between the domains we show that this adaptation behaviour can be achieved in almost any feed forward model by augmenting it with few standard layers and a simple new gradient reversal layer the resulting augmented architecture can be trained using standard backpropagation overall the whole approach can be implemented with little effort using any of the deep learning packages"
cs.DS|stat.ML,"we study the problem of selecting a subset of k random variables from a large set , in order to obtain the best linear prediction of another variable of interest this problem can be viewed in the context of both feature selection and sparse approximation we analyze the performance of widely used greedy heuristics , using insights from the maximization of submodular functions and spectral analysis we introduce the submodularity ratio as a key quantity to help understand why greedy algorithms perform well even when the variables are highly correlated using our techniques , we obtain the strongest known approximation guarantees for this problem , both in terms of the submodularity ratio and the smallest k sparse eigenvalue of the covariance matrix we further demonstrate the wide applicability of our techniques by analyzing greedy algorithms for the dictionary selection problem , and significantly improve the previously known guarantees our theoretical analysis is complemented by experiments on real world and synthetic data sets the experiments show that the submodularity ratio is a stronger predictor of the performance of greedy algorithms than other spectral parameters"
cs.AI,"most ethical work is done at a low level of formality this makes practical moral questions inaccessible to formal and natural sciences and can lead to misunderstandings in ethical discussion in this paper , we use bayesian inference to introduce a formalization of preference utilitarianism in physical world models , specifically cellular automata even though our formalization is not immediately applicable , it could provide ethics and ultimately the question of how to make the world better with a formal basis"
cs.SI,"spatial networks , in which nodes and edges are embedded in space , play a vital role in the study of complex systems for example , many social networks attach geo location information to each user , allowing the study of not only topological interactions between users , but spatial interactions as well the defining property of spatial networks is that edge distances are associated with a cost , which may subtly influence the topology of the network however , the cost function over distance is rarely known , thus developing a model of connections in spatial networks is a difficult task in this paper , we introduce a novel model for capturing the interaction between spatial effects and network structure our approach represents a unique combination of ideas from latent variable statistical models and spatial network modeling in contrast to previous work , we view the ability to form long short distance connections to be dependent on the individual nodes involved for example , a node 's specific surroundings \( e g network structure and node density \) may make it more likely to form a long distance link than other nodes with the same degree to capture this information , we attach a latent variable to each node which represents a node 's spatial reach these variables are inferred from the network structure using a markov chain monte carlo algorithm we experimentally evaluate our proposed model on 4 different types of real world spatial networks \( e g transportation , biological , infrastructure , and social \) we apply our model to the task of link prediction and achieve up to a 35 improvement over previous approaches in terms of the area under the roc curve additionally , we show that our model is particularly helpful for predicting links between nodes with low degrees in these cases , we see much larger improvements over previous models"
cs.LG|stat.ML,"we propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large the approach is based on iterative latent variable preselection , where we alternate between learning a 'selection function' to reveal the relevant latent variables , and use this to obtain a compact approximation of the posterior distribution for em this can make inference possible where the number of possible latent states is e g exponential in the number of latent variables , whereas an exact approach would be computationally unfeasible we learn the selection function entirely from the observed data and current em state via gaussian process regression this is by contrast with earlier approaches , where selections were hand designed for each problem setting we show our approach to perform as well as these bespoke selection functions on a wide variety of inference problems in particular , for the challenging case of a hierarchical model for object localization with occlusion , we achieve results that match a customized state of the art selection method , at a far lower computational cost"
cs.DM|cs.DS,"a coverage function f over a ground set m is associated with a universe u of weighted elements and m subsets a 1 , , a m of u , and for any subset t of m , f \( t \) is defined as the total weight of the elements in the union cup j in t a j coverage functions are an important special case of submodular functions , and arise in many applications , for instance as a class of utility functions of agents in combinatorial auctions set functions such as coverage functions often lack succinct representations , and in algorithmic applications , an access to a value oracle is assumed in this paper , we ask whether one can test if a given oracle is that of a coverage function or not we demonstrate an algorithm which makes o \( m u \) queries to an oracle of a coverage function and completely reconstructs it this gives a polytime tester for succinct coverage functions for which u is polynomially bounded in m in contrast , we demonstrate a set function which is far from coverage , but requires 2 tilde theta \( m \) queries to distinguish it from the class of coverage functions"
cs.AI|cs.LG|stat.ML,"contextual bandit algorithms have become popular for online recommendation systems such as digg , yahoo ! buzz , and news recommendation in general emph offline evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their partial label nature common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator however , creating simulator itself is often difficult and modeling bias is usually unavoidably introduced in this paper , we introduce a emph replay methodology for contextual bandit algorithm evaluation different from simulator based approaches , our method is completely data driven and very easy to adapt to different applications more importantly , our method can provide provably unbiased evaluations our empirical results on a large scale news article recommendation dataset collected from yahoo ! front page conform well with our theoretical results furthermore , comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method"
cs.AI|cs.DM|cs.LG,"this paper establishes theoretical bonafides for implicit concurrent multivariate effect evaluation implicit concurrency for short a broad and versatile computational learning efficiency thought to underlie general purpose , non local , noise tolerant optimization in genetic algorithms with uniform crossover \( ugas \) we demonstrate that implicit concurrency is indeed a form of efficient learning by showing that it can be used to obtain close to optimal bounds on the time and queries required to approximately correctly solve a constrained version \( k 7 , eta 1 5 \) of a recognizable computational learning problem learning parities with noisy membership queries we argue that a uga that treats the noisy membership query oracle as a fitness function can be straightforwardly used to approximately correctly learn the essential attributes in o \( log 1 585 n \) queries and o \( n log 1 585 n \) time , where n is the total number of attributes our proof relies on an accessible symmetry argument and the use of statistical hypothesis testing to reject a global null hypothesis at the 10 100 level of significance it is , to the best of our knowledge , the first relatively rigorous identification of efficient computational learning in an evolutionary algorithm on a non trivial learning problem"
cs.LG|stat.ML,"the success of machine learning on a given task dependson , among other things , which learning algorithm is selected and its associated hyperparameters selecting an appropriate learning algorithm and setting its hyperparameters for a given data set can be a challenging task , especially for users who are not experts in machine learning previous work has examined using meta features to predict which learning algorithm and hyperparameters should be used however , choosing a set of meta features that are predictive of algorithm performance is difficult here , we propose to apply collaborative filtering techniques to learning algorithm and hyperparameter selection , and find that doing so avoids determining which meta features to use and outperforms traditional meta learning approaches in many cases"
cs.DS,"we present several algorithms for computing the visibility polygon of a simple polygon p from a viewpoint inside the polygon , when the polygon resides in read only memory and only few working variables can be used the first algorithm uses a constant number of variables , and outputs the vertices of the visibility polygon in o \( n rout \) time , where rout denotes the number of reflex vertices of p that are part of the output the next two algorithms use o \( log rin \) variables , and output the visibility polygon in o \( n log rin \) randomized expected time or o \( n log 2 rin \) deterministic time , where rin is the number of reflex vertices of p"
cs.SI,"despite the emergence of event driven business process management , smart business networks , social networks , etc as important research areas in management , for all the attractiveness of these concepts , two major challenges remain around their design and the partner selection rules while learning from interaction events while smart business networks should provide advantages due to the quick connect of business partners for selected functions in a process common to several parties , literature does not provide constructive methods whereby the selection of temporary partners and functions can be done most discussions only rely solely on human judgment this paper introduces both computational geometry , and genetic programming , as systematic methods whereby to identify , characterize , and then display on a continuing basis from event monitoring such possible partnerships such techniques also allow to plan for their effect on the organizations and thus to carry out selection the two methods are being put in the context of emergence theory tessellations address the identification and categorization issues business maps address the display and monitoring challenge with the use of voronoii diagrams cellular automata mimicking living bodies , with genetic algorithms of which parameters are estimated by learning , address the selection and effect issues to illustrate the approach , some experimental results from the sourcing function in a high tech industry , are discussed they address the case of how to determine the selection process for a systems integrator to set up joint ventures with smaller technology suppliers"
cs.LG|stat.ML,"in conventional supervised pattern recognition tasks , model selection is typically accomplished by minimizing the classification error rate on a set of so called development data , subject to ground truth labeling by human experts or some other means in the context of speech processing systems and other large scale practical applications , however , such labeled development data are typically costly and difficult to obtain this article proposes an alternative semi supervised framework for likelihood based model selection that leverages unlabeled data by using trained classifiers representing each model to automatically generate putative labels the errors that result from this automatic labeling are shown to be amenable to results from robust statistics , which in turn provide for minimax optimal censored likelihood ratio tests that recover the nonparametric sign test as a limiting case this approach is then validated experimentally using a state of the art automatic speech recognition system to select between candidate word pronunciations using unlabeled speech data that only potentially contain instances of the words under test results provide supporting evidence for the utility of this approach , and suggest that it may also find use in other applications of machine learning"
cs.LG,"cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning reasons for this include its simplicity , speed and stability , as well as its competitive performance on ell 1 regularized smooth optimization problems surprisingly , very little is known about its finite time convergence behavior on these problems most existing results either just prove convergence or provide asymptotic rates we fill this gap in the literature by proving o \( 1 k \) convergence rates \( where k is the iteration counter \) for two variants of cyclic coordinate descent under an isotonicity assumption our analysis proceeds by comparing the objective values attained by the two variants with each other , as well as with the gradient descent algorithm we show that the iterates generated by the cyclic coordinate descent methods remain better than those of gradient descent uniformly over time"
cs.DS,"a permutation is said to be a square if it can be obtained by shuffling two order isomorphic patterns the definition is intended to be the natural counterpart to the ordinary shuffle of words and languages in this paper , we tackle the problem of recognizing square permutations from both the point of view of algebra and algorithms on the one hand , we present some algebraic and combinatorial properties of the shuffle product of permutations we follow an unusual line consisting in defining the shuffle of permutations by means of an unshuffling operator , known as a coproduct this strategy allows to obtain easy proofs for algebraic and combinatorial properties of our shuffle product we besides exhibit a bijection between square \( 213 , 231 \) avoiding permutations and square binary words on the other hand , using a containment free perfect matching criterion , we prove that recognizing square permutations is mathbf np complete"
cs.IT|cs.LG|stat.ML,"given a task of predicting y from x , a loss function l , and a set of probability distributions gamma , what is the optimal decision rule minimizing the worst case expected loss over gamma \? in this paper , we address this question by introducing a generalization of the principle of maximum entropy applying this principle to sets of distributions with a proposed structure , we develop a general minimax approach for supervised learning problems , that reduces to the maximum likelihood problem over generalized linear models through this framework , we develop two classification algorithms called the minimax svm and the minimax brier classifier the minimax svm , which is a relaxed version of the standard svm , minimizes the worst case 0 1 loss over the structured set of distribution , and by our numerical experiments can outperform the svm we also explore the application of the developed framework in robust feature selection"
cs.DM,"a spanning subgraph of a graph g is called a 0 , 2 factor of g , if for is a union of some disjoint cycles , paths and isolate vertices , that span the graph g it is easy to get a 0 , 2 factor of g and there would be many of 0 , 2 factors for a g a characteristic number for a 0 , 2 factor , which reflect the number of the paths and isolate vertices in it , is defineted the 0 , 2 factor of g is called maximum if its characteristic number is minimum , and is called characteristic number of g it to be proved that characteristic number of graph is a graph invariant and a polynomial time algorithm for computing a maximum 0 , 2 factor of a graph g has been given in this paper a 0 , 2 factor is called a 2 factor , if its characteristic number is zero that is , a 2 factor is a set of some disjoint cycles , that span g we propose a a polynomial time algorism for computing 2 factor from a 0 , 2 factor , which can be got easily a hamilton cycle is a 2 factor , therefore a necessary condition of a hamilton graph is that , the graph have a 2 factor or the characteristic number of the graph is zero the algorism , given in this paper , make it possible to examine the condition in polynomial time"
cs.DS|cs.SI,"the detection of triadic subgraph motifs is a common methodology in complex networks research the procedure usually applied in order to detect motifs evaluates whether a certain subgraph pattern is overrepresented in a network as a whole however , motifs do not necessarily appear frequently in every region of a graph for this reason , we recently introduced the framework of node specific pattern mining \( nospam \) this work is a manual for an implementation of nospam which can be downloaded from www mwinkler eu"
cs.LG|stat.ML,"recent work in distance metric learning focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints the learned transformations lead to improved retrieval , classification , and clustering algorithms due to the more accurate distance or similarity measures here , we introduce the problem of learning these transformations when the underlying constraint generation process is dynamic these dynamics can be due to changes in either the ground truth labels used to generate constraints or changes to the feature subspaces in which the class structure is apparent we propose and evaluate an adaptive , online algorithm for learning and tracking metrics as they change over time we demonstrate the proposed algorithm on both real and synthetic data sets and show significant performance improvements relative to previously proposed batch and online distance metric learning algorithms"
cs.AI|cs.LG,"the validation of any database mining methodology goes through an evaluation process where benchmarks availability is essential in this paper , we aim to randomly generate relational database benchmarks that allow to check probabilistic dependencies among the attributes we are particularly interested in probabilistic relational models \( prms \) , which extend bayesian networks \( bns \) to a relational data mining context and enable effective and robust reasoning over relational data even though a panoply of works have focused , separately , on the generation of random bayesian networks and relational databases , no work has been identified for prms on that track this paper provides an algorithmic approach for generating random prms from scratch to fill this gap the proposed method allows to generate prms as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies this can be of interest not only for machine learning researchers to evaluate their proposals in a common framework , but also for databases designers to evaluate the effectiveness of the components of a database management system"
cs.DM|cs.DS,"in the stable marriage and roommates problems , a set of agents is given , each of them having a strictly ordered preference list over some or all of the other agents a matching is a set of disjoint pairs of mutually accepted agents if any two agents mutually prefer each other to their partner , then they block the matching , otherwise , the matching is said to be stable in this paper we investigate the complexity of finding a solution satisfying additional constraints on restricted pairs of agents restricted pairs can be either forced or forbidden a stable solution must contain all of the forced pairs , while it must contain none of the forbidden pairs dias et al gave a polynomial time algorithm to decide whether such a solution exists in the presence of restricted edges if the answer is no , one might look for a solution close to optimal since optimality in this context means that the matching is stable and satisfies all constraints on restricted pairs , there are two ways of relaxing the constraints by permitting a solution to \( 1 \) be blocked by some pairs \( as few as possible \) , or \( 2 \) violate some constraints on restricted pairs \( again as few as possible \) our main theorems prove that for the \( bipartite \) stable marriage problem , case \( 1 \) leads to np hardness and inapproximability results , whilst case \( 2 \) can be solved in polynomial time for the non bipartite stable roommates instances , case \( 2 \) yields an np hard but 2 approximable problem in the case of np hard problems , we also discuss polynomially solvable special cases , arising from restrictions on the lengths of the preference lists , or upper bounds on the numbers of restricted pairs"
cs.LG|stat.ML,"generalized linear models with nonlinear feature transformations are widely used for large scale regression and classification problems with sparse inputs memorization of feature interactions through a wide set of cross product feature transformations are effective and interpretable , while generalization requires more feature engineering effort with less feature engineering , deep neural networks can generalize better to unseen feature combinations through low dimensional dense embeddings learned for the sparse features however , deep neural networks with embeddings can over generalize and recommend less relevant items when the user item interactions are sparse and high rank in this paper , we present wide deep learning jointly trained wide linear models and deep neural networks to combine the benefits of memorization and generalization for recommender systems we productionized and evaluated the system on google play , a commercial mobile app store with over one billion active users and over one million apps online experiment results show that wide deep significantly increased app acquisitions compared with wide only and deep only models we have also open sourced our implementation in tensorflow"
cs.AI,"this paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors in particulars , the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias free effect estimates in such models"
cs.DS,"we introduce a new approach to computing an approximately maximum s t flow in a capacitated , undirected graph this flow is computed by solving a sequence of electrical flow problems each electrical flow is given by the solution of a system of linear equations in a laplacian matrix , and thus may be approximately computed in nearly linear time using this approach , we develop the fastest known algorithm for computing approximately maximum s t flows for a graph having n vertices and m edges , our algorithm computes a \( 1 epsilon \) approximately maximum s t flow in time tilde o \( mn 1 3 epsilon 11 3 \) a dual version of our approach computes a \( 1 epsilon \) approximately minimum s t cut in time tilde o \( m n 4 3 eps 8 3 \) , which is the fastest known algorithm for this problem as well previously , the best dependence on m and n was achieved by the algorithm of goldberg and rao \( j acm 1998 \) , which can be used to compute approximately maximum s t flows in time tilde o \( m sqrt n epsilon 1 \) , and approximately minimum s t cuts in time tilde o \( m n 3 2 epsilon 3 \)"
cs.AI,"logic programming has developed as a rich field , built over a logical substratum whose main constituent is a nonclassical form of negation , sometimes coexisting with classical negation the field has seen the advent of a number of alternative semantics , with kripke kleene semantics , the well founded semantics , the stable model semantics , and the answer set semantics standing out as the most successful we show that all aforementioned semantics are particular cases of a generic semantics , in a framework where classical negation is the unique form of negation and where the literals in the bodies of the rules can be `marked' to indicate that they can be the targets of hypotheses a particular semantics then amounts to choosing a particular marking scheme and choosing a particular set of hypotheses when a literal belongs to the chosen set of hypotheses , all marked occurrences of that literal in the body of a rule are assumed to be true , whereas the occurrences of that literal that have not been marked in the body of the rule are to be derived in order to contribute to the firing of the rule hence the notion of hypothetical reasoning that is presented in this framework is not based on making global assumptions , but more subtly on making local , contextual assumptions , taking effect as indicated by the chosen marking scheme on the basis of the chosen set of hypotheses our approach offers a unified view on the various semantics proposed in logic programming , classical in that only classical negation is used , and links the semantics of logic programs to mechanisms that endow rule based systems with the power to harness hypothetical reasoning"
cs.IT|cs.LG,we show that forms of bayesian and mdl inference that are often applied to classification problems can be inconsistent this means there exists a learning problem such that for all amounts of data the generalization errors of the mdl classifier and the bayes classifier relative to the bayesian posterior both remain bounded away from the smallest achievable generalization error
stat.ML,"despite our extensive knowledge of biophysical properties of neurons , there is no commonly accepted algorithmic theory of neuronal function here we explore the hypothesis that single layer neuronal networks perform online symmetric nonnegative matrix factorization \( snmf \) of the similarity matrix of the streamed data by starting with the snmf cost function we derive an online algorithm , which can be implemented by a biologically plausible network with local learning rules we demonstrate that such network performs soft clustering of the data as well as sparse feature discovery the derived algorithm replicates many known aspects of sensory anatomy and biophysical properties of neurons including unipolar nature of neuronal activity and synaptic weights , local synaptic plasticity rules and the dependence of learning rate on cumulative neuronal activity thus , we make a step towards an algorithmic theory of neuronal function , which should facilitate large scale neural circuit simulations and biologically inspired artificial intelligence"
cs.SI,"evaluation of link prediction methods is a hard task in very large complex networks because of the inhibitive computational cost by setting a lower bound of the number of common neighbors \( cn \) , we propose a new framework to efficiently and precisely evaluate the performances of cn based similarity indices in link prediction for very large heterogeneous networks specifically , we propose a fast algorithm based on the parallel computing scheme to obtain all the node pairs with cn values larger than the lower bound furthermore , we propose a new measurement , called self predictability , to quantify the performance of the cn based similarity indices in link prediction , which on the other side can indicate the link predictability of a network"
cs.SI,"in this paper we present first experimental results about a small group of people exchanging private and public messages in a virtual community our goal is the study of the cognitive network that emerges during a chat seance we used the derrida coefficient and the triangle structure under the working assumption that moods and perceived mutual affinity can produce results complementary to a full semantic analysis the most outstanding outcome is the difference between the network obtained considering publicly exchanged messages and the one considering only privately exchanged messages in the former case , the network is very homogeneous , in the sense that each individual interacts in the same way with all the participants , whilst in the latter the interactions among different agents are very heterogeneous , and are based on the enemy of my enemy is my friend strategy finally a recent characterization of the triangular cliques has been considered in order to describe the intimate structure of the network experimental results confirm recent theoretical studies indicating that certain 3 vertex structures can be used as indicators for the network aging and some relevant dynamical features"
cs.LG|stat.ML,"in this paper , we propose an unifying view of several recently proposed structured sparsity inducing norms we consider the situation of a model simultaneously \( a \) penalized by a set function de ned on the support of the unknown parameter vector which represents prior knowledge on supports , and \( b \) regularized in lp norm we show that the natural combinatorial optimization problems obtained may be relaxed into convex optimization problems and introduce a notion , the lower combinatorial envelope of a set function , that characterizes the tightness of our relaxations we moreover establish links with norms based on latent representations including the latent group lasso and block coding , and with norms obtained from submodular functions"
cs.SI,"recent evidence shows that in many societies worldwide the relative sizes of the economic and social elites are continuously shrinking is this a natural social phenomenon \? what are the forces that shape this process \? we try to address these questions by studying a core periphery social structure composed of a social elite , namely , a relatively small but well connected and highly influential group of powerful individuals , and the rest of society , the periphery herein , we present a novel axiom based model for the forces governing the mutual influences between the elite and the periphery assuming a simple set of axioms , capturing the elite 's dominance , robustness , compactness and density , we are able to draw strong conclusions about the elite periphery structure in particular , we show that a balance of powers between elite and periphery and an elite size that is sub linear in the network size are universal properties of elites in social networks that satisfy our axioms we note that the latter is in controversy to the common belief that the elite size converges to a linear fraction of society \( most recently claimed to be 1 \) we accompany these findings with a large scale empirical study on about 100 real world networks , which supports our results"
cs.LG,"the class of boolean functions that are expressible as horn formulas , that is , conjunctions of horn clauses , is known to be learnable under different query learning settings , such as learning from membership and equivalence queries or learning from entailment we propose yet a different type of query the closure query closure queries are a natural extension of membership queries and also a variant , appropriate for the context of horn formulas , of the so called correction queries we present an algorithm that learns conjunctions of horn clauses in polynomial time , using closure and equivalence queries , and show how it relates to the canonical guigues duquenne basis for implicational systems we also show how the different query models mentioned relate to each other by either showing full fledged reductions by means of query simulation \( where possible \) , or by showing their connections in the context of particular algorithms that use them for learning horn formulas"
cs.LG|stat.ML,"we study a regularizer which is defined as a parameterized infimum of quadratics , and which we call the box norm we show that the k support norm , a regularizer proposed by argyriou et al , 2012 for sparse vector prediction problems , belongs to this family , and the box norm can be generated as a perturbation of the former we derive an improved algorithm to compute the proximity operator of the squared box norm , and we provide a method to compute the norm we extend the norms to matrices , introducing the spectral k support norm and spectral box norm we note that the spectral box norm is essentially equivalent to the cluster norm , a multitask learning regularizer introduced by jacob et al 2009a , and which in turn can be interpreted as a perturbation of the spectral k support norm centering the norm is important for multitask learning and we also provide a method to use centered versions of the norms as regularizers numerical experiments indicate that the spectral k support and box norms and their centered variants provide state of the art performance in matrix completion and multitask learning problems respectively"
cs.LG,"this paper proposes a framework dedicated to the construction of what we call discrete elastic inner product allowing one to embed sets of non uniformly sampled multivariate time series or sequences of varying lengths into inner product space structures this framework is based on a recursive definition that covers the case of multiple embedded time elastic dimensions we prove that such inner products exist in our general framework and show how a simple instance of this inner product class operates on some prospective applications , while generalizing the euclidean inner product classification experimentations on time series and symbolic sequences datasets demonstrate the benefits that we can expect by embedding time series or sequences into elastic inner spaces rather than into classical euclidean spaces these experiments show good accuracy when compared to the euclidean distance or even dynamic programming algorithms while maintaining a linear algorithmic complexity at exploitation stage , although a quadratic indexing phase beforehand is required"
cs.DM,"in this work , we study a family of random geometric graphs on hyperbolic spaces in this setting , n points are chosen randomly on a hyperbolic space and any two of them are joined by an edge with probability that depends on their hyperbolic distance , independently of every other pair in particular , when the positions of the points have been fixed , the distribution over the set of graphs on these points is the boltzmann distribution , where the hamiltonian is given by the sum of weighted indicator functions for each pair of points , with the weight being proportional to a real parameter beta 0 \( interpreted as the inverse temperature \) as well as to the hyperbolic distance between the corresponding points this class of random graphs was introduced by krioukov et al we provide a rigorous analysis of aspects of this model and its dependence on the parameter beta , verifying some of their observations we show that a phase transition occurs around beta 1 more specifically , we show that when beta 1 the degree of a typical vertex is bounded in probability \( in fact it follows a distribution which for large values exhibits a power law tail whose exponent depends only on the curvature of the space \) , whereas for beta 1 the degree is a random variable whose expected value grows polynomially in n when beta 1 , we establish logarithmic growth for the case beta 1 , we establish a connection with a class of inhomogeneous random graphs known as the chung lu model assume that we use the poincar 'e disc representation of a hyperbolic space if we condition on the distance of each one of the points from the origin , then the probability that two given points are adjacent is expressed through the kernel of this inhomogeneous random graph"
cs.DM,"given an infinite word x over an alphabet a a letter b occurring in x , and a total order sigma on a , we call the smallest word with respect to sigma starting with b in the shift orbit closure of x an extremal word of x in this paper we consider the extremal words of morphic words if x g \( f omega \( a \) \) for some morphisms f and g , we give two simple conditions on f and g that guarantees that all extremal words are morphic this happens , in particular , when x is a primitive morphic or a binary pure morphic word our techniques provide characterizations of the extremal words of the period doubling word and the chacon word and give a new proof of the form of the lexicographically least word in the shift orbit closure of the rudin shapiro word"
cs.LG|stat.ML,"biological and social systems consist of myriad interacting units the interactions can be intuitively represented in the form of a graph or network measurements of these graphs can reveal the underlying structure of these interactions , which provides insight into the systems that generated the graphs moreover , in applications such as neuroconnectomics , social networks , and genomics , graph data is accompanied by contextualizing measures on each node we leverage these node covariates to help uncover latent communities in a graph , using a modification of spectral clustering statistical guarantees are provided under a joint mixture model that we call the node contextualized stochastic blockmodel , including a bound on the mis clustering rate for most simulated conditions , covariate assisted spectral clustering yields superior results relative to both regularized spectral clustering without node covariates and an adaptation of canonical correlation analysis we apply covariate assisted spectral clustering to large brain graphs derived from diffusion mri data , using the node locations or neurological region membership as covariates in both cases , covariate assisted spectral clustering yields clusters that are easier to interpret neurologically"
cs.SI,"controlling complex networked systems to a desired state is a key research goal in contemporary science despite recent advances in studying the impact of network topology on controllability , a comprehensive understanding of the synergistic effect of network topology and individual dynamics on controllability is still lacking here we offer a theoretical study with particular interest in the diversity of dynamic units characterized by different types of individual dynamics interestingly , we find a global symmetry accounting for the invariance of controllability with respect to exchanging the densities of any two different types of dynamic units , irrespective of the network topology the highest controllability arises at the global symmetry point , at which different types of dynamic units are of the same density the lowest controllability occurs when all self loops are either completely absent or present with identical weights these findings further improve our understanding of network controllability and have implications for devising the optimal control of complex networked systems in a wide range of fields"
cs.AI,"this paper provides a self contained first introduction to description logics \( dls \) the main concepts and features are explained with examples before syntax and semantics of the dl sroiq are defined in detail additional sections review light weight dl languages , discuss the relationship to the web ontology language owl and give pointers to further reading"
cs.IT|cs.LG|cs.SI,"we propose an adaptive diffusion mechanism to optimize a global cost function in a distributed manner over a network of nodes the cost function is assumed to consist of a collection of individual components diffusion adaptation allows the nodes to cooperate and diffuse information in real time it also helps alleviate the effects of stochastic gradient noise and measurement noise through a continuous learning process we analyze the mean square error performance of the algorithm in some detail , including its transient and steady state behavior we also apply the diffusion algorithm to two problems distributed estimation with sparse parameters and distributed localization compared to well studied incremental methods , diffusion methods do not require the use of a cyclic path over the nodes and are robust to node and link failure diffusion methods also endow networks with adaptation abilities that enable the individual nodes to continue learning even when the cost function changes with time examples involving such dynamic cost functions with moving targets are common in the context of biological networks"
cs.AI,"accurate vehicular localization is important for various cooperative vehicle safety \( cvs \) applications such as collision avoidance , turning assistant , etc in this paper , we propose a cooperative vehicular distance measurement technique based on the sharing of gps pseudorange measurements and a weighted least squares method the classic double difference pseudorange solution , which was originally designed for high end survey level gps systems , is adapted to low end navigation level gps receivers for its wide availability in ground vehicles the carrier to noise ratio \( cnr \) of raw pseudorange measurements are taken into account for noise mitigation we present a dedicated short range communications \( dsrc \) based mechanism to implement the exchange of pseudorange information among neighboring vehicles as demonstrated in field tests , our proposed technique increases the accuracy of the distance measurement significantly compared with the distance obtained from the gps fixes"
cs.AI,"methods for learning word representations using large text corpora have received much attention lately due to their impressive performance in numerous natural language processing \( nlp \) tasks such as , semantic similarity measurement , and word analogy detection despite their success , these data driven word representation learning methods do not consider the rich semantic relational structure between words in a co occurring context on the other hand , already much manual effort has gone into the construction of semantic lexicons such as the wordnet that represent the meanings of words by defining the various relationships that exist among the words in a language we consider the question , can we improve the word representations learnt using a corpora by integrating the knowledge from semantic lexicons \? for this purpose , we propose a joint word representation learning method that simultaneously predicts the co occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon we use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into word representations on several benchmark datasets for semantic similarity and word analogy"
cs.LG,"we propose a novel linear semi supervised learning formulation that is derived from a solid probabilistic framework belief propagation we show that our formulation generalizes a number of label propagation algorithms described in the literature by allowing them to propagate generalized assumptions about influences between classes of neighboring nodes we call this formulation semi supervised learning with heterophily \( ssl h \) we also show how the affinity matrix can be learned from observed data with a simple convex optimization framework that is inspired by locally linear embedding we call this approach linear heterophily estimation \( lhe \) experiments on synthetic data show that both approaches combined can learn heterophily of a graph with 1m nodes , 10m edges and few labels in under 1min , and give better labeling accuracies than a baseline method in the case of small fraction of explicitly labeled nodes"
cs.DM,"the treewidth of control flow graphs arising from structured programs is known to be at most six however , as a control flow graph is inherently directed , it makes sense to consider a measure of width for digraphs instead we use the so called dag width and show that the dag width of control flow graphs arising from structured \( goto free \) programs is at most three additionally , we also give a linear time algorithm to compute the dag decomposition of these control flow graphs one consequence of this result is that parity games \( and hence the mu calculus model checking problem \) , which are known to be tractable on graphs of bounded dag width , can be solved efficiently in practice on control flow graphs"
cs.AI|cs.LG,"we explore the idea that authoring a piece of text is an act of maximizing one 's expected utility to make this idea concrete , we consider the societally important decisions of the supreme court of the united states extensive past work in quantitative political science provides a framework for empirically modeling the decisions of justices and how they relate to text we incorporate into such a model texts authored by amici curiae \( friends of the court separate from the litigants \) who seek to weigh in on the decision , then explicitly model their goals in a random utility model we demonstrate the benefits of this approach in improved vote prediction and the ability to perform counterfactual analysis"
cs.DM,"we generalize the notion of non crossing partition on a disk to general surfaces with boundary for this , we consider a surface sigma and introduce the number c sigma \( n \) of non crossing partitions of a set of n points laying on the boundary of sigma our proofs use bijective techniques arising from map enumeration , joint with the symbolic method and singularity analysis on generating functions an outcome of our results is that the exponential growth of c sigma \( n \) is the same as the one of the n th catalan number , i e , does not change when we move from the case where sigma is a disk to general surfaces with boundary"
cs.AI|cs.LG|stat.ML,"scalable and effective exploration remains a key challenge in reinforcement learning \( rl \) while there are methods with optimality guarantees in the setting of discrete state and action spaces , these methods cannot be applied in high dimensional deep rl scenarios as such , most contemporary rl relies on simple heuristics such as epsilon greedy exploration or adding gaussian noise to the controls this paper introduces variational information maximizing exploration \( vime \) , an exploration strategy based on maximization of information gain about the agent 's belief of environment dynamics we propose a practical implementation , using variational inference in bayesian neural networks which efficiently handles continuous state and action spaces vime modifies the mdp reward function , and can be applied with several different underlying rl algorithms we demonstrate that vime achieves significantly better performance compared to heuristic exploration methods across a variety of continuous control tasks and algorithms , including tasks with very sparse rewards"
cs.AI,"test functions are important to validate and compare the performance of optimization algorithms there have been many test or benchmark functions reported in the literature however , there is no standard list or set of benchmark functions ideally , test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way for this purpose , we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality , separability , and valley landscape this is by far the most complete set of functions so far in the literature , and tt can be expected this complete set of functions can be used for validation of new optimization in the future"
cs.DS|cs.IT,"we describe two constructions of \( very \) dense graphs which are edge disjoint unions of large em induced matchings the first construction exhibits graphs on n vertices with n choose 2 o \( n 2 \) edges , which can be decomposed into pairwise disjoint induced matchings , each of size n 1 o \( 1 \) the second construction provides a covering of all edges of the complete graph k n by two graphs , each being the edge disjoint union of at most n 2 delta induced matchings , where delta 0 058 this disproves \( in a strong form \) a conjecture of meshulam , substantially improves a result of birk , linial and meshulam on communicating over a shared channel , and \( slightly \) extends the analysis of h aa stad and wigderson of the graph test of samorodnitsky and trevisan for linearity additionally , our constructions settle a combinatorial question of vempala regarding a candidate rounding scheme for the directed steiner tree problem"
cs.LG|stat.ML,"using sparse inducing norms to learn robust models has received increasing attention from many fields for its attractive properties projection based methods have been widely applied to learning tasks constrained by such norms as a key building block of these methods , an efficient operator for euclidean projection onto the intersection of ell 1 and ell 1 , q norm balls \( q 2 text or infty \) is proposed in this paper we prove that the projection can be reduced to finding the root of an auxiliary function which is piecewise smooth and monotonic hence , a bisection algorithm is sufficient to solve the problem we show that the time complexity of our solution is o \( n g log g \) for q 2 and o \( n log n \) for q infty , where n is the dimensionality of the vector to be projected and g is the number of disjoint groups we confirm this complexity by experimentation empirical study reveals that our method achieves significantly better performance than classical methods in terms of running time and memory usage we further show that embedded with our efficient projection operator , projection based algorithms can solve regression problems with composite norm constraints more efficiently than other methods and give superior accuracy"
cs.DS,"materialisation precomputes all consequences of a set of facts and a datalog program so that queries can be evaluated directly \( i e , independently from the program \) rewriting optimises materialisation for datalog programs with equality by replacing all equal constants with a single representative and incremental maintenance algorithms can efficiently update a materialisation for small changes in the input facts both techniques are critical to practical applicability of datalog systems however , we are unaware of an approach that combines rewriting and incremental maintenance in this paper we present the first such combination , and we show empirically that it can speed up updates by several orders of magnitude compared to using either rewriting or incremental maintenance in isolation"
cs.DM,"we introduce a version of the cavity method for diluted mean field spin models that allows the computation of thermodynamic quantities similar to the franz parisi quenched potential in sparse random graph models this method is developed in the particular case of partially decimated random constraint satisfaction problems this allows to develop a theoretical understanding of a class of algorithms for solving constraint satisfaction problems , in which elementary degrees of freedom are sequentially assigned according to the results of a message passing procedure \( belief propagation \) we confront this theoretical analysis to the results of extensive numerical simulations"
cs.DS,"in a recent work , mossel and ross considered the shotgun assembly problem for a random jigsaw puzzle their model consists of a puzzle an n times n grid , where each vertex is viewed as a center of a piece they assume that each of the four edges adjacent to a vertex , is assigned one of q colors \( corresponding to jigs , or cut shapes \) uniformly at random mossel and ross asked how large should q q \( n \) be so that with high probability the puzzle can be assembled uniquely given the collection of individual tiles \? they showed that if q omega \( n 2 \) , then the puzzle can be assembled uniquely with high probability , while if q o \( n 2 3 \) , then with high probability the puzzle cannot be uniquely assembled here we improve the upper bound and show that for any eps 0 , the puzzle can be assembled uniquely with high probability if q geq n 1 eps the proof uses an algorithm of n theta \( 1 eps \) running time"
cs.SI,"compromising social network accounts has become a profitable course of action for cybercriminals by hijacking control of a popular media or business account , attackers can distribute their malicious messages or disseminate fake information to a large user base the impacts of these incidents range from a tarnished reputation to multi billion dollar monetary losses on financial markets in our previous work , we demonstrated how we can detect large scale compromises \( i e , so called campaigns \) of regular online social network users in this work , we show how we can use similar techniques to identify compromises of individual high profile accounts high profile accounts frequently have one characteristic that makes this detection reliable they show consistent behavior over time we show that our system , were it deployed , would have been able to detect and prevent three real world attacks against popular companies and news agencies furthermore , our system , in contrast to popular media , would not have fallen for a staged compromise instigated by a us restaurant chain for publicity reasons"
cs.DM,"answering a question of kalai and meshulam , we prove that graphs without induced cycles of length 3k have bounded chromatic number this implies the very first case of a much broader question asserting that every graph with large chromatic number induces a graph h such that the sum of the betti numbers of the independence complex of h is also large"
cs.DS,"the ppsz algorithm by paturi , pudl 'ak , saks , and zane \( focs 1998 \) is the fastest known algorithm for \( promise \) unique k sat we give an improved algorithm with exponentially faster bounds for unique 3 sat for uniquely satisfiable 3 cnf formulas , we do the following case distinction we call a clause critical if exactly one literal is satisfied by the unique satisfying assignment if a formula has many critical clauses , we observe that ppsz by itself is already faster if there are only few clauses allover , we use an algorithm by wahlstr om \( esa 2005 \) that is faster than ppsz in this case otherwise we have a formula with few critical and many non critical clauses non critical clauses have at least two literals satisfied we show how to exploit this to improve ppsz"
cs.AI|stat.ML,"we describe a new class of learning models called memory networks memory networks reason with inference components combined with a long term memory component they learn how to use these jointly the long term memory can be read and written to , with the goal of using it for prediction we investigate these models in the context of question answering \( qa \) where the long term memory effectively acts as a \( dynamic \) knowledge base , and the output is a textual response we evaluate them on a large scale qa task , and a smaller , but more complex , toy task generated from a simulated world in the latter , we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs"
stat.ML,"great successes of deep neural networks have been witnessed in various real applications many algorithmic and implementation techniques have been developed , however , theoretical understanding of many aspects of deep neural networks is far from clear a particular interesting issue is the usefulness of dropout , which was motivated from the intuition of preventing complex co adaptation of feature detectors in this paper , we study the rademacher complexity of different types of dropout , and our theoretical results disclose that for shallow neural networks \( with one or none hidden layer \) dropout is able to reduce the rademacher complexity in polynomial , whereas for deep neural networks it can amazingly lead to an exponential reduction of the rademacher complexity"
cs.DS,"let s s 1 , s 2 , s 3 , , s n be a given vector of n real numbers the rank of a real z with respect to s is defined as the number of elements s i in s such that s i is less than or equal to z we consider the following decision problem determine whether the odd numbered elements s 1 , s 3 , s 5 , are precisely the elements of s whose rank with respect to s is odd we prove a bound of theta \( n log n \) on the number of operations required to solve this problem in the algebraic computation tree model"
cs.SI,"we propose a new framework for education in conflict zones , considering the explosive growth of social media , web services , and mobile internet over the past decade moreover , we focus on one conflict zone , afghanistan , as a case study , because of its alarmingly high illiteracy rate , lack of qualified teachers , rough terrain , and relatively high mobile penetration of over 50 in several of afghanistan 's provinces , it is hard to currently sustain the traditional bricks and mortar school model , due to numerous incidents of schools , teachers , and students being attacked because of the ongoing insurgency and political instability our model improves the virtual school model , by addressing most of its disadvantages , to provide students in afghanistan with an opportunity to achieve standardised education , even when the security situation does not allow them to attend traditional schools one of the biggest advantages of this model is that it is sufficiently robust to deal with gender discrimination , imposed by culture or politics of the region"
cs.IT|stat.ML,"we develop mask iterative hard thresholding algorithms \( mask iht and mask dore \) for sparse image reconstruction of objects with known contour the measurements follow a noisy underdetermined linear model common in the compressive sampling literature assuming that the contour of the object that we wish to reconstruct is known and that the signal outside the contour is zero , we formulate a constrained residual squared error minimization problem that incorporates both the geometric information \( i e the knowledge of the object 's contour \) and the signal sparsity constraint we first introduce a mask iht method that aims at solving this minimization problem and guarantees monotonically non increasing residual squared error for a given signal sparsity level we then propose a double overrelaxation scheme for accelerating the convergence of the mask iht algorithm we also apply convex mask reconstruction approaches that employ a convex relaxation of the signal sparsity constraint in x ray computed tomography \( ct \) , we propose an automatic scheme for extracting the convex hull of the inspected object from the measured sinograms the obtained convex hull is used to capture the object contour information we compare the proposed mask reconstruction schemes with the existing large scale sparse signal reconstruction methods via numerical simulations and demonstrate that , by exploiting both the geometric contour information of the underlying image and sparsity of its wavelet coefficients , we can reconstruct this image using a significantly smaller number of measurements than the existing methods"
cs.SI,"detection of non overlapping and overlapping communities are essentially the same problem however , current algorithms focus either on finding overlapping or non overlapping communities we present a generalized framework that can identify both non overlapping and overlapping communities , without any prior input about the network or its community distribution to do so , we introduce a vertex based metric , genperm , that quantifies by how much a vertex belongs to each of its constituent communities our community detection algorithm is based on maximizing the genperm over all the vertices in the network we demonstrate , through experiments over synthetic and real world networks , that genperm is more effective than other metrics in evaluating community structure further , we show that due to its vertex centric property , genperm can be used to unfold several inferences beyond community detection , such as core periphery analysis and message spreading our algorithm for maximizing genperm outperforms six state of the art algorithms in accurately predicting the ground truth labels finally , we discuss the problem of resolution limit in overlapping communities and demonstrate that maximizing genperm can mitigate this problem"
cs.DM|cs.DS,"we uniquely characterize two members of the q clustering family in an axiomatic framework we introduce properties that use known tree constructions for the purpose of characterization to characterize the max sum clustering algorithm , we use the gomory hu construction , and to characterize single linkage , we use the maximum spanning tree although at first glance it seems these properties are `obviously' all that are necessary to characterize max sum and single linkage , we show that this is not the case , by investigating how subsets of properties interact we conclude by proposing additions to the taxonomy of clustering paradigms currently in use"
cs.DM|cs.DS,"we present a fast combinatorial 3 4 approximation algorithm for the maximum asymmetric tsp with weights zero and one the approximation factor of this algorithm matches the currently best one given by bl aser in 2004 and based on linear programming our algorithm first computes a maximum size matching and a maximum weight cycle cover without certain cycles of length two but possibly with em half edges a half edge of a given edge e is informally speaking a half of e that contains one of the endpoints of e then from the computed matching and cycle cover it extracts a set of paths , whose weight is large enough to be able to construct a traveling salesman tour with the claimed guarantee"
cs.LG,"in this paper we present a novel approach to spam filtering and demonstrate its applicability with respect to sms messages our approach requires minimum features engineering and a small set of la belled data samples features are extracted using topic modelling based on latent dirichlet allocation , and then a comprehensive data model is created using a stacked denoising autoencoder \( sda \) topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds given that the sms messages can be regarded as either spam \( unwanted \) or ham \( wanted \) , the sda is able to model the messages and accurately discriminate between the two classes without the need for a pre labelled training set the results are compared against the state of the art spam detection algorithms with our proposed approach achieving over 97 accuracy which compares favourably to the best reported algorithms presented in the literature"
cs.LG|cs.SI,"significant efforts have gone into the development of statistical models for analyzing data in the form of networks , such as social networks most existing work has focused on modeling static networks , which represent either a single time snapshot or an aggregate view over time there has been recent interest in statistical modeling of dynamic networks , which are observed at multiple points in time and offer a richer representation of many complex phenomena in this paper , we present a state space model for dynamic networks that extends the well known stochastic blockmodel for static networks to the dynamic setting we fit the model in a near optimal manner using an extended kalman filter \( ekf \) augmented with a local search we demonstrate that the ekf based algorithm performs competitively with a state of the art algorithm based on markov chain monte carlo sampling but is significantly less computationally demanding"
cs.AI,"we propose applying the categorical compositional scheme of 6 to conceptual space models of cognition in order to do this we introduce the category of convex relations as a new setting for categorical compositional semantics , emphasizing the convex structure important to conceptual space applications we show how conceptual spaces for composite types such as adjectives and verbs can be constructed we illustrate this new model on detailed examples"
cs.DM,"a em hole is a chordless cycle of length at least four a hole is em even \( resp em odd \) if it contains an even \( resp odd \) number of vertices a emph cap is a graph induced by a hole with an additional vertex that is adjacent to exactly two adjacent vertices on the hole in this note , we use a decomposition theorem by conforti et al \( 1999 \) to show that if a graph g does not contain any even hole or cap as an induced subgraph , then chi \( g \) le lfloor frac 3 2 omega \( g \) rfloor , where chi \( g \) and omega \( g \) are the chromatic number and the clique number of g , respectively this bound is attained by odd holes and the hajos graph the proof leads to a polynomial time 3 2 approximation algorithm for coloring \( even hole , cap \) free graphs"
cs.SI,"this paper deals with a very renowned website \( that is book crossing \) from two angles the first angle focuses on the direct relations between users and books many things can be inferred from this part of analysis such as who is more interested in book reading than others and why \? which books are most popular and which users are most active and why \? the task requires the use of certain social network analysis measures \( e g degree centrality \) what does it mean when two users like the same book \? is it the same when other two users have one thousand books in common \? who is more likely to be a friend of whom and why \? are there specific people in the community who are more qualified to establish large circles of social relations \? these questions \( and of course others \) were answered through the other part of the analysis , which will take us to probe the potential social relations between users in this community although these relationships do not exist explicitly , they can be inferred with the help of affiliation network analysis and techniques such as m slice"
cs.AI|cs.DS,"we study the bipartite boolean quadratic programming problem \( bbqp \) which is an extension of the well known boolean quadratic programming problem \( bqp \) applications of the bbqp include mining discrete patterns from binary data , approximating matrices by rank one binary matrices , computing the cut norm of a matrix , and solving optimisation problems such as maximum weight biclique , bipartite maximum weight cut , maximum weight induced sub graph of a bipartite graph , etc for the bbqp , we first present several algorithmic components , specifically , hillclimbers and mutations , and then show how to combine them in a high performance metaheuristic instead of hand tuning a standard metaheuristic to test the efficiency of the hybrid of the components , we chose to use an automated generation of a multi component metaheuristic to save human time , and also improve objectivity in the analysis and comparisons of components for this we designed a new metaheuristic schema which we call conditional markov chain search \( cmcs \) we show that cmcs is flexible enough to model several standard metaheuristics this flexibility is controlled by multiple numeric parameters , and so is convenient for automated generation we study the configurations revealed by our approach and show that the best of them outperforms the previous state of the art bbqp algorithm by several orders of magnitude in our experiments we use benchmark instances introduced in the preliminary version of this paper and described here , which have already become the de facto standard in the bbqp literature"
cs.LG|stat.ML,"crowdsourcing systems are popular for solving large scale labelling tasks with low paid \( or even non paid \) workers we study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular dawid skene model to address this inference problem , several algorithms have recently been proposed , but the best known guarantee is still significantly larger than the fundamental limit we close this gap under a simple but canonical scenario where each worker is assigned at most two tasks in particular , we introduce a tighter lower bound on the fundamental limit and prove that belief propagation \( bp \) exactly matches this lower bound the guaranteed optimality of bp is the strongest in the sense that it is information theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks in the general setting , when more than two tasks are assigned to each worker , we establish the dominance result on bp that it outperforms all existing algorithms with provable guarantees experimental results suggest that bp is close to optimal for all regimes considered , while all other algorithms show suboptimal performances in certain regimes"
cs.SI,"current social and activist movements find the opportunity in social media to effectively impact on the agenda of governing bodies and create `global' perceptions it is often claimed content related to the social and activist movements is online , to be accessed , supported or disputed and distributed from virtually anywhere at any time , in the public sphere of the internet this activity allows the enlargement of social movements and would increase the empowerment in the concerned communities the aim of this explorative study is to assess whether the temporal evolution of the normalised web distance \( nwd \) as defined by cilibrasi vit 'anyi \( 2007 \) between identifying terms concerning this activism could be used to measure the progress or decline of social empowerment through the internet the nwd relies on the page count number of single and joint queries , which in our study have been registered using a freely available web browser \( e g google search \) providing a time search window for temporal query results to explore this meta data technique , we introduce the case of a perceived wirikuta online movement , which originated in mexico with the aim to protect the huichols' sacred land and water resources from open mining projects for silver ore we conducted a small scale internet study relating the key terms `wirikuta' , `huichol' , and `wixarika' and their co occurrence with seven positive qualifiers \( e g `sacred land' \) , five negative qualifiers \( e g `violence' \) and one neutral qualifier \( `table' \) over time , annually from 1994 till 2013 we confirm close semantic clustering over time of traditional indigeneous identity terms of the huichol , and observe a slight convergence of key terms to `mines' and less pronounced to `sacred land' and a divergence with respect to `ancestors' indicating a complex image of a tendency of empowerment"
cs.DS,"unit two variable per inequality \( utvpi \) constraints form one of the largest class of integer constraints which are polynomial time solvable \( unless p np \) there is considerable interest in their use for constraint solving , abstract interpretation , spatial databases , and theorem proving in this paper we develop a new incremental algorithm for utvpi constraint satisfaction and implication checking that requires o \( m n log n p \) time and o \( n m p \) space to incrementally check satisfiability of m utvpi constraints on n variables and check implication of p utvpi constraints"
cs.AI,"in this paper , we discuss the method of bayesian regression and its efficacy for predicting price variation of bitcoin , a recently popularized virtual , cryptographic currency bayesian regression refers to utilizing empirical data as proxy to perform bayesian inference we utilize bayesian regression for the so called latent source model the bayesian regression for latent source model was introduced and discussed by chen , nikolov and shah \( 2013 \) and bresler , chen and shah \( 2014 \) for the purpose of binary classification they established theoretical as well as empirical efficacy of the method for the setting of binary classification in this paper , instead we utilize it for predicting real valued quantity , the price of bitcoin based on this price prediction method , we devise a simple strategy for trading bitcoin the strategy is able to nearly double the investment in less than 60 day period when run against real data trace"
cs.DS,"in this paper we introduce novel algorithmic strategies for effciently playing two player games in which the players have different or identical player roles in the case of identical roles , the players compete for the same objective \( that of winning the game \) the case with different player roles assumes that one of the players asks questions in order to identify a secret pattern and the other one answers them the purpose of the first player is to ask as few questions as possible \( or that the questions and their number satisfy some previously known constraints \) and the purpose of the secret player is to answer the questions in a way that will maximize the number of questions asked by the first player \( or in a way which forces the first player to break the constraints of the game \) we consider both previously known games \( or extensions of theirs \) and new types of games , introduced in this paper"
cs.DM,"in this paper we introduce a new game in this game there are two players who play as rival pirate gangs the goal is to gather more treasure than your rival the game is played on a graph and a player gathers treasure by moving to an unvisited vertex at the end of the game , the player with the most treasure wins we will show that this game is np hard , and we will also look at the structure of this game under the disjunctive sum we will show that there are cases where this game behaves like a normal play game , and cases where it behaves like a mis`ere play game we then leave an open problem about scoring play games in general"
cs.LG,"we present an improved analysis of mini batched stochastic dual coordinate ascent for regularized empirical loss minimization \( i e svm and svm type objectives \) our analysis allows for flexible sampling schemes , including where data is distribute across machines , and combines a dependence on the smoothness of the loss and or the data spread \( measured through the spectral norm \)"
cs.DM,"in this paper , we give a polynomial time algorithm which determines if a given triangle free graph with no induced seven vertex path is 3 colorable , and gives an explicit coloring if one exists"
cs.SI,"a minimal model based on individual interactions is proposed to study the non poisson statistical properties of human behavior individuals in the system interact with their neighbors , the probability of an individual acting correlates to its activity , and all individuals involved in action will change their activities randomly the model creates rich non poisson spatial temporal properties in the activities of individuals , in agreement with the patterns of human communication behaviors our findings provide insight into various human activities , embracing a range of realistic social interacting systems , particularly , intriguing bimodal phenomenons this model bridges priority queues and punctuated equilibrium , and our modeling and analysis is likely to shed light on non poisson phenomena in many complex systems"
cs.AI,"descriptions are often provided along with recommendations to help users' discovery recommending automatically generated music playlists \( e g personalised playlists \) introduces the problem of generating descriptions in this paper , we propose a method for generating music playlist descriptions , which is called as music captioning in the proposed method , audio content analysis and natural language processing are adopted to utilise the information of each track"
cs.DM,"the largest number factored on a quantum device reported until now was 143 that quantum computation , which used only 4 qubits , actually also factored much larger numbers such as 3599 , 13081 , and 44929 , without the awareness of the authors of that work furthermore , unlike the implementations of shor 's algorithm performed thus far , these 4 qubit factorizations do not need to use prior knowledge of the answer however , because they only use 4 qubits , these factorizations can also be performed trivially on classical computers we discover a class of numbers for which the power of quantum information actually comes into play we then demonstrate a 3 qubit factorization of 175 , which would be the first quantum factorization of a triprime"
cs.SI,"nowadays , travel surveys provide rich information about urban mobility and commuting patterns but , at the same time , they have drawbacks they are static pictures of a dynamic phenomena , are expensive to make , and take prolonged periods of time to finish however , the availability of mobile usage data \( call detail records \) makes the study of urban mobility possible at levels not known before this has been done in the past with good results mobile data makes possible to find and understand aggregated mobility patterns in this paper , we propose to analyze mobile data at individual level by estimating daily journeys , and use those journeys to build origin destiny matrices to understand urban flow we evaluate this approach with large anonymized cdrs from santiago , chile , and find that our method has a high correlation \( rho 0 89 \) with the current travel survey , and that it captures external anomalies in daily travel patterns , making our method suitable for inclusion into urban computing applications"
cs.AI,"choosing a good location when opening a new store is crucial for the future success of a business traditional methods include offline manual survey , which is very time consuming , and analytic models based on census data , which are un able to adapt to the dynamic market the rapid increase of the availability of big data from various types of mobile devices , such as online query data and offline positioning data , provides us with the possibility to develop automatic and accurate data driven prediction models for business store placement in this paper , we propose a demand distribution driven store placement \( d3sp \) framework for business store placement by mining search query data from baidu maps d3sp first detects the spatial temporal distributions of customer demands on different business services via query data from baidu maps , the largest online map search engine in china , and detects the gaps between demand and sup ply then we determine candidate locations via clustering such gaps in the final stage , we solve the location optimization problem by predicting and ranking the number of customers we not only deploy supervised regression models to predict the number of customers , but also learn to rank models to directly rank the locations we evaluate our framework on various types of businesses in real world cases , and the experiments results demonstrate the effectiveness of our methods d3sp as the core function for store placement has already been implemented as a core component of our business analytics platform and could be potentially used by chain store merchants on baidu nuomi"
cs.DS,"designing applications for hybrid cloud has many features , including dynamic virtualization management and route switching this makes it impossible to evaluate the query and hence the optimal distribution of data in this paper , we formulate the main challenges of designing and simulation , offer installation for processing"
cs.IT|stat.ML,"in this paper , we propose efficient algorithms for estimating the signal subspace of mobile users in a wireless communication environment with a multi antenna base station with m antennas we assume that , for reducing the rf front end complexity and overall a d conversion rate , the jsdm transmitter receiver is split into the product of a baseband linear projection \( digital \) and an rf reconfigurable beamforming network \( analog \) with only m ll m rf chains this implies that only m analog observations can be obtained for subspace estimation , and the standard sample covariance estimator is not available we develop efficient algorithms that estimate the dominant signal subspace from sampling only m o \( 2 sqrt m \) specific array elements according to a coprime scheme , and for a given p leq m , return a p dimensional beamformer that has a performance comparable with the best p dim beamformer designed by knowing the exact covariance matrix of the received signal we asses the performance of our proposed estimators both analytically and empirically via numerical simulations , and compare it with that of the other state of the art methods proposed in the literature , which are also reviewed and put in the context of estimating the subspace of the signal"
cs.LG,we propose a new training method for a feedforward neural network having the activation functions with the geometric contraction property the method consists of constructing a new functional that is less nonlinear in comparison with the classical functional by removing the nonlinearity of the activation functions from the output layer we validate this new method by a series of experiments that show an improved learning speed and also a better classification error
cs.DM|cs.DS,"a tournament t \( v , a \) is a directed graph in which there is exactly one arc between every pair of distinct vertices given a digraph on n vertices and an integer parameter k , the feedback arc set problem asks whether the given digraph has a set of k arcs whose removal results in an acyclic digraph the feedback arc set problem restricted to tournaments is known as the k feedback arc set in tournaments \( k fast \) problem in this paper we obtain a linear vertex kernel for k fast that is , we give a polynomial time algorithm which given an input instance t to k fast obtains an equivalent instance t' on o \( k \) vertices in fact , given any fixed e 0 , the kernelized instance has at most \( 2 e \) k vertices our result improves the previous known bound of o \( k 2 \) on the kernel size for k fast our kernelization algorithm solves the problem on a subclass of tournaments in polynomial time and uses a known polynomial time approximation scheme for k fast"
cs.LG,"the growing number of dimensionality reduction methods available for data visualization has recently inspired the development of quality assessment measures , in order to evaluate the resulting low dimensional representation independently from a methods' inherent criteria several \( existing \) quality measures can be \( re \) formulated based on the so called co ranking matrix , which subsumes all rank errors \( i e differences between the ranking of distances from every point to all others , comparing the low dimensional representation to the original data \) the measures are often based on the partioning of the co ranking matrix into 4 submatrices , divided at the k th row and column , calculating a weighted combination of the sums of each submatrix hence , the evaluation process typically involves plotting a graph over several \( or even all possible \) settings of the parameter k considering simple artificial examples , we argue that this parameter controls two notions at once , that need not necessarily be combined , and that the rectangular shape of submatrices is disadvantageous for an intuitive interpretation of the parameter we debate that quality measures , as general and flexible evaluation tools , should have parameters with a direct and intuitive interpretation as to which specific error types are tolerated or penalized therefore , we propose to replace k with two parameters to control these notions separately , and introduce a differently shaped weighting on the co ranking matrix the two new parameters can then directly be interpreted as a threshold up to which rank errors are tolerated , and a threshold up to which the rank distances are significant for the evaluation moreover , we propose a color representation of local quality to visually support the evaluation process for a given mapping , where every point in the mapping is colored according to its local contribution to the overall quality"
cs.AI,"part of the theory of logic programming and nonmonotonic reasoning concerns the study of fixed point semantics for these paradigms several different semantics have been proposed during the last two decades , and some have been more successful and acknowledged than others the rationales behind those various semantics have been manifold , depending on one 's point of view , which may be that of a programmer or inspired by commonsense reasoning , and consequently the constructions which lead to these semantics are technically very diverse , and the exact relationships between them have not yet been fully understood in this paper , we present a conceptually new method , based on level mappings , which allows to provide uniform characterizations of different semantics for logic programs we will display our approach by giving new and uniform characterizations of some of the major semantics , more particular of the least model semantics for definite programs , of the fitting semantics , and of the well founded semantics a novel characterization of the weakly perfect model semantics will also be provided"
cs.AI,"neurons are individually translated into simple gates to plan a brain based on human psychology and intelligence state machines , assumed previously learned in subconscious associative memory are shown to enable equation solving and rudimentary thinking using nanoprocessing within short term memory"
cs.SI,"in this paper we seek to understand the relationship between the online popularity of an article and its intrinsic quality prior experimental work suggests that the relationship between quality and popularity can be very distorted due to factors like social influence bias and inequality in visibility we conduct a study of popularity on two different social news aggregators , reddit and hacker news we define quality as the relative number of votes an article would have received if each article was shown , in a bias free way , to an equal number of users we propose a simple poisson regression method to estimate this quality metric from time series voting data we validate our methods on data from reddit and hacker news , as well the experimental data from prior work this method works well even though the collected data is subject to common social media biases using these estimates , we find that popularity on reddit and hacker news is a stronger reflection of intrinsic quality than expected"
cs.SI,"the basic indicators of a researcher 's productivity and impact are still the number of publications and their citation counts these metrics are clear , straightforward , and easy to obtain when a ranking of scholars is needed , for instance in grant , award , or promotion procedures , their use is the fastest and cheapest way of prioritizing some scientists over others however , due to their nature , there is a danger of oversimplifying scientific achievements therefore , many other indicators have been proposed including the usage of the pagerank algorithm known for the ranking of webpages and its modifications suited to citation networks nevertheless , this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity , its application should be well justified , particularly when compared to the standard citation counts in this study , we analyze three large datasets of computer science papers in the categories of artificial intelligence , software engineering , and theory and methods and apply 12 different ranking methods to the citation networks of authors we compare the resulting rankings with self compiled lists of outstanding researchers selected as frequent editorial board members of prestigious journals in the field and conclude that there is no evidence of pagerank based methods outperforming simple citation counts"
cs.LG|stat.ML,"we show how binary classification methods developed to work on i i d data can be used for solving statistical problems that are seemingly unrelated to classification and concern highly dependent time series specifically , the problems of time series clustering , homogeneity testing and the three sample problem are addressed the algorithms that we construct for solving these problems are based on a new metric between time series distributions , which can be evaluated using binary classification methods universal consistency of the proposed algorithms is proven under most general assumptions the theoretical results are illustrated with experiments on synthetic and real world data"
cs.DS,"we revisit the complexity of online computation in the cell probe model we consider a class of problems where we are first given a fixed pattern or vector f of n symbols and then one symbol arrives at a time in a stream after each symbol has arrived we must output some function of f and the n length suffix of the arriving stream cell probe bounds of omega \( delta lg n w \) have previously been shown for both convolution and hamming distance in this setting , where delta is the size of a symbol in bits and w in omega \( lg n \) is the cell size in bits however , when delta is a constant , as it is in many natural situations , these previous results no longer give us non trivial bounds we introduce a new lop sided information transfer proof technique which enables us to prove meaningful lower bounds even for constant size input alphabets we use our new framework to prove an amortised cell probe lower bound of omega \( lg 2 n \( w cdot lg lg n \) \) time per arriving bit for an online version of a well studied problem known as pattern matching with address errors this is the first non trivial cell probe lower bound for any online problem on bit streams that still holds when the cell sizes are large we also show the same bound for online convolution conditioned on a new combinatorial conjecture related to toeplitz matrices"
cs.LG|stat.ML,"big data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints big data always involve massive data but they also often include data streams and data heterogeneity recently some statistical methods have been adapted to process big data , like linear regression models , clustering methods and bootstrapping schemes based on decision trees combined with aggregation and bootstrap ideas , random forests were introduced by breiman in 2001 they are a powerful nonparametric statistical method allowing to consider in a single and versatile framework regression problems , as well as two class and multi class classification problems focusing on classification problems , this paper reviews available proposals about random forests in parallel environments as well as about online random forests then , we formulate various remarks for random forests in the big data context finally , we experiment three variants involving subsampling , big data bootstrap and mapreduce respectively , on two massive datasets \( 15 and 120 millions of observations \) , a simulated one as well as real world data"
cs.LG,"mirna and gene expression profiles have been proved useful for classifying cancer samples efficient classifiers have been recently sought and developed a number of attempts to classify cancer samples using mirna gene expression profiles are known in literature however , the use of semi supervised learning models have been used recently in bioinformatics , to exploit the huge corpuses of publicly available sets using both labeled and unlabeled sets to train sample classifiers , have not been previously considered when gene and mirna expression sets are used moreover , there is a motivation to integrate both mirna and gene expression for a semi supervised cancer classification as that provides more information on the characteristics of cancer samples in this paper , two semi supervised machine learning approaches , namely self learning and co training , are adapted to enhance the quality of cancer sample classification these approaches exploit the huge public corpuses to enrich the training data in self learning , mirna and gene based classifiers are enhanced independently while in co training , both mirna and gene expression profiles are used simultaneously to provide different views of cancer samples to our knowledge , it is the first attempt to apply these learning approaches to cancer classification the approaches were evaluated using breast cancer , hepatocellular carcinoma \( hcc \) and lung cancer expression sets results show up to 20 improvement in f1 measure over random forests and svm classifiers co training also outperforms low density separation \( lds \) approach by around 25 improvement in f1 measure in breast cancer"
stat.ML,"in this paper we introduce a novel method for linear system identification with quantized output data we model the impulse response as a zero mean gaussian process whose covariance \( kernel \) is given by the recently proposed stable spline kernel , which encodes information on regularity and exponential stability this serves as a starting point to cast our system identification problem into a bayesian framework we employ markov chain monte carlo \( mcmc \) methods to provide an estimate of the system in particular , we show how to design a gibbs sampler which quickly converges to the target distribution numerical simulations show a substantial improvement in the accuracy of the estimates over state of the art kernel based methods when employed in identification of systems with quantized data"
cs.DS|cs.SI,"the problem of computing the betweenness centrality \( bc \) is important in analyzing graphs in many practical applications like social networks , biological networks , transportation networks , electrical circuits , etc since this problem is computation intensive , researchers have been developing algorithms using high performance computing resources like supercomputers , clusters , and graphics processing units \( gpus \) current gpu algorithms for computing bc employ brandes' sequential algorithm with different trade offs for thread scheduling , data structures , and atomic operations in this paper , we study three gpu algorithms for computing bc of unweighted , directed , scale free networks we discuss and measure the trade offs of their design choices about balanced thread scheduling , atomic operations , synchronizations and latency hiding our program is written in nvidia cuda c and was tested on an nvidia tesla m2050 gpu"
cs.DS,"given a set cal r r 1 , r 2 , , r n of n randomly positioned axis parallel rectangles in 2d , the problem of computing the minimum clique cover \( mcc \) and maximum independent set \( mis \) for the intersection graph g \( cal r \) of the members in cal r are both computationally hard cite cc05 for the mcc problem , it is proved that polynomial time constant factor approximation is impossible to obtain cite pt11 though such a result is not proved yet for the mis problem , no polynomial time constant factor approximation algorithm exists in the literature we study the performance of greedy algorithms for computing these two parameters of g \( cal r \) experimental results shows that for each of the mcc and mis problems , the corresponding greedy algorithm produces a solution that is very close to its optimum solution scheinerman cite scheinerman80 showed that the size of mis is tightly bounded by sqrt n for a random instance of the 1d version of the problem , \( i e , for the interval graph \) our experiment shows that the size of independent set and the clique cover produced by the greedy algorithm is at least 2 sqrt n and at most 3 sqrt n , respectively thus the experimentally obtained approximation ratio of the greedy algorithm for mis problem is at most 3 2 and the same for the mcc problem is at least 2 3 finally we will provide refined greedy algorithms based on a concept of it simplicial rectangle the characteristics of this algorithm may be of interest in getting a provably constant factor approximation algorithm for random instance of both the problems we believe that the result also holds true for any finite dimension"
cs.DM,"this tutorial deal with the axiom of choice and some of its applications to topics related to computer science we will see that the axiom of choice is equivalent to some well known proof principles like zorn 's lemma or tuckey 's maximality principle we try to touch upon some topics , which appear to be important for developing mathematical structures within computer science"
cs.LG,"deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors in this paper , we analyze the propagation formulations behind the residual building blocks , which suggest that the forward and backward signals can be directly propagated from one block to any other block , when using identity mappings as the skip connections and after addition activation a series of ablation experiments support the importance of these identity mappings this motivates us to propose a new residual unit , which further makes training easy and improves generalization we report improved results using a 1001 layer resnet on cifar 10 100 , and a 200 layer resnet on imagenet"
cs.DM,this article provides structural characterization of simple graphs whose edge set can be partitioned into maximum matchings we use vizing 's classification of simple graphs based on edge chromatic index
cs.DM,"yannakakis' clique versus independent set problem \( cl is \) in communication complexity asks for the minimum number of cuts separating cliques from stable sets in a graph , called cs separator yannakakis provides a quasi polynomial cs separator , i e of size o \( n log n \) , and addresses the problem of finding a polynomial cs separator this question is still open even for perfect graphs we show that a polynomial cs separator almost surely exists for random graphs besides , if h is a split graph \( i e has a vertex partition into a clique and a stable set \) then there exists a constant c h for which we find a o \( n c h \) cs separator on the class of h free graphs this generalizes a result of yannakakis on comparability graphs we also provide a o \( n c k \) cs separator on the class of graphs without induced path of length k and its complement observe that on one side , c h is of order o \( h log h \) resulting from vapnik chervonenkis dimension , and on the other side , c k is exponential one of the main reason why yannakakis' cl is problem is fascinating is that it admits equivalent formulations our main result in this respect is to show that a polynomial cs separator is equivalent to the polynomial alon saks seymour conjecture , asserting that if a graph has an edge partition into k complete bipartite graphs , then its chromatic number is polynomially bounded in terms of k we also show that the classical approach to the stubborn problem \( arising in csp \) which consists in covering the set of all solutions by o \( n log n \) instances of 2 sat is again equivalent to the existence of a polynomial cs separator"
cs.IT|cs.SI,"the detection of community structure in networks is intimately related to finding a concise description of the network in terms of its modules this notion has been recently exploited by the map equation formalism \( m rosvall and c t bergstrom , pnas , 105 \( 4 \) , pp 1118 1123 , 2008 \) through an information theoretic description of the process of coding inter and intra community transitions of a random walker in the network at stationarity however , a thorough study of the relationship between the full markov dynamics and the coding mechanism is still lacking we show here that the original map coding scheme , which is both block averaged and one step , neglects the internal structure of the communities and introduces an upper scale , the `field of view' limit , in the communities it can detect as a consequence , map is well tuned to detect clique like communities but can lead to undesirable overpartitioning when communities are far from clique like we show that a signature of this behavior is a large compression gap the map description length is far from its ideal limit to address this issue , we propose a simple dynamic approach that introduces time explicitly into the map coding through the analysis of the weighted adjacency matrix of the time dependent multistep transition matrix of the markov process the resulting markov time sweeping induces a dynamical zooming across scales that can reveal \( potentially multiscale \) community structure above the field of view limit , with the relevant partitions indicated by a small compression gap"
cs.LG|stat.ML,"the generalized partially linear additive model \( gplam \) is a flexible and interpretable approach to building predictive models it combines features in an additive manner , allowing each to have either a linear or nonlinear effect on the response however , the choice of which features to treat as linear or nonlinear is typically assumed known thus , to make a gplam a viable approach in situations in which little is known a priori about the features , one must overcome two primary model selection challenges deciding which features to include in the model and determining which of these features to treat nonlinearly we introduce the sparse partially linear additive model \( splam \) , which combines model fitting and both of these model selection challenges into a single convex optimization problem splam provides a bridge between the lasso and sparse additive models through a statistical oracle inequality and thorough simulation , we demonstrate that splam can outperform other methods across a broad spectrum of statistical regimes , including the high dimensional \( p gg n \) setting we develop efficient algorithms that are applied to real data sets with half a million samples and over 45 , 000 features with excellent predictive performance"
cs.DS,"we present an exact algorithm that decides , for every fixed r geq 2 in time o \( m \) 2 o \( k 2 \) whether a given multiset of m clauses of size r admits a truth assignment that satisfies at least \( \( 2 r 1 \) m k \) 2 r clauses thus textsc max r sat is fixed parameter tractable when parameterized by the number of satisfied clauses above the tight lower bound \( 1 2 r \) m this solves an open problem of mahajan et al \( j comput system sci , 75 , 2009 \) our algorithm is based on a polynomial time data reduction procedure that reduces a problem instance to an equivalent algebraically represented problem with o \( k 2 \) variables this is done by representing the instance as an appropriate polynomial , and by applying a probabilistic argument combined with some simple tools from harmonic analysis to show that if the polynomial cannot be reduced to one of size o \( k 2 \) , then there is a truth assignment satisfying the required number of clauses we introduce a new notion of bikernelization from a parameterized problem to another one and apply it to prove that the above mentioned parameterized textsc max r sat admits a polynomial size kernel combining another probabilistic argument with tools from graph matching theory and signed graphs , we show that if an instance of textsc max 2 sat with m clauses has at least 3k variables after application of certain polynomial time reduction rules to it , then there is a truth assignment that satisfies at least \( 3m k \) 4 clauses we also outline how the fixed parameter tractability and polynomial size kernel results on textsc max r sat can be extended to more general families of boolean constraint satisfaction problems"
cs.DM,"characterization of k chordal graphs based on the existence of a simplicial path was shown in chv 'a tal et al note dirac type characterizations of graphs without long chordless cycles discrete mathematics , 256 , 445 448 , 2002 we give a characterization of k chordal graphs which is a generalization of the known characterization of chordal graphs due to g a dirac on rigid circuit graphs abh math sem univ hamburg , 25 , 71 76 , 1961 that use notions of a simplicial vertex and a simplicial ordering"
cs.AI|cs.DM,"we consider a dynamic vehicle routing problem with time windows and stochastic customers \( ds vrptw \) , such that customers may request for services as vehicles have already started their tours to solve this problem , the goal is to provide a decision rule for choosing , at each time step , the next action to perform in light of known requests and probabilistic knowledge on requests likelihood we introduce a new decision rule , called global stochastic assessment \( gsa \) rule for the ds vrptw , and we compare it with existing decision rules , such as msa in particular , we show that gsa fully integrates nonanticipativity constraints so that it leads to better decisions in our stochastic context we describe a new heuristic approach for efficiently approximating our gsa rule we introduce a new waiting strategy experiments on dynamic and stochastic benchmarks , which include instances of different degrees of dynamism , show that not only our approach is competitive with state of the art methods , but also enables to compute meaningful offline solutions to fully dynamic problems where absolutely no a priori customer request is provided"
cs.DM|cs.DS,"we consider the algorithmic problem of generating each subset of n 1 , 2 , ldots , n whose size is in some interval k , l , 0 leq k leq l leq n , exactly once \( cyclically \) by repeatedly adding or removing a single element , or by exchanging a single element for k 0 and l n this is the classical problem of generating all 2 n subsets of n by element additions removals , and for k l this is the classical problem of generating all binom n k subsets of n by element exchanges we prove the existence of such cyclic minimum change enumerations for a large range of values n , k , and l , improving upon and generalizing several previous results for all these existential results we provide optimal algorithms to compute the corresponding gray codes in constant time mathcal o \( 1 \) per generated set and space mathcal o \( n \) rephrased in terms of graph theory , our results establish the existence of \( almost \) hamilton cycles in the subgraph of the n dimensional cube q n induced by all levels k , l we reduce all remaining open cases to a generalized version of the middle levels conjecture , which asserts that the subgraph of q 2k 1 induced by all levels k c , k 1 c , c in 0 , 1 , ldots , k , has a hamilton cycle we also prove an approximate version of this conjecture , showing that this graph has a cycle that visits a \( 1 o \( 1 \) \) fraction of all vertices"
cs.LG|stat.ML,"several machine learning models , including neural networks , consistently misclassify adversarial examples inputs formed by applying small but intentionally worst case perturbations to examples from the dataset , such that the perturbed input results in the model outputting an incorrect answer with high confidence early attempts at explaining this phenomenon focused on nonlinearity and overfitting we argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature this explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them their generalization across architectures and training sets moreover , this view yields a simple and fast method of generating adversarial examples using this approach to provide examples for adversarial training , we reduce the test set error of a maxout network on the mnist dataset"
stat.ML,"canonical correlation analysis \( cca \) is a valuable method for interpreting cross covariance across related datasets of different dimensionality there are many potential applications of cca to neuroimaging data analysis for instance , cca can be used for finding functional similarities across fmri datasets collected from multiple subjects without resampling individual datasets to a template anatomy in this paper , we introduce pyrcca , an open source python module for executing cca between two or more datasets pyrcca can be used to implement cca with or without regularization , and with or without linear or a gaussian kernelization of the datasets we demonstrate an application of cca implemented with pyrcca to neuroimaging data analysis we use cca to find a data driven set of functional response patterns that are similar across individual subjects in a natural movie experiment we then demonstrate how this set of response patterns discovered by cca can be used to accurately predict subject responses to novel natural movie stimuli"
cs.LG|stat.ML,"traditionally , multitask learning \( mtl \) assumes that all the tasks are related this can lead to negative transfer when tasks are indeed incoherent recently , a number of approaches have been proposed that alleviate this problem by discovering the underlying task clusters or relationships however , they are limited to modeling these relationships at the task level , which may be restrictive in some applications in this paper , we propose a novel mtl formulation that captures task relationships at the feature level depending on the interactions among tasks and features , the proposed method construct different task clusters for different features , without even the need of pre specifying the number of clusters computationally , the proposed formulation is strongly convex , and can be efficiently solved by accelerated proximal methods experiments are performed on a number of synthetic and real world data sets under various degrees of task relationships , the accuracy of the proposed method is consistently among the best moreover , the feature specific task clusters obtained agree with the known plausible task structures of the data"
cs.SI,"biased random walks on complex networks are a particular type of walks whose motion is biased on properties of the destination node , such as its degree in recent years they have been exploited to design efficient strategies to explore a network , for instance by constructing maximally mixing trajectories or by sampling homogeneously the nodes in multiplex networks , the nodes are related through different types of links \( layers or communication channels \) , and the presence of connections at different layers multiplies the number of possible paths in the graph in this work we introduce biased random walks on multiplex networks and provide analytical solutions for their long term properties such as the stationary distribution and the entropy rate we focus on degree biased walks and distinguish between two subclasses of random walks extensive biased walks consider the properties of each node separately at each layer , intensive biased walks deal instead with intrinsically multiplex variables we study the effect of different structural properties , including the number of layers , the presence and sign of inter layer degree correlations , and the redundancy of edges across layers , on the steady state behaviour of the walkers , and we investigate how to design an efficient exploration of the system finally , we apply our results to the case of a multidimensional social network and to a multimodal transportation system , showing how an appropriate tuning of the bias parameters towards nodes which are truly multiplex allows to obtain a good trade off between a maximal entropy rate and a homogeneous sampling of the nodes of the network"
cs.LG,"this paper presents categorization of croatian texts using non standard words \( nsw \) as features non standard words are numbers , dates , acronyms , abbreviations , currency , etc nsws in croatian language are determined according to croatian nsw taxonomy for the purpose of this research , 390 text documents were collected and formed the skipez collection with 6 classes official , literary , informative , popular , educational and scientific text categorization experiment was conducted on three different representations of the skipez collection in the first representation , the frequencies of nsws are used as features in the second representation , the statistic measures of nsws \( variance , coefficient of variation , standard deviation , etc \) are used as features while the third representation combines the first two feature sets naive bayes , cn2 , c4 5 , knn , classification trees and random forest algorithms were used in text categorization experiments the best categorization results are achieved using the first feature set \( nsw frequencies \) with the categorization accuracy of 87 this suggests that the nsws should be considered as features in highly inflectional languages , such as croatian nsw based features reduce the dimensionality of the feature space without standard lemmatization procedures , and therefore the bag of nsws should be considered for further croatian texts categorization experiments"
cs.AI|cs.LG,"we present a method for automatically generating repair feedback for syntax errors for introductory programming problems syntax errors constitute one of the largest classes of errors \( 34 \) in our dataset of student submissions obtained from a mooc course on edx the previous techniques for generating automated feed back on programming assignments have focused on functional correctness and style considerations of student programs these techniques analyze the program ast of the program and then perform some dynamic and symbolic analyses to compute repair feedback unfortunately , it is not possible to generate asts for student pro grams with syntax errors and therefore the previous feedback techniques are not applicable in repairing syntax errors we present a technique for providing feedback on syntax errors that uses recurrent neural networks \( rnns \) to model syntactically valid token sequences our approach is inspired from the recent work on learning language models from big code \( large code corpus \) for a given programming assignment , we first learn an rnn to model all valid token sequences using the set of syntactically correct student submissions then , for a student submission with syntax errors , we query the learnt rnn model with the prefix to ken sequence to predict token sequences that can fix the error by either replacing or inserting the predicted token sequence at the error location we evaluate our technique on over 14 , 000 student submissions with syntax errors our technique can completely re pair 31 69 \( 4501 14203 \) of submissions with syntax errors and in addition partially correct 6 39 \( 908 14203 \) of the submissions"
cs.DM,"the main result of this paper is the decidability of the membership problem for 2 times 2 nonsingular integer matrices namely , we will construct the first algorithm that for any nonsingular 2 times 2 integer matrices m 1 , dots , m n and m decides whether m belongs to the semigroup generated by m 1 , dots , m n our algorithm relies on a translation of the numerical problem on matrices into combinatorial problems on words it also makes use of some algebraical properties of well known subgroups of mathrm gl \( 2 , mathbb z \) and various new techniques and constructions that help to limit an infinite number of possibilities by reducing them to the membership problem for regular languages"
cs.DS,"this paper shows that it is possible to improve the computational cost , the memory requirements and the accuracy of quick fourier transform \( qft \) algorithm for power of two fft \( fast fourier transform \) just introducing a slight modification in this algorithm the new algorithm requires the same number of additions and multiplications of split radix 3add 3mul , one of the most appreciated fft algorithms appeared in the literature , but employing only half of the trigonometric constants these results can elevate the qft approach to the level of most used fft procedures a new quite general way to describe fft algorithms , based on signal types and on a particular notation , is also proposed and used , highligting its advantages"
cs.IT|stat.ML,"can we recover a complex signal from its fourier magnitudes \? more generally , given a set of m measurements , y k mathbf a k mathbf x for k 1 , dots , m , is it possible to recover mathbf x in mathbb c n \( i e , length n complex vector \) \? this generalized phase retrieval \( gpr \) problem is a fundamental task in various disciplines , and has been the subject of much recent investigation natural nonconvex heuristics often work remarkably well for gpr in practice , but lack clear theoretical explanations in this paper , we take a step towards bridging this gap we prove that when the measurement vectors mathbf a k 's are generic \( i i d complex gaussian \) and the number of measurements is large enough \( m ge c n log 3 n \) , with high probability , a natural least squares formulation for gpr has the following benign geometric structure \( 1 \) there are no spurious local minimizers , and all global minimizers are equal to the target signal mathbf x , up to a global phase and \( 2 \) the objective function has a negative curvature around each saddle point this structure allows a number of iterative optimization methods to efficiently find a global minimizer , without special initialization to corroborate the claim , we describe and analyze a second order trust region algorithm"
cs.AI|cs.LG,"in this work , we present a mcts based go playing program which uses convolutional networks in all parts our method performs mcts in batches , explores the monte carlo search tree using thompson sampling and a convolutional network , and evaluates convnet based rollouts on the gpu we achieve strong win rates against open source go programs and attain competitive results against state of the art convolutional net based go playing programs"
cs.SI,"a deep community in a graph is a connected component that can only be seen after removal of nodes or edges from the rest of the graph this paper addresses the problem of detecting deep communities using a new centrality measure , called the local fiedler vector centrality \( lfvc \) the lfvc is associated with the sensitivity of algebraic connectivity to node or edge removals we prove that a greedy node edge removal strategy , based on successive maximization of lfvc , has bounded performance loss relative to the optimal , but intractable , combinatorial batch removal strategy under a stochastic block model framework , we show that the greedy lfvc strategy can extract deep communities with probability one as the number of observations becomes large we apply the greedy lfvc strategy to real world social network datasets compared with conventional community detection methods we demonstrate improved ability to identify important communities and key members in the network"
cs.SI,"tagging items with descriptive annotations or keywords is a very natural way to compress and highlight information about the properties of the given entity over the years several methods have been proposed for extracting a hierarchy between the tags for systems with a flat , egalitarian organization of the tags , which is very common when the tags correspond to free words given by numerous independent people here we present a complete framework for automated tag hierarchy extraction based on tag occurrence statistics along with proposing new algorithms , we are also introducing different quality measures enabling the detailed comparison of competing approaches from different aspects furthermore , we set up a synthetic , computer generated benchmark providing a versatile tool for testing , with a couple of tunable parameters capable of generating a wide range of test beds beside the computer generated input we also use real data in our studies , including a biological example with a pre defined hierarchy between the tags the encouraging similarity between the pre defined and reconstructed hierarchy , as well as the seemingly meaningful hierarchies obtained for other real systems indicate that tag hierarchy extraction is a very promising direction for further research with a great potential for practical applications"
cs.AI,"a major computational burden , while performing document clustering , is the calculation of similarity measure between a pair of documents similarity measure is a function that assign a real number between 0 and 1 to a pair of documents , depending upon the degree of similarity between them a value of zero means that the documents are completely dissimilar whereas a value of one indicates that the documents are practically identical traditionally , vector based models have been used for computing the document similarity the vector based models represent several features present in documents these approaches to similarity measures , in general , cannot account for the semantics of the document documents written in human languages contain contexts and the words used to describe these contexts are generally semantically related motivated by this fact , many researchers have proposed semantic based similarity measures by utilizing text annotation through external thesauruses like wordnet \( a lexical database \) in this paper , we define a semantic similarity measure based on documents represented in topic maps topic maps are rapidly becoming an industrial standard for knowledge representation with a focus for later search and extraction the documents are transformed into a topic map based coded knowledge and the similarity between a pair of documents is represented as a correlation between the common patterns the experimental studies on the text mining datasets reveal that this new similarity measure is more effective as compared to commonly used similarity measures in text clustering"
cs.AI,"divergence from a random baseline is a technique for the evaluation of document clustering it ensures cluster quality measures are performing work that prevents ineffective clusterings from giving high scores to clusterings that provide no useful result these concepts are defined and analysed using intrinsic and extrinsic approaches to the evaluation of document cluster quality this includes the classical clusters to categories approach and a novel approach that uses ad hoc information retrieval the divergence from a random baseline approach is able to differentiate ineffective clusterings encountered in the inex xml mining track it also appears to perform a normalisation similar to the normalised mutual information \( nmi \) measure but it can be applied to any measure of cluster quality when it is applied to the intrinsic measure of distortion as measured by rmse , subtraction from a random baseline provides a clear optimum that is not apparent otherwise this approach can be applied to any clustering evaluation this paper describes its use in the context of document clustering evaluation"
cs.SI,"background accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives novel event based methods that use the world wide web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking in this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease country counts text mined from real world data using biocaster for 18 data sets produced by biocaster , we compare 5 aberration detection algorithms \( ears c2 , c3 , w2 , f statistic and ewma \) for performance against expert moderated promed mail postings results we report sensitivity , specificity , positive predictive value \( ppv \) , negative predictive value \( npv \) , mean alerts 100 days and f1 , at 95 confidence interval \( ci \) for 287 promed mail postings on 18 outbreaks across 14 countries over a 366 day period results indicate that w2 had the best f1 with a slight benefit for day of week effect over c2 in drill down analysis we indicate issues arising from the granular choice of country level modeling , sudden drops in reporting due to day of week effects and reporting bias automatic alerting has been implemented in biocaster available from http born nii ac jp conclusions online health news alerts have the potential to enhance manual analytical methods by increasing throughput , timeliness and detection rates systematic evaluation of health news aberrations is necessary to push forward our understanding of the complex relationship between news report volumes and case numbers and to select the best performing features and algorithms"
cs.IT|cs.LG,"the poisson sampling technique eliminates dependencies among symbol appearances in a random sequence it has been used to simplify the analysis and strengthen the performance guarantees of randomized algorithms applying this method to universal compression , we relate the redundancies of fixed length and poisson sampled sequences , use the relation to derive a simple single letter formula that approximates the redundancy of any envelope class to within an additive logarithmic term as a first application , we consider i i d distributions over a small alphabet as a step envelope class , and provide a short proof that determines the redundancy of discrete distributions over a small al phabet up to the first order terms we then show the strength of our method by applying the formula to tighten the existing bounds on the redundancy of exponential and power law classes , in particular answering a question posed by boucheron , garivier and gassiat"
cs.AI,"we give a purely model theoretic characterization of the semantics of logic programs with negation as failure allowed in clause bodies in our semantics the meaning of a program is , as in the classical case , the unique minimum model in a program independent ordering we use an expanded truth domain that has an uncountable linearly ordered set of truth values between false \( the minimum element \) and true \( the maximum \) , with a zero element in the middle the truth values below zero are ordered like the countable ordinals the values above zero have exactly the reverse order negation is interpreted as reflection about zero followed by a step towards zero the only truth value that remains unaffected by negation is zero we show that every program has a unique minimum model m p , and that this model can be constructed with a t p iteration which proceeds through the countable ordinals furthermore , we demonstrate that m p can also be obtained through a model intersection construction which generalizes the well known model intersection theorem for classical logic programming finally , we show that by collapsing the true and false values of the infinite valued model m p to \( the classical \) true and false , we obtain a three valued model identical to the well founded one"
cs.DS|cs.IT,"we consider the following k sparse recovery problem design an m x n matrix a , such that for any signal x , given ax we can efficiently recover x' satisfying x x' 1 c min k sparse x x x 1 it is known that there exist matrices a with this property that have only o \( k log \( n k \) \) rows in this paper we show that this bound is tight our bound holds even for the more general randomized version of the problem , where a is a random variable and the recovery algorithm is required to work for any fixed x with constant probability \( over a \)"
cs.AI|stat.ML,"the goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values the least absolute shrinkage and selection operator \( lasso \) allows computationally efficient feature selection based on linear dependency between input features and output values in this paper , we consider a feature wise kernelized lasso for capturing non linear input output dependency we first show that , with particular choices of kernel functions , non redundant features with strong statistical dependence on output values can be found in terms of kernel based independence measures we then show that the globally optimal solution can be efficiently computed this makes the approach scalable to high dimensional problems the effectiveness of the proposed method is demonstrated through feature selection experiments with thousands of features"
cs.DS,we present a recursive way to partition hypergraphs which creates and exploits hypergraph geometry and is suitable for many core parallel architectures such partitionings are then used to bring sparse matrices in a recursive bordered block diagonal form \( for processor oblivious parallel lu decomposition \) or recursive separated block diagonal form \( for cache oblivious sparse matrix vector multiplication \) we show that the quality of the obtained partitionings and orderings is competitive by comparing obtained fill in for lu decomposition with superlu \( with better results for 8 of the 28 test matrices \) and comparing cut sizes for sparse matrix vector multiplication with mondriaan \( with better results for 4 of the 12 test matrices \) the main advantage of the new method is its speed it is on average 21 6 times faster than mondriaan
cs.AI,"evolutionary multi objective optimization is becoming a hot research area and quite a few papers regarding these algorithms have been published however the role of local search techniques has not been expanded adequately this paper studies the role of a local search technique called 2 opt for the multi objective travelling salesman problem \( motsp \) a new mutation operator called jumping gene \( jg \) is also used since 2 opt operator was intended for the single objective tsp , its domain has been expanded to motsp in this paper this new technique is applied to the list of kroab100 cities"
cs.SI,"online social networks \( osns \) are changing the way in which the information spreads throughout the internet a deep understanding of the information spreading in osns leads to both social and commercial benefits in this paper , we characterize the dynamic of information spreading \( e g , how fast and widely the information spreads against time \) in osns by developing a general and accurate model based on the interactive markov chains \( imcs \) and mean field theory this model explicitly reveals the impacts of the network topology on information spreading in osns further , we extend our model to feature the time varying user behaviors and the ever changing information popularity the complicated dynamic patterns of information spreading are captured by our model using six key parameters extensive tests based on renren 's dataset validate the accuracy of our model , which demonstrate that it can characterize the dynamic patterns of video sharing in renren precisely and predict future spreading tendency successfully"
cs.SI,"frigyes karinthy , in his 1929 short story l 'aancszemek \( chains \) suggested that any two persons are distanced by at most six friendship links \( the exact wording of the story is slightly ambiguous he bet us that , using no more than five individuals , one of whom is a personal acquaintance , he could contact the selected individual it is not completely clear whether the selected individual is part of the five , so this could actually allude to distance five or six in the language of graph theory , but the six degrees of separation phrase stuck after john guare 's 1990 eponymous play following milgram 's definition and guare 's interpretation , we will assume that degrees of separation is the same as distance minus one , where distance is the usual path length the number of arcs in the path \) stanley milgram in his famous experiment challenged people to route postcards to a fixed recipient by passing them only through direct acquaintances the average number of intermediaries on the path of the postcards lay between 4 4 and 5 7 , depending on the sample of people chosen we report the results of the first world scale social network graph distance computations , using the entire facebook network of active users \( approx721 million users , approx69 billion friendship links \) the average distance we observe is 4 74 , corresponding to 3 74 intermediaries or degrees of separation , showing that the world is even smaller than we expected , and prompting the title of this paper more generally , we study the distance distribution of facebook and of some interesting geographic subgraphs , looking also at their evolution over time the networks we are able to explore are almost two orders of magnitude larger than those analysed in the previous literature we report detailed statistical metadata showing that our measurements \( which rely on probabilistic algorithms \) are very accurate"
cs.SI,"while the effect of various lexical , syntactic , semantic and stylistic features have been addressed in persuasive language from a computational point of view , the persuasive effect of phonetics has received little attention by modeling a notion of euphony and analyzing four datasets comprising persuasive and non persuasive sentences in different domains \( political speeches , movie quotes , slogans and tweets \) , we explore the impact of sounds on different forms of persuasiveness we conduct a series of analyses and prediction experiments within and across datasets our results highlight the positive role of phonetic devices on persuasion"
cs.DM,"we consider straight line outerplanar drawings of outerplanar graphs in which a small number of distinct edge slopes are used , that is , the segments representing edges are parallel to a small number of directions we prove that delta 1 edge slopes suffice for every outerplanar graph with maximum degree delta ge 4 this improves on the previous bound of o \( delta 5 \) , which was shown for planar partial 3 trees , a superclass of outerplanar graphs the bound is tight for every delta ge 4 there is an outerplanar graph with maximum degree delta that requires at least delta 1 distinct edge slopes in an outerplanar straight line drawing"
cs.LG|stat.ML,"consider convex optimization problems subject to a large number of constraints we focus on stochastic problems in which the objective takes the form of expected values and the feasible set is the intersection of a large number of convex sets we propose a class of algorithms that perform both stochastic gradient descent and random feasibility updates simultaneously at every iteration , the algorithms sample a number of projection points onto a randomly selected small subsets of all constraints three feasibility update schemes are considered averaging over random projected points , projecting onto the most distant sample , projecting onto a special polyhedral set constructed based on sample points we prove the almost sure convergence of these algorithms , and analyze the iterates' feasibility error and optimality error , respectively we provide new convergence rate benchmarks for stochastic first order optimization with many constraints the rate analysis and numerical experiments reveal that the algorithm using the polyhedral set projection scheme is the most efficient one within known algorithms"
cs.DM|cs.DS,"we initiate the study of property testing of submodularity on the boolean hypercube submodular functions come up in a variety of applications in combinatorial optimization for a vast range of algorithms , the existence of an oracle to a submodular function is assumed but how does one check if this oracle indeed represents a submodular function \? consider a function f 0 , 1 n rightarrow r the distance to submodularity is the minimum fraction of values of f that need to be modified to make f submodular if this distance is more than epsilon 0 , then we say that f is epsilon far from being submodular the aim is to have an efficient procedure that , given input f that is epsilon far from being submodular , certifies that f is not submodular we analyze a very natural tester for this problem , and prove that it runs in subexponential time this gives the first non trivial tester for submodularity on the other hand , we prove an interesting lower bound \( that is , unfortunately , quite far from the upper bound \) suggesting that this tester cannot be very efficient in terms of epsilon this involves non trivial examples of functions which are far from submodular and yet do not exhibit too many local violations we also provide some constructions indicating the difficulty in designing a tester for submodularity we construct a partial function defined on exponentially many points that cannot be extended to a submodular function , but any strict subset of these values can be extended to a submodular function"
cs.LG,"applications such as face recognition that deal with high dimensional data need a mapping technique that introduces representation of low dimensional features with enhanced discriminatory power and a proper classifier , able to classify those complex features most of traditional linear discriminant analysis suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation moreover , their classification accuracy is affected by the small sample size problem which is often encountered in fr tasks in this short paper , we combine nonlinear kernel based mapping of data called kdda with support vector machine classifier to deal with both of the shortcomings in an efficient and cost effective manner the proposed here method is compared , in terms of classification accuracy , to other commonly used fr methods on umist face database results indicate that the performance of the proposed method is overall superior to those of traditional fr approaches , such as the eigenfaces , fisherfaces , and d lda methods and traditional linear classifiers"
cs.DM,"let g be a plane graph with outer cycle c and let \( l \( v \) v in v \( g \) \) be a family of sets such that l \( v \) ge 5 for every v in v \( g \) by an l coloring of a subgraph j of g we mean a \( proper \) coloring phi of j such that phi \( v \) in l \( v \) for every vertex v of j we prove a conjecture of dvorak et al that if h is a minimal subgraph of g such that c is a subgraph of h and every l coloring of c that extends to an l coloring of h also extends to an l coloring of g , then v \( h \) le 19 v \( c \) this is a lemma that plays an important role in subsequent papers , because it motivates the study of graphs embedded in surfaces that satisfy an isoperimetric inequality suggested by this result such study turned out to be quite profitable for the subject of list coloring graphs on surfaces"
cs.DS,"let cal p h 1 , , h s subset z y 1 , , y k , d geq deg \( h i \) for 1 leq i leq s , sigma bounding the bit length of the coefficients of the h i 's , and phi be a quantifier free cal p formula defining a convex semi algebraic set we design an algorithm returning a rational point in cal s if and only if cal s cap q neq emptyset it requires sigma bigo \( 1 \) d bigo \( k 3 \) bit operations if a rational point is outputted its coordinates have bit length dominated by sigma d bigo \( k 3 \) using this result , we obtain a procedure deciding if a polynomial f in z x 1 , , x n is a sum of squares of polynomials in q x 1 , , x n denote by d the degree of f , tau the maximum bit length of the coefficients in f , d n d choose n and k leq d \( d 1 \) n 2d choose n this procedure requires tau bigo \( 1 \) d bigo \( k 3 \) bit operations and the coefficients of the outputted polynomials have bit length dominated by tau d bigo \( k 3 \)"
cs.DS,"we study the pagerank mass of principal components in a bow tie web graph , as a function of the damping factor c using a singular perturbation approach , we show that the pagerank share of in and scc components remains high even for very large values of the damping factor , in spite of the fact that it drops to zero when c goes to one however , a detailed study of the out component reveals the presence ``dead ends'' \( small groups of pages linking only to each other \) that receive an unfairly high ranking when c is close to one we argue that this problem can be mitigated by choosing c as small as 1 2"
cs.AI,"object oriented application programing interfaces \( apis \) support software reuse by providing pre implemented functionalities due to the huge number of included classes , reusing and understanding large apis is a complex task otherwise , software components are admitted to be more reusable and understandable entities than object oriented ones thus , in this paper , we propose an approach for reengineering object oriented apis into component based ones we mine components as a group of classes based on the frequency they are used together and their ability to form a quality centric component to validate our approach , we experimented on 100 java applications that used android apis"
cs.DM|cs.DS,"the problem of publishing personal data without giving up privacy is becoming increasingly important an interesting formalization that has been recently proposed is the k anonymity this approach requires that the rows of a table are partitioned in clusters of size at least k and that all the rows in a cluster become the same tuple , after the suppression of some entries the natural optimization problem , where the goal is to minimize the number of suppressed entries , is known to be apx hard even when the records values are over a binary alphabet and k 3 , and when the records have length at most 8 and k 4 in this paper we study how the complexity of the problem is influenced by different parameters in this paper we follow this direction of research , first showing that the problem is w 1 hard when parameterized by the size of the solution \( and the value k \) then we exhibit a fixed parameter algorithm , when the problem is parameterized by the size of the alphabet and the number of columns finally , we investigate the computational \( and approximation \) complexity of the k anonymity problem , when restricting the instance to records having length bounded by 3 and k 3 we show that such a restriction is apx hard"
cs.LG|stat.ML,"we introduce a new algorithm , called adaptive sparse backfitting algorithm , for solving high dimensional sparse additive model \( spam \) utilizing symmetric , non negative definite smoothers unlike the previous sparse backfitting algorithm , our method is essentially a block coordinate descent algorithm that guarantees to converge to the optimal solution it bridges the gap between the population backfitting algorithm and that of the data version we also prove variable selection consistency under suitable conditions numerical studies on both synthesis and real data are conducted to show that adaptive sparse backfitting algorithm outperforms previous sparse backfitting algorithm in fitting and predicting high dimensional nonparametric models"
cs.DM|cs.DS,"the primary problem in property testing is to decide whether a given function satisfies a certain property , or is far from any function satisfying it this crucially requires a notion of distance between functions the most prevalent notion is the hamming distance over the em uniform distribution on the domain this restriction to uniformity is more a matter of convenience than of necessity , and it is important to investigate distances induced by more general distributions in this paper , we make significant strides in this direction we give simple and optimal testers for em bounded derivative properties over em arbitrary product distributions bounded derivative properties include fundamental properties such as monotonicity and lipschitz continuity our results subsume almost all known results \( upper and lower bounds \) on monotonicity and lipschitz testing we prove an intimate connection between bounded derivative property testing and binary search trees \( bsts \) we exhibit a tester whose query complexity is the sum of expected depths of optimal bsts for each marginal furthermore , we show this sum of depths is also a lower bound a fundamental technical contribution of this work is an em optimal dimension reduction theorem for all bounded derivative properties , which relates the distance of a function from the property to the distance of restrictions of the function to random lines such a theorem has been elusive even for monotonicity for the past 15 years , and our theorem is an exponential improvement to the previous best known result"
cs.DM|cs.DS,"this article introduces an algorithm , mergeshuffle , which is an extremely efficient algorithm to generate random permutations \( or to randomly permute an existing array \) it is easy to implement , runs in n log 2 n o \( 1 \) time , is in place , uses n log 2 n theta \( n \) random bits , and can be parallelized accross any number of processes , in a shared memory pram model finally , our preliminary simulations using openmp suggest it is more efficient than the rao sandelius algorithm , one of the fastest existing random permutation algorithms we also show how it is possible to further reduce the number of random bits consumed , by introducing a second algorithm balancedshuffle , a variant of the rao sandelius algorithm which is more conservative in the way it recursively partitions arrays to be shuffled while this algorithm is of lesser practical interest , we believe it may be of theoretical value our full code is available at https github com axel bacher mergeshuffle"
cs.DM,"from observational data alone , a causal dag is only identifiable up to markov equivalence interventional data generally improves identifiability however , the gain of an intervention strongly depends on the intervention target , that is , the intervened variables we present active learning \( that is , optimal experimental design \) strategies calculating optimal interventions for two different learning goals the first one is a greedy approach using single vertex interventions that maximizes the number of edges that can be oriented after each intervention the second one yields in polynomial time a minimum set of targets of arbitrary size that guarantees full identifiability this second approach proves a conjecture of eberhardt \( 2008 \) indicating the number of unbounded intervention targets which is sufficient and in the worst case necessary for full identifiability in a simulation study , we compare our two active learning approaches to random interventions and an existing approach , and analyze the influence of estimation errors on the overall performance of active learning"
cs.AI,the domain model is one of the important components used by adaptive learning systems to automatically generate customized courses for the learners in this paper our contribution is to propose a new tool for implementation of a domain model based on fuzzy relationships among concepts this tool allows the experts and teachers to find the best parameters in order to adapt the learners 's differences
cs.LG,"most modern convolutional neural networks \( cnns \) used for object recognition are built using the same principles alternating convolution and max pooling layers followed by a small number of fully connected layers we re evaluate the state of the art for object recognition from small images with convolutional networks , questioning the necessity of different components in the pipeline we find that max pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks following this finding and building on other recent work for finding simple network structures we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets \( cifar 10 , cifar 100 , imagenet \) to analyze the network we introduce a new variant of the deconvolution approach for visualizing features learned by cnns , which can be applied to a broader range of network structures than existing approaches"
cs.SI,"characterizing the community structure of complex networks is a key challenge in many scientific fields very diverse algorithms and methods have been proposed to this end , many working reasonably well in specific situations however , no consensus has emerged on which of these methods is the best to use in practice in part , this is due to the fact that testing their performance requires the generation of a comprehensive , standard set of synthetic benchmarks , a goal not yet fully achieved here , we present a type of benchmark that we call closed , in which an initial network of known community structure is progressively converted into a second network whose communities are also known this approach differs from all previously published ones , in which networks evolve toward randomness the use of this type of benchmark allows us to monitor the transformation of the community structure of a network moreover , we can predict the optimal behavior of the variation of information , a measure of the quality of the partitions obtained , at any moment of the process this enables us in many cases to determine the best partition among those suggested by different algorithms also , since any network can be used as a starting point , extensive studies and comparisons can be performed using a heterogeneous set of structures , including random ones these properties make our benchmarks a general standard for comparing community detection algorithms"
cs.DS,"we consider greedy contention managers for transactional memory for m x n execution windows of transactions with m threads and n transactions per thread assuming that each transaction conflicts with at most c other transactions inside the window , a trivial greedy contention manager can schedule them within cn time in this paper , we show that there are much better schedules we present and analyze two new randomized greedy contention management algorithms the first algorithm offline greedy produces a schedule of length o \( c n log \( mn \) \) with high probability , and gives competitive ratio o \( log \( mn \) \) for c n log \( mn \) the offline algorithm depends on knowing the conflict graph the second algorithm online greedy produces a schedule of length o \( c log \( mn \) n log 2 \( mn \) \) with high probability which is only a o \( log \( nm \) \) factor worse , but does not require knowledge of the conflict graph we also give an adaptive version which achieves similar worst case performance and c is determined on the fly under execution our algorithms provide new tradeoffs for greedy transaction scheduling that parameterize window sizes and transaction conflicts within the window"
cs.SI,"sampling techniques such as respondent driven sampling \( rds \) are widely used in epidemiology to sample hidden populations , such that properties of the network can be deduced from the sample we consider how similar techniques can be designed that allow the discovery of the structure , especially the community structure , of networks our method involves collecting samples of a network by random walks and reconstructing the network by probabilistically coalescing vertices , using vertex attributes to determine the probabilities even though our method can only approximately reconstruct a part of the original network , it can recover its community structure relatively well moreover , it can find the key vertices which , when immunized , can effectively reduce the spread of an infection through the original network"
cs.SI,"for a broad range single seed contagion processes acting on generalized random networks , we derive a unifying analytic expression for the possibility of global spreading events in a straightforward , physically intuitive fashion our reasoning lays bare a direct mechanical understanding of an archetypal spreading phenomena that is not evident in circuitous extant mathematical approaches"
cs.AI,"the efficiency of current cargo screening processes at sea and air ports is unknown as no benchmarks exists against which they could be measured some manufacturer benchmarks exist for individual sensors but we have not found any benchmarks that take a holistic view of the screening procedures assessing a combination of sensors and also taking operator variability into account just adding up resources and manpower used is not an effective way for assessing systems where human decision making and operator compliance to rules play a vital role for such systems more advanced assessment methods need to be used , taking into account that the cargo screening process is of a dynamic and stochastic nature our project aim is to develop a decision support tool \( cargo screening system simulator \) that will map the right technology and manpower to the right commodity threat combination in order to maximize detection rates in this paper we present a project outline and highlight the research challenges we have identified so far in addition we introduce our first case study , where we investigate the cargo screening process at the ferry port in calais"
cs.AI,"this paper tackles temporal resolution of documents , such as determining when a document is about or when it was written , based only on its text we apply techniques from information retrieval that predict dates via language models over a discretized timeline unlike most previous works , we rely it solely on temporal cues implicit in the text we consider both document likelihood and divergence based techniques and several smoothing methods for both of them our best model predicts the mid point of individuals' lives with a median of 22 and mean error of 36 years for wikipedia biographies from 3800 b c to the present day we also show that this approach works well when training on such biographies and predicting dates both for non biographical wikipedia pages about specific years \( 500 b c to 2010 a d \) and for publication dates of short stories \( 1798 to 2008 \) together , our work shows that , even in absence of temporal extraction resources , it is possible to achieve remarkable temporal locality across a diverse set of texts"
cs.SI,"as a generation of the classical percolation , clique percolation focuses on the connection of cliques in a graph , where the connection of two k cliques means they share at leat l k vertices in this paper , we develop a theoretical approach to study clique percolation in erd h o s r 'enyi graphs , which can give the exact solutions of the order parameter and the critical point we find that the fraction of cliques in the giant clique cluster always takes a continuous phase transition as the classical percolation however , the fraction of vertices in the giant clique cluster demonstrates an unnormal phase transitions for k 3 and l 1 , i e , the fraction of vertices in the giant clique cluster always takes value 1 above the critical point , however , a size independent constant at the critical point together with the analysis of the finite size scaling and cluster number distribution , we give a theoretical support and clarification for previous simulation studies of the clique percolation"
cs.DM,the resolution of a decades old open problem is described polygonal chains cannot lock in the plane
cs.AI,"with the boom in it technology , the data sets used in application are more and more larger and are described by a huge number of attributes , therefore , the feature selection become an important discipline in knowledge discovery and data mining , allowing the experts to select the most relevant features to improve the quality of their studies and to reduce the time processing of their algorithm in addition to that , the data used by the applications become richer they are now represented by a set of complex and structured objects , instead of simple numerical matrixes the purpose of our algorithm is to do feature selection on rich data , called boolean symbolic objects \( bsos \) these objects are described by multivalued features the bsos are considered as higher level units which can model complex data , such as cluster of individuals , aggregated data or taxonomies in this paper we will introduce a new feature selection criterion for bsos , and we will explain how we improved its complexity"
cs.DM,"although the results are correct , it was pointed out that the results follow from some previously known results accordingly , this version of the paper is withdrawn by the authors"
cs.SI,"with the rise of wikipedia as a first stop source for scientific knowledge , it is important to compare its representation of that knowledge to that of the academic literature this article approaches such a comparison through academic references made within the worlds 50 largest wikipedias previous studies have raised concerns that wikipedia editors may simply use the most easily accessible academic sources rather than sources of the highest academic status we test this claim by identifying the 250 most heavily used journals in each of 26 research fields \( 4 , 721 journals , 19 4m articles in total \) indexed by the scopus database , and modeling whether topic , academic status , and accessibility make articles from these journals more or less likely to be referenced on wikipedia we find that , controlling for field and impact factor , the odds that an open access journal is referenced on the english wikipedia are 47 higher compared to closed access journals moreover , in most of the worlds wikipedias a journals high status \( impact factor \) and accessibility \( open access policy \) both greatly increase the probability of referencing among the implications of this study is that the chief effect of open access policies may be to significantly amplify the diffusion of science , through an intermediary like wikipedia , to a broad public audience"
cs.DM|cs.DS,"the field of property testing of probability distributions , or distribution testing , aims to provide fast and \( most likely \) correct answers to questions pertaining to specific aspects of very large datasets in this work , we consider a property of particular interest , monotonicity of distributions we focus on the complexity of monotonicity testing across different models of access to the distributions and obtain results in these new settings that differ significantly from the known bounds in the standard sampling model"
cs.DS|cs.SI,"we present new , more efficient algorithms for estimating random walk scores such as personalized pagerank from a given source node to one or several target nodes these scores are useful for personalized search and recommendations on networks including social networks , user item networks , and the web past work has proposed using monte carlo or using linear algebra to estimate scores from a single source to every target , making them inefficient for a single pair our contribution is a new bidirectional algorithm which combines linear algebra and monte carlo to achieve significant speed improvements on a diverse set of six graphs , our algorithm is 70x faster than past state of the art algorithms we also present theoretical analysis while past algorithms require omega \( n \) time to estimate a random walk score of typical size frac 1 n on an n node graph to a given constant accuracy , our algorithm requires only o \( sqrt m \) expected time for an average target , where m is the number of edges , and is provably accurate in addition to our core bidirectional estimator for personalized pagerank , we present an alternative algorithm for undirected graphs , a generalization to arbitrary walk lengths and markov chains , an algorithm for personalized search ranking , and an algorithm for sampling random paths from a given source to a given set of targets we expect our bidirectional methods can be extended in other ways and will be useful subroutines in other graph analysis problems"
cs.DM|cs.DS,"graphlet analysis is an approach to network analysis that is particularly popular in bioinformatics we show how to set up a system of linear equations that relate the orbit counts and can be used in an algorithm that is significantly faster than the existing approaches based on direct enumeration of graphlets the algorithm requires existence of a vertex with certain properties we show that such vertex exists for graphlets of arbitrary size , except for complete graphs and c 4 , which are treated separately empirical analysis of running time agrees with the theoretical results"
cs.LG,"in this paper we study inexact dumped newton method implemented in a distributed environment we start with an original disco algorithm communication efficient distributed optimization of self concordant empirical loss , yuchen zhang and lin xiao , 2015 we will show that this algorithm may not scale well and propose an algorithmic modifications which will lead to less communications , better load balancing and more efficient computation we perform numerical experiments with an regularized empirical loss minimization instance described by a 273gb dataset"
cs.LG|stat.ML,"the strength of association between a pair of data vectors is represented by a nonnegative real number , called matching weight for dimensionality reduction , we consider a linear transformation of data vectors , and define a matching error as the weighted sum of squared distances between transformed vectors with respect to the matching weights given data vectors and matching weights , the optimal linear transformation minimizing the matching error is solved by the spectral graph embedding of yan et al \( 2007 \) this method is a generalization of the canonical correlation analysis , and will be called as matching correlation analysis \( mca \) in this paper , we consider a novel sampling scheme where the observed matching weights are randomly sampled from underlying true matching weights with small probability , whereas the data vectors are treated as constants we then investigate a cross validation by resampling the matching weights our asymptotic theory shows that the cross validation , if rescaled properly , computes an unbiased estimate of the matching error with respect to the true matching weights existing works of cross validation for resampling data vectors , instead of resampling matching weights , are not applicable here mca can be used for data vectors from multiple domains with different dimensions via the embarrassingly simple coding of shimodaira \( 2014 \) , and thus our results are directly applicable to the cross domain matching for searching closely related vectors across domains"
cs.AI,"the ke inference system is a tableau method developed by marco mondadori which was presented as an improvement , in the computational efficiency sense , over analytic tableaux in the literature , there is no description of a theorem prover based on the ke method for the c1 paraconsistent logic paraconsistent logics have several applications , such as in robot control and medicine these applications could benefit from the existence of such a prover we present a sound and complete ke system for c1 , an informal specification of a strategy for the c1 prover as well as problem families that can be used to evaluate provers for c1 the c1 ke system and the strategy described in this paper will be used to implement a ke based prover for c1 , which will be useful for those who study and apply paraconsistent logics"
cs.LG|stat.ML,"the recent proliferation of richly structured probabilistic models raises the question of how to automatically determine an appropriate model for a dataset we investigate this question for a space of matrix decomposition models which can express a variety of widely used models from unsupervised learning to enable model selection , we organize these models into a context free grammar which generates a wide variety of structures through the compositional application of a few simple rules we use our grammar to generically and efficiently infer latent components and estimate predictive likelihood for nearly 2500 structures using a small toolbox of reusable algorithms using a greedy search over our grammar , we automatically choose the decomposition structure from raw data by evaluating only a small fraction of all models the proposed method typically finds the correct structure for synthetic data and backs off gracefully to simpler models under heavy noise it learns sensible structures for datasets as diverse as image patches , motion capture , 20 questions , and u s senate votes , all using exactly the same code"
cs.LG,"higher order tensors are becoming prevalent in many scientific areas such as computer vision , social network analysis , data mining and neuroscience traditional tensor decomposition approaches face three major challenges model selecting , gross corruptions and computational efficiency to address these problems , we first propose a parallel trace norm regularized tensor decomposition method , and formulate it as a convex optimization problem this method does not require the rank of each mode to be specified beforehand , and can automatically determine the number of factors in each mode through our optimization scheme by considering the low rank structure of the observed tensor , we analyze the equivalent relationship of the trace norm between a low rank tensor and its core tensor then , we cast a non convex tensor decomposition model into a weighted combination of multiple much smaller scale matrix trace norm minimization finally , we develop two parallel alternating direction methods of multipliers \( admm \) to solve our problems experimental results verify that our regularized formulation is effective , and our methods are robust to noise or outliers"
cs.LG,"modeling data with multivariate count responses is a challenge problem due to the discrete nature of the responses such data are commonly observed in many applications such as biology , epidemiology and social studies the existing methods for univariate count response cannot be easily extended to the multivariate situation since the dependency among multiple responses needs to be properly accommodated in this paper , we propose a multivariate poisson log normal regression model for multivariate data with count responses an efficient monte carlo em algorithm is also developed to facilitate the model estimation by simultaneously estimating the regression coefficients and inverse covariance matrix over the latent variables , the proposed regression model takes advantages of association among multiple count responses to improve the model prediction performance simulation studies are conducted to systematically evaluate the performance of the proposed method in comparison with conventional methods the proposed method is further used to analyze a real influenza like illness data , showing accurate prediction and forecasting with meaningful interpretation"
cs.AI,"we show how coupling of local optimization processes can lead to better solutions than multi start local optimization consisting of independent runs this is achieved by minimizing the average energy cost of the ensemble , subject to synchronization constraints between the state vectors of the individual local minimizers from an augmented lagrangian which incorporates the synchronization constraints both as soft and hard constraints , a network is derived wherein the local minimizers interact and exchange information through the synchronization constraints from the viewpoint of neural networks , the array can be considered as a lagrange programming network for continuous optimization and as a cellular neural network \( cnn \) the penalty weights associated with the soft state synchronization constraints follow from the solution to a linear program this expresses that the energy cost of the ensemble should maximally decrease in this way successful local minimizers can implicitly impose their state to the others through a mechanism of master slave dynamics resulting into a cooperative search mechanism improved information spreading within the ensemble is obtained by applying the concept of small world networks this work suggests , in an interdisciplinary context , the importance of information exchange and state synchronization within ensembles , towards issues as evolution , collective behaviour , optimality and intelligence"
cs.AI,classical notions of disjunctive and cumulative scheduling are studied from the point of view of soft constraint satisfaction soft disjunctive scheduling is introduced as an instance of soft csp and preferences included in this problem are applied to generate a lower bound based on existing discrete capacity resource timetabling problems at purdue university and faculty of informatics at masaryk university considering individual course requirements of students demonstrate practical problems which are solved via proposed methods implementation of general preference constraint solver is discussed and first computational results for timetabling problem are presented
cs.AI|cs.LG,"deep convolutional neural networks \( cnns \) have demonstrated excellent performance in image classification , but still show room for improvement in object detection tasks with many categories , in particular for cluttered scenes and occlusion modern detection algorithms like regions with cnns \( girshick et al , 2014 \) rely on selective search \( uijlings et al , 2013 \) to propose regions which with high probability represent objects , where in turn cnns are deployed for classification selective search represents a family of sophisticated algorithms that are engineered with multiple segmentation , appearance and saliency cues , typically coming with a significant run time overhead furthermore , \( hosang et al , 2014 \) have shown that most methods suffer from low reproducibility due to unstable superpixels , even for slight image perturbations although cnns are subsequently used for classification in top performing object detection pipelines , current proposal methods are agnostic to how these models parse objects and their rich learned representations as a result they may propose regions which may not resemble high level objects or totally miss some of them to overcome these drawbacks we propose a boosting approach which directly takes advantage of hierarchical cnn features for detecting regions of interest fast we demonstrate its performance on imagenet 2013 detection benchmark and compare it with state of the art methods"
cs.DS|cs.IT|cs.LG,"it was recently shown that estimating the shannon entropy h \( p \) of a discrete k symbol distribution p requires theta \( k log k \) samples , a number that grows near linearly in the support size in many applications h \( p \) can be replaced by the more general renyi entropy of order alpha , h alpha \( p \) we determine the number of samples needed to estimate h alpha \( p \) for all alpha , showing that alpha 1 requires a super linear , roughly k 1 alpha samples , noninteger alpha 1 requires a near linear k samples , but , perhaps surprisingly , integer alpha 1 requires only theta \( k 1 1 alpha \) samples furthermore , developing on a recently established connection between polynomial approximation and estimation of additive functions of the form sum x f \( p x \) , we reduce the sample complexity for noninteger values of alpha by a factor of log k compared to the empirical estimator the estimators achieving these bounds are simple and run in time linear in the number of samples our lower bound provides an explicit construction of distributions with different renyi entropies that are hard to distinguish"
cs.LG,"hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene the classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from the corresponding hyperspectral signatures containing information like the signature 's energy or shape in this paper , we describe a technique that applies non homogeneous hidden markov chain \( nhmc \) models to hyperspectral signature classification the basic idea is to use statistical models \( such as nhmc \) to characterize wavelet coefficients which capture the spectrum semantics \( i e , structural information \) at multiple levels experimental results show that the approach based on nhmc models can outperform existing approaches relevant in classification tasks"
cs.LG,"we investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone we propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space in a systematic experiment designed to test how this behavioral pattern exhibits consistency over time , we collected touch data from users interacting with a smart phone using basic navigation maneuvers , i e , up down and left right scrolling we propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen the classifier achieves a median equal error rate of 0 for intra session authentication , 2 3 for inter session authentication and below 4 when the authentication test was carried out one week after the enrollment phase while our experimental findings disqualify this method as a standalone authentication mechanism for long term authentication , it could be implemented as a means to extend screen lock time or as a part of a multi modal biometric authentication system"
cs.LG|stat.ML,"in multiple instance learning , objects are sets \( bags \) of feature vectors \( instances \) rather than individual feature vectors in this paper we address the problem of how these bags can best be represented two standard approaches are to use \( dis \) similarities between bags and prototype bags , or between bags and prototype instances the first approach results in a relatively low dimensional representation determined by the number of training bags , while the second approach results in a relatively high dimensional representation , determined by the total number of instances in the training set in this paper a third , intermediate approach is proposed , which links the two approaches and combines their strengths our classifier is inspired by a random subspace ensemble , and considers subspaces of the dissimilarity space , defined by subsets of instances , as prototypes we provide guidelines for using such an ensemble , and show state of the art performances on a range of multiple instance learning problems"
cs.DM,"we develop a method for measuring homology classes this involves three problems first , we define the size of a homology class , using ideas from relative homology second , we define an optimal basis of a homology group to be the basis whose elements' size have the minimal sum we provide a greedy algorithm to compute the optimal basis and measure classes in it the algorithm runs in o \( beta 4 n 3 log 2 n \) time , where n is the size of the simplicial complex and beta is the betti number of the homology group third , we discuss different ways of localizing homology classes and prove some hardness results"
cs.LG,"a boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications in this paper , we develop a boltzmann machine that is capable of modelling thermodynamic observables for physical systems in thermal equilibrium through unsupervised learning , we train the boltzmann machine on data sets constructed with spin configurations importance sampled from the partition function of an ising hamiltonian at different temperatures using monte carlo \( mc \) methods the trained boltzmann machine is then used to generate spin states , for which we compare thermodynamic observables to those computed by direct mc sampling we demonstrate that the boltzmann machine can faithfully reproduce the observables of the physical system further , we observe that the number of neurons required to obtain accurate results increases as the system is brought close to criticality"
cs.SI,"recent progresses in data driven analysis methods , including network based approaches , are revolutionizing many classical disciplines these techniques can also be applied to food and nutrition , which must be studied to design healthy diets using nutritional information from over 1 , 000 raw foods , we systematically evaluated the nutrient composition of each food in regards to satisfying daily nutritional requirements the nutrient balance of a food was quantified herein as nutritional fitness , using the food 's frequency of occurrence in nutritionally adequate food combinations nutritional fitness offers prioritization of recommendable foods within a global network of foods , in which foods are connected based on the similarities of their nutrient compositions we identified a number of key nutrients , such as choline and alpha linolenic acid , whose levels in foods can critically affect the foods' nutritional fitness analogously , pairs of nutrients can have the same effect in fact , two nutrients can impact the nutritional fitness synergistically , although the individual nutrients alone may not this result , involving the tendency among nutrients to show correlations in their abundances across foods , implies a hidden layer of complexity when exploring for foods whose balance of nutrients within pairs holistically helps meet nutritional requirements interestingly , foods with high nutritional fitness successfully maintain this nutrient balance this effect expands our scope to a diverse repertoire of nutrient nutrient correlations , integrated under a common network framework that yields unexpected yet coherent associations between nutrients our nutrient profiling approach combined with a network based analysis provides a more unbiased , global view of the relationships between foods and nutrients , and can be extended towards nutritional policies , food marketing , and personalized nutrition"
cs.AI|cs.LG|stat.ML,"a stochastic combinatorial semi bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to constraints , and then observes stochastic weights of these items and receives their sum as a payoff in this paper , we close the problem of computationally and sample efficient learning in stochastic combinatorial semi bandits in particular , we analyze a ucb like algorithm for solving the problem , which is known to be computationally efficient and prove o \( k l \( 1 delta \) log n \) and o \( sqrt k l n log n \) upper bounds on its n step regret , where l is the number of ground items , k is the maximum number of chosen items , and delta is the gap between the expected returns of the optimal and best suboptimal solutions the gap dependent bound is tight up to a constant factor and the gap free bound is tight up to a polylogarithmic factor"
cs.SI,"behavioral economics tells us that emotions can profoundly affect individual behavior and decision making does this also apply to societies at large , i e , can societies experience mood states that affect their collective decision making \? by extension is the public mood correlated or even predictive of economic indicators \? here we investigate whether measurements of collective mood states derived from large scale twitter feeds are correlated to the value of the dow jones industrial average \( djia \) over time we analyze the text content of daily twitter feeds by two mood tracking tools , namely opinionfinder that measures positive vs negative mood and google profile of mood states \( gpoms \) that measures mood in terms of 6 dimensions \( calm , alert , sure , vital , kind , and happy \) we cross validate the resulting mood time series by comparing their ability to detect the public 's response to the presidential election and thanksgiving day in 2008 a granger causality analysis and a self organizing fuzzy neural network are then used to investigate the hypothesis that public mood states , as measured by the opinionfinder and gpoms mood time series , are predictive of changes in djia closing values our results indicate that the accuracy of djia predictions can be significantly improved by the inclusion of specific public mood dimensions but not others we find an accuracy of 87 6 in predicting the daily up and down changes in the closing values of the djia and a reduction of the mean average percentage error by more than 6"
cs.AI|stat.ML,"this paper presents a new anytime algorithm for the marginal map problem in graphical models the algorithm is described in detail , its complexity and convergence rate are studied , and relations to previous theoretical results for the problem are discussed it is shown that the algorithm runs in polynomial time if the underlying graph of the model has bounded tree width , and that it provides guarantees to the lower and upper bounds obtained within a fixed amount of computational resources experiments with both real and synthetic generated models highlight its main characteristics and show that it compares favorably against park and darwiche 's systematic search , particularly in the case of problems with many map variables and moderate tree width"
cs.LG|stat.ML,"in many applications , multivariate samples may harbor previously unrecognized heterogeneity at the level of conditional independence or network structure for example , in cancer biology , disease subtypes may differ with respect to subtype specific interplay between molecular components then , both subtype discovery and estimation of subtype specific networks present important and related challenges to enable such analyses , we put forward a mixture model whose components are sparse gaussian graphical models this brings together model based clustering and graphical modeling to permit simultaneous estimation of cluster assignments and cluster specific networks we carry out estimation within an l1 penalized framework , and investigate several specific penalization regimes we present empirical results on simulated data and provide general recommendations for the formulation and use of mixtures of l1 penalized gaussian graphical models"
cs.LG|stat.ML,"automated data driven decision systems are ubiquitous across a wide variety of online services , from online social networking and e commerce to e government these systems rely on complex learning methods and vast amounts of data to optimize the service functionality , satisfaction of the end user and profitability however , there is a growing concern that these automated decisions can lead to user discrimination , even in the absence of intent in this paper , we introduce fairness constraints , a mechanism to ensure fairness in a wide variety of classifiers in a principled manner fairness prevents a classifier from outputting predictions correlated with certain sensitive attributes in the data we then instantiate fairness constraints on three well known classifiers logistic regression , hinge loss and support vector machines \( svm \) and evaluate their performance in a real world dataset with meaningful sensitive human attributes experiments show that fairness constraints allow for an optimal trade off between accuracy and fairness"
cs.AI|cs.IT|stat.ML,"we present a theoretical analysis of maximum a posteriori \( map \) sequence estimation for binary symmetric hidden markov processes we reduce the map estimation to the energy minimization of an appropriately defined ising spin model , and focus on the performance of map as characterized by its accuracy and the number of solutions corresponding to a typical observed sequence it is shown that for a finite range of sufficiently low noise levels , the solution is uniquely related to the observed sequence , while the accuracy degrades linearly with increasing the noise strength for intermediate noise values , the accuracy is nearly noise independent , but now there are exponentially many solutions to the estimation problem , which is reflected in non zero ground state entropy for the ising model finally , for even larger noise intensities , the number of solutions reduces again , but the accuracy is poor it is shown that these regimes are different thermodynamic phases of the ising model that are related to each other via first order phase transitions"
cs.AI,"this paper presents a variation of apriori algorithm that includes the role of domain expert to guide and speed up the overall knowledge discovery task usually , the user is interested in finding relationships between certain attributes instead of the whole dataset moreover , he can help the mining algorithm to select the target database which in turn takes less time to find the desired association rules variants of the standard apriori and interactive apriori algorithms have been run on artificial datasets the results show that incorporating user 's preference in selection of target attribute helps to search the association rules efficiently both in terms of space and time"
cs.LG|stat.ML,"we propose a non parametric anomaly detection algorithm for high dimensional data we first rank scores derived from nearest neighbor graphs on n point nominal training data we then train limited complexity models to imitate these scores based on the max margin learning to rank framework a test point is declared as an anomaly at alpha false alarm level if the predicted score is in the alpha percentile the resulting anomaly detector is shown to be asymptotically optimal in that for any false alarm rate alpha , its decision region converges to the alpha percentile minimum volume level set of the unknown underlying density in addition , we test both the statistical performance and computational efficiency of our algorithm on a number of synthetic and real data experiments our results demonstrate the superiority of our algorithm over existing k nn based anomaly detection algorithms , with significant computational savings"
cs.DM,"this paper is concerned with the fast computation of a relation r on the edge set of connected graphs that plays a decisive role in the recognition of approximate cartesian products , the weak reconstruction of cartesian products , and the recognition of cartesian graph bundles with a triangle free basis a special case of r is the relation delta ast , whose convex closure yields the product relation sigma that induces the prime factor decomposition of connected graphs with respect to the cartesian product for the construction of r so called partial star products are of particular interest several special data structures are used that allow to compute partial star products in constant time these computations are tuned to the recognition of approximate graph products , but also lead to a linear time algorithm for the computation of delta ast for graphs with maximum bounded degree furthermore , we define emph quasi cartesian products as graphs with non trivial delta ast we provide several examples , and show that quasi cartesian products can be recognized in linear time for graphs with bounded maximum degree finally , we note that quasi products can be recognized in sublinear time with a parallelized algorithm"
cs.LG|cs.SI|stat.ML,"social trust prediction addresses the significant problem of exploring interactions among users in social networks naturally , this problem can be formulated in the matrix completion framework , with each entry indicating the trustness or distrustness however , there are two challenges for the social trust problem 1 \) the observed data are with sign \( 1 bit \) measurements 2 \) they are typically sampled non uniformly most of the previous matrix completion methods do not well handle the two issues motivated by the recent progress of max norm , we propose to solve the problem with a 1 bit max norm constrained formulation since max norm is not easy to optimize , we utilize a reformulation of max norm which facilitates an efficient projected gradient decent algorithm we demonstrate the superiority of our formulation on two benchmark datasets"
cs.LG|stat.ML,"we study a variant of the multi armed bandit problem with multiple plays in which the user wishes to sample the m out of k arms with the highest expected rewards , but at any given time can only sample l le m arms when l m , thompson sampling was recently shown to be asymptotically efficient we derive an asymptotic regret lower bound for any uniformly efficient algorithm in our new setting where may be less than m we then establish the asymptotic optimality of thompson sampling for bernoulli rewards , where our proof technique differs from earlier methods even when l m we also prove the asymptotic optimality of an algorithm based on upper confidence bounds , kl cucb , for single parameter exponential families and bounded , finitely supported rewards , a result which is new for all values of l"
cs.AI,"this paper pursues some applications of rough set theory \( rst \) and neural fuzzy model to analysis of lugeon data in the manner , using self organizing map \( som \) as a pre processing the data are scaled and then the dominant rules by rst , are elicited based on these rules variations of permeability in the different levels of shivashan dam , iran has been highlighted then , via using a combining of som and an adaptive neuro fuzzy inference system \( nfis \) another analysis on the data was carried out finally , a brief comparison between the obtained results of rst and som nfis \( briefly sonfis \) has been rendered"
cs.DS|cs.IT|cs.LG,"we study the use of very sparse random projections for compressed sensing \( sparse signal recovery \) when the signal entries can be either positive or negative in our setting , the entries of a gaussian design matrix are randomly sparsified so that only a very small fraction of the entries are nonzero our proposed decoding algorithm is simple and efficient in that the major cost is one linear scan of the coordinates we have developed two estimators \( i \) the em tie estimator , and \( ii \) the em absolute minimum estimator using only the tie estimator , we are able to recover a k sparse signal of length n using 1 551 ek log k delta measurements \( where delta leq 0 05 is the confidence \) using only the absolute minimum estimator , we can detect the support of the signal using ek log n delta measurements for a particular coordinate , the absolute minimum estimator requires fewer measurements \( i e , with a constant e instead of 1 551e \) thus , the two estimators can be combined to form an even more practical decoding framework prior studies have shown that existing one scan \( or roughly one scan \) recovery algorithms using sparse matrices would require substantially more \( e g , one order of magnitude \) measurements than l1 decoding by linear programming , when the nonzero entries of signals can be either negative or positive in this paper , following a known experimental setup , we show that , at the same number of measurements , the recovery accuracies of our proposed method are \( at least \) similar to the standard l1 decoding"
cs.LG|stat.ML,"spectral methods have greatly advanced the estimation of latent variable models , generating a sequence of novel and efficient algorithms with strong theoretical guarantees however , current spectral algorithms are largely restricted to mixtures of discrete or gaussian distributions in this paper , we propose a kernel method for learning multi view latent variable models , allowing each mixture component to be nonparametric the key idea of the method is to embed the joint distribution of a multi view latent variable into a reproducing kernel hilbert space , and then the latent parameters are recovered using a robust tensor power method we establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters thus , our non parametric tensor approach to learning latent variable models enjoys good sample and computational efficiencies moreover , the non parametric tensor power method compares favorably to em algorithm and other existing spectral algorithms in our experiments"
cs.DS,"given a language l that is online recognizable in linear time and space , we construct a linear time and space online recognition algorithm for the language l cdot mathrm pal , where mathrm pal is the language of all nonempty palindromes hence for every fixed positive k , mathrm pal k is online recognizable in linear time and space thus we solve an open problem posed by galil and seiferas in 1978"
cs.LG|stat.ML,"we consider the related tasks of matrix completion and matrix approximation from missing data and propose adaptive sampling procedures for both problems we show that adaptive sampling allows one to eliminate standard incoherence assumptions on the matrix row space that are necessary for passive sampling procedures for exact recovery of a low rank matrix , our algorithm judiciously selects a few columns to observe in full and , with few additional measurements , projects the remaining columns onto their span this algorithm exactly recovers an n times n rank r matrix using o \( nr mu 0 log 2 \( r \) \) observations , where mu 0 is a coherence parameter on the column space of the matrix in addition to completely eliminating any row space assumptions that have pervaded the literature , this algorithm enjoys a better sample complexity than any existing matrix completion algorithm to certify that this improvement is due to adaptive sampling , we establish that row space coherence is necessary for passive sampling algorithms to achieve non trivial sample complexity bounds for constructing a low rank approximation to a high rank input matrix , we propose a simple algorithm that thresholds the singular values of a zero filled version of the input matrix the algorithm computes an approximation that is nearly as good as the best rank r approximation using o \( nr mu log 2 \( n \) \) samples , where mu is a slightly different coherence parameter on the matrix columns again we eliminate assumptions on the row space"
cs.AI,"we construe smart meeting cinematography with a focus on professional situations such as meetings and seminars , possibly conducted in a distributed manner across socio spatially separated groups the basic objective in smart meeting cinematography is to interpret professional interactions involving people , and automatically produce dynamic recordings of discussions , debates , presentations etc in the presence of multiple communication modalities typical modalities include gestures \( e g , raising one 's hand for a question , applause \) , voice and interruption , electronic apparatus \( e g , pressing a button \) , movement \( e g , standing up , moving around \) etc rotunde , an instance of smart meeting cinematography concept , aims to \( a \) develop functionality driven benchmarks with respect to the interpretation and control capabilities of human cinematographers , real time video editors , surveillance personnel , and typical human performance in everyday situations \( b \) develop general tools for the commonsense cognitive interpretation of dynamic scenes from the viewpoint of visuo spatial cognition centred perceptual narrativisation particular emphasis is placed on declarative representations and interfacing mechanisms that seamlessly integrate within large scale cognitive \( interaction \) systems and companion technologies consisting of diverse ai sub components for instance , the envisaged tools would provide general capabilities for high level commonsense reasoning about space , events , actions , change , and interaction"
cs.DS|cs.LG,"the method of stable random projections is popular for efficiently computing the lp distances in high dimension \( where 0 p 2 \) , using small space because it adopts nonadaptive linear projections , this method is naturally suitable when the data are collected in a dynamic streaming fashion \( i e , turnstile data streams \) in this paper , we propose to use only the signs of the projected data and analyze the probability of collision \( i e , when the two signs differ \) we derive a bound of the collision probability which is exact when p 2 and becomes less sharp when p moves away from 2 interestingly , when p 1 \( i e , cauchy random projections \) , we show that the probability of collision can be accurately approximated as functions of the chi square similarity for example , when the \( un normalized \) data are binary , the maximum approximation error of the collision probability is smaller than 0 0192 in text and vision applications , the chi square similarity is a popular measure for nonnegative data when the features are generated from histograms our experiments confirm that the proposed method is promising for large scale learning applications"
cs.DS,"when the search algorithm quickselect compares keys during its execution in order to find a key of target rank , it must operate on the keys' representations or internal structures , which were ignored by the previous studies that quantified the execution cost for the algorithm in terms of the number of required key comparisons in this paper , we analyze running costs for the algorithm that take into account not only the number of key comparisons but also the cost of each key comparison we suppose that keys are represented as sequences of symbols generated by various probabilistic sources and that quickselect operates on individual symbols in order to find the target key we identify limiting distributions for the costs and derive integral and series expressions for the expectations of the limiting distributions these expressions are used to recapture previously obtained results on the number of key comparisons required by the algorithm"
cs.DS|cs.IT,"higher order fourier analysis , developed over prime fields , has been recently used in different areas of computer science , including list decoding , algorithmic decomposition and testing we extend the tools of higher order fourier analysis to analyze functions over general fields using these new tools , we revisit the results in the above areas for any fixed finite field mathbb k , we show that the list decoding radius of the generalized reed muller code over mathbb k equals the minimum distance of the code previously , this had been proved over prime fields bl14 and for the case when mathbb k 1 divides the order of the code gkz08 for any fixed finite field mathbb k , we give a polynomial time algorithm to decide whether a given polynomial p mathbb k n to mathbb k can be decomposed as a particular composition of lesser degree polynomials this had been previously established over prime fields bha14 , bht15 for any fixed finite field mathbb k , we prove that all locally characterized affine invariant properties of functions f mathbb k n to mathbb k are testable with one sided error the same result was known when mathbb k is prime bfhhl13 and when the property is linear ks08 moreover , we show that for any fixed finite field mathbb f , an affine invariant property of functions f mathbb k n to mathbb f , where mathbb k is a growing field extension over mathbb f , is testable if it is locally characterized by constraints of bounded weight"
cs.AI,"self organizing map \( som \) is a neural network model which is used to obtain a topology preserving mapping from the \( usually high dimensional \) input feature space to an output map space of fewer dimensions \( usually two or three in order to facilitate visualization \) neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space despite the fact that growing variants of som overcome the fixed structure limitation they increase computational cost and also do not allow the removal of a neuron after its introduction in this paper , a variant of som is proposed called amsom \( adaptive moving self organizing map \) that on the one hand creates a more flexible structure where neuron positions are dynamically altered during training and on the other hand tackles the drawback of having a predefined grid by allowing neuron addition and or removal during training experiments using multiple literature datasets show that the proposed method improves training performance of som , leads to a better visualization of the input dataset and provides a framework for determining the optimal number and structure of neurons"
cs.AI|cs.DS,"the fastest known exact algorithms for scorebased structure discovery in bayesian networks on n nodes run in time and space 2nno \( 1 \) the usage of these algorithms is limited to networks on at most around 25 nodes mainly due to the space requirement here , we study space time tradeoffs for finding an optimal network structure when little space is available , we apply the gurevich shelah recurrence originally proposed for the hamiltonian path problem and obtain time 22n sno \( 1 \) in space 2sno \( 1 \) for any s n 2 , n 4 , n 8 , we assume the indegree of each node is bounded by a constant for the more practical setting with moderate amounts of space , we present a novel scheme it yields running time 2n \( 3 2 \) pno \( 1 \) in space 2n \( 3 4 \) pno \( 1 \) for any p 0 , 1 , , n 2 these bounds hold as long as the indegrees are at most 0 238n furthermore , the latter scheme allows easy and efficient parallelization beyond previous algorithms we also explore empirically the potential of the presented techniques"
cs.DM,"let p be a set of points in general position in the plane join all pairs of points in p with straight line segments the number of segment crossings in such a drawing , denoted by crg \( p \) , is the emph rectilinear crossing number of p a emph halving line of p is a line passing though two points of p that divides the rest of the points of p in \( almost \) half the number of halving lines of p is denoted by h \( p \) similarly , a k emph edge , 0 leq k leq n 2 1 , is a line passing through two points of p and leaving exactly k points of p on one side the number of \( le k \) edges of p is denoted by e leq k \( p \) let rcr \( n \) , h \( n \) , and e leq k \( n \) denote the minimum of crg \( p \) , the maximum of h \( p \) , and the minimum of e leq k \( p \) , respectively , over all sets p of n points in general position in the plane we show that the previously best known lower bound on e leq k \( n \) is tight for k lceil \( 4n 2 \) 9 rceil and improve it for all k geq lceil \( 4n 2 \) 9 rceil this in turn improves the lower bound on rcr \( n \) from 0 37968 binom n 4 theta \( n 3 \) to 277 729 binom n 4 theta \( n 3 \) geq 0 37997 binom n 4 theta \( n 3 \) we also give the exact values of rcr \( n \) and h \( n \) for all n leq27 exact values were known only for n leq18 and odd n leq21 for the crossing number , and for n leq14 and odd n leq21 for halving lines"
cs.LG|stat.ML,"training deep belief networks \( dbns \) requires optimizing a non convex function with an extremely large number of parameters naturally , existing gradient descent \( gd \) based methods are prone to arbitrarily poor local minima in this paper , we rigorously show that such local minima can be avoided \( upto an approximation error \) by using the dropout technique , a widely used heuristic in this domain in particular , we show that by randomly dropping a few nodes of a one hidden layer neural network , the training objective function , up to a certain approximation error , decreases by a multiplicative factor on the flip side , we show that for training convex empirical risk minimizers \( erm \) , dropout in fact acts as a stabilizer or regularizer that is , a simple dropout based gd method for convex erms is stable in the face of arbitrary changes to any one of the training points using the above assertion , we show that dropout provides fast rates for generalization error in learning \( convex \) generalized linear models \( glm \) moreover , using the above mentioned stability properties of dropout , we design dropout based differentially private algorithms for solving erms the learned glm thus , preserves privacy of each of the individual training points while providing accurate predictions for new test points finally , we empirically validate our stability assertions for dropout in the context of convex erms and show that surprisingly , dropout significantly outperforms \( in terms of prediction accuracy \) the l2 regularization based methods for several benchmark datasets"
cs.LG|stat.ML,"we study the problem of large scale , multi label visual recognition with a large number of possible classes we propose a method for augmenting a trained neural network classifier with auxiliary capacity in a manner designed to significantly improve upon an already well performing model , while minimally impacting its computational footprint using the predictions of the network itself as a descriptor for assessing visual similarity , we define a partitioning of the label space into groups of visually similar entities we then augment the network with auxilliary hidden layer pathways with connectivity only to these groups of label units we report a significant improvement in mean average precision on a large scale object recognition task with the augmented model , while increasing the number of multiply adds by less than 3"
cs.DM,"the existence of a partition of the common set of the vertices of two forests into two subsets , when difference of their capacities in the neighborhood of each vertex of each forest not greater than 2 is proved , and an example , which shows that improvement of the specified constant is impossible is brought"
cs.DM,"we show that deciding whether a given set of circles can be packed into a rectangle , an equilateral triangle , or a unit square are np hard problems , settling the complexity of these natural packing problems on the positive side , we show that any set of circles of total area 1 can be packed into a square of size 4 sqrt pi 2 2567 these results are motivated by problems arising in the context of origami design"
cs.LG,"how to build a machine learning method that can continuously gain structured visual knowledge by learning structured facts \? our goal in this paper is to address this question by proposing a problem setting , where training data comes as structured facts in images with different types including \( 1 \) objects \( e g , boy \) , \( 2 \) attributes \( e g , boy , tall \) , \( 3 \) actions \( e g , boy , playing \) , \( 4 \) interactions \( e g , boy , riding , a horse \) each structured fact has a semantic language view \( e g , boy , playing \) and a visual view \( an image with this fact \) a human is able to efficiently gain visual knowledge by learning facts in a never ending process , and as we believe in a structured way \( e g , understanding playing is the action part of boy , playing , and hence can generalize to recognize girl , playing if just learn girl additionally \) inspired by human visual perception , we propose a model that is \( 1 \) able to learn a representation , we name as wild card , which covers different types of structured facts , \( 2 \) could flexibly get fed with structured fact language visual view pairs in a never ending way to gain more structured knowledge , \( 3 \) could generalize to unseen facts , and \( 4 \) allows retrieval of both the fact language view given the visual view \( i e , image \) and vice versa we also propose a novel method to generate hundreds of thousands of structured fact pairs from image caption data , which are necessary to train our model and can be useful for other applications"
cs.DS|cs.IT,"an important problem in wireless sensor networks is to find the minimal number of randomly deployed sensors making a network connected with a given probability in practice sensors are often deployed one by one along a trajectory of a vehicle , so it is natural to assume that arbitrary probability density functions of distances between successive sensors in a segment are given the paper computes the probability of connectivity and coverage of 1 dimensional networks and gives estimates for a minimal number of sensors for important distributions"
cs.AI,"in cooperative multi agent sequential decision making under uncertainty , agents must coordinate to find an optimal joint policy that maximises joint value typical algorithms exploit additive structure in the value function , but in the fully observable multi agent mdp setting \( mmdp \) such structure is not present we propose a new optimal solver for transition independent mmdps , in which agents can only affect their own state but their reward depends on joint transitions we represent these dependencies compactly in conditional return graphs \( crgs \) using crgs the value of a joint policy and the bounds on partially specified joint policies can be efficiently computed we propose core , a novel branch and bound policy search algorithm building on crgs core typically requires less runtime than the available alternatives and finds solutions to problems previously unsolvable"
cs.AI|cs.DS,"this paper introduces a multi period inspector scheduling problem \( mpisp \) , which is a new variant of the multi trip vehicle routing problem with time windows \( vrptw \) in the mpisp , each inspector is scheduled to perform a route in a given multi period planning horizon at the end of each period , each inspector is not required to return to the depot but has to stay at one of the vertices for recuperation if the remaining time of the current period is insufficient for an inspector to travel from his her current vertex a to a certain vertex b , he she can choose either waiting at vertex a until the start of the next period or traveling to a vertex c that is closer to vertex b therefore , the shortest transit time between any vertex pair is affected by the length of the period and the departure time we first describe an approach of computing the shortest transit time between any pair of vertices with an arbitrary departure time to solve the mpisp , we then propose several local search operators adapted from classical operators for the vrptw and integrate them into a tabu search framework in addition , we present a constrained knapsack model that is able to produce an upper bound for the problem finally , we evaluate the effectiveness of our algorithm with extensive experiments based on a set of test instances our computational results indicate that our approach generates high quality solutions"
cs.LG,"we investigate the use of deep neural networks for the novel task of class generic object detection we show that neural networks originally designed for image recognition can be trained to detect objects within images , regardless of their class , including objects for which no bounding box labels have been provided in addition , we show that bounding box labels yield a 1 performance increase on the imagenet recognition challenge"
cs.DS,"we study the random m ary search tree model \( where m stands for the number of branches of a search tree \) , an important problem for data storage in computer science , using a variety of statistical physics techniques that allow us to obtain exact asymptotic results in particular , we show that the probability distributions of extreme observables associated with a random search tree such as the height and the balanced height of a tree have a traveling front structure in addition , the variance of the number of nodes needed to store a data string of a given size n is shown to undergo a striking phase transition at a critical value of the branching ratio m c 26 we identify the mechanism of this phase transition , show that it is generic and occurs in various other problems as well new results are obtained when each element of the data string is a d dimensional vector we show that this problem also has a phase transition at a critical dimension , d c pi sin 1 \( 1 sqrt 8 \) 8 69363"
cs.DM|cs.DS,"we propose a modification of a key component in the doomsday algorithm for calculating the day of the week of any calendar date in particular , we propose to replace the calculation of the required term lfloor frac x 12 rfloor x bmod 12 lfloor frac x bmod 12 4 rfloor with frac x 11 \( x bmod 2 \) 2 11 \( frac x 11 \( x bmod 2 \) 2 bmod 2 \) bmod 7 for a 2 digit input year x although our expression looks daunting and complicated , we will explain why it is actually easy to calculate mentally"
cs.AI,"applied ontology is a relatively new field which aims to apply theories and methods from diverse disciplines such as philosophy , cognitive science , linguistics and formal logics to perform or improve domain specific tasks to support the development of effective research methodologies for applied ontology , we critically discuss the question how its research results should be evaluated we propose that results in applied ontology must be evaluated within their domain of application , based on some ontology based task within the domain , and discuss quantitative measures which would facilitate the objective evaluation and comparison of research results in applied ontology"
cs.IT|stat.ML,"sparse linear \( or generalized linear \) models combine a standard likelihood function with a sparse prior on the unknown coefficients these priors can conveniently be expressed as a maximization over zero mean gaussians with different variance hyperparameters standard map estimation \( type i \) involves maximizing over both the hyperparameters and coefficients , while an empirical bayesian alternative \( type ii \) first marginalizes the coefficients and then maximizes over the hyperparameters , leading to a tractable posterior approximation the underlying cost functions can be related via a dual space framework from wipf et al \( 2011 \) , which allows both the type i or type ii objectives to be expressed in either coefficient or hyperparmeter space this perspective is useful because some analyses or extensions are more conducive to development in one space or the other herein we consider the estimation of a trade off parameter balancing sparsity and data fit as this parameter is effectively a variance , natural estimators exist by assessing the problem in hyperparameter \( variance \) space , transitioning natural ideas from type ii to solve what is much less intuitive for type i in contrast , for analyses of update rules and sparsity properties of local and global solutions , as well as extensions to more general likelihood models , we can leverage coefficient space techniques developed for type i and apply them to type ii for example , this allows us to prove that type ii inspired techniques can be successful recovering sparse coefficients when unfavorable restricted isometry properties \( rip \) lead to failure of popular l1 reconstructions it also facilitates the analysis of type ii when non gaussian likelihood models lead to intractable integrations"
cs.DS,"distributed aggregation allows the derivation of a given global aggregate property from many individual local values in nodes of an interconnected network system simple aggregates such as minima maxima , counts , sums and averages have been thoroughly studied in the past and are important tools for distributed algorithms and network coordination nonetheless , this kind of aggregates may not be comprehensive enough to characterize biased data distributions or when in presence of outliers , making the case for richer estimates of the values on the network this work presents spectra , a distributed algorithm for the estimation of distribution functions over large scale networks the estimate is available at all nodes and the technique depicts important properties , namely robust when exposed to high levels of message loss , fast convergence speed and fine precision in the estimate it can also dynamically cope with changes of the sampled local property , not requiring algorithm restarts , and is highly resilient to node churn the proposed approach is experimentally evaluated and contrasted to a competing state of the art distribution aggregation technique"
cs.AI|cs.LG,"bayesian optimisation has been successfully applied to a variety of reinforcement learning problems however , the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables state features that are randomly determined by the environment in a physical setting but are controllable in a simulator this paper considers the problem of finding an optimal policy while taking into account the impact of environment variables we present the alternating optimisation and quadrature algorithm which uses bayesian optimisation and bayesian quadrature to address such settings and is robust to the presence of significant rare events , which may not be observable under random sampling but have a considerable impact on determining the optimal policy our experimental results show that our approach learns better and faster than existing methods"
cs.DM,"the problem of ranking can be described as follows we have a set of combinatorial objects s , such as , say , the k subsets of n things , and we can imagine that they have been arranged in some list , say lexicographically , and we want to have a fast method for obtaining the rank of a given object in the list this problem is widely known in combinatorial analysis , computer science and information theory ranking is closely connected with the hashing problem , especially with perfect hashing and with generating of random combinatorial objects in information theory the ranking problem is closely connected with so called enumerative encoding , which may be described as follows there is a set of words s and an enumerative code has to one to one encode every s in s by a binary word code \( s \) the length of the code \( s \) must be the same for all s in s clearly , code \( s \) geq log s \( here and below log x log 2 x \) \) the suggested method allows the exponential growth of the speed of encoding and decoding for all combinatorial problems of enumeration which are considered , including the enumeration of permutations , compositions and others"
cs.DS,"link weights are the principal parameters of shortest path routing protocols , the most commonly used protocols for ip networks the problem of optimally setting link weights for unique shortest path routing is addressed due to the complexity of the constraints involved , there exist challenges to formulate the problem properly , so that a solution algorithm may be developed which could prove to be more efficient than those already in existence in this paper , a novel complete formulation with a polynomial number of constraints is first introduced and then mathematically proved to be correct it is further illustrated that the formulation has advantages over a prior one in terms of both constraint structure and model size for a proposed decomposition method to solve the problem"
cs.DS,"the kronecker product is a key algorithm and is ubiquitous across the physical , biological , and computation social sciences thus considerations of optimal implementation are important the need to have high performance and computational reproducibility is paramount moreover , due to the need to compose multiple kronecker products , issues related to data structures , layout and indexing algebra require a new look at an old problem this paper discusses the outer product tensor product and a special case of the tensor product the kronecker product , along with optimal implementation when composed , and mapped to complex processor memory hierarchies we discuss how the use of ``a mathematics of arrays \( moa \) , and the psi calculus , \( a calculus of indexing with shapes \) , provides optimal , verifiable , reproducible , scalable , and portable implementations of both hardware and software"
cs.LG,"the prevention of dangerous chemical accidents is a primary problem of industrial manufacturing in the accidents of dangerous chemicals , the oil gas explosion plays an important role the essential task of the explosion prevention is to estimate the better explosion limit of a given oil gas in this paper , support vector machines \( svm \) and logistic regression \( lr \) are used to predict the explosion of oil gas lr can get the explicit probability formula of explosion , and the explosive range of the concentrations of oil gas according to the concentration of oxygen meanwhile , svm gives higher accuracy of prediction furthermore , considering the practical requirements , the effects of penalty parameter on the distribution of two types of errors are discussed"
cs.DS,"let p subset mathbb r 2 be a set of n points in this paper we show two new algorithms , one to compute the number of triangulations of p , and one to compute the number of pseudo triangulations of p we show that our algorithms run in time o \( t \( p \) \) and o \( pt \( p \) \) respectively , where t \( p \) and pt \( p \) are the largest number of triangulation paths \( t paths \) and pseudo triangulations paths \( pt paths \) , respectively , that the algorithms encounter during their execution moreover , we show that t \( p \) o \( 9 n \) , which is the first non trivial bound on t \( p \) to be known while there already are algorithms that count triangulations in o left \( 2 n right \) , and o left \( 3 1414 n right \) , there are sets of points where the number of t paths is o \( 2 n \) in such cases the algorithm herein presented could potentially be faster furthermore , it is not clear whether the already known algorithms can be modified to count pseudo triangulations so that their running times remain o \( c n \) , for some small constant c in mathbb r therefore , for counting pseudo triangulations \( and possibly other similar structures \) our approach seems better"
cs.SI,"all real networks are different , but many have some structural properties in common there seems to be no consensus on what the most common properties are , but scale free degree distributions , strong clustering , and community structure are frequently mentioned without question surprisingly , there exists no simple generative mechanism explaining all the three properties at once in growing networks here we show how latent network geometry coupled with preferential attachment of nodes to this geometry fills this gap we call this mechanism geometric preferential attachment \( gpa \) , and validate it against the internet gpa gives rise to soft communities that provide a different perspective on the community structure in networks the connections between gpa and cosmological models , including inflation , are also discussed"
cs.LG,"this paper exclusively reports the efficiency of ais inmaca ais inmaca has created good impact on solving major problems in bioinformatics like protein region identification and promoter region prediction with less time \( pokkuluri kiran sree , 2014 \) this ais inmaca is now came with several variations \( pokkuluri kiran sree , 2014 \) towards projecting it as a tool in bioinformatics for solving many problems in bioinformatics so this paper will be very much useful for so many researchers who are working in the domain of bioinformatics with cellular automata"
cs.IT|cs.LG|stat.ML,"a robust algorithm for non negative matrix factorization \( nmf \) is presented in this paper with the purpose of dealing with large scale data , where the separability assumption is satisfied in particular , we modify the linear programming \( lp \) algorithm of 9 by introducing a reduced set of constraints for exact nmf in contrast to the previous approaches , the proposed algorithm does not require the knowledge of factorization rank \( extreme rays 3 or topics 7 \) furthermore , motivated by a similar problem arising in the context of metabolic network analysis 13 , we consider an entirely different regime where the number of extreme rays or topics can be much larger than the dimension of the data vectors the performance of the algorithm for different synthetic data sets are provided"
stat.ML,"the multilingual nature of the world makes translation a crucial requirement today parallel dictionaries constructed by humans are a widely available resource , but they are limited and do not provide enough coverage for good quality translation purposes , due to out of vocabulary words and neologisms this motivates the use of statistical translation systems , which are unfortunately dependent on the quantity and quality of training data such systems have a very limited availability especially for some languages and very narrow text domains in this research we present our improvements to current comparable corpora mining methodologies by re implementation of the comparison algorithms \( using needleman wunch algorithm \) , introduction of a tuning script and computation time improvement by gpu acceleration experiments are carried out on bilingual data extracted from the wikipedia , on various domains for the wikipedia itself , additional cross lingual comparison heuristics were introduced the modifications made a positive impact on the quality and quantity of mined data and on the translation quality"
cs.LG|stat.ML,"we consider two variables that are related to each other by an invertible function while it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause , we presently show that even in the deterministic \( noise free \) case , there are asymmetries that can be exploited for causal inference our method is based on the idea that if the function and the probability density of the cause are chosen independently , then the distribution of the effect will , in a certain sense , depend on the function we provide a theoretical analysis of this method , showing that it also works in the low noise regime , and link it to information geometry we report strong empirical results on various real world data sets from different domains"
cs.IT|cs.LG,"an extension of the traditional two armed bandit problem is considered , in which the decision maker has access to some side information before deciding which arm to pull at each time t , before making a selection , the decision maker is able to observe a random variable x t that provides some information on the rewards to be obtained the focus is on finding uniformly good rules \( that minimize the growth rate of the inferior sampling time \) and on quantifying how much the additional information helps various settings are considered and for each setting , lower bounds on the achievable inferior sampling time are developed and asymptotically optimal adaptive schemes achieving these lower bounds are constructed"
cs.AI,"knowledge representation and reasoning capacities are vital to cognitive robotics because they provide higher level cognitive functions for reasoning about actions , environments , goals , perception , etc although answer set programming \( asp \) is well suited for modelling such functions , there was so far no seamless way to use asp in a robotic environment we address this shortcoming and show how a recently developed reactive asp system can be harnessed to provide appropriate reasoning capacities within a robotic system to be more precise , we furnish a package integrating the reactive asp solver oclingo with the popular open source robotic middleware ros the resulting system , rosoclingo , provides a generic way by which an asp program can be used to control the behaviour of a robot and to respond to the results of the robot 's actions"
cs.DS,"this paper presents an asynchronous distributed algorithm to manage multiple trees for peer to peer streaming in a flow level model it is assumed that videos are cut into substreams , with or without source coding , to be distributed to all nodes the algorithm guarantees that each node receives sufficiently many substreams within delay logarithmic in the number of peers the algorithm works by constantly updating the topology so that each substream is distributed through trees to as many nodes as possible without interference competition among trees for limited upload capacity is managed so that both coverage and balance are achieved the algorithm is robust in that it efficiently eliminates cycles and maintains tree structures in a distributed way the algorithm favors nodes with higher degree , so it not only works for live streaming and video on demand , but also in the case a few nodes with large degree act as servers and other nodes act as clients a proof of convergence of the algorithm is given assuming instantaneous update of depth information , and for the case of a single tree it is shown that the convergence time is stochastically tightly bounded by a small constant times the log of the number of nodes these theoretical results are complemented by simulations showing that the algorithm works well even when most assumptions for the theoretical tractability do not hold"
cs.AI,"simple type theory is suited as framework for combining classical and non classical logics this claim is based on the observation that various prominent logics , including \( quantified \) multimodal logics and intuitionistic logics , can be elegantly embedded in simple type theory furthermore , simple type theory is sufficiently expressive to model combinations of embedded logics and it has a well understood semantics off the shelf reasoning systems for simple type theory exist that can be uniformly employed for reasoning within and about combinations of logics"
cs.LG|stat.ML,"massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources to train these architectures at scale , we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets we investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results \( 1 \) massively multitask networks obtain predictive accuracies significantly better than single task methods , \( 2 \) the predictive power of multitask networks improves as additional tasks and data are added , \( 3 \) the total amount of data and the total number of tasks both contribute significantly to multitask improvement , and \( 4 \) multitask networks afford limited transferability to tasks not in the training set our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process"
cs.LG,"data mining is a way of extracting data or uncovering hidden patterns of information from databases so , there is a need to prevent the inference rules from being disclosed such that the more secure data sets cannot be identified from non sensitive attributes this can be done through removing or adding certain item sets in the transactions sanitization the purpose is to hide the inference rules , so that the user may not be able to discover any valuable information from other non sensitive data and any organisation can release all samples of their data without the fear of knowledge discovery in databases which can be achieved by investigating frequently occurring item sets , rules that can be mined from them with the objective of hiding them another way is to release only limited samples in the new database so that there is no information loss and it also satisfies the legitimate needs of the users the major problem is uncovering hidden patterns , which causes a threat to the database security sensitive data are inferred from non sensitive data based on the semantics of the application the user has , commonly known as the inference problem two fundamental approaches to protect sensitive rules from disclosure are that , preventing rules from being generated by hiding the frequent sets of data items and reducing the importance of the rules by setting their confidence below a user specified threshold"
cs.AI|cs.LG|stat.ML,"human activity recognition \( har \) in ubiquitous computing is beginning to adopt deep learning to substitute for well established analysis techniques that rely on hand crafted feature extraction and classification techniques from these isolated applications of custom deep architectures it is , however , difficult to gain an overview of their suitability for problems ranging from the recognition of manipulative gestures to the segmentation and identification of physical activities like running or ascending stairs in this paper we rigorously explore deep , convolutional , and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors we describe how to train recurrent approaches in this setting , introduce a novel regularisation approach , and illustrate how they outperform the state of the art on a large benchmark dataset across thousands of recognition experiments with randomly sampled model configurations we investigate the suitability of each model for different tasks in har , explore the impact of hyperparameters using the fanova framework , and provide guidelines for the practitioner who wants to apply deep learning in their problem setting"
cs.LG,"non negative matrix factorization \( nmf \) approximates a given matrix as a product of two non negative matrices multiplicative algorithms deliver reliable results , but they show slow convergence for high dimensional data and may be stuck away from local minima gradient descent methods have better behavior , but only apply to smooth losses such as the least squares loss in this article , we propose a first order primal dual algorithm for non negative decomposition problems \( where one factor is fixed \) with the kl divergence , based on the chambolle pock algorithm all required computations may be obtained in closed form and we provide an efficient heuristic way to select step sizes by using alternating optimization , our algorithm readily extends to nmf and , on synthetic examples , face recognition or music source separation datasets , it is either faster than existing algorithms , or leads to improved local optima , or both"
cs.AI|cs.LG|stat.ML,"probabilistic techniques are central to data analysis , but different approaches can be difficult to apply , combine , and compare this paper introduces composable generative population models \( cgpms \) , a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques examples include hierarchical bayesian models , multivariate kernel methods , discriminative machine learning , clustering algorithms , dimensionality reduction , and arbitrary probabilistic programs we also demonstrate the integration of cgpms into bayesdb , a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language the practical value is illustrated in two ways first , cgpms are used in an analysis that identifies satellite data records which probably violate kepler 's third law , by composing causal probabilistic programs with non parametric bayes in under 50 lines of probabilistic code second , for several representative data analysis tasks , we report on lines of code and accuracy measurements of various cgpms , plus comparisons with standard baseline solutions from python and matlab libraries"
cs.IT|stat.ML,"we propose a modified chi beta divergence , give some of its properties , and show that this leads to the definition of a generalized fisher information we give generalized cram 'er rao inequalities , involving this fisher information , an extension of the fisher information matrix , and arbitrary norms and power of the estimation error in the case of a location parameter , we obtain new characterizations of the generalized q gaussians , for instance as the distribution with a given moment that minimizes the generalized fisher information finally we indicate how the generalized fisher information can lead to new uncertainty relations"
cs.DM,"this paper describes a new exact algorithm for the equitable coloring problem , a coloring problem where the sizes of two arbitrary color classes differ in at most one unit based on the well known dsatur algorithm for the classic coloring problem , a new pruning criterion arising from equity constraints is proposed and analyzed the good performance of the algorithm is shown through computational experiments over random and benchmark instances"
cs.AI,"while game theory is widely used to model strategic interactions , a natural question is where do the game representations come from \? one answer is to learn the representations from data if one wants to learn both the payoffs and the players' strategies , a naive approach is to learn them both directly from the data this approach ignores the fact the players might be playing reasonably good strategies , so there is a connection between the strategies and the data the main contribution of this paper is to make this connection while learning we formulate the learning problem as a weighted constraint satisfaction problem , including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the payoffs we use quantal response equilibrium as our notion of rationality for quantifying the latter fit our results show that incorporating rationality constraints can improve learning when the amount of data is limited"
cs.DM,"in this paper we study properties of intersection graphs of k bend paths in the rectangular grid a k bend path is a path with at most k 90 degree turns the class of graphs representable by intersections of k bend paths is denoted by b k vpg we show here that for every fixed k , b k vpg is a proper subset of b k 1 vpg and that recognition of graphs from b k vpg is np complete even when the input graph is given by a b k 1 vpg representation we also show that the class b k vpg \( for k 0 \) is in no inclusion relation with the class of intersection graphs of straight line segments in the plane"
cs.SI,"point of interest \( poi \) recommendation is an important application in location based social networks \( lbsns \) , which learns the user preference and mobility pattern from check in sequences to recommend pois however , previous poi recommendation systems model check in sequences based on either tensor factorization or markov chain model , which cannot capture contextual check in information in sequences the contextual check in information implies the complementary functions among pois that compose an individual 's daily check in sequence in this paper , we exploit the embedding learning technique to capture the contextual check in information and further propose the textit textbf se quential textit textbf e mbedding textit textbf r ank \( textit seer \) model for poi recommendation in particular , the textit seer model learns user preferences via a pairwise ranking model under the sequential constraint modeled by the poi embedding learning method furthermore , we incorporate two important factors , i e , temporal influence and geographical influence , into the textit seer model to enhance the poi recommendation system due to the temporal variance of sequences on different days , we propose a temporal poi embedding model and incorporate the temporal poi representations into a temporal preference ranking model to establish the textit t emporal textit seer \( textit t seer \) model in addition , we incorporate the geographical influence into the textit t seer model and develop the textit textbf geo temporal textit textbf seer \( textit gt seer \) model"
cs.DM|cs.DS,"we give a simple polynomial time algorithm to exactly count the number of euler tours \( ets \) of any eulerian generalized series parallel graph , and show how to adapt this algorithm to exactly sample a random et of the given generalized series parallel graph note that the class of generalized seriesparallel graphs includes all outerplanar graphs we can perform the counting in time o \( m delta 3 \) , where delta is the maximum degree of the graph with m edges we use o \( m delta 2 log delta \) bits to store intermediate values during our computations to date , these are the first known polynomial time algorithms to count or sample ets of any class of graphs there are no other known polynomial time algorithms to even approximately count or sample ets of any other class of graphs the problem of counting ets is known to be p complete for general graphs \( brightwell and winkler , 2005 3 \) and also for planar graphs \( creed , 2009 4 \)"
cs.LG|stat.ML,"this article studies the achievable guarantees on the error rates of certain learning algorithms , with particular focus on refining logarithmic factors many of the results are based on a general technique for obtaining bounds on the error rates of sample consistent classifiers with monotonic error regions , in the realizable case we prove bounds of this type expressed in terms of either the vc dimension or the sample compression size this general technique also enables us to derive several new bounds on the error rates of general sample consistent learning algorithms , as well as refined bounds on the label complexity of the cal active learning algorithm additionally , we establish a simple necessary and sufficient condition for the existence of a distribution free bound on the error rates of all sample consistent learning rules , converging at a rate inversely proportional to the sample size we also study learning in the presence of classification noise , deriving a new excess error rate guarantee for general vc classes under tsybakov 's noise condition , and establishing a simple and general necessary and sufficient condition for the minimax excess risk under bounded noise to converge at a rate inversely proportional to the sample size"
cs.IT|stat.ML,"adaptive sampling results in dramatic improvements in the recovery of sparse signals in white gaussian noise a sequential adaptive sampling and refinement procedure called distilled sensing \( ds \) is proposed and analyzed ds is a form of multi stage experimental design and testing because of the adaptive nature of the data collection , ds can detect and localize far weaker signals than possible from non adaptive measurements in particular , reliable detection and localization \( support estimation \) using non adaptive samples is possible only if the signal amplitudes grow logarithmically with the problem dimension here it is shown that using adaptive sampling , reliable detection is possible provided the amplitude exceeds a constant , and localization is possible when the amplitude exceeds any arbitrarily slowly growing function of the dimension"
cs.DS,"we present a new locality sensitive hashing \( lsh \) algorithm for c approximate nearest neighbor search in ell p with 1 p 2 for a database of n points in ell p , we achieve o \( dn rho \) query time and o \( dn n 1 rho \) space , where rho le o \( \( ln c \) 2 c p \) this improves upon the previous best upper bound rho le 1 c by datar et al \( socg 2004 \) , and is close to the lower bound rho ge 1 c p by o'donnell , wu and zhou \( itcs 2011 \) the proof is a simple generalization of the lsh scheme for ell 2 by andoni and indyk \( focs 2006 \)"
cs.DM,"an emph s graph is a graph with two kinds of edges emph subdivisible edges and emph real edges a emph realisation of an s graph b is any graph obtained by subdividing subdivisible edges of b into paths of arbitrary length \( at least one \) given an s graph b , we study the decision problem pi b whose instance is a graph g and question is does g contain a realisation of b as an induced subgraph \? for several b 's , the complexity of pi b is known and here we give the complexity for several more our np completeness proofs for pi b 's rely on the np completeness proof of the following problem let cal s be a set of graphs and d be an integer let gamma cal s d be the problem whose instance is \( g , x , y \) where g is a graph whose maximum degree is at most d , with no induced subgraph in cal s and x , y in v \( g \) are two non adjacent vertices of degree 2 the question is does g contain an induced cycle passing through x , y \? among several results , we prove that gamma 3 emptyset is np complete we give a simple criterion on a connected graph h to decide whether gamma infty h is polynomial or np complete the polynomial cases rely on the algorithm three in a tree , due to chudnovsky and seymour"
cs.DS|stat.ML,"we propose a primitive called pjoin , for predictive join , which combines and extends the operations join and link , which valiant proposed as the basis of a computational theory of cortex we show that pjoin can be implemented in valiant 's model we also show that , using pjoin , certain reasonably complex learning and pattern matching tasks can be performed , in a way that involves phenomena which have been observed in cognition and the brain , namely memory based prediction and downward traffic in the cortical hierarchy"
cs.AI|cs.LG,"we propose a framework for analyzing and comparing distributions , allowing us to design statistical tests to determine if two samples are drawn from different distributions our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel hilbert space \( rkhs \) we present two tests based on large deviation bounds for the test statistic , while a third is based on the asymptotic distribution of this statistic the test statistic can be computed in quadratic time , although efficient linear time approximations are available several classical metrics on distributions are recovered when the function space used to compute the difference in expectations is allowed to be more general \( eg a banach space \) we apply our two sample tests to a variety of problems , including attribute matching for databases using the hungarian marriage method , where they perform strongly excellent performance is also obtained when comparing distributions over graphs , for which these are the first such tests"
cs.AI,"this article is devoted to the study of methods to change defeasible logic programs \( de l p s \) which are the knowledge bases used by the defeasible logic programming \( delp \) interpreter delp is an argumentation formalism that allows to reason over potentially inconsistent de l p s argument theory change \( atc \) studies certain aspects of belief revision in order to make them suitable for abstract argumentation systems in this article , abstract arguments are rendered concrete by using the particular rule based defeasible logic adopted by delp the objective of our proposal is to define prioritized argument revision operators `a la atc for de l p s , in such a way that the newly inserted argument ends up undefeated after the revision , thus warranting its conclusion in order to ensure this warrant , the de l p has to be changed in concordance with a minimal change principle to this end , we discuss different minimal change criteria that could be adopted finally , an algorithm is presented , implementing the argument revision operations"
cs.LG,"evolutionary algorithms \( eas \) , a large class of general purpose optimization algorithms inspired from the natural phenomena , are widely used in various industrial optimizations and often show excellent performance this paper presents an attempt towards revealing their general power from a statistical view of eas by summarizing a large range of eas into the sampling and learning framework , we show that the framework directly admits a general analysis on the probable absolute approximate \( paa \) query complexity we particularly focus on the framework with the learning subroutine being restricted as a binary classification , which results in the sampling and classification \( sac \) algorithms with the help of the learning theory , we obtain a general upper bound on the paa query complexity of sac algorithms we further compare sac algorithms with the uniform search in different situations under the error target independence condition , we show that sac algorithms can achieve polynomial speedup to the uniform search , but not super polynomial speedup under the one side error condition , we show that super polynomial speedup can be achieved this work only touches the surface of the framework its power under other conditions is still open"
cs.AI,"we consider the computational complexity of producing the best possible offspring in a crossover , given two solutions of the parents the crossover operators are studied on the class of boolean linear programming problems , where the boolean vector of variables is used as the solution representation by means of efficient reductions of the optimized gene transmitting crossover problems \( ogtc \) we show the polynomial solvability of the ogtc for the maximum weight set packing problem , the minimum weight set partition problem and for one of the versions of the simple plant location problem we study a connection between the ogtc for linear boolean programming problem and the maximum weight independent set problem on 2 colorable hypergraph and prove the np hardness of several special cases of the ogtc problem in boolean linear programming"
cs.AI|cs.LG|stat.ML,"mechanical devices such as engines , vehicles , aircrafts , etc , are typically instrumented with numerous sensors to capture the behavior and health of the machine however , there are often external factors or variables which are not captured by sensors leading to time series which are inherently unpredictable for instance , manual controls and or unmonitored environmental conditions or load may lead to inherently unpredictable time series detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity , or prediction models that utilize prediction errors to detect anomalies we propose a long short term memory networks based encoder decoder scheme for anomaly detection \( encdec ad \) that learns to reconstruct 'normal' time series behavior , and thereafter uses reconstruction error to detect anomalies we experiment with three publicly available quasi predictable time series datasets power demand , space shuttle , and ecg , and two real world engine datasets with both predictive and unpredictable behavior we show that encdec ad is robust and can detect anomalies from predictable , unpredictable , periodic , aperiodic , and quasi periodic time series further , we show that encdec ad is able to detect anomalies from short time series \( length as small as 30 \) as well as long time series \( length as large as 500 \)"
cs.IT|cs.LG,"we derive an information theoretic lower bound for sample complexity in sparse recovery problems where inputs can be chosen sequentially and adaptively this lower bound is in terms of a simple mutual information expression and unifies many different linear and nonlinear observation models using this formula we derive bounds for adaptive compressive sensing \( cs \) , group testing and 1 bit cs problems we show that adaptivity cannot decrease sample complexity in group testing , 1 bit cs and cs with linear sparsity in contrast , we show there might be mild performance gains for cs in the sublinear regime our unified analysis also allows characterization of gains due to adaptivity from a wider perspective on sparse problems"
stat.ML,"as inductive inference and machine learning methods in computer science see continued success , researchers are aiming to describe even more complex probabilistic models and inference algorithms what are the limits of mechanizing probabilistic inference \? we investigate the computability of conditional probability , a fundamental notion in probability theory and a cornerstone of bayesian statistics , and show that there are computable joint distributions with noncomputable conditional distributions , ruling out the prospect of general inference algorithms , even inefficient ones specifically , we construct a pair of computable random variables in the unit interval such that the conditional distribution of the first variable given the second encodes the halting problem nevertheless , probabilistic inference is possible in many common modeling settings , and we prove several results giving broadly applicable conditions under which conditional distributions are computable in particular , conditional distributions become computable when measurements are corrupted by independent computable noise with a sufficiently smooth density"
cs.SI,"opinion dynamics , aiming to understand the evolution of collective behavior through various interaction mechanisms of opinions , represents one of the most challenges in natural and social science to elucidate this issue clearly , binary opinion model becomes a useful framework , where agents can take an independent opinion inspired by the realistic observations , here we propose two basic interaction mechanisms of binary opinion model one is the so called bso model in which players benefit from holding the same opinion the other is called bdo model in which players benefit from taking different opinions in terms of these two basic models , the synthetical effect of opinion preference and equivocators on the evolution of binary opinion is studied under the framework of evolutionary game theory \( egt \) , where the replicator equation \( re \) is employed to mimick the evolution of opinions by means of numerous simulations , we show the theoretical equilibrium states of binary opinion dynamics , and mathematically analyze the stability of each equilibrium state as well"
cs.DS,"we consider the problem of finding minimum link rectilinear paths in rectilinear polygonal domains in the plane a path or a polygon is rectilinear if all its edges are axis parallel given a set mathcal p of h pairwise disjoint rectilinear polygonal obstacles with a total of n vertices in the plane , a minimum link rectilinear path between two points is a rectilinear path that avoids all obstacles with the minimum number of edges in this paper , we present a new algorithm for finding minimum link rectilinear paths among mathcal p after the plane is triangulated , with respect to any source point s , our algorithm builds an o \( n \) size data structure in o \( n h log h \) time , such that given any query point t , the number of edges of a minimum link rectilinear path from s to t can be computed in o \( log n \) time and the actual path can be output in additional time linear in the number of the edges of the path the previously best algorithm computes such a data structure in o \( n log n \) time"
cs.DS,"this paper surveys results on complexity of the optimal recombination problem \( orp \) , which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm , given two parent solutions we consider efficient reductions of the orps , allowing to establish polynomial solvability or np hardness of the orps , as well as direct proofs of hardness results"
cs.DS,we study geometrical properties of the complete set of solutions of the random 3 satisfiability problem we show that even for moderate system sizes the number of clusters corresponds surprisingly well with the theoretic asymptotic prediction we locate the freezing transition in the space of solutions which has been conjectured to be relevant in explaining the onset of computational hardness in random constraint satisfaction problems
cs.DM|cs.DS,"a network topology with low average shortest path length \( aspl \) provides efficient data transmission while the number of nodes and the number of links incident to each node are often limited due to physical constraints in this paper , we consider the construction of low aspl graphs under these constraints by using stochastic local search \( sls \) algorithms since the aspl cannot be calculated efficiently , the aspl is not suitable for the evaluation function of sls algorithms we first derive an equality and bounds for the aspl of graphs of diameter 3 then , we propose use the simpliest upper bound represented by the number of triangles and squares in the graph as an evaluation function for graphs of diameter 3 we show that the proposed evaluation function can be evaluated in o \( 1 \) time as the number of nodes and the maximum degree tend to infinity by using some data tables by using the simulated annealing with the proposed evaluation function , we construct low aspl regular graphs of diameter 3 with 10 000 nodes"
cs.AI|cs.IT,"the intention of the present study is to establish general framework for automated problem solving by approaching the task universal algebraically introducing knowledge as realizations of generalized free algebra based nets , graphs with gluing forms connecting in and out edges to nodes nets are caused to undergo transformations in conceptual level by type wise differentiated intervening net rewriting systems dispersing problems to abstract parts , matching being determined by substitution relations achieved sets of conceptual nets constitute congruent classes new results are obtained within construction of problem solving systems where solution algorithms are derived parallel with other candidates applied to the same net classes by applying parallel transducer paths consisting of net rewriting systems to net classes congruent quotient algebras are established and the manifested class rewriting comprises all solution candidates whenever produced nets are in anticipated languages liable to acceptance of net automata"
cs.LG,"the classification of high dimensional data defined on graphs is particularly difficult when the graph geometry is unknown we introduce a haar scattering transform on graphs , which computes invariant signal descriptors it is implemented with a deep cascade of additions , subtractions and absolute values , which iteratively compute orthogonal haar wavelet transforms multiscale neighborhoods of unknown graphs are estimated by minimizing an average total variation , with a pair matching algorithm of polynomial complexity supervised classification with dimension reduction is tested on data bases of scrambled images , and for signals sampled on unknown irregular grids on a sphere"
cs.LG,"big data streams are being generated in a faster , bigger , and more commonplace in this scenario , hoeffding trees are an established method for classification several extensions exist , including high performing ensemble setups such as online and leveraging bagging also , k nearest neighbors is a popular choice , with most extensions dealing with the inherent performance limitations over a potentially infinite stream at the same time , gradient descent methods are becoming increasingly popular , owing in part to the successes of deep learning although deep neural networks can learn incrementally , they have so far proved too sensitive to hyper parameter options and initial conditions to be considered an effective `off the shelf' data streams solution in this work , we look at combinations of hoeffding trees , nearest neighbour , and gradient descent methods with a streaming preprocessing approach in the form of a random feature functions filter for additional predictive power we further extend the investigation to implementing methods on gpus , which we test on some large real world datasets , and show the benefits of using gpus for data stream learning due to their high scalability our empirical evaluation yields positive results for the novel approaches that we experiment with , highlighting important issues , and shed light on promising future directions in approaches to data stream classification"
cs.DS|cs.SI,"the push algorithm was proposed first by jeh and widom in the context of personalized pagerank computations \( albeit the name push algorithm was actually used by andersen , chung and lang in a subsequent paper \) in this note we describe the algorithm at a level of generality that make the computation of the spectral ranking of any nonnegative matrix possible actually , the main contribution of this note is that the description is very simple \( almost trivial \) , and it requires only a few elementary linear algebra computations along the way , we give new precise ways of estimating the convergence of the algorithm , and describe some of the contribution of the existing literature , which again turn out to be immediate when recast in our framework"
cs.DS|cs.IT,"in 1733 , georges louis leclerc , comte de buffon in france , set the ground of geometric probability theory by defining an enlightening problem what is the probability that a needle thrown randomly on a ground made of equispaced parallel strips lies on two of them \? in this work , we show that the solution to this problem , and its generalization to n dimensions , allows us to discover a quantized form of the johnson lindenstrauss \( jl \) lemma , i e , one that combines a linear dimensionality reduction procedure with a uniform quantization of precision delta 0 in particular , given a finite set mathcal s subset mathbb r n of s points and a distortion level epsilon 0 , as soon as m m 0 o \( epsilon 2 log s \) , we can \( randomly \) construct a mapping from \( mathcal s , ell 2 \) to \( , \( delta , mathbb z \) m , ell 1 \) that approximately preserves the pairwise distances between the points of mathcal s interestingly , compared to the common jl lemma , the mapping is quasi isometric and we observe both an additive and a multiplicative distortions on the embedded distances these two distortions , however , decay as o \( sqrt log s m \) when m increases moreover , for coarse quantization , i e , for high delta compared to the set radius , the distortion is mainly additive , while for small delta we tend to a lipschitz isometric embedding finally , we show that there exists almost a quasi isometric embedding of \( mathcal s , ell 2 \) in \( \( delta mathbb z \) m , ell 2 \) this one involves a non linear distortion of the ell 2 distance in mathcal s that vanishes for distant points in this set noticeably , the additive distortion in this case is slower and decays as o \( \( log s m \) 1 4 \)"
cs.SI,"online social networks are becoming a major growth point of the internet , as individuals , companies and governments constantly desire to interact with one another , the ability of the internet to deliver this networking capabilities grows stronger in this paper , we looked at the structure and components of the member profile and the challenges of privacy issues faced by individuals and governments that participate in social networking we also looked at how it can be used to distort national security , how it became the new weapons of mass mobilization and also how social networks have became the rallying forces for revolutions and social justice"
cs.SI,"we address the issue of the reducibility of the dynamics on a multilayer network to an equivalent process on an aggregated single layer network as a typical example of models for opinion formation in social networks , we implement the voter model on a two layer multiplex network , and we study its dynamics as a function of two control parameters , namely the fraction of edges simultaneously existing in both layers of the network \( edge overlap \) , and the fraction of nodes participating in both layers \( interlayer connectivity or degree of multiplexity \) we compute the asymptotic value of the number of active links \( interface density \) in the thermodynamic limit , and the time to reach an absorbing state for finite systems , and we compare the numerical results with the analytical predictions on equivalent single layer networks obtained through various possible aggregation procedures we find a large region of parameters where the interface density of large multiplexes gives systematic deviations from that of the aggregates we show that neither of the standard aggregation procedures is able to capture the highly nonlinear increase in the lifetime of a finite size multiplex at small interlayer connectivity these results indicate that multiplexity should be appropriately taken into account when studying voter model dynamics , and that , in general , single layer approximations might be not accurate enough to properly understand processes occurring on multiplex networks , since they might flatten out relevant dynamical details"
cs.DS,"in this paper , we re evaluate the basic strategies for label correcting algorithms for the multiobjective shortest path \( mosp \) problem , i e , node and label selection in contrast to common believe , we show that when carefully implemented the node selection strategy usually beats the label selection strategy moreover , we present a new pruning method which is easy to implement and performs very well on real world road networks in this study , we test our hypotheses on artificial mosp instances from the literature with up to 15 objectives and real world road networks with up to almost 160 , 000 nodes"
cs.LG,"we simulate the training of a set of state of the art neural networks , the maxout networks \( goodfellow et al , 2013a \) , on three benchmark datasets the mnist , cifar10 and svhn , with three distinct arithmetics floating point , fixed point and dynamic fixed point for each of those datasets and for each of those arithmetics , we assess the impact of the precision of the computations on the final error of the training we find that very low precision computation is sufficient not just for running trained networks but also for training them for example , almost state of the art results were obtained on most datasets with around 10 bits for computing activations and gradients , and 12 bits for storing updated parameters"
cs.LG,"in this paper , we show that by feeding the weights of a deep neural network \( dnn \) during training into a deep q network \( dqn \) as its states , this dqn can learn policies to accelerate the training of that dnn the actions of the dqn modify different hyperparameters during training empirically , this acceleration leads to better generalization performance of the dnn"
cs.AI|cs.LG,"characterizing driving styles of human drivers using vehicle sensor data , e g , gps , is an interesting research problem and an important real world requirement from automotive industries a good representation of driving features can be highly valuable for autonomous driving , auto insurance , and many other application scenarios however , traditional methods mainly rely on handcrafted features , which limit machine learning algorithms to achieve a better performance in this paper , we propose a novel deep learning solution to this problem , which could be the first attempt of studying deep learning for driving behavior analysis the proposed approach can effectively extract high level and interpretable features describing complex driving patterns from gps data it also requires significantly less human experience and work the power of the learned driving style representations are validated through the driver identification problem using a large real dataset"
cs.DM,"we survey an abstract theory of connectivity , based on symmetric submodular set functions we start by developing robertson and seymour 's fundamental duality between branch decompositions \( related to the better known tree decompositions \) and so called tangles , which may be viewed as highly connected regions in a connectivity system we move on to studying canonical decompositions of connectivity systems into their maximal tangles last , but not least , we will discuss algorithmic aspect of the theory"
cs.DS,"the art gallery problem \( agp \) asks for placing a minimum number of stationary guards in a polygonal region p , such that all points in p are guarded the problem is known to be np hard , and its inherent continuous structure \( with both the set of points that need to be guarded and the set of points that can be used for guarding being uncountably infinite \) makes it difficult to apply a straightforward formulation as an integer linear program we use an iterative primal dual relaxation approach for solving agp instances to optimality at each stage , a pair of lp relaxations for a finite candidate subset of primal covering and dual packing constraints and variables is considered these correspond to possible guard positions and points that are to be guarded particularly useful are cutting planes for eliminating fractional solutions we identify two classes of facets , based on edge cover and set cover \( sc \) inequalities solving the separation problem for the latter is np complete , but exploiting the underlying geometric structure , we show that large subclasses of fractional sc solutions cannot occur for the agp this allows us to separate the relevant subset of facets in polynomial time we also characterize all facets for finite agp relaxations with coefficients in 0 , 1 , 2 finally , we demonstrate the practical usefulness of our approach our cutting plane technique yields a significant improvement in terms of speed and solution quality due to considerably reduced integrality gaps as compared to the approach by kr oller et al"
cs.LG,"one of the key obstacles in making learning protocols realistic in applications is the need to supervise them , a costly process that often requires hiring domain experts we consider the framework to use the world knowledge as indirect supervision world knowledge is general purpose knowledge , which is not designed for any specific domain then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning in this paper , we provide an example of using world knowledge for domain dependent document clustering we provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types , and represent the data with world knowledge as a heterogeneous information network then we propose a clustering algorithm that can cluster multiple types and incorporate the sub type information as constraints in the experiments , we use two existing knowledge bases as our sources of world knowledge one is freebase , which is collaboratively collected knowledge about entities and their organizations the other is yago2 , a knowledge base automatically extracted from wikipedia and maps knowledge to the linguistic knowledge base , wordnet experimental results on two text benchmark datasets \( 20newsgroups and rcv1 \) show that incorporating world knowledge as indirect supervision can significantly outperform the state of the art clustering algorithms as well as clustering algorithms enhanced with world knowledge features"
cs.DS,"in analysing a well known hash coding method , knuth gave an exact expression for the average number of rejections encountered by players of a variant of musical chairs we study a variant more closely related to musical chairs itself and deduce the same expression by a purely combinatorial approach"
cs.DM|cs.DS,"in this paper we study the max cut problem on power law graphs \( plgs \) with power law exponent beta we prove some new approximability results on that problem in particular we show that there exist polynomial time approximation schemes \( ptas \) for max cut on plgs for the power law exponent beta in the interval \( 0 , 2 \) for beta 2 we show that for some epsilon 0 , max cut is np hard to approximate within approximation ratio 1 epsilon , ruling out the existence of a ptas in this case moreover we give an approximation algorithm with improved constant approximation ratio for the case of beta 2"
cs.LG,"digital music has become prolific in the web in recent decades automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience when manual annotations and user preference data is lacking \( e g for new artists \) these systems must rely on emph content based methods besides powerful machine learning tools for classification and retrieval , a key component for successful recommendation is the emph audio content representation good representations should capture informative musical patterns in the audio signal of songs these representations should be concise , to enable efficient \( low storage , easy indexing , fast search \) management of huge music repositories , and should also be easy and fast to compute , to enable real time interaction with a user supplying new songs to the system before designing new audio features , we explore the usage of traditional local features , while adding a stage of encoding with a pre computed emph codebook and a stage of pooling to get compact vectorial representations we experiment with different encoding methods , namely emph the lasso , emph vector quantization \( vq \) and emph cosine similarity \( cs \) we evaluate the representations' quality in two music information retrieval applications query by tag and query by example our results show that concise representations can be used for successful performance in both applications we recommend using top tau vq encoding , which consistently performs well in both applications , and requires much less computation time than the lasso"
cs.LG|stat.ML,"overcomplete latent representations have been very popular for unsupervised feature learning in recent years in this paper , we specify which overcomplete models can be identified given observable moments of a certain order we consider probabilistic admixture or topic models in the overcomplete regime , where the number of latent topics can greatly exceed the size of the observed word vocabulary while general overcomplete topic models are not identifiable , we establish generic identifiability under a constraint , referred to as topic persistence our sufficient conditions for identifiability involve a novel set of higher order expansion conditions on the topic word matrix or the population structure of the model this set of higher order expansion conditions allow for overcomplete models , and require the existence of a perfect matching from latent topics to higher order observed words we establish that random structured topic models are identifiable w h p in the overcomplete regime our identifiability results allows for general \( non degenerate \) distributions for modeling the topic proportions , and thus , we can handle arbitrarily correlated topics in our framework our identifiability results imply uniqueness of a class of tensor decompositions with structured sparsity which is contained in the class of tucker decompositions , but is more general than the candecomp parafac \( cp \) decomposition"
cs.AI|cs.LG|stat.ML,"monte carlo tree search \( mcts \) methods are drawing great interest after yielding breakthrough results in computer go this paper proposes a bayesian approach to mcts that is inspired by distributionfree approaches such as uct 13 , yet significantly differs in important respects the bayesian framework allows potentially much more accurate \( bayes optimal \) estimation of node values and node uncertainties from a limited number of simulation trials we further propose propagating inference in the tree via fast analytic gaussian approximation methods this can make the overhead of bayesian inference manageable in domains such as go , while preserving high accuracy of expected value estimates we find substantial empirical outperformance of uct in an idealized bandit tree test environment , where we can obtain valuable insights by comparing with known ground truth additionally we rigorously prove on policy and off policy convergence of the proposed methods"
cs.DS,"we provide a new algorithm to determine stuttering equivalence with time complexity o \( m log n \) , where n is the number of states and m is the number of transitions of a kripke structure this algorithm can also be used to determine branching bisimulation in o \( m \( log mathit act log n \) \) time where mathit act is the set of actions in a labelled transition system theoretically , our algorithm substantially improves upon existing algorithms which all have time complexity o \( m n \) at best moreover , it has better or equal space complexity practical results confirm these findings showing that our algorithm can outperform existing algorithms with orders of magnitude , especially when the sizes of the kripke structures are large the importance of our algorithm stretches far beyond stuttering equivalence and branching bisimulation the known o \( m n \) algorithms were already far more efficient \( both in space and time \) than most other algorithms to determine behavioural equivalences \( including weak bisimulation \) and therefore it was often used as an essential preprocessing step this new algorithm makes this use of stuttering equivalence and branching bisimulation even more attractive"
cs.LG|stat.ML,"notwithstanding the popularity of conventional clustering algorithms such as k means and probabilistic clustering , their clustering results are sensitive to the presence of outliers in the data even a few outliers can compromise the ability of these algorithms to identify meaningful hidden structures rendering their outcome unreliable this paper develops robust clustering algorithms that not only aim to cluster the data , but also to identify the outliers the novel approaches rely on the infrequent presence of outliers in the data which translates to sparsity in a judiciously chosen domain capitalizing on the sparsity in the outlier domain , outlier aware robust k means and probabilistic clustering approaches are proposed their novelty lies on identifying outliers while effecting sparsity in the outlier domain through carefully chosen regularization a block coordinate descent approach is developed to obtain iterative algorithms with convergence guarantees and small excess computational complexity with respect to their non robust counterparts kernelized versions of the robust clustering algorithms are also developed to efficiently handle high dimensional data , identify nonlinearly separable clusters , or even cluster objects that are not represented by vectors numerical tests on both synthetic and real datasets validate the performance and applicability of the novel algorithms"
cs.DM,"we derive a quadratic time algorithm for the genus distribution of any 3 regular , biconnected series parallel graph , which we extend to any biconnected series parallel graph of maximum degree at most 3 since the biconnected components of every graph of treewidth 2 are series parallel graphs , this yields , by use of bar amalgamation , a quadratic time algorithm for every graph of treewidth at most 2 and maximum degree at most 3"
cs.DS|cs.IT,"one of the most famous and investigated lossless data compression scheme is the one introduced by lempel and ziv about 40 years ago this compression scheme is known as dictionary based compression and consists of squeezing an input string by replacing some of its substrings with \( shorter \) codewords which are actually pointers to a dictionary of phrases built as the string is processed surprisingly enough , although many fundamental results are nowadays known about upper bounds on the speed and effectiveness of this compression process and references therein \) , ``we are not aware of any parsing scheme that achieves optimality when the lz77 dictionary is in use under any constraint on the codewords other than being of equal length'' n rajpoot and c sahinalp handbook of lossless data compression , chapter dictionary based data compression academic press , 2002 pag 159 here optimality means to achieve the minimum number of bits in compressing each individual input string , without any assumption on its generating source in this paper we provide the first lz based compressor which computes the bit optimal parsing of any input string in efficient time and optimal space , for a general class of variable length codeword encodings which encompasses most of the ones typically used in data compression and in the design of search engines and compressed indexes"
cs.AI,"characterization of semiconductor devices is used to gather as much data about the device as possible to determine weaknesses in design or trends in the manufacturing process in this paper , we propose a novel multiple trip point characterization concept to overcome the constraint of single trip point concept in device characterization phase in addition , we use computational intelligence techniques \( e g neural network , fuzzy and genetic algorithm \) to further manipulate these sets of multiple trip point values and tests based on semiconductor test equipments , our experimental results demonstrate an excellent design parameter variation analysis in device characterization phase , as well as detection of a set of worst case tests that can provoke the worst case variation , while traditional approach was not capable of detecting them"
cs.DS|cs.LG,"strongly rayleigh distributions are natural generalizations of product and determinantal probability distributions and satisfy strongest form of negative dependence properties we show that the natural monte carlo markov chain \( mcmc \) is rapidly mixing in the support of a em homogeneous strongly rayleigh distribution as a byproduct , our proof implies markov chains can be used to efficiently generate approximate samples of a k determinantal point process this answers an open question raised by deshpande and rademacher"
cs.DS,"we consider a modified random walk which uses unvisited edges whenever possible , and makes a simple random walk otherwise we call such a walk an edge process we assume there is a rule a , which tells the walk which unvisited edge to use whenever there is a choice in the simplest case , a is a uniform random choice over unvisited edges incident with the current walk position however we do not exclude arbitrary choices of rule a for example , the rule could be determined on line by an adversary , or could vary from vertex to vertex for even degree expander graphs , of bounded maximum degree , we have the following result let g be an n vertex even degree expander graph , for which every vertex is in at least one vertex induced cycle of length l any edge process on g has cover time \( n \( n log n \) l \) this result is independent of the rule a used to select the order of the unvisited edges , which can be chosen on line by an adversary as an example , with high probability , random r regular graphs , \( r at least 4 , even \) , are expanders for which l omega \( log n \) thus , for almost all such graphs , the vertex cover time of the edge process is theta \( n \) this improves the vertex cover time of such graphs by a factor of log n , compared to the omega \( n log n \) cover time of any weighted random walk"
cs.LG,"event classification at sentence level is an important information extraction task with applications in several nlp , ir , and personalization systems multi label binary relevance \( br \) are the state of art methods in this work , we explored new multi label methods known for capturing relations between event types these new methods , such as the ensemble chain of classifiers , improve the f1 on average across the 6 labels by 2 8 over the binary relevance the low occurrence of multi label sentences motivated the reduction of the hard imbalanced multi label classification problem with low number of occurrences of multiple labels per instance to an more tractable imbalanced multiclass problem with better results \( 4 6 \) we report the results of adding new features , such as sentiment strength , rhetorical signals , domain id \( source id and date \) , and key phrases in both single label and multi label event classification scenarios"
cs.LG,"we introduce the dense captioning task , which requires a computer vision system to both localize and describe salient regions in images in natural language the dense captioning task generalizes object detection when the descriptions consist of a single word , and image captioning when one predicted region covers the full image to address the localization and description task jointly we propose a fully convolutional localization network \( fcln \) architecture that processes an image with a single , efficient forward pass , requires no external regions proposals , and can be trained end to end with a single round of optimization the architecture is composed of a convolutional network , a novel dense localization layer , and recurrent neural network language model that generates the label sequences we evaluate our network on the visual genome dataset , which comprises 94 , 000 images and 4 , 100 , 000 region grounded captions we observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings"
cs.DM,"a bipartite graph g \( l , r e \) with at least one edge is said to be identifiable if for every vertex v in l , the subgraph induced by its non neighbors has a matching of cardinality l 1 an ell subgraph of g is an induced subgraph of g obtained by deleting from it some vertices in l together with all their neighbors the identifiable subgraph problem is the problem of determining whether a given bipartite graph contains an identifiable ell subgraph we show that the identifiable subgraph problem is polynomially solvable , along with the version of the problem in which the task is to delete as few vertices from l as possible together with all their neighbors so that the resulting ell subgraph is identifiable we also complement a known apx hardness result for the complementary problem in which the task is to minimize the number of remaining vertices in l , by showing that two parameterized variants of the problem are w 1 hard"
stat.ML,"geographic object based image analysis \( geobia \) framework has gained increasing interest recently following this popular paradigm , we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions they capture the same scene with different sensors and are naturally fused together through the hierarchical representation , where coarser levels are built from a low spatial resolution \( lsr \) or medium spatial resolution \( msr \) image while finer levels are generated from a high spatial resolution \( hsr \) or very high spatial resolution \( vhsr \) image such a representation allows one to benefit from the context information thanks to the coarser levels , and subregions spatial arrangement information thanks to the finer levels two dedicated structured kernels are then used to perform machine learning directly on the constructed hierarchical representation this strategy overcomes the limits of conventional geobia classification procedures that can handle only one or very few pre selected scales experiments run on an urban classification task show that the proposed approach can highly improve the classification accuracy w r t conventional approaches working on a single scale"
cs.AI,"the framework of algorithmic knowledge assumes that agents use deterministic knowledge algorithms to compute the facts they explicitly know we extend the framework to allow for randomized knowledge algorithms we then characterize the information provided by a randomized knowledge algorithm when its answers have some probability of being incorrect we formalize this information in terms of evidence a randomized knowledge algorithm returning ``yes'' to a query about a fact phi provides evidence for phi being true finally , we discuss the extent to which this evidence can be used as a basis for decisions"
cs.LG,"in existing works that learn representation for object detection , the relationship between a candidate window and the ground truth bounding box of an object is simplified by thresholding their overlap this paper shows information loss in this simplification and picks up the relative location size information discarded by thresholding we propose a representation learning pipeline to use the relationship as supervision for improving the learned representation in object detection such relationship is not limited to object of the target category , but also includes surrounding objects of other categories we show that image regions with multiple contexts and multiple rotations are effective in capturing such relationship during the representation learning process and in handling the semantic and visual variation caused by different window object configurations experimental results show that the representation learned by our approach can improve the object detection accuracy by 6 4 in mean average precision \( map \) on ilsvrc2014 on the challenging ilsvrc2014 test dataset , 48 6 map is achieved by our single model and it is the best among published results on pascal voc , it outperforms the state of the art result of fast rcnn by 3 3 in absolute map"
cs.DM|cs.DS,"given a weighted and complete graph g \( v , e \) , v denotes the set of n objects to be clustered , and the weight d \( u , v \) associated with an edge \( u , v \) belonging to e denotes the dissimilarity between objects u and v the diameter of a cluster is the maximum dissimilarity between pairs of objects in the cluster , and the split of a cluster is the minimum dissimilarity between objects within the cluster and objects outside the cluster in this paper , we propose a new criterion for measuring the goodness of clusters the ratio of the minimum split to the maximum diameter , and the objective is to maximize the ratio for k 2 , we present an exact algorithm for k 3 , we prove that the problem is np hard and present a factor of 2 approximation algorithm on the precondition that the weights associated with e satisfy the triangle inequality the worst case runtime of both algorithms is o \( n 3 \) we compare the proposed algorithms with the normalized cut by applying them to image segmentation the experimental results on both natural and synthetic images demonstrate the effectiveness of the proposed algorithms"
cs.LG|stat.ML,"recent research has made significant progress on the problem of bounding log partition functions for exponential family graphical models such bounds have associated dual parameters that are often used as heuristic estimates of the marginal probabilities required in inference and learning however these variational estimates do not give rigorous bounds on marginal probabilities , nor do they give estimates for probabilities of more general events than simple marginals in this paper we build on this recent work by deriving rigorous upper and lower bounds on event probabilities for graphical models our approach is based on the use of generalized chernoff bounds to express bounds on event probabilities in terms of convex optimization problems these optimization problems , in turn , require estimates of generalized log partition functions simulations indicate that this technique can result in useful , rigorous bounds to complement the heuristic variational estimates , with comparable computational cost"
cs.LG|cs.SI|stat.ML,"we introduce the nonparametric metadata dependent relational \( nmdr \) model , a bayesian nonparametric stochastic block model for network data the nmdr allows the entities associated with each node to have mixed membership in an unbounded collection of latent communities learned regression models allow these memberships to depend on , and be predicted from , arbitrary node metadata we develop efficient mcmc algorithms for learning nmdr models from partially observed node relationships retrospective mcmc methods allow our sampler to work directly with the infinite stick breaking representation of the nmdr , avoiding the need for finite truncations our results demonstrate recovery of useful latent communities from real world social and ecological networks , and the usefulness of metadata in link prediction tasks"
cs.AI,"we study constraint satisfaction problems on the so called 'planted' random ensemble we show that for a certain class of problems , e g graph coloring , many of the properties of the usual random ensemble are quantitatively identical in the planted random ensemble we study the structural phase transitions , and the easy hard easy pattern in the average computational complexity we also discuss the finite temperature phase diagram , finding a close connection with the liquid glass solid phenomenology"
cs.LG|stat.ML,"off the shelf gaussian process \( gp \) covariance functions encode smoothness assumptions on the structure of the function to be modeled to model complex and non differentiable functions , these smoothness assumptions are often too restrictive one way to alleviate this limitation is to find a different representation of the data by introducing a feature space this feature space is often learned in an unsupervised way , which might lead to data representations that are not useful for the overall regression task in this paper , we propose manifold gaussian processes , a novel supervised method that jointly learns a transformation of the data into a feature space and a gp regression from the feature space to observed space the manifold gp is a full gp and allows to learn data representations , which are useful for the overall regression task as a proof of concept , we evaluate our approach on complex non smooth functions where standard gps perform poorly , such as step functions and robotics tasks with contacts"
stat.ML,"although software analytics has experienced rapid growth as a research area , it has not yet reached its full potential for wide industrial adoption most of the existing work in software analytics still relies heavily on costly manual feature engineering processes , and they mainly address the traditional classification problems , as opposed to predicting future events we present a vision for emph deepsoft , an emph end to end generic framework for modeling software and its development process to predict future risks and recommend interventions deepsoft , partly inspired by human memory , is built upon the powerful deep learning based long short term memory architecture that is capable of learning long term temporal dependencies that occur in software evolution such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction deepsoft provides a new approach for research into modeling of source code , risk prediction and mitigation , developer modeling , and automatically generating code patches from bug reports"
cs.LG|stat.ML,"most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort in many scenarios , however , it is desirable to label an entire batch of examples at once , for example , when labels can be acquired in parallel this motivates us to study batch active learning , which iteratively selects batches of k 1 examples to be labeled we propose a novel batch active learning method that leverages the availability of high quality and efficient sequential active learning policies by attempting to approximate their behavior when applied for k steps specifically , our algorithm first uses monte carlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over k step executions the algorithm then attempts to select a set of k examples that best matches this distribution , leading to a combinatorial optimization problem that we term bounded coordinated matching while we show this problem is np hard in general , we give an efficient greedy solution , which inherits approximation bounds from supermodular minimization theory our experimental results on eight benchmark datasets show that the proposed approach is highly effective"
cs.LG|stat.ML,"in this paper we propose a multi armed bandit inspired , pool based active learning algorithm for the problem of binary classification by carefully constructing an analogy between active learning and multi armed bandits , we utilize ideas such as lower confidence bounds , and self concordant regularization from the multi armed bandit literature to design our proposed algorithm our algorithm is a sequential algorithm , which in each round assigns a sampling distribution on the pool , samples one point from this distribution , and queries the oracle for the label of this sampled point the design of this sampling distribution is also inspired by the analogy between active learning and multi armed bandits we show how to derive lower confidence bounds required by our algorithm experimental comparisons to previously proposed active learning algorithms show superior performance on some standard uci datasets"
cs.SI,"to uncover the mechanisms underlying the collaborative production of knowledge , we investigate a very large online question and answer system that includes the question asking and answering activities of millions of users over five years we created knowledge networks in which nodes are questions and edges are the successive answering activities of users we find that these networks have two common properties 1 \) the mitigation of degree inequality among nodes and 2 \) the assortative mixing of nodes this means that , while the system tends to reduce attention investment on old questions in order to supply sufficient attention to new questions , it is not easy for novel knowledge be integrated into the existing body of knowledge we propose a mixing model to combine preferential attachment and reversed preferential attachment processes to model the evolution of knowledge networks and successfully reproduce the ob served patterns our mixing model is not only theoretically interesting but also provide insights into the management of online communities"
cs.SI,"we study the strategic formation of multi layer networks , where each layer represents a different type of relationship between the nodes in the network and is designed to maximize some utility that depends on the topology of that layer and those of the other layers we start by generalizing distance based network formation to the two layer setting , where edges are constructed in one layer \( with fixed cost per edge \) to minimize distances between nodes that are neighbors in another layer we show that designing an optimal network in this setting is np hard despite the underlying complexity of the problem , we characterize certain properties of the optimal networks we then formulate a multi layer network formation game where each layer corresponds to a player that is optimally choosing its edge set in response to the edge sets of the other players we consider utility functions that view the different layers as strategic substitutes by applying our results about optimal networks , we show that players with low edge costs drive players with high edge costs out of the game , and that hub and spoke networks that are commonly observed in real world transportation systems arise as nash equilibria in this game"
cs.AI|cs.DS,"the constraint satisfaction problem \( csp \) is a general problem central to computer science and artificial intelligence although the csp is np hard in general , considerable effort has been spent on identifying tractable subclasses the main two approaches consider structural properties \( restrictions on the hypergraph of constraint scopes \) and relational properties \( restrictions on the language of constraint relations \) recently , some authors have considered hybrid properties that restrict the constraint hypergraph and the relations simultaneously our key contribution is the novel concept of a csp pattern and classes of problems defined by forbidden patterns \( which can be viewed as forbidding generic subproblems \) we describe the theoretical framework which can be used to reason about classes of problems defined by forbidden patterns we show that this framework generalises relational properties and allows us to capture known hybrid tractable classes although we are not close to obtaining a dichotomy concerning the tractability of general forbidden patterns , we are able to make some progress in a special case classes of problems that arise when we can only forbid binary negative patterns \( generic subproblems in which only inconsistent tuples are specified \) in this case we are able to characterise very large classes of tractable and np hard forbidden patterns this leaves the complexity of just one case unresolved and we conjecture that this last case is tractable"
cs.AI|cs.LG,"in undirected graphical models , learning the graph structure and learning the functions that relate the predictive variables \( features \) to the responses given the structure are two topics that have been widely investigated in machine learning and statistics learning graphical models in two stages will have problems because graph structure may change after considering the features the main contribution of this paper is the proposed method that learns the graph structure and functions on the graph at the same time general graphical models with binary outcomes conditioned on predictive variables are proved to be equivalent to multivariate bernoulli model the reparameterization of the potential functions in graphical model by conditional log odds ratios in multivariate bernoulli model offers advantage in the representation of the conditional independence structure in the model additionally , we impose a structure penalty on groups of conditional log odds ratios to learn the graph structure these groups of functions are designed with overlaps to enforce hierarchical function selection in this way , we are able to shrink higher order interactions to obtain a sparse graph structure simulation studies show that the method is able to recover the graph structure the analysis of county data from census bureau gives interesting relations between unemployment rate , crime and others discovered by the model"
cs.DM,"using computer algorithms we establish that the ramsey number r \( 3 , k 10 e \) is equal to 37 , which solves the smallest open case for ramsey numbers of this type we also obtain new upper bounds for the cases of r \( 3 , k k e \) for 11 le k le 16 , and show by construction a new lower bound 55 le r \( 3 , k 13 e \) the new upper bounds on r \( 3 , k k e \) are obtained by using the values and lower bounds on e \( 3 , k l e , n \) for l le k , where e \( 3 , k k e , n \) is the minimum number of edges in any triangle free graph on n vertices without k k e in the complement we complete the computation of the exact values of e \( 3 , k k e , n \) for all n with k leq 10 and for n leq 34 with k 11 , and establish many new lower bounds on e \( 3 , k k e , n \) for higher values of k using the maximum triangle free graph generation method , we determine two other previously unknown ramsey numbers , namely r \( 3 , k 10 k 3 e \) 31 and r \( 3 , k 10 p 3 e \) 31 for graphs g on 10 vertices , besides g k 10 , this leaves 6 other open besides g k 10 , this leaves 6 open cases of the form r \( 3 , g \) the hardest among them appears to be g k 10 2k 2 , for which we establish the bounds 31 le r \( 3 , k 10 2k 2 \) le 33"
cs.AI|stat.ML,"this paper presents new results for the \( partial \) maximum a posteriori \( map \) problem in bayesian networks , which is the problem of querying the most probable state configuration of some of the network variables given evidence first , it is demonstrated that the problem remains hard even in networks with very simple topology , such as binary polytrees and simple trees \( including the naive bayes structure \) such proofs extend previous complexity results for the problem inapproximability results are also derived in the case of trees if the number of states per variable is not bounded although the problem is shown to be hard and inapproximable even in very simple scenarios , a new exact algorithm is described that is empirically fast in networks of bounded treewidth and bounded number of states per variable the same algorithm is used as basis of a fully polynomial time approximation scheme for map under such assumptions approximation schemes were generally thought to be impossible for this problem , but we show otherwise for classes of networks that are important in practice the algorithms are extensively tested using some well known networks as well as random generated cases to show their effectiveness"
cs.DM,"we investigate the least number of palindromic factors in an infinite word we first consider general alphabets , and give answers to this problem for periodic and non periodic words , closed or not under reversal of factors we then investigate the same problem when the alphabet has size two"
cs.SI|stat.ML,"community detection is one of the fundamental problems of network analysis , for which a number of methods have been proposed most model based or criteria based methods have to solve an optimization problem over a discrete set of labels to find communities , which is computationally infeasible some fast spectral algorithms have been proposed for specific methods or models , but only on a case by case basis here we propose a general approach for maximizing a function of a network adjacency matrix over discrete labels by projecting the set of labels onto a subspace approximating the leading eigenvectors of the expected adjacency matrix this projection onto a low dimensional space makes the feasible set of labels much smaller and the optimization problem much easier we prove a general result about this method and show how to apply it to several previously proposed community detection criteria , establishing its consistency for label estimation in each case and demonstrating the fundamental connection between spectral properties of the network and various model based approaches to community detection simulations and applications to real world data are included to demonstrate our method performs well for multiple problems over a wide range of parameters"
cs.AI,"we introduce priors and algorithms to perform bayesian inference in gaussian models defined by acyclic directed mixed graphs such a class of graphs , composed of directed and bi directed edges , is a representation of conditional independencies that is closed under marginalization and arises naturally from causal models which allow for unmeasured confounding monte carlo methods and a variational approximation for such models are presented our algorithms for bayesian inference allow the evaluation of posterior distributions for several quantities of interest , including causal effects that are not identifiable from data alone but could otherwise be inferred where informative prior knowledge about confounding is available"
cs.DS|stat.ML,"given a similarity graph between items , correlation clustering \( cc \) groups similar items together and dissimilar ones apart one of the most popular cc algorithms is kwikcluster an algorithm that serially clusters neighborhoods of vertices , and obtains a 3 approximation ratio unfortunately , kwikcluster in practice requires a large number of clustering rounds , a potential bottleneck for large graphs we present c4 and clusterwild ! , two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds and achieve nearly linear speedups , provably c4 uses concurrency control to enforce serializability of a parallel clustering process , and guarantees a 3 approximation ratio clusterwild ! is a coordination free algorithm that abandons consistency for the benefit of better scaling this leads to a provably small loss in the 3 approximation ratio we provide extensive experimental results for both algorithms , where we outperform the state of the art , both in terms of clustering accuracy and running time we show that our algorithms can cluster billion edge graphs in under 5 seconds on 32 cores , while achieving a 15x speedup"
cs.SI,"metrics derived from twitter and other social media often referred to as altmetrics are increasingly used to estimate the broader social impacts of scholarship such efforts , however , may produce highly misleading results , as the entities that participate in conversations about science on these platforms are largely unknown for instance , if altmetric activities are generated mainly by scientists , does it really capture broader social impacts of science \? here we present a systematic approach to identifying and analyzing scientists on twitter our method can be easily adapted to identify other stakeholder groups in science we investigate the demographics , sharing behaviors , and interconnectivity of the identified scientists our work contributes to the literature both methodologically and conceptually we provide new methods for disambiguating and identifying particular actors on social media and describing the behaviors of scientists , thus providing foundational information for the construction and use of indicators on the basis of social media metrics"
cs.DS,"an induced matching in a graph is a set of edges whose endpoints induce a 1 regular subgraph it is known that any n vertex graph has at most 10 n 5 approx 1 5849 n maximal induced matchings , and this bound is best possible we prove that any n vertex triangle free graph has at most 3 n 3 approx 1 4423 n maximal induced matchings , and this bound is attained by any disjoint union of copies of the complete bipartite graph k 3 , 3 our result implies that all maximal induced matchings in an n vertex triangle free graph can be listed in time o \( 1 4423 n \) , yielding the fastest known algorithm for finding a maximum induced matching in a triangle free graph"
cs.DM,"we present digenes , a new discovery system that aims to help researchers in graph theory while its main task is to find extremal graphs for a given \( function of \) invariants , it also provides some basic support in proof conception this has already been proved to be very useful to find new conjectures since the autographix system of caporossi and hansen \( discrete math 212 2000 \) however , unlike existing systems , digenes can be used both with directed or undirected graphs in this paper , we present the principles and functionality of digenes , describe the genetic algorithms that have been designed to achieve them , and give some computational results and open questions this do arise some interesting questions regarding genetic algorithms design particular to this field , such as crossover definition"
cs.LG|stat.ML,"we extend the theory of matrix completion to the case where we make poisson observations for a subset of entries of a low rank matrix we consider the \( now \) usual matrix recovery formulation through maximum likelihood with proper constraints on the matrix m , and establish theoretical upper and lower bounds on the recovery error our bounds are nearly optimal up to a factor on the order of o \( sqrt r \) these bounds are obtained by adapting the arguments used for one bit matrix completion \( although these two problems are different in nature \) and the adaptation requires new techniques exploiting properties of the poisson likelihood function and tackling the difficulties posed by the heavy tail and locally sub gaussian characteristic of the poisson distribution our results highlight a few important distinctions of poisson matrix completion compared to the prior work in matrix completion including having to impose a minimum signal to noise requirement on each observed entry we also develop an efficient iterative algorithm and demonstrate its good performance in recovering solar flare images"
cs.DS,"we give a new , flow type convex program describing equilibrium solutions to linear arrow debreu markets whereas convex formulations were previously known nenakov , primak 83 jain 07 cornet '89 , our program exhibits several new features it gives a simple necessary and sufficient condition and a concise proof of the existence and rationality of equilibria , settling an open question raised by vazirani as a consequence we also obtain a simple new proof of mertens 's result that the equilibrium prices form a convex polyhedral set"
cs.SI,"detecting community structures in social networks has gained considerable attention in recent years however , lack of prior knowledge about the number of communities , and their overlapping nature have made community detection a challenging problem moreover , many of the existing methods only consider static networks , while most of real world networks are dynamic and evolve over time hence , finding consistent overlapping communities in dynamic networks without any prior knowledge about the number of communities is still an interesting open research problem in this paper , we present an overlapping community detection method for dynamic networks called dynamic bayesian overlapping community detector \( dbocd \) dbocd assumes that in every snapshot of network , overlapping parts of communities are dense areas and utilizes link communities instead of common node communities using recurrent chinese restaurant process and community structure of the network in the last snapshot , dbocd simultaneously extracts the number of communities and soft community memberships of nodes while maintaining the consistency of communities over time we evaluated dbocd on both synthetic and real dynamic data sets to assess its ability to find overlapping communities in different types of network evolution the results show that dbocd outperforms the recent state of the art dynamic community detection methods"
cs.LG,"we show that collaborative filtering can be viewed as a sequence prediction problem , and that given this interpretation , recurrent neural networks offer very competitive approach in particular we study how the long short term memory \( lstm \) can be applied to collaborative filtering , and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation we show that the lstm is competitive in all aspects , and largely outperforms other methods in terms of item coverage and short term predictions"
cs.DM,"we study the maximal exponent of abelian powers and abelian repetitions occurring in infinite sturmian words more precisely , we give a formula for computing the maximal exponent of an abelian power of period m occurring in any sturmian word s alpha of rotation angle alpha , and a formula for computing the maximal exponent of an abelian power of period m starting at a given position n in the sturmian word s alpha , rho of rotation angle alpha and initial point rho starting from these results , if k m \( resp k' m \) denotes the maximal exponent of an abelian power \( resp an abelian repetition \) of period m , we prove that limsup k m m limsup k' m m geq sqrt 5 for any sturmian word s alpha , and the equality holds for the infinite fibonacci word we further prove that the above superior limits are finite if and only if the development in continued fraction of alpha has bounded partial quotients , equivalently if and only if s alpha is beta power free for some real number beta concerning the infinite fibonacci word , we prove that emph i \) the longest prefix that is an abelian repetition of period f j , j 1 , has length f j \( f j 1 f j 1 1 \) 2 if j is even or f j \( f j 1 f j 1 \) 2 if j is odd , where f n is the n th fibonacci number emph ii \) the smallest abelian period of any factor of the fibonacci word is a fibonacci number from the previous results , we derive the exact formula for the smallest abelian periods of the fibonacci finite words more precisely , we prove that for j geq 3 , the fibonacci word f j , of length f j , has smallest abelian period equal to f lfloor j 2 rfloor if j 0 , 1 , 2 mod 4 , or to f 1 lfloor j 2 rfloor if j 3 mod 4"
cs.DS,"we address the problem of detecting race conditions in programs that use semaphores for synchronization netzer and miller showed that it is np complete to detect race conditions in programs that use many semaphores we show in this paper that it remains np complete even if only two semaphores are used in the parallel programs for the tractable case , i e , using only one semaphore , we give two algorithms for detecting race conditions from the trace of executing a parallel program on p processors , where n semaphore operations are executed the first algorithm determines in o \( n \) time whether a race condition exists between any two given operations the second algorithm runs in o \( np log n \) time and outputs a compact representation from which one can determine in o \( 1 \) time whether a race condition exists between any two given operations the second algorithm is near optimal in that the running time is only o \( log n \) times the time required simply to write down the output"
cs.DM,"an independent set in a graph is a set of pairwise non adjacent vertices , and a \( g \) is the size of a maximum independent set in the graph g if s k is the number of independent sets of cardinality k in g , then i \( g x \) s 0 s 1 x s 2 x 2 s a x a , a a \( g \) , is called the independence polynomial of g \( i gutman and f harary , 1983 \) if s a i f \( i \) s i holds for every i , then i \( g x \) is called f symmetric \( f palindromic \) if f \( i \) 1 , then i \( g x \) is symmetric \( palindromic \) the corona of the graphs g and h is the graph g h obtained by joining each vertex of g to all the vertices of a copy of h in this paper we show that if h is a graph with p vertices , q edges , and alpha \( h \) 2 , then i \( g h x \) is f symmetric for some elegant function f in particular , if h k r e , we show that i \( g h x \) is symmetric and unimodal , with a unique mode this finding generalizes results due to \( stevanovic , 1998 \) and \( mandrescu , 2012 \) claiming that i \( g \( k 2 e \) x \) i \( g 2k 1 x \) is symmetric and unimodal for every graph g"
cs.SI,"this paper explores the application of role based access control to social networks , from the perspective of social network analysis each tie , composed of a relation , a sender and a receiver , involves the sender 's assignation of the receiver to a role with permissions the model is not constrained to system defined relations and lets users define them unilaterally it benefits of rbac 's advantages , such as policy neutrality , simplification of security administration and permissions on other roles tie rbac has been implemented in a core for building social network sites , social stream"
cs.AI,"many ubiquitous computing projects have addressed health and wellness behaviors such as healthy eating healthy meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity however , learning people 's food preferences and making healthy recommendations that appeal to their palate is challenging existing approaches either only learn high level preferences or require a prolonged learning period we propose yum me , a personalized healthy meal recommender system designed to meet individuals' health goals , dietary restrictions , and fine grained food preferences marrying ideas from user preference learning and healthy eating promotion , yum me enables a simple and accurate food preference profiling procedure via an image based online learning framework , and projects the learned profile into the domain of healthy food options to find ones that will appeal to the user we present the design and implementation of yum me , and further discuss the most critical component of it fooddist , a state of the art food image analysis model we demonstrate fooddist 's superior performance through careful benchmarking , and discuss its applicability across a wide array of dietary applications we validate the feasibility and effectiveness of yum me through a 60 person user study , in which yum me improves the recommendation acceptance rate by 42 63 over the traditional food preference survey"
cs.LG|stat.ML,we seek to learn an effective policy for a markov decision process \( mdp \) with continuous states via q learning given a set of basis functions over state action pairs we search for a corresponding set of linear weights that minimizes the mean bellman residual our algorithm uses a kalman filter model to estimate those weights and we have developed a simpler approximate kalman filter model that outperforms the current state of the art projected td learning methods on several standard benchmark problems
cs.SI,"comparative benefits provided by the basic social strategies including collectivism and egoism are investigated within the framework of democratic decision making in particular , we study the mechanism of growing snowball of cooperation"
cs.DM,we study the spectral distribution of the threshold network model the results contain an explicit description and its asymptotic behaviour
cs.DS,"we discuss in this paper a method of finding skyline or non dominated points in a set p of n p points with respect to a set s of n s sites a point p i in p is non dominated if and only if for each p j in p , j not i , there exists at least one point s in s that is closer to p i than p j we reduce this problem of determining non dominated points to the problem of finding sites that have non empty cells in an additive voronoi diagram with a convex distance function the weights of the additive voronoi diagram are derived from the co ordinates of the points of p and the convex distance function is derived from s in the 2 dimensional plane , this reduction gives a o \( \( n s n p \) log n s n p log n p \) time randomized incremental algorithm to find the non dominated points"
cs.AI,"designing algorithms capable of efficiently constructing minimal models of cnfs is an important task in ai this paper provides new results along this research line and presents new algorithms for performing minimal model finding and checking over positive propositional cnfs and model minimization over propositional cnfs an algorithmic schema , called the generalized elimination algorithm \( gea \) is presented , that computes a minimal model of any positive cnf the schema generalizes the elimination algorithm \( ea \) bp97 , which computes a minimal model of positive head cycle free \( hcf \) cnf theories while the ea always runs in polynomial time in the size of the input hcf cnf , the complexity of the gea depends on the complexity of the specific eliminating operator invoked therein , which may in general turn out to be exponential therefore , a specific eliminating operator is defined by which the gea computes , in polynomial time , a minimal model for a class of cnf that strictly includes head elementary set free \( hef \) cnf theories gll06 , which form , in their turn , a strict superset of hcf theories furthermore , in order to deal with the high complexity associated with recognizing hef theories , an incomplete variant of the gea \( called igea \) is proposed the resulting schema , once instantiated with an appropriate elimination operator , always constructs a model of the input cnf , which is guaranteed to be minimal if the input theory is hef in the light of the above results , the main contribution of this work is the enlargement of the tractability frontier for the minimal model finding and checking and the model minimization problems"
cs.LG,"a particular case of recurrent neural network \( rnn \) was introduced at the beginning of the 2000s under the name of echo state networks \( esns \) the esn model overcomes the limitations during the training of the rnns while introducing no significant disadvantages although the model presents some well identified drawbacks when the parameters are not well initialised the performance of an esn is highly dependent on its internal parameters and pattern of connectivity of the hidden hidden weights often , the tuning of the network parameters can be hard and can impact in the accuracy of the models in this work , we investigate the performance of a specific boosting technique \( called l2 boost \) with esns as single predictors the l2 boost technique has been shown to be an effective tool to combine weak predictors in regression problems in this study , we use an ensemble of random initialized esns \( without control their parameters \) as weak predictors of the boosting procedure we evaluate our approach on five well know time series benchmark problems additionally , we compare this technique with a baseline approach that consists of averaging the prediction of an ensemble of esns"
cs.AI|cs.LG,"in this article , we work towards the goal of developing agents that can learn to act in complex worlds we develop a probabilistic , relational planning rule representation that compactly models noisy , nondeterministic action effects , and show how such rules can be effectively learned through experiments in simple planning domains and a 3d simulated blocks world with realistic physics , we demonstrate that this learning algorithm allows agents to effectively model world dynamics"
cs.LG|stat.ML,"we propose a novel hierarchical model for multitask bipartite ranking the proposed approach combines a matrix variate gaussian process with a generative model for task wise bipartite ranking in addition , we employ a novel trace constrained variational inference approach to impose low rank structure on the posterior matrix variate gaussian process the resulting posterior covariance function is derived in closed form , and the posterior mean function is the solution to a matrix variate regression with a novel spectral elastic net regularizer further , we show that variational inference for the trace constrained matrix variate gaussian process combined with maximum likelihood parameter estimation for the bipartite ranking model is jointly convex our motivating application is the prioritization of candidate disease genes the goal of this task is to aid the identification of unobserved associations between human genes and diseases using a small set of observed associations as well as kernels induced by gene gene interaction networks and disease ontologies our experimental results illustrate the performance of the proposed model on real world datasets moreover , we find that the resulting low rank solution improves the computational scalability of training and testing as compared to baseline models"
cs.DM,"a good deal of research has been done and published on coloring of the vertices of graphs for several years while studying of the excellent work of those maestros , we get inspire to work on the vertex coloring of graphs in case of a particular triangular closed path structure what we achieve from the front view of a pyramidal structure from here we achieve a repetitive nature of vertex coloring in case of odd and even number of horizontal lines within this triangular structure in order to apply this repetitive nature of vertex coloring in case of a binary tree , we get a success in krafts inequality actually our work mainly deals with a particular triangular closed path vertex coloring and repetition of the vertex coloring nature in case of the krafts inequality in the field of information theory and coding"
cs.LG,"as the emerging field of machine learning , deep learning shows excellent ability in solving complex learning problems however , the size of the networks becomes increasingly large scale due to the demands of the practical applications , which poses significant challenge to construct a high performance implementations of deep learning neural networks in order to improve the performance as well to maintain the low power cost , in this paper we design dlau , which is a scalable accelerator architecture for large scale deep learning networks using fpga as the hardware prototype the dlau accelerator employs three pipelined processing units to improve the throughput and utilizes tile techniques to explore locality for deep learning applications experimental results on the state of the art xilinx fpga board demonstrate that the dlau accelerator is able to achieve up to 36 1x speedup comparing to the intel core2 processors , with the power consumption at 234mw"
cs.LG,"this paper presents a novel l1 norm semi supervised learning algorithm for robust image analysis by giving new l1 norm formulation of laplacian regularization which is the key step of graph based semi supervised learning since our l1 norm laplacian regularization is defined directly over the eigenvectors of the normalized laplacian matrix , we successfully formulate semi supervised learning as an l1 norm linear reconstruction problem which can be effectively solved with sparse coding by working with only a small subset of eigenvectors , we further develop a fast sparse coding algorithm for our l1 norm semi supervised learning due to the sparsity induced by sparse coding , the proposed algorithm can deal with the noise in the data to some extent and thus has important applications to robust image analysis , such as noise robust image classification and noise reduction for visual and textual bag of words \( bow \) models in particular , this paper is the first attempt to obtain robust image representation by sparse co refinement of visual and textual bow models the experimental results have shown the promising performance of the proposed algorithm"
cs.SI,"in this paper , we develop the themes presented at the 2003 joint complexity conference at the london school of economics and subsequently published in the intelligencer \( 2004 \) and o tempo das redes \( 2008 \) following the data analysis of the 9 11 high jacker network developed by valdis krebs from open sources , we apply social network theory to examine salient arguments regarding terrorism as seen from the standpoint of complex adaptive systems theory in particular , we explore the concepts of group cohesion , adhesion and alternative network mappings derived from node removal"
cs.AI,"in this work we present sift , a 3 step algorithm for the analysis of the structural information represented by means of a taxonomy the major advantage of this algorithm is the capability to leverage the information inherent to the hierarchical structures of taxonomies to infer correspondences which can allow to merge them in a later step this method is particular relevant in scenarios where taxonomy alignment techniques exploiting textual information from taxonomy nodes cannot operate successfully"
cs.LG|stat.ML,"in this paper we consider the task of estimating the non zero pattern of the sparse inverse covariance matrix of a zero mean gaussian random vector from a set of iid samples note that this is also equivalent to recovering the underlying graph structure of a sparse gaussian markov random field \( gmrf \) we present two novel greedy approaches to solving this problem the first estimates the non zero covariates of the overall inverse covariance matrix using a series of global forward and backward greedy steps the second estimates the neighborhood of each node in the graph separately , again using greedy forward and backward steps , and combines the intermediate neighborhoods to form an overall estimate the principal contribution of this paper is a rigorous analysis of the sparsistency , or consistency in recovering the sparsity pattern of the inverse covariance matrix surprisingly , we show that both the local and global greedy methods learn the full structure of the model with high probability given just o \( d log \( p \) \) samples , which is a emph significant improvement over state of the art ell 1 regularized gaussian mle \( graphical lasso \) that requires o \( d 2 log \( p \) \) samples moreover , the restricted eigenvalue and smoothness conditions imposed by our greedy methods are much weaker than the strong irrepresentable conditions required by the ell 1 regularization based methods we corroborate our results with extensive simulations and examples , comparing our local and global greedy methods to the ell 1 regularized gaussian mle as well as the neighborhood greedy method to that of nodewise ell 1 regularized linear regression \( neighborhood lasso \)"
cs.LG|stat.ML,"boosted decision trees typically yield good accuracy , precision , and roc area however , because the outputs from boosting are not well calibrated posterior probabilities , boosting yields poor squared error and cross entropy we empirically demonstrate why adaboost predicts distorted probabilities and examine three calibration methods for correcting this distortion platt scaling , isotonic regression , and logistic correction we also experiment with boosting using log loss instead of the usual exponential loss experiments show that logistic correction and boosting with log loss work well when boosting weak models such as decision stumps , but yield poor performance when boosting more complex models such as full decision trees platt scaling and isotonic regression , however , significantly improve the probabilities predicted by"
cs.AI,"graphical models of probabilistic dependencies have been extensively investigated in the context of classical uncertainty however , in some domains \( most notably , in computational physics and quantum computing \) the nature of the relevant uncertainty is non classical , and the laws of classical probability theory are superseded by those of quantum mechanics in this paper we introduce markovian entanglement networks \( men \) , a novel class of graphical representations of quantum mechanical dependencies in the context of such non classical systems men are the quantum mechanical analogue of markovian networks , a family of undirected graphical representations which , in the classical domain , exploit a notion of conditional independence among subsystems after defining a notion of conditional independence appropriate to our domain \( conditional separability \) , we prove that the conditional separabilities induced by a quantum mechanical wave function are effectively reflected in the graphical structure of men specifically , we show that for any wave function there exists a men which is a perfect map of its conditional separabilities next , we show how the graphical structure of men can be used to effectively classify the pure states of three qubit systems we also demonstrate that , in large systems , exploiting conditional independencies may dramatically reduce the computational burden of various inference tasks in principle , the graph theoretic representation of conditional independencies afforded by men may not only facilitate the classical simulation of quantum systems , but also provide a guide to the efficient design and complexity analysis of quantum algorithms and circuits"
cs.DS,"we present an incremental , scalable and efficient dimension reduction technique for tensors that is based on sparse random linear coding data is stored in a compactified representation with fixed size , which makes memory requirements low and predictable component encoding and decoding are performed on line without computationally expensive re analysis of the data set the range of tensor indices can be extended dynamically without modifying the component representation this idea originates from a mathematical model of semantic memory and a method known as random indexing in natural language processing we generalize the random indexing algorithm to tensors and present signal to noise ratio simulations for representations of vectors and matrices we present also a mathematical analysis of the approximate orthogonality of high dimensional ternary vectors , which is a property that underpins this and other similar random coding approaches to dimension reduction to further demonstrate the properties of random indexing we present results of a synonym identification task the method presented here has some similarities with random projection and tucker decomposition , but it performs well at high dimensionality only \( n 10 3 \) random indexing is useful for a range of complex practical problems , e g , in natural language processing , data mining , pattern recognition , event detection , graph searching and search engines prototype software is provided it supports encoding and decoding of tensors of order 1 in a unified framework , i e , vectors , matrices and higher order tensors"
cs.SI,"the quest for historically impactful science and technology provides invaluable insight into the innovation dynamics of human society , yet many studies are limited to qualitative and small scale approaches here , we investigate scientific evolution through systematic analysis of a massive corpus of digitized english texts between 1800 and 2008 our analysis reveals great predictability for long prevailing scientific concepts based on the levels of their prior usage interestingly , once a threshold of early adoption rates is passed even slightly , scientific concepts can exhibit sudden leaps in their eventual lifetimes we developed a mechanistic model to account for such results , indicating that slowly but commonly adopted science and technology surprisingly tend to have higher innate strength than fast and commonly adopted ones the model prediction for disciplines other than science was also well verified our approach sheds light on unbiased and quantitative analysis of scientific evolution in society , and may provide a useful basis for policy making"
cs.SI,"the social brain hypothesis predicts that humans have an average of about 150 relationships at any given time within this 150 , there are layers of friends of an ego , where the number of friends in a layer increases as the emotional closeness decreases here we analyse a mobile phone dataset , firstly , to ascertain whether layers of friends can be identified based on call frequency we then apply different clustering algorithms to break the call frequency of egos into clusters and compare the number of alters in each cluster with the layer size predicted by the social brain hypothesis in this dataset we find strong evidence for the existence of a layered structure the clustering yields results that match well with previous studies for the innermost and outermost layers , but for layers in between we observe large variability"
cs.LG,"we perform a study of the factors affecting training time in multi device deep learning systems given a specification of a convolutional neural network , we study how to minimize the time to train this model on a cluster of commodity cpus and gpus our first contribution focuses on the single node setting , in which we show that by using standard batching and data parallel techniques throughput can be improved by at least 5 5x over state of the art systems when training on cpus this ensures an end to end training time directly proportional to the throughput of a device regardless of its underlying hardware , allowing each node in the cluster to be treated as a black box our second contribution is a theoretical and empirical study of the tradeoffs affecting end to end training time in a multiple device setting we identify the degree of asynchronous parallelization as a key feature affecting both hardware and statistical efficiency we show that asynchrony can be viewed as introducing a momentum parameter , which we use to limit our search space in turn , this leads to a simpler optimizer , which is our third contribution our optimizer involves a predictive model for the total time to convergence and selects an allocation of resources to minimize that time we demonstrate that the most popular distributed deep learning systems fall within our tradeoff space but do not optimize within the space by doing such optimization , our prototype runs 1 9x to 12x faster than the fastest state of the art systems"
cs.DM,"in this note , we give an explicit polynomial time executable strategy for peter winkler 's hat guessing game that gives superior results if the distribution of hats is imbalanced while winkler 's strategy guarantees in any case that lfloor n 2 rfloor of the n player guess their hat color correct , our strategy ensures that the players produce max r , b 1 2 n 2 3 2 correct guesses for any distribution of r red and b n r blue hats we also show that any strategy ensuring max r , b f \( n \) correct guesses necessarily has f \( n \) omega \( sqrt n \)"
cs.LG|stat.ML,"in this study , we intend to solve a mutual information problem in interacting molecules of any type , such as proteins , nucleic acids , and small molecules using machine learning techniques , we accurately predict pairwise interactions , which can be of medical and biological importance graphs are are useful in this problem for their generality to all types of molecules , due to the inherent association of atoms through atomic bonds subgraphs can represent different molecular domains these domains can be biologically significant as most molecules only have portions that are of functional significance and can interact with other domains thus , we use subgraphs as features in different machine learning algorithms to predict if two drugs interact and predict potential single molecule effects"
cs.LG|stat.ML,we extend the herding algorithm to continuous spaces by using the kernel trick the resulting kernel herding algorithm is an infinite memory deterministic process that learns to approximate a pdf with a collection of samples we show that kernel herding decreases the error of expectations of functions in the hilbert space at a rate o \( 1 t \) which is much faster than the usual o \( 1 pt \) for iid random samples we illustrate kernel herding by approximating bayesian predictive distributions
cs.DM,we introduce the concept of nic planar graphs and present the full characterization of nic planar complete k partite graphs
cs.SI,"an interrelation between a topological design of network and efficient algorithm on it is important for its applications to communication or transportation systems in this paper , we propose a design principle for a reliable routing in a store carry forward manner based on autonomously moving message ferries on a special structure of fractal like network , which consists of a self similar tiling of equilateral triangles as a collective adaptive mechanism , the routing is realized by a relay of cyclic message ferries corresponded to a concatenation of the triangle cycles and using some good properties of the network structure it is recoverable for local accidents in the hierarchical network structure moreover , the design principle is theoretically supported with a calculation method for the optimal service rates of message ferries derived from a tandem queue model for stochastic processes on a chain of edges in the network these results obtained from a combination of complex network science and computer science will be useful for developing a resilient network system"
cs.LG|stat.ML,"recent reports have described that learning bayesian networks are highly sensitive to the chosen equivalent sample size \( ess \) in the bayesian dirichlet equivalence uniform \( bdeu \) this sensitivity often engenders some unstable or undesirable results this paper describes some asymptotic analyses of bdeu to explain the reasons for the sensitivity and its effects furthermore , this paper presents a proposal for a robust learning score for ess by eliminating the sensitive factors from the approximation of log bdeu"
cs.AI,"topic models are a way to discover underlying themes in an otherwise unstructured collection of documents in this study , we specifically used the latent dirichlet allocation \( lda \) topic model on a dataset of yelp reviews to classify restaurants based off of their reviews furthermore , we hypothesize that within a city , restaurants can be grouped into similar clusters based on both location and similarity we used several different clustering methods , including k means clustering and a probabilistic mixture model , in order to uncover and classify districts , both well known and hidden \( i e cultural areas like chinatown or hearsay like the best street for italian restaurants \) within a city we use these models to display and label different clusters on a map we also introduce a topic similarity heatmap that displays the similarity distribution in a city to a new restaurant"
cs.SI,"a mass of traces of human activities show rich dynamic patterns in this article , we comprehensively investigate the dynamic patterns of 50 thousands of researchers' activities in sciencenet , the largest multi disciplinary academic community in china through statistical analyses , we found that \( i \) there exists a power law scaling between the frequency of visits to an academic forum and the number of corresponding visitors , with the exponent being about 1 33 \( ii \) the expansion process of academic forums obeys the heaps' law , namely the number of distinct visited forums to the number of visits grows in a power law form with exponent being about 0 54 \( iii \) the probability distributions of time interval and the number of visits taken to revisit the same academic forum both follow power laws , indicating the existence of memory effect in academic forum activities on the basis of these empirical results , we propose a dynamic model that incorporates the exploration , preferential return and memory effect , which can well reproduce the observed scaling laws"
cs.LG|stat.ML,"directed acyclic graphs \( dags \) are a popular framework to express multivariate probability distributions acyclic directed mixed graphs \( admgs \) are generalizations of dags that can succinctly capture much richer sets of conditional independencies , and are especially useful in modeling the effects of latent variables implicitly unfortunately there are currently no good parameterizations of general admgs in this paper , we apply recent work on cumulative distribution networks and copulas to propose one one general construction for admg models we consider a simple parameter estimation approach , and report some encouraging experimental results"
cs.SI,"how far and how fast does information spread in social media \? researchers have recently examined a number of factors that affect information diffusion in online social networks , including the novelty of information , users' activity levels , who they pay attention to , and how they respond to friends' recommendations using urls as markers of information , we carry out a detailed study of retweeting , the primary mechanism by which information spreads on the twitter follower graph our empirical study examines how users respond to an incoming stimulus , i e , a tweet \( message \) from a friend , and reveals that retweeting behavior is constrained by a few simple principles the principle of least effort combined with limited attention plays a dominant role in retweeting behavior specifically , we observe that users retweet information when it is most visible , such as when it near the top of their twitter stream moreover , our measurements quantify how a user 's limited attention is divided among incoming tweets , providing novel evidence that highly connected individuals are less likely to propagate an arbitrary tweet our study indicates that the finite ability to process incoming information constrains social contagion , and we conclude that rapid decay of visibility is the primary barrier to information propagation online"
cs.LG,"this paper proposes the stacked approximated regression machine \( sarm \) , a novel , simple yet powerful deep learning \( dl \) baseline we start by discussing the relationship between regularized regression models and feed forward networks , with emphasis on the non negative sparse coding and convolutional sparse coding models we demonstrate how these models are naturally converted into a unified feed forward network structure , which coincides with popular dl components sarm is constructed by stacking multiple unfolded and truncated regression models compared to the pcanet , whose feature extraction layers are completely linear , sarm naturally introduces non linearities , by embedding sparsity regularization the parameters of sarm are easily obtained , by solving a series of light weight problems , e g , pca or ksvd extensive experiments are conducted , which show that sarm outperforms the existing simple deep baseline , pcanet , and is on par with many state of the art deep models , but with much lower computational loads"
cs.AI,"infinite loops and redundant computations are long recognized open problems in prolog two ways have been explored to resolve these problems loop checking and tabling loop checking can cut infinite loops , but it cannot be both sound and complete even for function free logic programs tabling seems to be an effective way to resolve infinite loops and redundant computations however , existing tabulated resolutions , such as oldt resolution , slg resolution , and tabulated sls resolution , are non linear because they rely on the solution lookup mode in formulating tabling the principal disadvantage of non linear resolutions is that they cannot be implemented using a simple stack based memory structure like that in prolog moreover , some strictly sequential operators such as cuts may not be handled as easily as in prolog in this paper , we propose a hybrid method to resolve infinite loops and redundant computations we combine the ideas of loop checking and tabling to establish a linear tabulated resolution called tp resolution tp resolution has two distinctive features \( 1 \) it makes linear tabulated derivations in the same way as prolog except that infinite loops are broken and redundant computations are reduced it handles cuts as effectively as prolog \( 2 \) it is sound and complete for positive logic programs with the bounded term size property the underlying algorithm can be implemented by an extension to any existing prolog abstract machines such as wam or atoam"
cs.DM|cs.DS,"there is substantial literature dealing with fixed parameter algorithms for the dominating set problem on various families of graphs in this paper , we give a k o \( dk \) n time algorithm for finding a dominating set of size at most k in a d degenerated graph with n vertices this proves that the dominating set problem is fixed parameter tractable for degenerated graphs for graphs that do not contain k h as a topological minor , we give an improved algorithm for the problem with running time \( o \( h \) \) hk n for graphs which are k h minor free , the running time is further reduced to \( o \( log h \) \) hk 2 n fixed parameter tractable algorithms that are linear in the number of vertices of the graph were previously known only for planar graphs for the families of graphs discussed above , the problem of finding an induced cycle of a given length is also addressed for every fixed h and k , we show that if an h minor free graph g with n vertices contains an induced cycle of size k , then such a cycle can be found in o \( n \) expected time as well as in o \( n log n \) worst case time some results are stated concerning the \( im \) possibility of establishing linear time algorithms for the more general family of degenerated graphs"
cs.IT|cs.LG,"compressed sensing \( cs \) is a novel technique for simultaneous signal sampling and compression based on the existence of a sparse representation of signal and a projected dictionary pp d , where pp in mathbb r m times d is the projection matrix and d in mathbb r d times n is the dictionary to exactly recover the signal with a small number of measurements m , the projected dictionary pp d is expected to be of low mutual coherence several previous methods attempt to find the projection pp such that the mutual coherence of pp d can be as low as possible however , they do not minimize the mutual coherence directly and thus their methods are far from optimal also the solvers they used lack of the convergence guarantee and thus there has no guarantee on the quality of their obtained solutions this work aims to address these issues we propose to find an optimal projection by minimizing the mutual coherence of pp d directly this leads to a nonconvex nonsmooth minimization problem we then approximate it by smoothing and solve it by alternate minimization we further prove the convergence of our algorithm to the best of our knowledge , this is the first work which directly minimizes the mutual coherence of the projected dictionary with a convergence guarantee numerical experiments demonstrate that the proposed method can recover sparse signals better than existing methods"
cs.AI|cs.LG|stat.ML,"unlike traditional programs \( such as operating systems or word processors \) which have large amounts of code , machine learning tasks use programs with relatively small amounts of code \( written in machine learning libraries \) , but voluminous amounts of data just like developers of traditional programs debug errors in their code , developers of machine learning tasks debug and fix errors in their data however , algorithms and tools for debugging and fixing errors in data are less common , when compared to their counterparts for detecting and fixing errors in code in this paper , we consider classification tasks where errors in training data lead to misclassifications in test points , and propose an automated method to find the root causes of such misclassifications our root cause analysis is based on pearl 's theory of causation , and uses pearl 's ps \( probability of sufficiency \) as a scoring metric our implementation , psi , encodes the computation of ps as a probabilistic program , and uses recent work on probabilistic programs and transformations on probabilistic programs \( along with gray box models of machine learning algorithms \) to efficiently compute ps psi is able to identify root causes of data errors in interesting data sets"
cs.LG|stat.ML,"actively sampled data can have very different characteristics than passively sampled data therefore , it 's promising to investigate using different inference procedures during al than are used during passive learning \( pl \) this general idea is explored in detail for the focused case of al with cost weighted svms for imbalanced data , a situation that arises for many hlt tasks the key idea behind the proposed initpa method for addressing imbalance is to base cost models during al on an estimate of overall corpus imbalance computed via a small unbiased sample rather than the imbalance in the labeled training data , which is the leading method used during pl"
cs.SI|stat.ML,"in this paper , we present a novel way to summarize the structure of large graphs , based on non parametric estimation of edge density in directed multigraphs following coclustering approach , we use a clustering of the vertices , with a piecewise constant estimation of the density of the edges across the clusters , and address the problem of automatically and reliably inferring the number of clusters , which is the granularity of the coclustering we use a model selection technique with data dependent prior and obtain an exact evaluation criterion for the posterior probability of edge density estimation models we demonstrate , both theoretically and empirically , that our data dependent modeling technique is consistent , resilient to noise , valid non asymptotically and asymptotically behaves as an universal approximator of the true edge density in directed multigraphs we evaluate our method using artificial graphs and present its practical interest on real world graphs the method is both robust and scalable it is able to extract insightful patterns in the unsupervised learning setting and to provide state of the art accuracy when used as a preparation step for supervised learning"
cs.DS,"we review some of quantum algorithms for search problems grover 's search algorithm , its generalization to amplitude amplification , the applications of amplitude amplification to various problems and the recent quantum algorithms based on quantum walks"
cs.LG|stat.ML,"protein function prediction is the important problem in modern biology in this paper , the un normalized , symmetric normalized , and random walk graph laplacian based semi supervised learning methods will be applied to the integrated network combined from multiple networks to predict the functions of all yeast proteins in these multiple networks these multiple networks are network created from pfam domain structure , co participation in a protein complex , protein protein interaction network , genetic interaction network , and network created from cell cycle gene expression measurements multiple networks are combined with fixed weights instead of using convex optimization to determine the combination weights due to high time complexity of convex optimization method this simple combination method will not affect the accuracy performance measures of the three semi supervised learning methods experiment results show that the un normalized and symmetric normalized graph laplacian based methods perform slightly better than random walk graph laplacian based method for integrated network moreover , the accuracy performance measures of these three semi supervised learning methods for integrated network are much better than the best accuracy performance measures of these three methods for the individual network"
stat.ML,"this study is aimed at answering the famous question of how the approximation errors at each iteration of approximate dynamic programming \( adp \) affect the quality of the final results considering the fact that errors at each iteration affect the next iteration to this goal , convergence of value iteration scheme of adp for deterministic nonlinear optimal control problems with undiscounted cost functions is investigated while considering the errors existing in approximating respective functions the boundedness of the results around the optimal solution is obtained based on quantities which are known in a general optimal control problem and assumptions which are verifiable moreover , since the presence of the approximation errors leads to the deviation of the results from optimality , sufficient conditions for stability of the system operated by the result obtained after a finite number of value iterations , along with an estimation of its region of attraction , are derived in terms of a calculable upper bound of the control approximation error finally , the process of implementation of the method on an orbital maneuver problem is investigated through which the assumptions made in the theoretical developments are verified and the sufficient conditions are applied for guaranteeing stability and near optimality"
cs.SI,"relevance and importance are the main factors when humans build network connections we propose an evolutionary network model based on preferential attachment \( pa \) considering these factors we analyze and compute several important features of the network class generated by this algorithm including scale free degree distribution , high clustering coefficient , small world property and core periphery structure we then compare this model with other network models and empirical data such as inter city road transportation and air traffic networks"
cs.LG,"this work focuses on top k recommendation in domains where underlying data distribution shifts overtime we propose to learn a time dependent bias for each item over whatever existing recommendation engine such a bias learning process alleviates data sparsity in constructing the engine , and at the same time captures recent trend shift observed in data we present an alternating optimization framework to resolve the bias learning problem , and develop methods to handle a variety of commonly used recommendation evaluation criteria , as well as large number of items and users in practice the proposed algorithm is examined , both offline and online , using real world data sets collected from the largest retailer worldwide empirical results demonstrate that the bias learning can almost always boost recommendation performance we encourage other practitioners to adopt it as a standard component in recommender systems where temporal dynamics is a norm"
cs.SI,"in this work , we are interested in finding the most efficient use of a budget to promote an opinion by paying agents within a group to supplant their true opinions we model opinions as continuous scalars ranging from 0 to 1 with 1 \( 0 \) representing extremely positive \( negative \) opinion we focus on asymmetric confidence between agents the iterative update of an agent corresponds to the best response to other agents' actions the resulting confidence matrix can be seen as an equivalent markov chain we provide simple and efficient algorithms to solve this problem and we show through an example how to solve the stated problem in practice"
cs.DS,"the generalised sudoku problem with n symbols is known to be np complete , and hence is equivalent to any other np complete problem , even for the standard restricted version where n is a perfect square in particular , generalised sudoku is equivalent to the , classical , hamiltonian cycle problem a constructive algorithm is given that reduces generalised sudoku to the hamiltonian cycle problem , where the resultant instance of hamiltonian cycle problem is sparse , and has o \( n 3 \) vertices the hamiltonian cycle problem instance so constructed is a directed graph , and so a \( known \) conversion to undirected hamiltonian cycle problem is also provided so that it can be submitted to the best heuristics a simple algorithm for obtaining the valid sudoku solution from the hamiltonian cycle is provided techniques to reduce the size of the resultant graph are also discussed"
cs.DM|cs.DS,"we prove that any graph g with n points has a distribution mathcal t over spanning trees such that for any edge \( u , v \) the expected stretch e t sim mathcal t d t \( u , v \) d g \( u , v \) is bounded by tilde o \( log n \) our result is obtained via a new approach of building ``highways'' between portals and a new strong diameter probabilistic decomposition theorem"
cs.AI,"partial monitoring games are repeated games where the learner receives feedback that might be different from adversary 's move or even the reward gained by the learner recently , a general model of combinatorial partial monitoring \( cpm \) games was proposed cite lincombinatorial2014 , where the learner 's action space can be exponentially large and adversary samples its moves from a bounded , continuous space , according to a fixed distribution the paper gave a confidence bound based algorithm \( gcb \) that achieves o \( t 2 3 log t \) distribution independent and o \( log t \) distribution dependent regret bounds the implementation of their algorithm depends on two separate offline oracles and the distribution dependent regret additionally requires existence of a unique optimal action for the learner adopting their cpm model , our first contribution is a phased exploration with greedy exploitation \( pege \) algorithmic framework for the problem different algorithms within the framework achieve o \( t 2 3 sqrt log t \) distribution independent and o \( log 2 t \) distribution dependent regret respectively crucially , our framework needs only the simpler argmax oracle from gcb and the distribution dependent regret does not require existence of a unique optimal action our second contribution is another algorithm , pege2 , which combines gap estimation with a pege algorithm , to achieve an o \( log t \) regret bound , matching the gcb guarantee but removing the dependence on size of the learner 's action space however , like gcb , pege2 requires access to both offline oracles and the existence of a unique optimal action finally , we discuss how our algorithm can be efficiently applied to a cpm problem of practical interest namely , online ranking with feedback at the top"
cs.DS|cs.IT,"bayesian networks are basic graphical models , used widely both in statistics and artificial intelligence these statistical models of conditional independence structure are described by acyclic directed graphs whose nodes correspond to \( random \) variables in consideration a quite important topic is the learning of bayesian network structures , which is determining the best fitting statistical model on the basis of given data although there are learning methods based on statistical conditional independence tests , contemporary methods are mainly based on maximization of a suitable quality criterion that evaluates how good the graph explains the occurrence of the observed data this leads to a nonlinear combinatorial optimization problem that is in general np hard to solve in this paper we deal with the complexity of learning restricted bayesian network structures , that is , we wish to find network structures of highest score within a given subset of all possible network structures for this , we introduce a new unique algebraic representative for these structures , called the characteristic imset we show that these imsets are always 0 1 vectors and that they have many nice properties that allow us to simplify long proofs for some known results and to easily establish new complexity results for learning restricted bayes network structures"
cs.IT|cs.LG|cs.SI,"in a paper that initiated the modern study of the stochastic block model , decelle , krzakala , moore and zdeborov 'a made a fascinating conjecture denote by k the number of balanced communities , a n the probability of connecting inside clusters and b n across clusters , and set mathrm snr \( a b \) 2 \( k \( a \( k 1 \) b \) \) for any k geq 2 , it is possible to detect efficiently communities whenever mathrm snr 1 \( the ks bound \) , whereas for k geq 5 , it is possible to detect communities information theoretically for some mathrm snr 1 massouli 'e , mossel et al and bordenave et al succeeded in proving that the ks bound is efficiently achievable for k 2 , while mossel et al proved that the ks bound cannot be crossed information theoretically for k 2 the above conjecture remained open beyond two communities this paper proves this conjecture specifically , an acyclic belief propagation \( abp \) algorithm is developed and proved to detect communities for any k whenever mathrm snr 1 , i e , down the ks bound , with a complexity of o \( n log n \) the parameters are also learned efficiently down the ks bound thus abp improves upon the state of the art both in terms of complexity and universality in achieving the ks bound this also gives a rigorous instance where message passing succeeds with optimal guarantees while handling cycles and a random initialization further , a non efficient algorithm sampling a typical clustering is shown to break down the ks bound at k 5 the gap between the ks bound and the information theoretic bound is shown to be large in some cases if a 0 , the ks bound reads b gtrsim k 2 whereas the information theoretic bound reads b gtrsim k ln \( k \) the results extend to general sbms"
cs.SI,"we present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of highly clustered random graphs introduced by gleeson j p gleeson , phys rev e 80 , 036107 \( 2009 \) a condition for the existence of global cascades is also derived applications of this approach include analyses of percolation , and watts 's model we show how our techniques can be used to study the effects of in group bias in cascades on social networks"
cs.DM|cs.DS,"the bin packing problem is one of the most important optimization problems in recent years , due to its np hard nature , several approximation algorithms have been presented it is proved that the best algorithm for the bin packing problem has the approximation ratio 3 2 and the time order o \( n \) , unless p np in this paper , first , a 3 2 approximation algorithm is presented , then a modification to ffd algorithm is proposed to decrease time order to linear finally , these suggested approximation algorithms are compared with some other approximation algorithms the experimental results show the suggested algorithms perform efficiently in summary , the main goal of the research is presenting methods which not only enjoy the best theoretical criteria , but also perform considerably efficient in practice"
cs.AI,"previous studies in open information extraction \( open ie \) are mainly based on extraction patterns they manually define patterns or automatically learn them from a large corpus however , these approaches are limited when grasping the context of a sentence , and they fail to capture implicit relations in this paper , we address this problem with the following methods first , we exploit long short term memory \( lstm \) networks to extract higher level features along the shortest dependency paths , connecting headwords of relations and arguments the path level features from lstm networks provide useful clues regarding contextual information and the validity of arguments second , we constructed samples to train lstm networks without the need for manual labeling in particular , feedback negative sampling picks highly negative samples among non positive samples through a model trained with positive samples the experimental results show that our approach produces more precise and abundant extractions than state of the art open ie systems to the best of our knowledge , this is the first work to apply deep learning to open ie"
cs.LG|stat.ML,"a central question for active learning \( al \) is what is the optimal selection \? defining optimality by classifier loss produces a new characterisation of optimal al behaviour , by treating expected loss reduction as a statistical target for estimation this target forms the basis of model retraining improvement \( mri \) , a novel approach providing a statistical estimation framework for al this framework is constructed to address the central question of al optimality , and to motivate the design of estimation algorithms mri allows the exploration of optimal al behaviour , and the examination of al heuristics , showing precisely how they make sub optimal selections the abstract formulation of mri is used to provide a new guarantee for al , that an unbiased mri estimator should outperform random selection this mri framework reveals intricate estimation issues that in turn motivate the construction of new statistical al algorithms one new algorithm in particular performs strongly in a large scale experimental study , compared to standard al methods this competitive performance suggests that practical efforts to minimise estimation bias may be important for al applications"
cs.DS|cs.SI,"spatially embedded random networks such as the waxman random graph have been used in a variety of settings for synthesizing networks however , little thought has been put into fast generation of these networks existing techniques are o \( n 2 \) where n is the number of nodes in the graph in this paper we present an o \( n e \) algorithm , where e is the number of edges"
cs.LG,"training of large scale deep neural networks is often constrained by the available computational resources we study the effect of limited precision data representation and computation on neural network training within the context of low precision fixed point computations , we observe the rounding scheme to play a crucial role in determining the network 's behavior during training our results show that deep networks can be trained using only 16 bit wide fixed point number representation when using stochastic rounding , and incur little to no degradation in the classification accuracy we also demonstrate an energy efficient hardware accelerator that implements low precision fixed point arithmetic with stochastic rounding"
cs.SI,"we propose an algorithm for detecting communities of links in networks which uses local information , is based on a new evaluation function , and allows for pervasive overlaps of communities the complexity of the clustering task requires the application of a memetic algorithm that combines probabilistic evolutionary strategies with deterministic local searches in part 2 we will present results of experiments with a large citation network of astrophysical papers"
cs.AI,"a first order conditional logic is considered , with semantics given by a variant of epsilon semantics , where p q means that pr \( q p \) approaches 1 super polynomially faster than any inverse polynomial this type of convergence is needed for reasoning about security protocols a complete axiomatization is provided for this semantics , and it is shown how a qualitative proof of the correctness of a security protocol can be automatically converted to a quantitative proof appropriate for reasoning about concrete security"
cs.DM|cs.IT,"in this paper , we present a new lower bound on the size of separating hash families of type w 1 q 1 , w 2 where w 1 w 2 our result extends the paper by guo et al on binary frameproof codes this bound compares well against known general bounds , and is especially useful when trying to bound the size of strong separating hash families we also show that our new bound is tight by constructing hash families that meet the new bound with equality"
cs.SI,"this paper develops a generalization of the pagerank model of page centralities in the global webgraph of hyperlinks the webgraph of adjacencies is generalized to a valued directed graph , and the scalar dampening coefficient for walks through the graph is relaxed to allow for heterogeneous values a visitation count approach may be employed to apply the more general model , based on the number of visits to a page and the page 's proportionate allocations of these visits to other nodes of the webgraph"
cs.AI,"spam is commonly known as unsolicited or unwanted email messages in the internet causing potential threat to internet security users spend a valuable amount of time deleting spam emails more importantly , ever increasing spam emails occupy server storage space and consume network bandwidth keyword based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters the evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam this paper investigates the possibilities of modeling spammer behavioral patterns by well known classification algorithms such as na ive bayesian classifier \( na ive bayes \) , decision tree induction \( dti \) and support vector machines \( svms \) preliminary experimental results demonstrate a promising detection rate of around 92 , which is considerably an enhancement of performance compared to similar spammer behavior modeling research"
cs.DM|cs.DS,"lam , gusfield , and sridhar \( 2009 \) showed that a set of three state characters has a perfect phylogeny if and only if every subset of three characters has a perfect phylogeny they also gave a complete characterization of the sets of three three state characters that do not have a perfect phylogeny however , it is not clear from their characterization how to find a subset of three characters that does not have a perfect phylogeny without testing all triples of characters in this note , we build upon their result by giving a simple characterization of when a set of three state characters does not have a perfect phylogeny that can be inferred from testing all pairs of characters"
cs.DS,"lsm tree has been widely used in cloud computing systems by google , facebook , and amazon , to achieve high performance for write intensive workloads however , in lsm tree , random key value queries can experience long latency and low throughput due to the interference from the compaction , a basic operation in the algorithm , to caching lsm tree relies on frequent compaction operations to merge data into a sorted structure after a compaction , the original data are reorganized and written to other locations on the disk as a result , the cached data are invalidated since their referencing addresses are changed , causing serious performance degradations we propose dlsm in order to re enable high speed caching during intensive writes dlsm is an lsm tree with a compaction buffer on the disk , working as a cushion to minimize the cache invalidation caused by compactions the compaction buffer maintains a series of snapshots of the frequently compacted data , which represent a consistent view of the corresponding data in the underlying lsm tree being updated in a much lower rate than that of compactions , data in the compaction buffer are almost stationary in dlsm , an object is referenced by the disk address of the corresponding block either in the compaction buffer for frequently compacted data , or in the underlying lsm tree for infrequently compacted data thus , hot objects can be effectively kept in the cache without harmful invalidations with the help of a small on disk compaction buffer , dlsm achieves a high query performance by enabling effective caching , while retaining all merits of lsm tree for write intensive data processing we have implemented dlsm based on leveldb our evaluations show that with a standard dram cache , dlsm can achieve 5 8x performance improvement over lsm with the same cache on hdd storage"
cs.AI,"in this paper , we present some high level information fusion approaches for numeric and symbolic data we study the interest of such method particularly for classifier fusion a comparative study is made in a context of sea bed characterization from sonar images the classi fication of kind of sediment is a difficult problem because of the data complexity we compare high level information fusion and give the obtained performance"
cs.AI,the paper presents an approach to verification of a multi agent data analysis algorithm we base correct simulation of the multi agent system by a finite integer model for verification we use model checking tool spin protocols of agents are written in promela language and properties of the multi agent data analysis system are expressed in logic ltl we run several experiments with spin and the model
cs.DS|cs.SI,"modeling information that resides on vertices of large graphs is a key problem in several real life applications , ranging from social networks to the internet of things signal processing on graphs and , in particular , graph wavelets can exploit the intrinsic smoothness of these datasets in order to represent them in a both compact and accurate manner however , how to discover wavelet bases that capture the geometry of the data wrt the signal and the graph structure remains an open question in this paper , we study the problem of computing graph wavelet bases via sparse cuts in order to produce low dimensional encodings of data driven bases such problem is connected to other hard problems in graph theory \( e g multiway cuts \) and thus requires an efficient heuristic we formulate the basis discovery task as a relaxation of a vector optimization problem , which leads to an elegant solution as a regularized eigenvalue computation moreover , we propose several strategies in order to scale our algorithm to large graphs the experimental results demonstrate that the proposed algorithm can effectively encode both the graph structure and signal , producing compressed and accurate representations for vertex values in a wide range of datasets \( e g sensor network , gene expression \) and outperforming the best baseline by up to 50"
cs.LG,"in this paper we present a comprehensive framework for learning robust low rank representations by combining and extending recent ideas for learning fast sparse coding regressors with structured non convex optimization techniques this approach connects robust principal component analysis \( rpca \) with dictionary learning techniques and allows its approximation via trainable encoders we propose an efficient feed forward architecture derived from an optimization algorithm designed to exactly solve robust low dimensional projections this architecture , in combination with different training objective functions , allows the regressors to be used as online approximants of the exact offline rpca problem or as rpca based neural networks simple modifications of these encoders can handle challenging extensions , such as the inclusion of geometric data transformations we present several examples with real data from image , audio , and video processing when used to approximate rpca , our basic implementation shows several orders of magnitude speedup compared to the exact solvers with almost no performance degradation we show the strength of the inclusion of learning to the rpca approach on a music source separation application , where the encoders outperform the exact rpca algorithms , which are already reported to produce state of the art results on a benchmark database our preliminary implementation on an ipad shows faster than real time performance with minimal latency"
cs.SI,"crowd sensing is a new paradigm that leverages pervasive sensor equipped mobile devices to provide sensing services like forensic analysis , documenting public spaces , and collaboratively constructing statistical models extensive user participation is indispensable for achieving good service quality nowadays , most of existing mechanisms focus on guaranteeing good service quality based on instantaneous extensive user participation for crowd sensing applications little attention has been dedicated to maximizing long term service quality for crowd sensing applications due to their asymmetric interests , preferences , selfish behaviors , etc to fill these gaps , in this paper , we derive the closed expression of the marginal sensing data quality based on the monopoly aggregation in economics furthermore , we design marginalquality based incentive mechanisms for long term crowd sensing applications , not only to enhance extensive user participation by maximizing the expected total profits of mobile users , but also to stimulate mobile users to produce high quality contents by applying the marginal quality finally , simulation results show that our mechanisms outperform the existing solutions"
cs.DS|cs.SI,"we study a new notion of graph centrality based on absorbing random walks given a graph g \( v , e \) and a set of query nodes q subseteq v , we aim to identify the k most central nodes in g with respect to q specifically , we consider central nodes to be absorbing for random walks that start at the query nodes q the goal is to find the set of k central nodes that minimizes the expected length of a random walk until absorption the proposed measure , which we call k absorbing random walk centrality , favors diverse sets , as it is beneficial to place the k absorbing nodes in different parts of the graph so as to intercept random walks that start from different query nodes although similar problem definitions have been considered in the literature , e g , in information retrieval settings where the goal is to diversify web search results , in this paper we study the problem formally and prove some of its properties we show that the problem is np hard , while the objective function is monotone and supermodular , implying that a greedy algorithm provides solutions with an approximation guarantee on the other hand , the greedy algorithm involves expensive matrix operations that make it prohibitive to employ on large datasets to confront this challenge , we develop more efficient algorithms based on spectral clustering and on personalized pagerank"
cs.LG|stat.ML,"we address the problem of estimating the parameters of a time homogeneous markov chain given only noisy , aggregate data this arises when a population of individuals behave independently according to a markov chain , but individual sample paths cannot be observed due to limitations of the observation process or the need to protect privacy instead , only population level counts of the number of individuals in each state at each time step are available when these counts are exact , a conditional least squares \( cls \) estimator is known to be consistent and asymptotically normal we initiate the study of method of moments estimators for this problem to handle the more realistic case when observations are additionally corrupted by noise we show that cls can be interpreted as a simple plug in method of moments estimator however , when observations are noisy , it is not consistent because it fails to account for additional variance introduced by the noise we develop a new , simpler method of moments estimator that bypasses this problem and is consistent under noisy observations"
cs.DM,"a b coloring of a graph is a proper coloring such that every color class contains a vertex that is adjacent to all other color classes the b chromatic number of a graph g , denoted by chi b \( g \) , is the maximum number t such that g admits a b coloring with t colors a graph g is called b continuous if it admits a b coloring with t colors , for every t chi \( g \) , ldots , chi b \( g \) , and b monotonic if chi b \( h 1 \) geq chi b \( h 2 \) for every induced subgraph h 1 of g , and every induced subgraph h 2 of h 1 we investigate the b chromatic number of graphs with stability number two these are exactly the complements of triangle free graphs , thus including all complements of bipartite graphs the main results of this work are the following we characterize the b colorings of a graph with stability number two in terms of matchings with no augmenting paths of length one or three we derive that graphs with stability number two are b continuous and b monotonic we prove that it is np complete to decide whether the b chromatic number of co bipartite graph is at most a given threshold we describe a polynomial time dynamic programming algorithm to compute the b chromatic number of co trees extending several previous results , we show that there is a polynomial time dynamic programming algorithm for computing the b chromatic number of tree cographs moreover , we show that tree cographs are b continuous and b monotonic"
cs.LG,"in this paper , we systematically analyse the connecting architectures of recurrent neural networks \( rnns \) our main contribution is twofold first , we present a rigorous graph theoretic framework describing the connecting architectures of rnns in general second , we propose three architecture complexity measures of rnns \( a \) the recurrent depth , which captures the rnn 's over time nonlinear complexity , \( b \) the feedforward depth , which captures the local input output nonlinearity \( similar to the depth in feedforward neural networks \( fnns \) \) , and \( c \) the recurrent skip coefficient which captures how rapidly the information propagates over time our experimental results show that rnns might benefit from larger recurrent depth and feedforward depth we further demonstrate that increasing recurrent skip coefficient offers performance boosts on long term dependency problems , as we improve the state of the art for sequential mnist dataset"
cs.AI,"the constructive solid geometry \( csg \) is a data model providing a set of binary boolean operators such as union , difference and intersection in this work , these operators are used to compute topological relations between objects defined by the constraints of the nine intersection model \( 9 im \) from egenhofer with the help of these constraints , we define a procedure to compute the topological relations on csg objects these topological relations are disjoint , contains , inside , covers , coveredby , equals and overlaps , and are defined in a top level ontology with a specific semantic definition on relation such as transitive , symmetric , asymmetric , functional , reflexive , and irreflexive the results of topological relations computation are stored in the ontology allowing after what to infer on these topological relationships in addition , logic rules based on the semantic web language allows the definition of logic programs that define which topological relationships have to be computed on which kind of objects for instance , a building that overlaps a railway is a railstation"
cs.LG|stat.ML,"we present a framework for supervised subspace tracking , when there are two time series x t and y t , one being the high dimensional predictors and the other being the response variables and the subspace tracking needs to take into consideration of both sequences it extends the classic online subspace tracking work which can be viewed as tracking of x t only our online sufficient dimensionality reduction \( osdr \) is a meta algorithm that can be applied to various cases including linear regression , logistic regression , multiple linear regression , multinomial logistic regression , support vector machine , the random dot product model and the multi scale union of subspace model osdr reduces data dimensionality on the fly with low computational complexity and it can also handle missing data and dynamic data osdr uses an alternating minimization scheme and updates the subspace via gradient descent on the grassmannian manifold the subspace update can be performed efficiently utilizing the fact that the grassmannian gradient with respect to the subspace in many settings is rank one \( or low rank in certain cases \) the optimization problem for osdr is non convex and hard to analyze in general we provide convergence analysis of osdr in a simple linear regression setting the good performance of osdr compared with the conventional unsupervised subspace tracking are demonstrated via numerical examples on simulated and real data"
cs.AI,"in many cases , government data is still locked in several data silos , even within the boundaries of a single \( inter \) national public organization with disparate and distributed organizational units and departments spread across multiple sites opening data and enabling its unified querying from a single site in an efficient and effective way is a semantic application integration and open government data challenge this paper describes how nara is using semantic web technology to implement an application integration approach within the boundaries of its organization via opening and querying multiple governmental data sources from a single site the generic approach proposed , namely s3 ai , provides support to answering unified , ontology mediated , federated queries to data produced and exploited by disparate applications , while these are being located in different organizational sites s3 ai preserves ownership , autonomy and independency of applications and data the paper extensively demonstrates s3 ai , using the d2rq and fuseki technologies , for addressing the needs of a governmental it helpdesk support case"
cs.DM,"the firefighter problem is a monotone dynamic process in graphs that can be viewed as modeling the use of a limited supply of vaccinations to stop the spread of an epidemic in more detail , a fire spreads through a graph , from burning vertices to their unprotected neighbors in every round , a small amount of unburnt vertices can be protected by firefighters how many firefighters per turn , on average , are needed to stop the fire from advancing \? we prove tight lower and upper bounds on the amount of firefighters needed to control a fire in the cartesian planar grid and in the strong planar grid , resolving two conjectures of ng and raff"
cs.LG|stat.ML,"learning structured outputs with general structures is computationally challenging , except for tree structured models thus we propose an efficient boosting based algorithm adaboost mrf for this task the idea is based on the realization that a graph is a superimposition of trees different from most existing work , our algorithm can handle partial labelling , and thus is particularly attractive in practice where reliable labels are often sparsely observed in addition , our method works exclusively on trees and thus is guaranteed to converge we apply the adaboost mrf algorithm to an indoor video surveillance scenario , where activities are modelled at multiple levels"
cs.LG|stat.ML,"discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence recently , some techniques to discover an identifiable causal structure have been explored based on non gaussianity of the observed data distribution however , most of these are limited to continuous data in this paper , we present a novel causal model for binary data and propose a new approach to derive an identifiable causal structure governing the data based on skew bernoulli distributions of external noise experimental evaluation shows excellent performance for both artificial and real world data sets"
cs.LG,"traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand crafted features however , of late , deep learning techniques have offered a compelling alternative that of automatically learning problem specific features with this new paradigm , every problem in computer vision is now being re examined from a deep learning perspective therefore , it has become important to understand what kind of deep networks are suitable for a given problem although general surveys of this fast moving paradigm \( i e deep networks \) exist , a survey specific to computer vision is missing we specifically consider one form of deep networks widely used in computer vision convolutional neural networks \( cnns \) we start with alexnet as our base cnn and then examine the broad variations proposed over time to suit different applications we hope that our recipe style survey will serve as a guide , particularly for novice practitioners intending to use deep learning techniques for computer vision"
cs.LG,"bidirectional recurrent neural networks \( rnn \) are trained to predict both in the positive and negative time directions simultaneously they have not been used commonly in unsupervised tasks , because a probabilistic interpretation of the model is difficult as an example of an unsupervised task , we study the problem of filling in gaps in high dimensional time series with complex dynamics although unidirectional rnns have recently been trained successfully to model such time series , inference in the negative time direction is non trivial we propose two probabilistic interpretations of bidirectional rnns that can be used to reconstruct missing gaps efficiently our experiments on text data show that both proposed methods are much more accurate than unidirectional reconstructions , although a bit less accurate than a computationally complex bidirectional bayesian inference on the unidirectional rnn we also provide results on music data for which the bayesian inference is computationally infeasible"
cs.AI,"clustering problems are considered amongst the prominent challenges in statistics and computational science clustering of nodes in wireless sensor networks which is used to prolong the life time of networks is one of the difficult tasks of clustering procedure in order to perform nodes clustering , a number of nodes are determined as cluster heads and other ones are joined to one of these heads , based on different criteria e g euclidean distance so far , different approaches have been proposed for this process , where swarm and evolutionary algorithms contribute in this regard in this study , a novel algorithm is proposed based on artificial fish swarm algorithm \( afsa \) for clustering procedure in the proposed method , the performance of the standard afsa is improved by increasing balance between local and global searches furthermore , a new mechanism has been added to the base algorithm for improving convergence speed in clustering problems performance of the proposed technique is compared to a number of state of the art techniques in this field and the outcomes indicate the supremacy of the proposed technique"
cs.LG|cs.SI|stat.ML,"we consider the link prediction problem in a partially observed network , where the objective is to make predictions in the unobserved portion of the network many existing methods reduce link prediction to binary classification problem however , the dominance of absent links in real world networks makes misclassification error a poor performance metric instead , researchers have argued for using ranking performance measures , like auc , ap and ndcg , for evaluation our main contribution is to recast the link prediction problem as a learning to rank problem and use effective learning to rank techniques directly during training this is in contrast to existing work that uses ranking measures only during evaluation our approach is able to deal with the class imbalance problem by using effective , scalable learning to rank techniques during training furthermore , our approach allows us to combine network topology and node features as a demonstration of our general approach , we develop a link prediction method by optimizing the cross entropy surrogate , originally used in the popular listnet ranking algorithm we conduct extensive experiments on publicly available co authorship , citation and metabolic networks to demonstrate the merits of our method"
cs.AI,"both in the plane and in space , we invert the nonlinear ullman transformation for 3 points and 3 orthographic cameras while ullman 's theorem assures a unique reconstruction modulo a reflection for 3 cameras and 4 points , we find a locally unique reconstruction for 3 cameras and 3 points explicit reconstruction formulas allow to decide whether picture data of three cameras seeing three points can be realized as a point camera configuration"
cs.DS|cs.IT,"the problem of online packet scheduling to minimize the required conventional grid energy for transmitting a fixed number of packets given a common deadline is considered the total number of packets arriving within the deadline is known , but the packet arrival times are unknown , and can be arbitrary the proposed algorithm tries to finish the transmission of each packet assuming all future packets are going to arrive at equal time intervals within the left over time the proposed online algorithm is shown to have competitive ratio that is logarithmic in the number of packet arrivals the hybrid energy paradigm is also considered , where in addition to grid energy , energy is also available via extraction from renewable sources the objective here is to minimize the grid energy use a suitably modified version of the previous algorithm is also shown to have competitive ratio that is logarithmic in the number of packet arrivals"
cs.SI,"campaigners are increasingly using online social networking platforms for promoting products , ideas and information a popular method of promoting a product or even an idea is incentivizing individuals to evangelize the idea vigorously by providing them with referral rewards in the form of discounts , cash backs , or social recognition due to budget constraints on scarce resources such as money and manpower , it may not be possible to provide incentives for the entire population , and hence incentives need to be allocated judiciously to appropriate individuals for ensuring the highest possible outreach size we aim to do the same by formulating and solving an optimization problem using percolation theory in particular , we compute the set of individuals that are provided incentives for minimizing the expected cost while ensuring a given outreach size we also solve the problem of computing the set of individuals to be incentivized for maximizing the outreach size for given cost budget the optimization problem turns out to be non trivial it involves quantities that need to be computed by numerically solving a fixed point equation our primary contribution is , that for a fairly general cost structure , we show that the optimization problems can be solved by solving a simple linear program we believe that our approach of using percolation theory to formulate an optimization problem is the first of its kind"
cs.DM|cs.DS,"a graph g is em k choosable if for every assignment of a set s \( v \) of k colors to every vertex v of g , there is a proper coloring of g that assigns to each vertex v a color from s \( v \) we consider the complexity of deciding whether a given graph is k choosable for some constant k in particular , it is shown that deciding whether a given planar graph is 4 choosable is np hard , and so is the problem of deciding whether a given planar triangle free graph is 3 choosable we also obtain simple constructions of a planar graph which is not 4 choosable and a planar triangle free graph which is not 3 choosable"
cs.DM|cs.DS,"we introduce a variant of the capacitated vehicle routing problem that is encountered in sensor networks for scientific data collection consider an undirected graph g \( v cup mathbf sink , e \) each vertex v in v holds a constant sized reading normalized to 1 byte that needs to be communicated to the mathbf sink the communication protocol is defined such that readings travel in packets the packets have a capacity of k bytes we define a em packet hop to be the communication of a packet from a vertex to its neighbor each packet hop drains one unit of energy and therefore , we need to communicate the readings to the mathbf sink with the fewest number of hops we show this problem to be np hard and counter it with a simple distributed \( 2 frac 3 2k \) approximation algorithm called tt spt that uses the shortest path tree rooted at the mathbf sink we also show that tt spt is absolutely optimal when g is a tree and asymptotically optimal when g is a grid furthermore , tt spt has two nice properties firstly , the readings always travel along a shortest path toward the mathbf sink , which makes it an appealing solution to the convergecast problem as it fits the natural intuition secondly , each node employs a very elementary packing strategy given all the readings that enter into the node , it sends out as many fully packed packets as possible followed by at most 1 partial packet we show that any solution that has either one of the two properties cannot be a \( 2 epsilon \) approximation , for any fixed epsilon 0 this makes spt optimal for the class of algorithms that obey either one of those properties"
cs.AI,"we show that tools from circuit complexity can be used to study decompositions of global constraints in particular , we study decompositions of global constraints into conjunctive normal form with the property that unit propagation on the decomposition enforces the same level of consistency as a specialized propagation algorithm we prove that a constraint propagator has a a polynomial size decomposition if and only if it can be computed by a polynomial size monotone boolean circuit lower bounds on the size of monotone boolean circuits thus translate to lower bounds on the size of decompositions of global constraints for instance , we prove that there is no polynomial sized decomposition of the domain consistency propagator for the alldifferent constraint"
cs.SI,"emergence of localized eigenvectors can cause a failure of standard spectral method for graph partitioning the spectral method using the non backtracking matrix was proposed as a way to overcome this problem the numerical experiments on several examples of real networks show that , indeed , the non backtracking matrix does not exhibit localization of eigenvectors we show that , however , localized eigenvectors of the non backtracking matrix out of the spectral band can exist , deteriorating the performance of graph partitioning"
cs.LG,"we propose a multi agent algorithm able to automatically discover relevant regularities in a given dataset , determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters each agent operates independently by performing a markovian random walk on a suitable weighted graph representation of the input dataset such a weighted graph representation is induced by the specific parameter configuration of the dissimilarity measure adopted by the agent , which searches and takes decisions autonomously for one cluster at a time results show that the algorithm is able to discover parameter configurations that yield a consistent and interpretable collection of clusters moreover , we demonstrate that our algorithm shows comparable performances with other similar state of the art algorithms when facing specific clustering problems"
stat.ML,"a reliable human skin detection method that is adaptable to different human skin colours and illu mination conditions is essential for better human skin segmentation even though different human skin colour detection solutions have been successfully applied , they are prone to false skin detection and are not able to cope with the variety of human skin colours across different ethnic moreover , existing methods require high computational cost in this paper , we propose a novel human skin de tection approach that combines a smoothed 2d histogram and gaussian model , for automatic human skin detection in colour image \( s \) in our approach an eye detector is used to refine the skin model for a specific person the proposed approach reduces computational costs as no training is required and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination to the best of our knowledge , this is the first method to employ fusion strategy for this purpose qualitative and quantitative results on three standard public datasets and a comparison with state of the art methods have shown the effectiveness and robustness of the proposed approach"
cs.AI|cs.LG,"local policy search is a popular reinforcement learning approach for handling large state spaces formally , it searches locally in a paramet erized policy space in order to maximize the associated value function averaged over some predefined distribution it is probably commonly b elieved that the best one can hope in general from such an approach is to get a local optimum of this criterion in this article , we show th e following surprising result emph any \( approximate \) emph local optimum enjoys a emph global performance guarantee we compare this g uarantee with the one that is satisfied by direct policy iteration , an approximate dynamic programming algorithm that does some form of poli cy search if the approximation error of local policy search may generally be bigger \( because local search requires to consider a space of s tochastic policies \) , we argue that the concentrability coefficient that appears in the performance bound is much nicer finally , we discuss several practical and theoretical consequences of our analysis"
cs.DS,"we describe a parallel iterative least squares solver named texttt lsrn that is based on random normal projection texttt lsrn computes the min length solution to min x in mathbb r n a x b 2 , where a in mathbb r m times n with m gg n or m ll n , and where a may be rank deficient tikhonov regularization may also be included since a is only involved in matrix matrix and matrix vector multiplications , it can be a dense or sparse matrix or a linear operator , and texttt lsrn automatically speeds up when a is sparse or a fast linear operator the preconditioning phase consists of a random normal projection , which is embarrassingly parallel , and a singular value decomposition of size lceil gamma min \( m , n \) rceil times min \( m , n \) , where gamma is moderately larger than 1 , e g , gamma 2 we prove that the preconditioned system is well conditioned , with a strong concentration result on the extreme singular values , and hence that the number of iterations is fully predictable when we apply lsqr or the chebyshev semi iterative method as we demonstrate , the chebyshev method is particularly efficient for solving large problems on clusters with high communication cost numerical results demonstrate that on a shared memory machine , texttt lsrn outperforms lapack 's dgelsd on large dense problems , and matlab 's backslash \( suitesparseqr \) on sparse problems further experiments demonstrate that texttt lsrn scales well on an amazon elastic compute cloud cluster"
cs.IT|cs.LG,"we consider the problem of joint universal variable rate lossy coding and identification for parametric classes of stationary beta mixing sources with general \( polish \) alphabets compression performance is measured in terms of lagrangians , while identification performance is measured by the variational distance between the true source and the estimated source provided that the sources are mixing at a sufficiently fast rate and satisfy certain smoothness and vapnik chervonenkis learnability conditions , it is shown that , for bounded metric distortions , there exist universal schemes for joint lossy compression and identification whose lagrangian redundancies converge to zero as sqrt v n log n n as the block length n tends to infinity , where v n is the vapnik chervonenkis dimension of a certain class of decision regions defined by the n dimensional marginal distributions of the sources furthermore , for each n , the decoder can identify n dimensional marginal of the active source up to a ball of radius o \( sqrt v n log n n \) in variational distance , eventually with probability one the results are supplemented by several examples of parametric sources satisfying the regularity conditions"
cs.DM,"we prove that posets of bounded height whose cover graphs belong to a fixed class with bounded expansion have bounded dimension bounded expansion , introduced by ne v s et v r il and ossona de mendez as a model for sparsity in graphs , is a property that is naturally satisfied by a wide range of graph classes , from graph structure theory \( graphs excluding a minor or a topological minor \) to graph drawing \( e g graphs with constant book thickness \) therefore , our theorem generalizes a number of results including the most recent one for posets of bounded height with cover graphs excluding a fixed graph as a topological minor \( walczak , soda 2015 \) we also show that the result is in a sense best possible , as it does not extend to nowhere dense classes in fact , it already fails for cover graphs with locally bounded treewidth"
cs.DS,"in this paper is presented an heuristic that , in polynomial time and space in the input dimension , determines if a circuit describes a tautology or a contradiction if the circuit is neither a tautology nor a contradiction , then the heuristic finds an assignment to the circuit inputs such that the circuit is satisfied"
cs.DS,the article discribe methods of verifing the conditions of access in computer systems based on take grant protection model
cs.DS,"crdts are distributed data types that make eventual consistency of a distributed object possible and non ad hoc specifically , state based crdts achieve this by sharing local state changes through shipping the entire state , that is then merged to other replicas with an idempotent , associative , and commutative join operation , ensuring convergence this imposes a large communication overhead as the state size becomes larger we introduce delta state conflict free replicated datatypes \( delta crdt \) , which make use of delta mutators , defined in such a way to return a delta state , typically , with a much smaller size than the full state delta states are joined to the local state as well as to the remote states \( after being shipped \) this can achieve the best of both worlds small messages with an incremental nature , as in operation based crdts , disseminated over unreliable communication channels , as in traditional state based crdts we introduce the delta crdt framework , and we explain it through establishing a correspondence to current state based crdts in addition , we present two anti entropy algorithms a basic one that provides eventual convergence , and another one that ensures both convergence and causal consistency we also introduce two delta crdt specifications of well known replicated datatypes"
cs.DS,"the analysis of algorithms mostly relies on counting classic elementary operations like additions , multiplications , comparisons , swaps etc this approach is often sufficient to quantify an algorithm 's efficiency in some cases , however , features of modern processor architectures like pipelined execution and memory hierarchies have significant impact on running time and need to be taken into account to get a reliable picture one such example is quicksort it has been demonstrated experimentally that under certain conditions on the hardware the classically optimal balanced choice of the pivot as median of a sample gets harmful the reason lies in mispredicted branches whose rollback costs become dominating in this paper , we give the first precise analytical investigation of the influence of pipelining and the resulting branch mispredictions on the efficiency of \( classic \) quicksort and yaroslavskiy 's dual pivot quicksort as implemented in oracle 's java 7 library for the latter it is still not fully understood why experiments prove it 10 faster than a highly engineered implementation of a classic single pivot version for different branch prediction strategies , we give precise asymptotics for the expected number of branch misses caused by the aforementioned quicksort variants when their pivots are chosen from a sample of the input we conclude that the difference in branch misses is too small to explain the superiority of the dual pivot algorithm"
cs.DM|cs.DS,"permutation pattern matching \( or ppm \) is a decision problem whose input is a pair of permutations pi and tau , represented as sequences of integers , and the task is to determine whether tau contains a subsequence order isomorphic to pi bose , buss and lubiw proved that ppm is np complete on general inputs we show that ppm is np complete even when pi has no decreasing subsequence of length 3 and tau has no decreasing subsequence of length 4 this provides the first known example of ppm being hard when one or both of pi and sigma are restricted to a proper hereditary class of permutations this hardness result is tight in the sense that ppm is known to be polynomial when both pi and tau avoid a decreasing subsequence of length 3 , as well as when pi avoids a decreasing subsequence of length 2 the result is also tight in another sense we will show that for any hereditary proper subclass c of the class of permutations avoiding a decreasing sequence of length 3 , there is a polynomial algorithm solving ppm instances where pi is from c and tau is arbitrary we also obtain analogous hardness and tractability results for the class of so called skew merged patterns from these results , we deduce a complexity dichotomy for the ppm problem restricted to pi belonging to av \( rho \) , where av \( rho \) denotes the class of permutations avoiding a permutation rho specifically , we show that the problem is polynomial when rho is in the set 1 , 12 , 21 , 132 , 213 , 231 , 312 , and it is np complete for any other rho"
cs.IT|cs.LG,"finding a basis matrix \( dictionary \) by which objective signals are represented sparsely is of major relevance in various scientific and technological fields we consider a problem to learn a dictionary from a set of training signals we employ techniques of statistical mechanics of disordered systems to evaluate the size of the training set necessary to typically succeed in the dictionary learning the results indicate that the necessary size is much smaller than previously estimated , which theoretically supports and or encourages the use of dictionary learning in practical situations"
cs.DM,the generalized discretizable molecular distance geometry problem is a distance geometry problems that can be solved by a combinatorial algorithm called ``branch and prune'' it was observed empirically that the number of solutions of yes instances is always a power of two we give a proof that this event happens with probability one
cs.AI,"questions related to the evolution of language have recently known an impressive increase of interest \( briscoe , 2002 \) this short paper aims at questioning the scientific status of these models and their relations to attested data we show that one cannot directly model non linguistic factors \( exogenous factors \) even if they play a crucial role in language evolution we then examine the relation between linguistic models and attested language data , as well as their contribution to cognitive linguistics"
cs.SI,"today , online social networks such as facebook , linkedin and twitter are the most popular platforms on the internet , on which millions of users register to share personal information with their friends a large amount of data , social links and statistics about users are collected by online social networks services and they create big digital mines of various statistical data leakage of personal information is a significant concern for social network users besides information propagation , some new attacks on online social networks such as identity clone attack \( ica \) have been identified ica attempts to create a fake online identity of a victim to fool their friends into believing the authenticity of the fake identity to establish social links in order to reap the private information of the victims friends which is not shared in their public profiles there are some identity validation services that perform users identity validation , but they are passive services and they only protect users who are informed on privacy concerns and online identity issues this paper starts with an explanation of two types of profile cloning attacks are explained and a new approach for detecting clone identities is proposed by defining profile similarity and strength of relationship measures according to similar attributes and strength of relationship among users which are computed in detection steps , it will be decided which profile is clone and which one is genuine by a predetermined threshold finally , the experimental results are presented to demonstrate the effectiveness of the proposed approach"
cs.LG|stat.ML,"discussion of latent variable graphical model selection via convex optimization by venkat chandrasekaran , pablo a parrilo and alan s willsky arxiv 1008 1290"
cs.AI,"the ability to reason with natural language is a fundamental prerequisite for many nlp tasks such as information extraction , machine translation and question answering to quantify this ability , systems are commonly tested whether they can recognize textual entailment , i e , whether one sentence can be inferred from another one however , in most nlp applications only single source sentences instead of sentence pairs are available hence , we propose a new task that measures how well a model can generate an entailed sentence from a source sentence we take entailment pairs of the stanford natural language inference corpus and train an lstm with attention on a manually annotated test set we found that 82 of generated sentences are correct , an improvement of 10 3 over an lstm baseline a qualitative analysis shows that this model is not only capable of shortening input sentences , but also inferring new statements via paraphrasing and phrase entailment we then apply this model recursively to input output pairs , thereby generating natural language inference chains that can be used to automatically construct an entailment graph from source sentences finally , by swapping source and target sentences we can also train a model that given an input sentence invents additional information to generate a new sentence"
cs.AI,this paper presents a bimodal logic for reasoning about knowledge during knowledge acquisition one of the modalities represents \( effort during \) non deterministic time and the other represents knowledge the semantics of this logic are tree like spaces which are a generalization of semantics used for modeling branching time and historical necessity a finite system of axiom schemes is shown to be canonically complete for the formentioned spaces a characterization of the satisfaction relation implies the small model property and decidability for this system
cs.AI|cs.IT|cs.LG,"identifying and understanding modular organizations is centrally important in the study of complex systems several approaches to this problem have been advanced , many framed in information theoretic terms our treatment starts from the complementary point of view of statistical modeling and prediction of dynamical systems it is known that for finite amounts of training data , simpler models can have greater predictive power than more complex ones we use the trade off between model simplicity and predictive accuracy to generate optimal multiscale decompositions of dynamical networks into weakly coupled , simple modules state dependent and causal versions of our method are also proposed"
cs.AI,"this survey note describes a brief systemic view to approaches for evaluation of hierarchical composite \( modular \) systems the list of considered issues involves the following \( i \) basic assessment scales \( quantitative scale , ordinal scale , multicriteria description , two kinds of poset like scales \) , \( ii \) basic types of scale transformations problems , \( iii \) basic types of scale integration methods evaluation of the modular systems is considered as assessment of system components \( and their compatibility \) and integration of the obtained local estimates into the total system estimate \( s \) this process is based on the above mentioned problems \( i e , scale transformation and integration \) illustrations of the assessment problems and evaluation approaches are presented \( including numerical examples \)"
cs.IT|stat.ML,"we improve existing results in the field of compressed sensing and matrix completion when sampled data may be grossly corrupted we introduce three new theorems 1 \) in compressed sensing , we show that if the m times n sensing matrix has independent gaussian entries , then one can recover a sparse signal x exactly by tractable ell1 minimimization even if a positive fraction of the measurements are arbitrarily corrupted , provided the number of nonzero entries in x is o \( m \( log \( n m \) 1 \) \) 2 \) in the very general sensing model introduced in a probabilistic and ripless theory of compressed sensing by candes and plan , and assuming a positive fraction of corrupted measurements , exact recovery still holds if the signal now has o \( m \( log 2 n \) \) nonzero entries 3 \) finally , we prove that one can recover an n times n low rank matrix from m corrupted sampled entries by tractable optimization provided the rank is on the order of o \( m \( n log 2 n \) \) again , this holds when there is a positive fraction of corrupted samples"
cs.AI|cs.LG,"words and phrases acquire meaning from the way they are used in society , from their relative semantics to other words and phrases for computers the equivalent of `society' is `database , ' and the equivalent of `use' is `way to search the database ' we present a new theory of similarity between words and phrases based on information distance and kolmogorov complexity to fix thoughts we use the world wide web as database , and google as search engine the method is also applicable to other search engines and databases this theory is then applied to construct a method to automatically extract similarity , the google similarity distance , of words and phrases from the world wide web using google page counts the world wide web is the largest database on earth , and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality we give applications in hierarchical clustering , classification , and language translation we give examples to distinguish between colors and numbers , cluster names of paintings by 17th century dutch masters and names of books by english novelists , the ability to understand emergencies , and primes , and we demonstrate the ability to do a simple automatic english spanish translation finally , we use the wordnet database as an objective baseline against which to judge the performance of our method we conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our google distance , resulting in an a mean agreement of 87 with the expert crafted wordnet categories"
cs.LG,"in this work , the trepan algorithm is enhanced and extended for extracting decision trees from neural networks we empirically evaluated the performance of the algorithm on a set of databases from real world events this benchmark enhancement was achieved by adapting single test trepan and c4 5 decision tree induction algorithms to analyze the datasets the models are then compared with x trepan for comprehensibility and classification accuracy furthermore , we validate the experimentations by applying statistical methods finally , the modified algorithm is extended to work with multi class regression problems and the ability to comprehend generalized feed forward networks is achieved"
cs.AI,"the onemax problem is a standard benchmark optimisation problem for a binary search space recent work on applying a bandit based random mutation hill climbing algorithm to the noisy onemax problem showed that it is important to choose a good value for the resampling number to make a careful trade off between taking more samples in order to reduce noise , and taking fewer samples to reduce the total computational cost this paper extends that observation , by deriving an analytical expression for the running time of the rmhc algorithm with resampling applied to the noisy onemax problem , and showing both theoretically and empirically that the optimal resampling number increases with the number of dimensions in the search space"
cs.SI,"with computing and sensing woven into the fabric of everyday life , we live in an era where we are awash in a flood of data from which we can gain rich insights augmented reality \( ar \) is able to collect and help analyze the growing torrent of data about user engagement metrics within our personal mobile and wearable devices this enables us to blend information from our senses and the digitalized world in a myriad of ways that was not possible before ar and big data have a logical maturity that inevitably converge them the tread of harnessing ar and big data to breed new interesting applications is starting to have a tangible presence in this paper , we explore the potential to capture value from the marriage between ar and big data technologies , following with several challenges that must be addressed to fully realize this potential"
cs.LG,"in this paper , we propose a path following replicator dynamic , and investigate its potentials in uncovering the underlying cluster structure of a graph the proposed dynamic is a generalization of the discrete replicator dynamic the replicator dynamic has been successfully used to extract dense clusters of graphs however , it is often sensitive to the degree distribution of a graph , and usually biased by vertices with large degrees , thus may fail to detect the densest cluster to overcome this problem , we introduce a dynamic parameter , called path parameter , into the evolution process the path parameter can be interpreted as the maximal possible probability of a current cluster containing a vertex , and it monotonically increases as evolution process proceeds by limiting the maximal probability , the phenomenon of some vertices dominating the early stage of evolution process is suppressed , thus making evolution process more robust to solve the optimization problem with a fixed path parameter , we propose an efficient fixed point algorithm the time complexity of the path following replicator dynamic is only linear in the number of edges of a graph , thus it can analyze graphs with millions of vertices and tens of millions of edges on a common pc in a few minutes besides , it can be naturally generalized to hypergraph and graph with edges of different orders we apply it to four important problems maximum clique problem , densest k subgraph problem , structure fitting , and discovery of high density regions the extensive experimental results clearly demonstrate its advantages , in terms of robustness , scalability and flexility"
cs.SI,"we present the results of detailed numerical study of a model for the sharing and sorting of informations in a community consisting of a large number of agents the information gathering takes place in a sequence of mutual bipartite interactions where randomly selected pairs of agents communicate with each other to enhance their knowledge and sort out the common information though our model is less restricted compared to the well established naming game , yet the numerical results strongly indicate that the whole set of exponents characterizing this model are different from those of the naming game and they assume non trivial values finally it appears that in analogy to the emergence of clusters in the phenomenon of percolation , one can define clusters of agents here having the same information we have studied in detail the growth of the largest cluster in this article and performed its finite size scaling analysis"
cs.AI,"recently there have been some unexpected results concerning fuzzy description logics \( fdls \) with general concept inclusions \( gcis \) they show that , unlike the classical case , the dl alc with gcis does not have the finite model property under lukasiewicz logic or product logic and , specifically , knowledge base satisfiability is an undecidable problem for product logic we complete here the analysis by showing that knowledge base satisfiability is also an undecidable problem for lukasiewicz logic"
cs.LG|stat.ML,"in this paper , we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty , which encourages adjacent graphs to share similar structures a motivating example is the analysis of brain networks of alzheimer 's disease using neuroimaging data specifically , we may wish to estimate a brain network for the normal controls \( nc \) , a brain network for the patients with mild cognitive impairment \( mci \) , and a brain network for alzheimer 's patients \( ad \) we expect the two brain networks for nc and mci to share common structures but not to be identical to each other similarly for the two brain networks for mci and ad the proposed formulation can be solved using a second order method our key technical contribution is to establish the necessary and sufficient condition for the graphs to be decomposable based on this key property , a simple screening rule is presented , which decomposes the large graphs into small subgraphs and allows an efficient estimation of multiple independent \( small \) subgraphs , dramatically reducing the computational cost we perform experiments on both synthetic and real data our results demonstrate the effectiveness and efficiency of the proposed approach"
cs.DS,"given a graph g cellularly embedded on a surface sigma of genus g , a cut graph is a subgraph of g such that cutting sigma along g yields a topological disk we provide a fixed parameter tractable approximation scheme for the problem of computing the shortest cut graph , that is , for any varepsilon 0 , we show how to compute a \( 1 varepsilon \) approximation of the shortest cut graph in time f \( varepsilon , g \) n 3 our techniques first rely on the computation of a spanner for the problem using the technique of brick decompositions , to reduce the problem to the case of bounded tree width then , to solve the bounded tree width case , we introduce a variant of the surface cut decomposition of ru 'e , sau and thilikos , which may be of independent interest"
cs.DM,"we give necessary and sufficient conditions for the existence of telescopers for rational functions of two variables in the continuous , discrete and q discrete settings and characterize which operators can occur as telescopers using this latter characterization , we reprove results of furstenberg and zeilberger concerning diagonals of power series representing rational functions the key concept behind these considerations is a generalization of the notion of residue in the continuous case to an analogous concept in the discrete and q discrete cases"
cs.AI|cs.DS,"in time series analysis research there is a strong interest in discrete representations of real valued data streams one approach that emerged over a decade ago and is still considered state of the art is the symbolic aggregate approximation algorithm this discretization algorithm was the first symbolic approach that mapped a real valued time series to a symbolic representation that was guaranteed to lower bound euclidean distance the interest of this paper concerns the sax assumption of data being highly gaussian and the use of the standard normal curve to choose partitions to discretize the data though not necessarily , but generally , and certainly in its canonical form , the sax approach chooses partitions on the standard normal curve that would produce an equal probability for each symbol in a finite alphabet to occur this procedure is generally valid as a time series is normalized before the rest of the sax algorithm is applied however there exists a caveat to this assumption of equi probability due to the intermediate step of piecewise aggregate approximation \( paa \) what we will show in this paper is that when paa is applied the distribution of the data is indeed altered , resulting in a shrinking standard deviation that is proportional to the number of points used to create a segment of the paa representation and the degree of auto correlation within the series data that exhibits statistically significant auto correlation is less affected by this shrinking distribution as the standard deviation of the data contracts , the mean remains the same , however the distribution is no longer standard normal and therefore the partitions based on the standard normal curve are no longer valid for the assumption of equal probability"
cs.SI|stat.ML,"the empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large scale network structure most often , the suitability of the annotations as topological descriptors itself is not assessed , and without this it is not possible to ultimately distinguish between actual shortcomings of the community detection algorithms on one hand , and the incompleteness , inaccuracy or structured nature of the data annotations themselves on the other in this work we present a principled method to access both aspects simultaneously we construct a joint generative model for the data and metadata , and a non parametric bayesian framework to infer its parameters from annotated datasets we assess the quality of the metadata not according to its direct alignment with the network communities , but rather in its capacity to predict the placement of edges in the network we also show how this feature can be used to predict the connections to missing nodes when only the metadata is available by investigating a wide range of datasets , we show that while there are seldom exact agreements between metadata tokens and the inferred data groups , the metadata is often informative of the network structure nevertheless , and can improve the prediction of missing nodes this shows that the method uncovers meaningful patterns in both the data and metadata , without requiring or expecting a perfect agreement between the two"
cs.AI,"this paper proposes a novel solution to spam detection inspired by a model of the adaptive immune system known as the crossregulation model we report on the testing of a preliminary algorithm on six e mail corpora we also compare our results statically and dynamically with those obtained by the naive bayes classifier and another binary classification method we developed previously for biomedical text mining applications we show that the cross regulation model is competitive against those and thus promising as a bio inspired algorithm for spam detection in particular , and binary classification in general"
cs.DS|cs.LG,"we show that , under a standard hardness assumption , there is no computationally efficient algorithm that given n samples from an unknown distribution can give valid answers to n 3 o \( 1 \) adaptively chosen statistical queries a statistical query asks for the expectation of a predicate over the underlying distribution , and an answer to a statistical query is valid if it is close to the correct expectation over the distribution our result stands in stark contrast to the well known fact that exponentially many statistical queries can be answered validly and efficiently if the queries are chosen non adaptively \( no query may depend on the answers to previous queries \) moreover , a recent work by dwork et al shows how to accurately answer exponentially many adaptively chosen statistical queries via a computationally inefficient algorithm and how to answer a quadratic number of adaptive queries via a computationally efficient algorithm the latter result implies that our result is tight up to a linear factor in n conceptually , our result demonstrates that achieving statistical validity alone can be a source of computational intractability in adaptive settings for example , in the modern large collaborative research environment , data analysts typically choose a particular approach based on previous findings false discovery occurs if a research finding is supported by the data but not by the underlying distribution while the study of preventing false discovery in statistics is decades old , to the best of our knowledge our result is the first to demonstrate a computational barrier in particular , our result suggests that the perceived difficulty of preventing false discovery in today 's collaborative research environment may be inherent"
cs.DS,"the performance of any algorithm will largely depend on the setting of its algorithm dependent parameters the optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems however , such parameter tuning itself is a tough optimization problem in this paper , we present a framework for self tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself using the firefly algorithm as an example , we show that this framework works well it is also found that different parameters may have different sensitivities , and thus require different degrees of tuning parameters with high sensitivities require fine tuning to achieve optimality"
cs.DS,"an effective technique for solving optimization problems over massive data sets is to partition the data into smaller pieces , solve the problem on each piece and compute a representative solution from it , and finally obtain a solution inside the union of the representative solutions for all pieces this technique can be captured via the concept of em composable core sets , and has been recently applied to solve diversity maximization problems as well as several clustering problems however , for coverage and submodular maximization problems , impossibility bounds are known for this technique cite immm14 in this paper , we focus on efficient construction of a randomized variant of composable core sets where the above idea is applied on a em random clustering of the data we employ this technique for the coverage , monotone and non monotone submodular maximization problems our results significantly improve upon the hardness results for non randomized core sets , and imply improved results for submodular maximization in a distributed and streaming settings in summary , we show that a simple greedy algorithm results in a 1 3 approximate randomized composable core set for submodular maximization under a cardinality constraint this is in contrast to a known o \( log k over sqrt k \) impossibility result for \( non randomized \) composable core set our result also extends to non monotone submodular functions , and leads to the first 2 round mapreduce based constant factor approximation algorithm with o \( n \) total communication complexity for either monotone or non monotone functions finally , using an improved analysis technique and a new algorithm mathsf pseudogreedy , we present an improved 0 545 approximation algorithm for monotone submodular maximization , which is in turn the first mapreduce based algorithm beating factor 1 2 in a constant number of rounds"
cs.LG|stat.ML,"research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types the first employs a bayesian framework , where optimality improves with increased computational time this is because the resulting planning task takes the form of a dynamic programming problem on a belief tree with an infinite number of states the second type employs relatively simple algorithm which are shown to suffer small regret within a distribution free framework this paper presents a lower bound and a high probability upper bound on the optimal value function for the nodes in the bayesian belief tree , which are analogous to similar bounds in pomdps the bounds are then used to create more efficient strategies for exploring the tree the resulting algorithms are compared with the distribution free algorithm ucb1 , as well as a simpler baseline algorithm on multi armed bandit problems"
cs.LG,"in this paper , we consider learning dictionary models over a network of agents , where each agent is only in charge of a portion of the dictionary elements this formulation is relevant in big data scenarios where large dictionary models may be spread over different spatial locations and it is not feasible to aggregate all dictionaries in one location due to communication and privacy considerations we first show that the dual function of the inference problem is an aggregation of individual cost functions associated with different agents , which can then be minimized efficiently by means of diffusion strategies the collaborative inference step generates dual variables that are used by the agents to update their dictionaries without the need to share these dictionaries or even the coefficient models for the training data this is a powerful property that leads to an effective distributed procedure for learning dictionaries over large networks \( e g , hundreds of agents in our experiments \) furthermore , the proposed learning strategy operates in an online manner and is able to respond to streaming data , where each data sample is presented to the network once"
cs.DS,"we describe a linearizable , wait free implementation of a one bit swap object from a single max register and an unbounded array of test and set bits each swap operation takes at most three steps using standard randomized constructions , the max register and test and set bits can be replaced by read write registers , at the price of raising the cost of a swap operation to an expected o \( max \( log n , min \( log t , n \) \) \) steps , where t is the number of times the swap object has previously changed its value and n is the number of processes"
cs.LG,"many of the recent trajectory optimization algorithms alternate between local approximation of the dynamics and conservative policy update however , linearly approximating the dynamics in order to derive the new policy can bias the update and prevent convergence to the optimal policy in this article , we propose a new model free algorithm that backpropagates a local quadratic time dependent q function , allowing the derivation of the policy update in closed form our policy update ensures exact kl constraint satisfaction without simplifying assumptions on the system dynamics demonstrating improved performance in comparison to related trajectory optimization algorithms linearizing the dynamics"
cs.DS,"winfree 's abstract tile assembly model \( atam \) is a model of molecular self assembly of dna complexes known as tiles , which float freely in solution and attach one at a time to a growing seed assembly based on specific binding sites on their four sides we show that there is a polynomial time algorithm that , given an n x n square , finds the minimal tile system \( i e , the system with the smallest number of distinct tile types \) that uniquely self assembles the square , answering an open question of adleman , cheng , goel , huang , kempe , moisset de espanes , and rothemund \( combinatorial optimization problems in self assembly , stoc 2002 \) our investigation leading to this algorithm reveals other positive and negative results about the relationship between the size of a tile system and its temperature \( the binding strength threshold required for a tile to attach \)"
cs.DS|cs.IT,"efficient optimal prefix coding has long been accomplished via the huffman algorithm however , there is still room for improvement and exploration regarding variants of the huffman problem length limited huffman coding , useful for many practical applications , is one such variant , for which codes are restricted to the set of codes in which none of the n codewords is longer than a given length , l max binary length limited coding can be done in o \( n l max \) time and o \( n \) space via the widely used package merge algorithm and with even smaller asymptotic complexity using a lesser known algorithm in this paper these algorithms are generalized without increasing complexity in order to introduce a minimum codeword length constraint l min , to allow for objective functions other than the minimization of expected codeword length , and to be applicable to both binary and nonbinary codes nonbinary codes were previously addressed using a slower dynamic programming approach these extensions have various applications including fast decompression and a modified version of the game ``twenty questions'' and can be used to solve the problem of finding an optimal code with limited fringe , that is , finding the best code among codes with a maximum difference between the longest and shortest codewords the previously proposed method for solving this problem was nonpolynomial time , whereas solving this using the novel linear space algorithm requires only o \( n \( l max l min \) 2 \) time , or even less if l max l min is not o \( log n \)"
stat.ML,"this paper introduces a novel graph analytic approach for detecting anomalies in network flow data called graphprints building on foundational network mining techniques , our method represents time slices of traffic as a graph , then counts graphlets small induced subgraphs that describe local topology by performing outlier detection on the sequence of graphlet counts , anomalous intervals of traffic are identified , and furthermore , individual ips experiencing abnormal behavior are singled out initial testing of graphprints is performed on real network data with an implanted anomaly evaluation shows false positive rates bounded by 2 84 at the time interval level , and 0 05 at the ip level with 100 true positive rates at both"
cs.LG|stat.ML,"nonnegative matrix factorization \( nmf \) is a powerful class of feature extraction techniques that has been successfully applied in many fields , namely in signal and image processing current nmf techniques have been limited to a single objective problem in either its linear or nonlinear kernel based formulation in this paper , we propose to revisit the nmf as a multi objective problem , in particular a bi objective one , where the objective functions defined in both input and feature spaces are taken into account by taking the advantage of the sum weighted method from the literature of multi objective optimization , the proposed bi objective nmf determines a set of nondominated , pareto optimal , solutions instead of a single optimal decomposition moreover , the corresponding pareto front is studied and approximated experimental results on unmixing real hyperspectral images confirm the efficiency of the proposed bi objective nmf compared with the state of the art methods"
cs.SI,"from the viewpoint of networks , a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks , whereby a directed link represents the result of a single game previously proposed network based ranking systems are derived from static networks , i e , aggregation of the results of games over time however , the score of a player \( or team \) fluctuates over time defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods to account for this factor , we propose a dynamic variant of such a network based ranking system and apply it to professional men 's tennis data we derive a set of linear online update equations for the score of each player the proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts"
cs.AI,"we introduce a problem set up we call the iterated matching pennies \( imp \) game and show that it is a powerful framework for the study of three problems adversarial learnability , conventional \( i e , non adversarial \) learnability and approximability using it , we are able to derive the following theorems \( 1 \) it is possible to learn by example all of sigma 0 1 cup pi 0 1 as well as some supersets \( 2 \) in adversarial learning \( which we describe as a pursuit evasion game \) , the pursuer has a winning strategy \( in other words , sigma 0 1 can be learned adversarially , but pi 0 1 not \) \( 3 \) some languages in pi 0 1 cannot be approximated by any language in sigma 0 1 we show corresponding results also for sigma 0 i and pi 0 i for arbitrary i"
cs.AI,it is proposed to apply modern methods of nonlinear nonequilibrium statistical mechanics to develop software algorithms that will optimally respond to targets within short response times with minimal computer resources this statistical mechanics algorithm for response to targets \( smart \) can be developed with a view towards its future implementation into a hardwired statistical algorithm multiprocessor \( sam \) to enhance the efficiency and speed of response to targets \( smart sam \)
cs.DM|cs.DS,"a ride sharing problem is considered where we are given a graph , whose edges are equipped with a travel cost , plus a set of objects , each associated with a transportation request given by a pair of origin and destination nodes a vehicle travels through the graph , carrying each object from its origin to its destination without any bound on the number of objects that can be simultaneously transported the vehicle starts and terminates its ride at given nodes , and the goal is to compute a minimum cost ride satisfying all requests this ride sharing problem is shown to be tractable on paths by designing a o \( h log h n \) algorithm , with h being the number of distinct requests and with n being the number of nodes in the path the algorithm is then used as a subroutine to efficiently solve instances defined over cycles , hence covering all graphs with maximum degree 2 this traces the frontier of tractability , since bf np hard instances are exhibited over trees whose maximum degree is 3"
cs.LG,"one of the key challenges in applying reinforcement learning to complex robotic control tasks is the need to gather large amounts of experience in order to find an effective policy for the task at hand model based reinforcement learning can achieve good sample efficiency , but requires the ability to learn a model of the dynamics that is good enough to learn an effective policy in this work , we develop a modelbased reinforcement learning algorithm that combines prior knowledge from previous tasks with online adaptation of the dynamics model these two ingredients enable highly sample efficient learning even in regimes where estimating the true dynamics is very difficult , since the online model adaptation allows the method to locally compensate for unmodeled variation in the dynamics we encode the prior experience into a neural network dynamics model , and adapt it online by progressively refitting a local linear model of the dynamics our experimental results show that this approach can be used to solve a variety of complex robotic manipulation tasks in just a single attempt , using prior data from other manipulation behaviors"
cs.LG,"fast convergence speed is a desired property for training latent dirichlet allocation \( lda \) , especially in online and parallel topic modeling for massive data sets this paper presents a novel residual belief propagation \( rbp \) algorithm to accelerate the convergence speed for training lda the proposed rbp uses an informed scheduling scheme for asynchronous message passing , which passes fast convergent messages with a higher priority to influence those slow convergent messages at each learning iteration extensive empirical studies confirm that rbp significantly reduces the training time until convergence while achieves a much lower predictive perplexity than other state of the art training algorithms for lda , including variational bayes \( vb \) , collapsed gibbs sampling \( gs \) , loopy belief propagation \( bp \) , and residual vb \( rvb \)"
cs.AI,"the semantic web initiative puts emphasis not primarily on putting data on the web , but rather on creating links in a way that both humans and machines can explore the web of data when such users access the web , they leave a trail as web servers maintain a history of requests web usage mining approaches have been studied since the beginning of the web given the log 's huge potential for purposes such as resource annotation , personalization , forecasting etc however , the impact of any such efforts has not really gone beyond generating statistics detailing who , when , and how web pages maintained by a web server were visited"
cs.AI,"top n recommender systems have been extensively studied however , the sparsity of user item activities has not been well resolved while many hybrid systems were proposed to address the cold start problem , the profile information has not been sufficiently leveraged furthermore , the heterogeneity of profiles between users and items intensifies the challenge in this paper , we propose a content based top n recommender system by learning the global term weights in profiles to achieve this , we bring in pathsim , which could well measures the node similarity with heterogeneous relations \( between users and items \) starting from the original tf idf value , the global term weights gradually converge , and eventually reflect both profile and activity information to facilitate training , the derivative is reformulated into matrix form , which could easily be paralleled we conduct extensive experiments , which demonstrate the superiority of the proposed method"
cs.DS,"as the babar experiment progresses , it produces new and unforeseen requirements and increasing demands on capacity and feature base the current system is being utilized well beyond its original design specifications , and has scaled appropriately , maintaining data consistency and durability the persistent event storage system has remained largely unchanged since the initial implementation , and thus includes many design features which have become performance bottlenecks programming interfaces were designed before sufficient usage information became available performance and efficiency were traded off for added flexibility to cope with future demands with significant experience in managing actual production data under our belt , we are now in a position to recraft the system to better suit current needs the event store redesign is intended to eliminate redundant features while adding new ones , increase overall performance , and contain the physical storage cost of the world 's largest database"
cs.AI,"we describe a sketch interpretation system that detects and classifies clock numerals created by subjects taking the clock drawing test , a clinical tool widely used to screen for cognitive impairments \( e g , dementia \) we describe how it balances appearance and context , and document its performance on some 2 , 000 drawings \( about 24k clock numerals \) produced by a wide spectrum of patients we calibrate the utility of different forms of context , describing experiments with conditional random fields trained and tested using a variety of features we identify context that contributes to interpreting otherwise ambiguous or incomprehensible strokes we describe st slices , a novel representation that enables unpeeling the layers of ink that result when people overwrite , which often produces ink impossible to analyze if only the final drawing is examined we characterize when st slices work , calibrate their impact on performance , and consider their breadth of applicability"
cs.LG,"deep learning has become a ubiquitous technology to improve machine intelligence however , most of the existing deep models are structurally very complex , making them difficult to be deployed on the mobile platforms with limited computational power in this paper , we propose a novel network compression method called dynamic network surgery , which can remarkably reduce the network complexity by making on the fly connection pruning unlike the previous methods which accomplish this task in a greedy way , we properly incorporate connection splicing into the whole process to avoid incorrect pruning and make it as a continual network maintenance the effectiveness of our method is proved with experiments without any accuracy loss , our method can efficiently compress the number of parameters in lenet 5 and alexnet by a factor of bm 108 times and bm 17 7 times respectively , proving that it outperforms the recent pruning method by considerable margins code will be made publicly available"
cs.DS|cs.SI,"the amount of available data about complex systems is increasing every year , measurements of larger and larger systems are collected and recorded a natural representation of such data is given by networks , whose size is following the size of the original system the current trend of multiple cores in computing infrastructures call for a parallel reimplementation of earlier methods here we present the grid version of cfinder , which can locate overlapping communities in directed , weighted or undirected networks based on the clique percolation method \( cpm \) we show that the computation of the communities can be distributed among several cpu s or computers although switching to the parallel version not necessarily leads to gain in computing time , it definitely makes the community structure of extremely large networks accessible"
cs.AI,"we position a narrative centred computational model for high level knowledge representation and reasoning in the context of a range of assistive technologies concerned with visuo spatial perception and cognition tasks our proposed narrative model encompasses aspects such as emph space , events , actions , change , and interaction from the viewpoint of commonsense reasoning and learning in large scale cognitive systems the broad focus of this paper is on the domain of human activity interpretation in smart environments , ambient intelligence etc in the backdrop of a smart meeting cinematography domain , we position the proposed narrative model , preliminary work on perceptual narrativisation , and the immediate outlook on constructing general purpose open source tools for perceptual narrativisation acm classification i 2 artificial intelligence i 2 0 general cognitive simulation , i 2 4 knowledge representation formalisms and methods , i 2 10 vision and scene understanding architecture and control structures , motion , perceptual reasoning , shape , video analysis general keywords cognitive systems human computer interaction spatial cognition and computation commonsense reasoning spatial and temporal reasoning assistive technologies"
cs.LG,"in this paper we describe a novel framework for the discovery of the topical content of a data corpus , and the tracking of its complex structural changes across the temporal dimension in contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the markovian assumption which overly restricts the type of changes that the model can capture our key technical contribution is a framework based on \( i \) discretization of time into epochs , \( ii \) epoch wise topic discovery using a hierarchical dirichlet process based model , and \( iii \) a temporal similarity graph which allows for the modelling of complex topic changes emergence and disappearance , evolution , splitting , and merging the power of the proposed framework is demonstrated on two medical literature corpora concerned with the autism spectrum disorder \( asd \) and the metabolic syndrome \( mets \) both increasingly important research subjects with significant social and healthcare consequences in addition to the collected asd and metabolic syndrome literature corpora which we made freely available , our contribution also includes an extensive empirical analysis of the proposed framework we describe a detailed and careful examination of the effects that our algorithms 's free parameters have on its output , and discuss the significance of the findings both in the context of the practical application of our algorithm as well as in the context of the existing body of work on temporal topic analysis our quantitative analysis is followed by several qualitative case studies highly relevant to the current research on asd and mets , on which our algorithm is shown to capture well the actual developments in these fields"
cs.LG|stat.ML,"we provide a general theoretical analysis of expected out of sample utility , also referred to as decision theoretic classification , for non decomposable binary classification metrics such as f measure and jaccard coefficient our key result is that the expected out of sample utility for many performance metrics is provably optimized by a classifier which is equivalent to a signed thresholding of the conditional probability of the positive class our analysis bridges a gap in the literature on binary classification , revealed in light of recent results for non decomposable metrics in population utility maximization style classification our results identify checkable properties of a performance metric which are sufficient to guarantee a probability ranking principle we propose consistent estimators for optimal expected out of sample classification as a consequence of the probability ranking principle , computational requirements can be reduced from exponential to cubic complexity in the general case , and further reduced to quadratic complexity in special cases we provide empirical results on simulated and benchmark datasets evaluating the performance of the proposed algorithms for decision theoretic classification and comparing them to baseline and state of the art methods in population utility maximization for non decomposable metrics"
cs.LG,"very deep convolutional networks with hundreds or more layers have lead to significant reductions in error on competitive benchmarks like the imagenet or coco tasks although the unmatched expressiveness of the many deep layers can be highly desirable at test time , training very deep networks comes with its own set of challenges the gradients can vanish , the forward flow often diminishes and the training time can be painfully slow even on modern computers in this paper we propose stochastic depth , a training procedure that enables the seemingly contradictory setup to train short networks and obtain deep networks we start with very deep networks but during training , for each mini batch , randomly drop a subset of layers and bypass them with the identity function the resulting networks are short \( in expectation \) during training and deep during testing training residual networks with stochastic depth is compellingly simple to implement , yet effective we show that this approach successfully addresses the training difficulties of deep networks and complements the recent success of residual and highway networks it reduces training time substantially and improves the test errors on almost all data sets significantly \( cifar 10 , cifar 100 , svhn \) intriguingly , we show that with stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error \( 4 91 \) on cifar 10"
stat.ML,"estimating the level set of a signal from measurements is a task that arises in a variety of fields , including medical imaging , astronomy , and digital elevation mapping motivated by scenarios where accurate and complete measurements of the signal may not available , we examine here a simple procedure for estimating the level set of a signal from highly incomplete measurements , which may additionally be corrupted by additive noise the proposed procedure is based on box constrained total variation \( tv \) regularization we demonstrate the performance of our approach , relative to existing state of the art techniques for level set estimation from compressive measurements , via several simulation examples"
cs.AI,"when agents are acting together , they may need a simple mechanism to decide on joint actions one possibility is to have the agents express their preferences in the form of a ballot and use a voting rule to decide the winning action \( s \) unfortunately , agents may try to manipulate such an election by misreporting their preferences fortunately , it has been shown that it is np hard to compute how to manipulate a number of different voting rules however , np hardness only bounds the worst case complexity recent theoretical results suggest that manipulation may often be easy in practice to address this issue , i suggest studying empirically if computational complexity is in practice a barrier to manipulation the basic tool used in my investigations is the identification of computational phase transitions such an approach has been fruitful in identifying hard instances of propositional satisfiability and other np hard problems i show that phase transition behaviour gives insight into the hardness of manipulating voting rules , increasing concern that computational complexity is indeed any sort of barrier finally , i look at the problem of computing manipulation of other , related problems like stable marriage and tournament problems"
cs.DM|cs.IT,"binary matrix codes with restricted row and column weights are a desirable method of coded modulation for power line communication in this work , we construct such matrix codes that are obtained as products of affine codes cosets of binary linear codes additionally , the constructions have the property that they are systematic subsequently , we generalize our construction to irregular product of affine codes , where the component codes are affine codes of different rates"
cs.LG,"we present a method for extracting depth information from a rectified image pair we train a convolutional neural network to predict how well two image patches match and use it to compute the stereo matching cost the cost is refined by cross based cost aggregation and semiglobal matching , followed by a left right consistency check to eliminate errors in the occluded regions our stereo method achieves an error rate of 2 61 on the kitti stereo dataset and is currently \( august 2014 \) the top performing method on this dataset"
cs.DM,"a textit maximum stable set in a graph g is a stable set of maximum cardinality s is a textit local maximum stable set of g , and we write s in psi \( g \) , if s is a maximum stable set of the subgraph induced by s cup n \( s \) , where n \( s \) is the neighborhood of s nemhauser and trotter jr \( 1975 \) , proved that any s in psi \( g \) is a subset of a maximum stable set of g in \( levit mandrescu , 2002 \) we have shown that the family psi \( t \) of a forest t forms a greedoid on its vertex set the cases where g is bipartite , triangle free , well covered , while psi \( g \) is a greedoid , were analyzed in \( levit mandrescu , 2002 \) , \( levit mandrescu , 2004 \) , \( levit mandrescu , 2007 \) , respectively in this paper we demonstrate that if g is a very well covered graph of girth geq4 , then the family psi \( g \) is a greedoid if and only if g has a unique perfect matching"
cs.AI|cs.LG|stat.ML,"generalized linear models \( glms \) and single index models \( sims \) provide powerful generalizations of linear regression , where the target variable is assumed to be a \( possibly unknown \) 1 dimensional function of a linear predictor in general , these problems entail non convex estimation procedures , and , in practice , iterative local search heuristics are often used kalai and sastry \( 2009 \) recently provided the first provably efficient method for learning sims and glms , under the assumptions that the data are in fact generated under a glm and under certain monotonicity and lipschitz constraints however , to obtain provable performance , the method requires a fresh sample every iteration in this paper , we provide algorithms for learning glms and sims , which are both computationally and statistically efficient we also provide an empirical study , demonstrating their feasibility in practice"
cs.AI|cs.LG|stat.ML,"in this paper , we present a novel two stage metric learning algorithm we first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points then , we define the distance in the input data space as the fisher information distance on the associated statistical manifold this induces in the input data space a new family of distance metric with unique properties unlike kernelized metric learning , we do not require the similarity measure to be positive semi definite moreover , it can also be interpreted as a local metric learning algorithm with well defined distance approximation we evaluate its performance on a number of datasets it outperforms significantly other metric learning methods and svm"
cs.LG,"in this work , we address the problem of spelling correction in the arabic language utilizing the new corpus provided by qalb \( qatar arabic language bank \) project which is an annotated corpus of sentences with errors and their corrections the corpus contains edit , add before , split , merge , add after , move and other error types we are concerned with the first four error types as they contribute more than 90 of the spelling errors in the corpus the proposed system has many models to address each error type on its own and then integrating all the models to provide an efficient and robust system that achieves an overall recall of 0 59 , precision of 0 58 and f1 score of 0 58 including all the error types on the development set our system participated in the qalb 2014 shared task automatic arabic error correction and achieved an f1 score of 0 6 , earning the sixth place out of nine participants"
cs.DS,"here we study the complexity of string problems as a function of the size of a program that generates input we consider straight line programs \( slp \) , since all algorithms on slp generated strings could be applied to processing lz compressed texts the main result is a new algorithm for pattern matching when both a text t and a pattern p are presented by slps \( so called fully compressed pattern matching problem \) we show how to find a first occurrence , count all occurrences , check whether any given position is an occurrence or not in time o \( n 2m \) here m , n are the sizes of straight line programs generating correspondingly p and t then we present polynomial algorithms for computing fingerprint table and compressed representation of all covers \( for the first time \) and for finding periods of a given compressed string \( our algorithm is faster than previously known \) on the other hand , we show that computing the hamming distance between two slp generated strings is np and conp hard"
cs.DM,"hoffman proved that for a simple graph g , the chromatic number chi \( g \) obeys chi \( g \) le 1 frac lambda 1 lambda n where lambda 1 and lambda n are the maximal and minimal eigenvalues of the adjacency matrix of g respectively lov 'asz later showed that chi \( g \) le 1 frac lambda 1 lambda n for any \( perhaps negatively \) weighted adjacency matrix in this paper , we give a probabilistic proof of lov 'asz 's theorem , then extend the technique to derive generalizations of hoffman 's theorem when allowed a certain proportion of edge conflicts using this result , we show that if a 3 uniform hypergraph is 2 colorable , then bar d le frac 3 2 lambda min where bar d is the average degree and lambda min is the minimal eigenvalue of the underlying graph we generalize this further for k uniform hypergraphs , for the cases k 4 and 5 , by considering several variants of the underlying graph"
cs.SI,"one of the hallmarks of emotional intelligence is the ability to regulate emotions research suggests that cognitive reappraisal a technique that involves reinterpreting the meaning of a thought or situation can down regulate negative emotions , without incurring significant psychological or physiological costs habitual use of this strategy is also linked to many key indices of physical and emotional health unfortunately , this technique is not always easy to apply thinking flexibly about stressful thoughts and situations requires creativity and poise , faculties that often elude us when we need them the most in this paper , we propose an assistive technology that coordinates collective intelligence on demand , to help individuals reappraise stressful thoughts and situations in two experiments , we assess key features of our design and we demonstrate the feasibility of crowdsourcing empathetic reappraisals with on demand workforces , such as amazon 's mechanical turk"
cs.AI,"the quick medical reference \( qmr \) is a compendium of statistical knowledge connecting diseases to findings \( symptoms \) the information in qmr can be represented as a bayesian network the inference problem \( or , in more medical language , giving a diagnosis \) for the qmr is to , given some findings , find the probability of each disease rejection sampling and likelihood weighted sampling \( a k a likelihood weighting \) are two simple algorithms for making approximate inferences from an arbitrary bayesian net \( and from the qmr bayesian net in particular \) heretofore , the samples for these two algorithms have been obtained with a conventional classical computer in this paper , we will show that two analogous algorithms exist for the qmr bayesian net , where the samples are obtained with a quantum computer we expect that these two algorithms , implemented on a quantum computer , can also be used to make inferences \( and predictions \) with other bayesian nets"
cs.AI|cs.LG,"deep neural networks are able to learn powerful representations from large quantities of labeled input data , however they cannot always generalize well across changes in input distributions domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift in this paper , we address the case when the target domain is unlabeled , requiring unsupervised adaptation coral is a frustratingly easy unsupervised domain adaptation method that aligns the second order statistics of the source and target distributions with a linear transformation here , we extend coral to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks \( deep coral \) experiments on standard benchmark datasets show state of the art performance"
cs.LG,"discourse relations bind smaller linguistic elements into coherent texts however , automatically identifying discourse relations is difficult , because it requires understanding the semantics of the linked sentences a more subtle challenge is that it is not enough to represent the meaning of each sentence of a discourse relation , because the relation may depend on links between lower level elements , such as entity mentions our solution computes distributional meaning representations by composition up the syntactic parse tree a key difference from previous work on compositional distributional semantics is that we also compute representations for entity mentions , using a novel downward compositional pass discourse relations are predicted not only from the distributional representations of the sentences , but also of their coreferent entity mentions the resulting system obtains substantial improvements over the previous state of the art in predicting implicit discourse relations in the penn discourse treebank"
cs.LG,"the field of object detection has made significant advances riding on the wave of region based convnets , but their training procedure still includes many heuristics and hyperparameters that are costly to tune we present a simple yet surprisingly effective online hard example mining \( ohem \) algorithm for training region based convnet detectors our motivation is the same as it has always been detection datasets contain an overwhelming number of easy examples and a small number of hard examples automatic selection of these hard examples can make training more effective and efficient ohem is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use but more importantly , it yields consistent and significant boosts in detection performance on benchmarks like pascal voc 2007 and 2012 its effectiveness increases as datasets become larger and more difficult , as demonstrated by the results on the ms coco dataset moreover , combined with complementary advances in the field , ohem leads to state of the art results of 78 9 and 76 3 map on pascal voc 2007 and 2012 respectively"
cs.AI,"we report on a project to use a theorem prover to find proofs of the theorems in tarskian geometry these theorems start with fundamental properties of betweenness , proceed through the derivations of several famous theorems due to gupta and end with the derivation from tarski 's axioms of hilbert 's 1899 axioms for geometry they include the four challenge problems left unsolved by quaife , who two decades ago found some otter proofs in tarskian geometry \( solving challenges issued in wos 's 1998 book \) there are 212 theorems in this collection we were able to find otter proofs of all these theorems we developed a methodology for the automated preparation and checking of the input files for those theorems , to ensure that no human error has corrupted the formal development of an entire theory as embodied in two hundred input files and proofs we distinguish between proofs that were found completely mechanically \( without reference to the steps of a book proof \) and proofs that were constructed by some technique that involved a human knowing the steps of a book proof proofs of length 40 100 , roughly speaking , are difficult exercises for a human , and proofs of 100 250 steps belong in a ph d thesis or publication 29 of the proofs in our collection are longer than 40 steps , and ten are longer than 90 steps we were able to derive completely mechanically all but 26 of the 183 theorems that have short proofs \( 40 or fewer deduction steps \) we found proofs of the rest , as well as the 29 hard theorems , using a method that requires consulting the book proof at the outset our subformula strategy enabled us to prove four of the 29 hard theorems completely mechanically these are ph d level proofs , of length up to 108"
cs.DM,let g be a triangle free graph with maximum degree delta \( g \) we show that the chromatic number c hi \( g \) is less than 67 \( 1 o \( 1 \) \) delta log delta
cs.AI|cs.IT|cs.LG,"feature markov decision processes \( phimdps \) are well suited for learning agents in general environments nevertheless , unstructured \( phi \) mdps are limited to relatively simple environments structured mdps like dynamic bayesian networks \( dbns \) are used for large scale real world problems in this article i extend phimdp to phidbn the primary contribution is to derive a cost criterion that allows to automatically extract the most relevant features from the environment , leading to the best dbn representation i discuss all building blocks required for a complete general learning algorithm"
cs.DM,"let f in mathbb c x , y , s , t be an irreducible constant degree polynomial , and let a , b , c , d subset mathbb c be finite sets of size n we show that f vanishes on at most o \( n 8 3 \) points of the cartesian product a times b times c times d , unless f has a special group related form a similar statement holds for a , b , c , d of unequal sizes this is a four dimensional extension of our recent improved analysis of the original elekes szab 'o theorem in three dimensions"
cs.SI,"we consider the problem of maximizing influence in a social network we focus on the case that the social network is a directed bipartite graph whose arcs join senders to receivers we consider both the case of deterministic networks and probabilistic graphical models , that is , the so called cascade model the problem is to find the set of the k most influential senders for a given integer k although this problem is np hard , there is a polynomial time approximation algorithm due to kempe , kleinberg and tardos in this work we consider convex relaxation for the problem we prove that convex optimization can recover the exact optimizer in the case that the network is constructed according to a generative model in which influential nodes are planted but then obscured with noise we also demonstrate computationally that the convex relaxation can succeed on a more realistic generative model called the forest fire model"
cs.LG|stat.ML,"we introduce bilbowa \( bilingual bag of words without alignments \) , a simple and computationally efficient model for learning bilingual distributed representations of words which can scale to large datasets and does not require word aligned training data instead it trains directly on monolingual data and extracts a bilingual signal from a smaller set of raw text sentence aligned data we introduce a novel sampled bag of words cross lingual objective and use it to regularize two noise contrastive language models for efficient cross lingual feature learning we evaluate our model on cross lingual document classification and lexical translation on the wmt11 data our code will be made available as part of the open source word2vec toolkit"
cs.LG|stat.ML,"coresets are efficient representations of datasets such that models trained on a coreset are provably competitive with models trained on the original dataset as such , they have been successfully used to scale up clustering models such as k means and gaussian mixture models to massive datasets however , until now , the algorithms and corresponding theory were usually specific to each clustering problem we propose a single , practical algorithm to construct strong coresets for a large class of hard and soft clustering problems based on bregman divergences this class includes hard clustering with popular distortion measures such as the squared euclidean distance , the mahalanobis distance , kl divergence , itakura saito distance and relative entropy the corresponding soft clustering problems are directly related to popular mixture models due to a dual relationship between bregman divergences and exponential family distributions our results recover existing coreset constructions for k means and gaussian mixture models and imply polynomial time approximations schemes for various hard clustering problems"
cs.SI,"in online social networks \( osn \) , users quite usually disclose sensitive information about themselves by publishing messages at the same time , they are \( in many cases \) unable to properly manage the access to this sensitive information due to the following issues i \) the rigidness of the access control mechanism implemented by the osn , and ii \) many users lack of technical knowledge about data privacy and access control to tackle these limitations , in this paper , we propose a dynamic , transparent and privacy driven access control mechanism for textual messages published in osns the notion of privacy driven is achieved by analyzing the semantics of the messages to be published and , according to that , assessing the degree of sensitiveness of their contents for this purpose , the proposed system relies on an automatic semantic annotation mechanism that , by using knowledge bases and linguistic tools , is able to associate a meaning to the information to be published by means of this annotation , our mechanism automatically detects the information that is sensitive according to the privacy requirements of the publisher of data , with regard to the type of reader that may access such data finally , our access control mechanism automatically creates sanitized versions of the users' publications according to the type of reader that accesses them as a result , our proposal , which can be integrated in already existing social networks , provides an automatic , seamless and content driven protection of user publications , which are coherent with her privacy requirements and the type of readers that access them complementary to the system design , we also discuss the feasibility of the system by illustrating it through a real example and evaluate its accuracy and effectiveness over standard approaches"
cs.DM,"we address combinatorial problems that can be formulated as minimization of a partially separable function of discrete variables \( energy minimization in graphical models , weighted constraint satisfaction , pseudo boolean optimization , 0 1 polynomial programming \) for polyhedral relaxations of such problems it is generally not true that variables integer in the relaxed solution will retain the same values in the optimal discrete solution those which do are called persistent such persistent variables define a part of a globally optimal solution once identified , they can be excluded from the problem , reducing its size to any polyhedral relaxation we associate a sufficient condition proving persistency of a subset of variables we set up a specially constructed linear program which determines the set of persistent variables maximal with respect to the relaxation the condition improves as the relaxation is tightened and possesses all its invariances the proposed framework explains a variety of existing methods originating from different areas of research and based on different principles a theoretical comparison is established that relates these methods to the standard linear relaxation and proves that the proposed technique identifies same or larger set of persistent variables"
cs.AI|cs.LG|stat.ML,"we study how to communicate findings of bayesian inference to third parties , while preserving the strong guarantee of differential privacy our main contributions are four different algorithms for private bayesian inference on proba bilistic graphical models these include two mechanisms for adding noise to the bayesian updates , either directly to the posterior parameters , or to their fourier transform so as to preserve update consistency we also utilise a recently introduced posterior sampling mechanism , for which we prove bounds for the specific but general case of discrete bayesian networks and we introduce a maximum a posteriori private mechanism our analysis includes utility and privacy bounds , with a novel focus on the influence of graph structure on privacy worked examples and experiments with bayesian na i ve bayes and bayesian linear regression illustrate the application of our mechanisms"
cs.AI,"this work aims to investigate the reliability of software products as an important attribute of computer programs it helps to decide the degree of trustworthiness a program has in accomplishing its specific functions this is done using the software reliability growth models \( srgms \) through the estimation of their parameters the parameters are estimated in this work based on the available failure data and with the search techniques of swarm intelligence , namely , the cuckoo search \( cs \) due to its efficiency , effectiveness and robustness a number of srgms is studied , and the results are compared to particle swarm optimization \( pso \) , ant colony optimization \( aco \) and extended aco results show that cs outperformed both pso and aco in finding better parameters tested using identical datasets it was sometimes outperformed by the extended aco also in this work , the percentages of training data to testing data are investigated to show their impact on the results"
cs.LG|stat.ML,"recent years have witnessed amazing outcomes from big models trained by big data most popular algorithms for model training are iterative due to the surging volumes of data , we can usually afford to process only a fraction of the training data in each iteration typically , the data are either uniformly sampled or sequentially accessed in this paper , we study how the data access pattern can affect model training we propose an active sampler algorithm , where training data with more learning value to the model are sampled more frequently the goal is to focus training effort on valuable instances near the classification boundaries , rather than evident cases , noisy data or outliers we show the correctness and optimality of active sampler in theory , and then develop a light weight vectorized implementation active sampler is orthogonal to most approaches optimizing the efficiency of large scale data analytics , and can be applied to most analytics models trained by stochastic gradient descent \( sgd \) algorithm extensive experimental evaluations demonstrate that active sampler can speed up the training procedure of svm , feature selection and deep learning , for comparable training quality by 1 6 2 2x"
cs.SI,"traditional mathematical models of epidemic disease had for decades conventionally considered static structure for contacts recently , an upsurge of theoretical inquiry has strived towards rendering the models more realistic by incorporating the temporal aspects of networks of contacts , societal and online , that are of interest in the study of epidemics \( and other similar diffusion processes \) however , temporal dynamics have predominantly focused on link fluctuations and nodal activities , and less attention has been paid to the growth of the underlying network many real networks grow online networks are evidently in constant growth , and societal networks can grow due to migration flux and reproduction the effect of network growth on the epidemic properties of networks is hitherto unknown mainly due to the predominant focus of the network growth literature on the so called steady state this paper takes a step towards alleviating this gap we analytically study the degree dynamics of a given arbitrary network that is subject to growth we use the theoretical findings to predict the epidemic properties of the network as a function of time we observe that the introduction of new individuals into the network can enhance or diminish its resilience against endemic outbreaks , and investigate how this regime shift depends upon the connectivity of newcomers and on how they establish connections to existing nodes throughout , theoretical findings are corroborated with monte carlo simulations over synthetic and real networks the results shed light on the effects of network growth on the future epidemic properties of networks , and offers insights for devising a priori immunization strategies"
cs.AI|stat.ML,"purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate on the other hand , purely mechanistic approaches need to identify and specify all the interactions in the problem at hand \( which may not be feasible \) and still leave the issue of how to parameterize the system in this paper , we present a hybrid approach using gaussian processes and differential equations to combine data driven modelling with a physical model of the system we show how different , physically inspired , kernel functions can be developed through sensible , simple , mechanistic assumptions about the underlying system the versatility of our approach is illustrated with three case studies from motion capture , computational biology and geostatistics"
cs.SI,"random walks on bipartite networks have been used extensively to design personalized recommendation methods while aging has been identified as a key component in the growth of information networks , most research has focused on the networks' structural properties and neglected the often available time information time has been largely ignored both by the investigated recommendation methods as well as by the methodology used to evaluate them we show that this time unaware approach overestimates the methods' recommendation performance motivated by microscopic rules of network growth , we propose a time aware modification of an existing recommendation method and show that by combining the temporal and structural aspects , it outperforms the existing methods the performance improvements are particularly striking in systems with fast aging"
cs.DM,"we study the hard core model defined on independent sets , where each independent set i in a graph g is weighted proportionally to lambda i , for a positive real parameter lambda for large lambda , computing the partition function \( namely , the normalizing constant which makes the weighting a probability distribution on a finite graph \) on graphs of maximum degree d ge 3 , is a well known computationally challenging problem more concretely , let lambda c \( t d \) denote the critical value for the so called uniqueness threshold of the hard core model on the infinite d regular tree recent breakthrough results of dror weitz \( 2006 \) and allan sly \( 2010 \) have identified lambda c \( t d \) as a threshold where the hardness of estimating the above partition function undergoes a computational transition we focus on the well studied particular case of the square lattice integers 2 , and provide a new lower bound for the uniqueness threshold , in particular taking it well above lambda c \( t 4 \) our technique refines and builds on the tree of self avoiding walks approach of weitz , resulting in a new technical sufficient criterion \( of wider applicability \) for establishing strong spatial mixing \( and hence uniqueness \) for the hard core model our new criterion achieves better bounds on strong spatial mixing when the graph has extra structure , improving upon what can be achieved by just using the maximum degree applying our technique to integers 2 we prove that strong spatial mixing holds for all lambda 2 3882 , improving upon the work of weitz that held for lambda 27 16 1 6875 our results imply a fully polynomial deterministic approximation algorithm for estimating the partition function , as well as rapid mixing of the associated glauber dynamics to sample from the hard core distribution"
cs.LG|stat.ML,we propose a novel learning method for multilayered neural networks which uses feedforward supervisory signal and associates classification of a new input with that of pre trained input the proposed method effectively uses rich input information in the earlier layer for robust leaning and revising internal representation in a multilayer neural network
cs.DM,"we prove that , in all dimensions d 4 , every simple open polygonal chain and every tree may be straightened , and every simple closed polygonal chain may be convexified these reconfigurations can be achieved by algorithms that use polynomial time in the number of vertices , and result in a polynomial number of ``moves '' these results contrast to those known for d 2 , where trees can ``lock , '' and for d 3 , where open and closed chains can lock"
cs.SI,"we introduce and study random bipartite networks with hidden variables nodes in these networks are characterized by hidden variables which control the appearance of links between node pairs we derive analytic expressions for the degree distribution , degree correlations , the distribution of the number of common neighbors , and the bipartite clustering coefficient in these networks we also establish the relationship between degrees of nodes in original bipartite networks and in their unipartite projections we further demonstrate how hidden variable formalism can be applied to analyze topological properties of networks in certain bipartite network models , and verify our analytical results in numerical simulations"
cs.LG|cs.SI,"in the network inference problem , one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph in this paper , we approach this problem from the sparse recovery perspective we introduce a general model of cascades , including the voter model and the independent cascade model , for which we provide the first algorithm which recovers the graph 's edges with high probability and o \( s log m \) measurements where s is the maximum degree of the graph and m is the number of nodes furthermore , we show that our algorithm also recovers the edge weights \( the parameters of the diffusion process \) and is robust in the context of approximate sparsity finally we prove an almost matching lower bound of omega \( s log frac m s \) and validate our approach empirically on synthetic graphs"
cs.DM|cs.IT,"lifted maximum rank distance \( mrd \) codes , which are constant dimension codes , are considered it is shown that a lifted mrd code can be represented in such a way that it forms a block design known as a transversal design a slightly different representation of this design makes it similar to a q analog of a transversal design the structure of these designs is used to obtain upper bounds on the sizes of constant dimension codes which contain a lifted mrd code codes which attain these bounds are constructed these codes are the largest known codes for the given parameters these transversal designs can be also used to derive a new family of linear codes in the hamming space bounds on the minimum distance and the dimension of such codes are given"
cs.IT|cs.LG|stat.ML,"we propose minimum regret search \( mrs \) , a novel acquisition function for bayesian optimization mrs bears similarities with information theoretic approaches such as entropy search \( es \) however , while es aims in each query at maximizing the information gain with respect to the global maximum , mrs aims at minimizing the expected immediate regret of its ultimate recommendation for the optimum while empirically es and mrs perform similar in most of the cases , mrs produces fewer outliers with high regret than es we provide empirical results both for a synthetic single task optimization problem as well as for a simulated multi task robotic control problem"
cs.LG,"we propose a new framework , called hierarchical multi resolution mesh network \( hmmn \) , which establishes a dynamic brain network for each time resolution of the fmri signal to represent the underlying cognitive process the edge weights of the multi resolution network are then used to train an ensemble learning architecture , called , fuzzy stacked generalization \( fsg \) for brain decoding the suggested framework , first , decomposes the fmri signal into various time resolutions using wavelet transforms then , for each time resolution , a local mesh is formed around each brain region the locality is defined with respect to a neighborhood system based on functional connectivity the edge weights of each mesh are estimated by ridge regression the local meshes are ensembled to form a dynamic network at each time resolution in the final step , the edge weights of the networks are used for brain decoding this task is achieved by fusing the multi resolution edge weights as the input to the fuzzy stacked generalization \( fsg \) architecture our results on human connectome project task fmri dataset reflect that the suggested model , hmmn , can successfully discriminate tasks by extracting complementary information obtained from mesh edge weights of multiple subbands"
cs.DM,"matrix partition problems generalize a number of natural graph partition problems , and have been studied for several standard graph classes we prove that each matrix partition problem has only finitely many minimal obstructions for split graphs previously such a result was only known for the class of cographs \( in particular , there are matrix partition problems which have infinitely many minimal chordal obstructions \) we provide \( close \) upper and lower bounds on the maximum size of a minimal split obstruction this shows for the first time that some matrices have exponential sized minimal obstructions of any kind \( not necessarily split graphs \) we also discuss matrix partitions for bipartite and co bipartite graphs"
cs.DM,"a homomorphism from a graph g to a graph h is locally bijective , surjective , or injective if its restriction to the neighborhood of every vertex of g is bijective , surjective , or injective , respectively we prove that the problems of testing whether a given graph g allows a homomorphism to a given graph h that is locally bijective , surjective , or injective , respectively , are np complete , even when g has pathwidth at most 5 , 4 , or 2 , respectively , or when both g and h have maximum degree 3 we complement these hardness results by showing that the three problems are polynomial time solvable if g has bounded treewidth and in addition g or h has bounded maximum degree"
cs.AI|cs.LG,"most computational models of analogy assume they are given a delineated source domain and often a specified target domain these systems do not address how analogs can be isolated from large domains and spontaneously retrieved from long term memory , a process we call spontaneous analogy we present a system that represents relational structures as feature bags using this representation , our system leverages perceptual algorithms to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long term memory we provide a demonstration of our approach that takes a set of unsegmented stories , constructs an ontology of analogical schemas \( corresponding to plot devices \) , and uses this ontology to efficiently find analogs within new stories , yielding significant time savings over linear analog retrieval at a small accuracy cost"
cs.DM|cs.DS,"in this paper , we consider the planted partition model , in which n ks vertices of a random graph are partitioned into k clusters , each of size s edges between vertices in the same cluster and different clusters are included with constant probability p and q , respectively \( where 0 le q p le 1 \) we give an efficient algorithm that , with high probability , recovers the clustering as long as the cluster sizes are are least omega \( sqrt n \) our algorithm is based on projecting the graph 's adjacency matrix onto the space spanned by its largest eigenvalues and using the result to recover one cluster at a time while certainly not the first to use this approach , our algorithm has the advantage of being simple , and we employ a novel technique to prove its correctness"
cs.AI,"in this paper we discuss some reasons why temporal logic might not be suitable to model real life norms to show this , we present a novel deontic logic contrary to duty derived permission paradox based on the interaction of obligations , permissions and contrary to duty obligations the paradox is inspired by real life norms"
cs.DM|cs.DS,"we consider the problem of finding a spanning tree with maximum number of leaves \( maxleaf \) a 2 approximation algorithm is known for this problem , and a 3 2 approximation algorithm when restricted to graphs where every vertex has degree 3 \( cubic graphs \) maxleaf is known to be apx hard in general , and np hard for cubic graphs we show that the problem is also apx hard for cubic graphs the apx hardness of the related problem minimum connected dominating set for cubic graphs follows"
cs.AI|stat.ML,"arriving at a complete probabilistic knowledge of a domain , i e , learning how variables interact , is indeed a demanding task in reality , settings often arise for which an individual merely possesses partial knowledge of the domain , and yet , is expected to give adequate answers to a variety of posed queries that is , although precise answers to some queries , in principle , cannot be achieved , a range of plausible answers is attainable for each query given the available partial knowledge in this paper , we propose multi context model \( mcm \) , a new graphical model to represent the state of partial knowledge as to a domain mcm is a middle ground between probabilistic logic , bayesian logic , and probabilistic graphical models for this model we discuss how to \( i \) gradually construct a contradictory free mcm , i e , to form partial beliefs regarding a domain in a gradual and probabilistically consistent way , and \( ii \) perform inference , i e , to evaluate a probability of interest involving some variables of a domain"
cs.SI,"online social systems are multiplex in nature as multiple links may exist between the same two users across different social networks in this work , we introduce a framework for studying links and interactions between users beyond the individual social network exploring the cross section of two popular online platforms twitter and location based social network foursquare we represent the two together as a composite multilayer online social network through this paradigm we study the interactions of pairs of users differentiating between those with links on one or both networks we find that users with multiplex links , who are connected on both networks , interact more and have greater neighbourhood overlap on both platforms , in comparison with pairs who are connected on just one of the social networks in particular , the most frequented locations of users are considerably closer , and similarity is considerably greater among multiplex links we present a number of structural and interaction features , such as the multilayer adamic adar coefficient , which are based on the extension of the concept of the node neighbourhood beyond the single network our evaluation , which aims to shed light on the implications of multiplexity for the link generation process , shows that multilayer features , constructed from properties across social networks , perform better than their single network counterparts in predicting links across networks we propose that combining information from multiple networks in a multilayer configuration can provide new insights into user interactions on online social networks , and can significantly improve link prediction overall with valuable applications to social bootstrapping and friend recommendations"
cs.AI|cs.LG,"we introduce an unsupervised discriminative model for the task of retrieving experts in online document collections we exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way we compare our model to state of the art unsupervised statistical vector space and probabilistic generative approaches our proposed log linear model achieves the retrieval performance levels of state of the art document centric methods with the low inference cost of so called profile centric approaches it yields a statistically significant improved ranking over vector space and generative models in most cases , matching the performance of supervised methods on various benchmarks that is , by using solely text we can do as well as methods that work with external evidence and or relevance feedback a contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching"
cs.DS,"given a graph g , an identifying code c subseteq v \( g \) is a vertex set such that for any two distinct vertices v 1 , v 2 in v \( g \) , the sets n v 1 cap c and n v 2 cap c are distinct and nonempty \( here n v denotes a vertex v and its neighbors \) we study the case when g is the infinite hexagonal grid h cohen et al constructed two identifying codes for h with density 3 7 and proved that any identifying code for h must have density at least 16 39 approx0 410256 both their upper and lower bounds were best known until now here we prove a lower bound of 12 29 approx0 413793"
cs.AI,we propose a general bayesian network model for application in a wide class of problems of therapy monitoring we discuss the use of stochastic simulation as a computational approach to inference on the proposed class of models as an illustration we present an application to the monitoring of cytotoxic chemotherapy in breast cancer
cs.SI,"while recent advances in spatial and temporal networks have enabled researchers to more accurately describe many real world systems , existing models do not capture the combined constraint that space and time impose on the relationships and interactions present in a spatio temporal complex network this has important consequences , often resulting in an over simplification of the resilience of a system and obscuring the network 's true structure in this paper , we study the response of spatio temporal complex networks to random error and systematic attack firstly , we propose a model of spatio temporal paths in time varying spatially embedded networks this model captures the property that , in many real world systems , interaction between nodes is non instantaneous and governed by the space in which they are embedded secondly , using numerical experiments on four empirical examples of such systems , we study the effect of node failure on a network 's topological , temporal , and spatial structure we find that networks exhibit divergent behaviour with respect to random error and systematic attack finally , to identify weaknesses specific to the behaviour of a spatio temporal system , we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting temporally efficient flow through the network we explore the disruption to each system caused by attack strategies based on each of these centrality measures this exposes the complex nature of fragility in a spatio temporal system , showing that there is a variety of failure modes when a network is subject to systematic attack"
cs.SI,"the dynamics of individuals is of essential importance for understanding the evolution of social systems most existing models assume that individuals in diverse systems , ranging from social networks to e commerce , all tend to what is already popular we develop an analytical time aware framework which shows that when individuals make choices which item to buy , for example in online social systems , a small fraction of them is consistently successful in discovering popular items long before they actually become popular we argue that these users , whom we refer to as discoverers , are fundamentally different from the previously known opinion leaders , influentials , and innovators we use the proposed framework to demonstrate that discoverers are present in a wide range of systems once identified , they can be used to predict the future success of items we propose a network model which reproduces the discovery patterns observed in the real data furthermore , data produced by the model pose a fundamental challenge to classical ranking algorithms which neglect the time of link creation and thus fail to discriminate between discoverers and ordinary users in the data our results open the door to qualitative and quantitative study of fine temporal patterns in social systems and have far reaching implications for network modeling and algorithm design"
cs.LG|stat.ML,"extreme learning machine \( elm \) as a neural network algorithm has shown its good performance , such as fast speed , simple structure etc , but also , weak robustness is an unavoidable defect in original elm for blended data we present a new machine learning framework called larsen elm for overcoming this problem in our paper , we would like to show two key steps in larsen elm in the first step , preprocessing , we select the input variables highly related to the output using least angle regression \( lars \) in the second step , training , we employ genetic algorithm \( ga \) based selective ensemble and original elm in the experiments , we apply a sum of two sines and four datasets from uci repository to verify the robustness of our approach the experimental results show that compared with original elm and other methods such as op elm , gasen elm and lsboost , larsen elm significantly improve robustness performance while keeping a relatively high speed"
cs.IT|cs.LG|stat.ML,"we present a new method for online prediction and learning of tensors \( n way arrays , n 2 \) from sequential measurements we focus on the specific case of 3 d tensors and exploit a recently developed framework of structured tensor decompositions proposed in 1 in this framework it is possible to treat 3 d tensors as linear operators and appropriately generalize notions of rank and positive definiteness to tensors in a natural way using these notions we propose a generalization of the matrix exponentiated gradient descent algorithm 2 to a tensor exponentiated gradient descent algorithm using an extension of the notion of von neumann divergence to tensors then following a similar construction as in 3 , we exploit this algorithm to propose an online algorithm for learning and prediction of tensors with provable regret guarantees simulations results are presented on semi synthetic data sets of ratings evolving in time under local influence over a social network the result indicate superior performance compared to other \( online \) convex tensor completion methods"
cs.DM|cs.IT,"in this paper , we consider the problem of designing dna sequences \( codewords \) for dna storage systems and dna computing that are unlikely to fold back onto themselves to form undesirable secondary structures the paper addresses both the issue of enumerating the sequences with such properties and the problem of practical code construction"
cs.SI,"understanding the long term impact that changes in a city 's transportation infrastructure have on its spatial interactions remains a challenge the difficulty arises from the fact that the real impact may not be revealed in static or aggregated mobility measures , as these are remarkably robust to perturbations more generally , the lack of longitudinal , cross sectional data demonstrating the evolution of spatial interactions at a meaningful urban scale also hinders us from evaluating the sensitivity of movement indicators , limiting our capacity to understand the evolution of urban mobility in depth using very large mobility records distributed over three years we quantify the impact of the completion of a metro line extension the circle line \( ccl \) in singapore we find that the commonly used movement indicators are almost identical before and after the project was completed however , in comparing the temporal community structure across years , we do observe significant differences in the spatial reorganization of the affected geographical areas the completion of ccl enables travelers to re identify their desired destinations collectively with lower transport cost , making the community structure more consistent these changes in locality are dynamic , and characterized over short time scales , offering us a different approach to identify and analyze the long term impact of new infrastructures on cities and their evolution dynamics"
cs.AI,"the main contribution of this paper , is to propose a novel semantic approach based on a natural language processing technique in order to ensure a semantic unification of unstructured process patterns which are expressed not only in different formats but also , in different forms this approach is implemented using the gate text engineering framework and then evaluated leading up to high quality results motivating us to continue in this direction"
cs.DS,"we present two stateless algorithms that guarantee to deliver the message to every device in a designated geographic area flooding and planar geocasting due to the algorithms' statelessness , intermediate devices do not have to keep message data between message transmissions we formally prove the algorithms correct , estimate their message complexity and evaluate their performance through simulation"
cs.DM|cs.DS,"a emph t treewidth modulator of a graph g is a set x subseteq v \( g \) such that the treewidth of g x is at most some constant t 1 in this paper , we present a novel algorithm to compute a decomposition scheme for graphs g that come equipped with a t treewidth modulator this decomposition , called a emph protrusion decomposition , is the cornerstone in obtaining the following two main results we first show that any parameterized graph problem \( with parameter k \) that has emph finite integer index and is emph treewidth bounding admits a linear kernel on h topological minor free graphs , where h is some arbitrary but fixed graph a parameterized graph problem is called treewidth bounding if all positive instances have a t treewidth modulator of size o \( k \) , for some constant t this result partially extends previous meta theorems on the existence of linear kernels on graphs of bounded genus bodlaender et al , focs 2009 and h minor free graphs fomin et al , soda 2010 our second application concerns the planar mathcal f deletion problem let mathcal f be a fixed finite family of graphs containing at least one planar graph given an n vertex graph g and a non negative integer k , planar mathcal f deletion asks whether g has a set x subseteq v \( g \) such that x leq k and g x is h minor free for every h in mathcal f very recently , an algorithm for planar mathcal f deletion with running time 2 o \( k \) n log 2 n \( such an algorithm is called emph single exponential \) has been presented in fomin et al , focs 2012 under the condition that every graph in mathcal f is connected using our algorithm to construct protrusion decompositions as a building block , we get rid of this connectivity constraint and present an algorithm for the general planar mathcal f deletion problem running in time 2 o \( k \) n 2"
cs.DM,"we investigate algorithms for canonical labelling of site graphs , i e graphs in which edges bind vertices on sites with locally unique names we first show that the problem of canonical labelling of site graphs reduces to the problem of canonical labelling of graphs with edge colourings we then present two canonical labelling algorithms based on edge enumeration , and a third based on an extension of hopcroft 's partition refinement algorithm all run in quadratic worst case time individually however , one of the edge enumeration algorithms runs in sub quadratic time for graphs with many automorphisms , and the partition refinement algorithm runs in sub quadratic time for graphs with few bisimulation equivalences this suite of algorithms was chosen based on the expectation that graphs fall in one of those two categories if that is the case , a combined algorithm runs in sub quadratic worst case time whether this expectation is reasonable remains an interesting open problem"
cs.AI,"in this paper , first we present a new explanation for the relation between logical circuits and artificial neural networks , logical circuits and fuzzy logic , and artificial neural networks and fuzzy inference systems then , based on these results , we propose a new neuro fuzzy computing system which can effectively be implemented on the memristor crossbar structure one important feature of the proposed system is that its hardware can directly be trained using the hebbian learning rule and without the need to any optimization the system also has a very good capability to deal with huge number of input out training data without facing problems like overtraining"
cs.AI|cs.LG,"domain knowledge is crucial for effective performance in autonomous control systems typically , human effort is required to encode this knowledge into a control algorithm in this paper , we present an approach to language grounding which automatically interprets text in the context of a complex control application , such as a game , and uses domain knowledge extracted from the text to improve control performance both text analysis and control strategies are learned jointly using only a feedback signal inherent to the application to effectively leverage textual information , our method automatically extracts the text segment most relevant to the current game state , and labels it with a task centric predicate structure this labeled text is then used to bias an action selection policy for the game , guiding it towards promising regions of the action space we encode our model for text analysis and game playing in a multi layer neural network , representing linguistic decisions via latent variables in the hidden layers , and game action quality via the output layer operating within the monte carlo search framework , we estimate model parameters using feedback from simulated games we apply our approach to the complex strategy game civilization ii using the official game manual as the text guide our results show that a linguistically informed game playing agent significantly outperforms its language unaware counterpart , yielding a 34 absolute improvement and winning over 65 of games when playing against the built in ai of civilization"
cs.AI,"making a decision in a changeable and dynamic environment is an arduous task owing to the lack of information , their uncertainties and the unawareness of planners about the future evolution of incidents the use of a decision support system is an efficient solution of this issue such a system can help emergency planners and responders to detect possible emergencies , as well as to suggest and evaluate possible courses of action to deal with the emergency we are interested in our work to the modeling of a monitoring preventive and emergency management system , wherein we stress the generic aspect in this paper we propose an agent based architecture of this system and we describe a first step of our approach which is the modeling of information and their representation using a multiagent system"
cs.LG,"extraction of electrocardiography \( ecg or ekg \) signals of mother and baby is a challenging task , because one single device is used and it receives a mixture of multiple heart beats in this paper , we would like to design a filter to separate the signals from each other"
cs.DS,"we study two fundamental problems in computational geometry finding the maximum inscribed ball \( maxib \) inside a polytope defined by m hyperplanes in a d dimensional space , and finding the minimum enclosing ball \( mineb \) of a set of n points in a d dimensional space we translate both geometry problems into purely algebraic optimization questions , and apply ideas from first order convex optimizations to obtain simple and nearly linear time algorithms for maxib , our algorithm produces a \( 1 varepsilon \) approximate solution in time tilde o \( m d alpha varepsilon \) , compared to the best known running time tilde o \( m d alpha 3 varepsilon 3 \) of xie , snoeyink , and xu xsx06 , where alpha geq 1 is the aspect ratio of the polytope for mineb , our algorithm produces a \( 1 varepsilon \) approximate solution in time tilde o \( n d sqrt varepsilon \) this result matches the previous best running time , while yielding a simpler algorithm for both problems , we provide an empirical evaluation showing the improved performance of our algorithms"
cs.AI,"the ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments based on many known properties of cortical neurons , a recent study proposed hierarchical temporal memory \( htm \) sequence memory as a theoretical framework for sequence learning in the cortex in this paper , we analyze properties of htm sequence memory and apply it to various sequence learning and prediction problems we show the model is able to continuously learn a large number of variable order temporal sequences using an unsupervised hebbian like learning rule the sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence we compare the htm sequence memory and other sequence learning algorithms , including the autoregressive integrated moving average \( arima \) model and long short term memory \( lstm \) , on sequence prediction problems with both artificial and real world data the htm model not only achieves comparable or better accuracy than state of the art algorithms , but also exhibits a set of properties that is critical for sequence learning these properties include continuous online learning , the ability to handle multiple predictions and branching sequences , robustness to sensor noise and fault tolerance , and good performance without task specific hyper parameters tuning therefore the htm sequence memory not only advances our understanding of how the brain may solve the sequence learning problem , but is also applicable to a wide range of real world problems such as discrete and continuous sequence prediction , anomaly detection , and sequence classification"
cs.AI|cs.LG,"recently , the next item basket recommendation system , which considers the sequential relation between bought items , has drawn attention of researchers the utilization of sequential patterns has boosted performance on several kinds of recommendation tasks inspired by natural language processing \( nlp \) techniques , we propose a novel neural network \( nn \) based next song recommender , cnn rec , in this paper then , we compare the proposed system with several nn based and classic recommendation systems on the next song recommendation task verification results indicate the proposed system outperforms classic systems and has comparable performance with the state of the art system"
cs.AI|cs.SI,"biomedical taxonomies , thesauri and ontologies in the form of the international classification of diseases \( icd \) as a taxonomy or the national cancer institute thesaurus as an owl based ontology , play a critical role in acquiring , representing and processing information about human health with increasing adoption and relevance , biomedical ontologies have also significantly increased in size for example , the 11th revision of the icd , which is currently under active development by the who contains nearly 50 , 000 classes representing a vast variety of different diseases and causes of death this evolution in terms of size was accompanied by an evolution in the way ontologies are engineered because no single individual has the expertise to develop such large scale ontologies , ontology engineering projects have evolved from small scale efforts involving just a few domain experts to large scale projects that require effective collaboration between dozens or even hundreds of experts , practitioners and other stakeholders understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations we uncover how large ontology engineering projects , such as the icd in its 11th revision , unfold by analyzing usage logs of five different biomedical ontology engineering projects of varying sizes and scopes using markov chains we discover intriguing interaction patterns \( e g , which properties users subsequently change \) that suggest that large collaborative ontology engineering projects are governed by a few general principles that determine and drive development from our analysis , we identify commonalities and differences between different projects that have implications for project managers , ontology editors , developers and contributors working on collaborative ontology engineering projects and tools in the biomedical domain"
cs.LG|stat.ML,"to devise efficient solutions for approximating a mean partition in consensus clustering , dimitriadou et al 3 presented a necessary condition of optimality for a consensus function based on least square distances we show that their result is pivotal for deriving interesting properties of consensus clustering beyond optimization for this , we present the necessary condition of optimality in a slightly stronger form in terms of the mean partition theorem and extend it to the expected partition theorem to underpin its versatility , we show three examples that apply the mean partition theorem \( i \) equivalence of the mean partition and optimal multiple alignment , \( ii \) construction of profiles and motifs , and \( iii \) relationship between consensus clustering and cluster stability"
cs.SI,"recent years have seen the world become a closely connected society with the emergence of different types of social networks online social networks have provided a way to bridge long distances and establish numerous communication channels which were not possible earlier these networks exhibit interesting behavior under intentional attacks and random failures where different structural properties influence the resilience in different ways in this paper , we perform two sets of experiments and draw conclusions from the results pertaining to the resilience of social networks the first experiment performs a comparative analysis of four different classes of networks namely small world networks , scale free networks , small world scale free networks and random networks with four semantically different social networks under different attack strategies the second experiment compares the resilience of these semantically different social networks under different attack strategies empirical analysis reveals interesting behavior of different classes of networks with different attack strategies"
cs.LG,"one shot learning is usually tackled by using generative models or discriminative embeddings discriminative methods based on deep learning , which are very effective in other learning scenarios , are ill suited for one shot learning as they need large amounts of training data in this paper , we propose a method to learn the parameters of a deep model in one shot we construct the learner as a second deep network , called a learnet , which predicts the parameters of a pupil network from a single exemplar in this manner we obtain an efficient feed forward one shot learner , trained end to end by minimizing a one shot classification objective in a learning to learn formulation in order to make the construction feasible , we propose a number of factorizations of the parameters of the pupil network we demonstrate encouraging results by learning characters from single exemplars in omniglot , and by tracking visual objects from a single initial exemplar in the visual object tracking benchmark"
cs.SI,"structural balance theory has been developed in sociology and psychology to explain how interacting agents , e g , countries , political parties , opinionated individuals , with mixed trust and mistrust relationships evolve into polarized camps recent results have shown that structural balance is necessary for polarization in networks with fixed , strongly connected neighbor relationships when the opinion dynamics are described by degroot type averaging rules we develop this line of research in this paper in two steps first , we consider fixed , not necessarily strongly connected , neighbor relationships it is shown that if the network includes a strongly connected subnetwork containing mistrust , which influences the rest of the network , then no opinion clustering is possible when that subnetwork is not structurally balanced all the opinions become neutralized in the end in contrast , it is shown that when that subnetwork is indeed structurally balanced , the agents of the subnetwork evolve into two polarized camps and the opinions of all other agents in the network spread between these two polarized opinions second , we consider time varying neighbor relationships we show that the opinion separation criteria carry over if the conditions for fixed graphs are extended to joint graphs the results are developed for both discrete time and continuous time models"
cs.SI,"all online sharing systems gather data that reflects users' collective behaviour and their shared activities this data can be used to extract different kinds of relationships , which can be grouped into layers , and which are basic components of the multidimensional social network proposed in the paper the layers are created on the basis of two types of relations between humans , i e direct and object based ones which respectively correspond to either social or semantic links between individuals for better understanding of the complexity of the social network structure , layers and their profiles were identified and studied on two , spanned in time , snapshots of the flickr population additionally , for each layer , a separate strength measure was proposed the experiments on the flickr photo sharing system revealed that the relationships between users result either from semantic links between objects they operate on or from social connections of these users moreover , the density of the social network increases in time the second part of the study is devoted to building a social recommender system that supports the creation of new relations between users in a multimedia sharing system its main goal is to generate personalized suggestions that are continuously adapted to users' needs depending on the personal weights assigned to each layer in the multidimensional social network the conducted experiments confirmed the usefulness of the proposed model"
cs.AI|cs.LG|stat.ML,"learning from electronic medical records \( emr \) is challenging due to their relational nature and the uncertain dependence between a patient 's past and future health status statistical relational learning is a natural fit for analyzing emrs but is less adept at handling their inherent latent structure , such as connections between related medications or diseases one way to capture the latent structure is via a relational clustering of objects we propose a novel approach that , instead of pre clustering the objects , performs a demand driven clustering during learning we evaluate our algorithm on three real world tasks where the goal is to use emrs to predict whether a patient will have an adverse reaction to a medication we find that our approach is more accurate than performing no clustering , pre clustering , and using expert constructed medical heterarchies"
cs.DS|cs.SI,"large real world networks are typically scale free recent research has shown that such graphs are described best in a geometric space more precisely , the internet can be mapped to a hyperbolic space such that geometric greedy routing performs close to optimal \( bogun 'a , papadopoulos , and krioukov nature communications , 1 62 , 2010 \) this observation pushed the interest in hyperbolic networks as a natural model for scale free networks hyperbolic random graphs follow a power law degree distribution with controllable exponent beta and show high clustering \( gugelmann , panagiotou , and peter icalp , pp 573 585 , 2012 \) for understanding the structure of the resulting graphs and for analyzing the behavior of network algorithms , the next question is bounding the size of the diameter the only known explicit bound is mathcal o \( \( log n \) 32 \( \( 3 beta \) \( 5 beta \) \) 1 \) \( kiwi and mitsche analco , pp 26 39 , 2015 \) we present two much simpler proofs for an improved upper bound of mathcal o \( \( log n \) 2 \( 3 beta \) \) and a lower bound of omega \( log n \)"
cs.LG,"we trained a convolutional neural network \( cnn \) to map raw pixels from a single front facing camera directly to steering commands this end to end approach proved surprisingly powerful with minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways it also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads the system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal we never explicitly trained it to detect , for example , the outline of roads compared to explicit decomposition of the problem , such as lane marking detection , path planning , and control , our end to end system optimizes all processing steps simultaneously we argue that this will eventually lead to better performance and smaller systems better performance will result because the internal components self optimize to maximize overall system performance , instead of optimizing human selected intermediate criteria , e g , lane detection such criteria understandably are selected for ease of human interpretation which does n't automatically guarantee maximum system performance smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps we used an nvidia devbox and torch 7 for training and an nvidia drive \( tm \) px self driving car computer also running torch 7 for determining where to drive the system operates at 30 frames per second \( fps \)"
cs.AI,"huge datasets in cyber security , such as network traffic logs , can be analyzed using machine learning and data mining methods however , the amount of collected data is increasing , which makes analysis more difficult many machine learning methods have not been designed for big datasets , and consequently are slow and difficult to understand we address the issue of efficient network traffic classification by creating an intrusion detection framework that applies dimensionality reduction and conjunctive rule extraction the system can perform unsupervised anomaly detection and use this information to create conjunctive rules that classify huge amounts of traffic in real time we test the implemented system with the widely used kdd cup 99 dataset and real world network logs to confirm that the performance is satisfactory this system is transparent and does not work like a black box , making it intuitive for domain experts , such as network administrators"
cs.DS,"we address the problem of testing whether a dynamic graph is temporally connected , i e a temporal path \( em journey \) exists between all pairs of vertices we consider a discrete version of the problem , where the topology is given as an evolving graph g g 1 , g 2 , , g k in which only the set of \( directed \) edges varies two cases are studied , depending on whether a single edge or an unlimited number of edges can be crossed in a same g i \( strict journeys it vs non strict journeys \) for strict journeys , two existing algorithms designed for other problems can be adapted however , we show that a dedicated approach achieves a better time complexity than one of these two algorithms in all cases , and than the other one for those graphs whose density is low at any time \( though arbitrary over time \) the time complexity of our algorithm is o \( k mu n \) , where k g is the number of time steps and mu max \( e i \) is the maximum em instant density , to be contrasted with m cup e i , the em cumulated density indeed , it is not uncommon for a mobility scenario to satisfy , for instance , both mu o \( n \) and m theta \( n 2 \) we characterize the key values of k , mu and m for which our algorithm should be used for non strict journeys , for which no algorithm is known , we show that a similar strategy can be used to answer the question , still in o \( k mu n \) time"
cs.LG|stat.ML,"we propose a simple and efficient time series clustering framework particularly suited for low signal to noise ratio \( snr \) , by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information we extend the sparse k means algorithm by incorporating structured sparsity , and use it to exploit the multi scale property of wavelets and group structure in multivariate signals finally , we extract features invariant to translation and scaling with the scattering transform , which corresponds to a convolutional network with filters given by a wavelet operator , and use the network 's structure in sparse clustering by promoting sparsity , this transform can yield a low dimensional representation of signals that gives improved clustering results on several real datasets"
cs.LG|stat.ML,"we propose an efficient method for approximating natural gradient descent in neural networks which we call kronecker factored approximate curvature \( k fac \) k fac is based on an efficiently invertible approximation of a neural network 's fisher information matrix which is neither diagonal nor low rank , and in some cases is completely non sparse it is derived by approximating various large blocks of the fisher \( corresponding to entire layers \) as factoring as kronecker products between two much smaller matrices while only several times more expensive to compute than the plain stochastic gradient , the updates produced by k fac make much more progress optimizing the objective , which results in an algorithm that can be much faster than stochastic gradient descent with momentum in practice and unlike some previously proposed approximate natural gradient newton methods such as hessian free methods , k fac works very well in highly stochastic optimization regimes"
cs.DS,"for g n , let b 1 , , b n g be n g independent vectors in mathbb r n with a common distribution invariant by rotation considering these vectors as a basis for the euclidean lattice they generate , the aim of this paper is to provide asymptotic results when n to infty concerning the property that such a random basis is reduced in the sense of sc lenstra , lenstra lov 'asz the proof passes by the study of the process \( r g 1 \( n \) , r g 2 \( n \) , , r n 1 \( n \) \) where r j \( n \) is the ratio of lengths of two consecutive vectors b n j 1 and b n j built from \( b 1 , , b n g \) by the gram schmidt orthogonalization procedure , which we believe to be interesting in its own we show that , as n to infty , the process \( r j \( n \) 1 \) j tends in distribution in some sense to an explicit process \( mathcal r j 1 \) j some properties of this latter are provided"
cs.DS,"we study boolean functions of an arbitrary number of input variables that can be realized by simple iterative constructions based on constant size primitives this restricted type of construction needs little global coordination or control and thus is a candidate for neurally feasible computation valiant 's construction of a majority function can be realized in this manner and , as we show , can be generalized to any uniform threshold function we study the rate of convergence , finding that while linear convergence to the correct function can be achieved for any threshold using a fixed set of primitives , for quadratic convergence , the size of the primitives must grow as the threshold approaches 0 or 1 we also study finite realizations of this process and the learnability of the functions realized we show that the constructions realized are accurate outside a small interval near the target threshold , where the size of the construction grows as the inverse square of the interval width this phenomenon , that errors are higher closer to thresholds \( and thresholds closer to the boundary are harder to represent \) , is a well known cognitive finding"
cs.IT|cs.LG|stat.ML,"we consider the problem of recovering the sparsest vector in a subspace mathcal s subseteq mathbb r p with mathrm dim \( mathcal s \) n p this problem can be considered a homogeneous variant of the sparse recovery problem , and finds applications in sparse dictionary learning , sparse pca , and other problems in signal processing and machine learning simple convex heuristics for this problem provably break down when the fraction of nonzero entries in the target sparse vector substantially exceeds 1 sqrt n in contrast , we exhibit a relatively simple nonconvex approach based on alternating directions , which provably succeeds even when the fraction of nonzero entries is omega \( 1 \) to our knowledge , this is the first practical algorithm to achieve this linear scaling this result assumes a planted sparse model , in which the target sparse vector is embedded in an otherwise random subspace empirically , our proposed algorithm also succeeds in more challenging data models arising , e g , from sparse dictionary learning"
cs.SI,"traditionally , scholarly impact and visibility have been measured by counting publications and citations in the scholarly literature however , increasingly scholars are also visible on the web , establishing presences in a growing variety of social ecosystems but how wide and established is this presence , and how do measures of social web impact relate to their more traditional counterparts \? to answer this , we sampled 57 presenters from the 2010 leiden sti conference , gathering publication and citations counts as well as data from the presenters' web footprints we found web presence widespread and diverse 84 of scholars had homepages , 70 were on linkedin , 23 had public google scholar profiles , and 16 were on twitter for sampled scholars' publications , social reference manager bookmarks were compared to scopus and web of science citations we found that mendeley covers more than 80 of sampled articles , and that mendeley bookmarks are significantly correlated \( r 45 \) to scopus citation counts"
cs.LG|stat.ML,"a l1 norm penalized orthogonal forward regression \( l1 pofr \) algorithm is proposed based on the concept of leaveone out mean square error \( loomse \) firstly , a new l1 norm penalized cost function is defined in the constructed orthogonal space , and each orthogonal basis is associated with an individually tunable regularization parameter secondly , due to orthogonal computation , the loomse can be analytically computed without actually splitting the data set , and moreover a closed form of the optimal regularization parameter in terms of minimal loomse is derived thirdly , a lower bound for regularization parameters is proposed , which can be used for robust loomse estimation by adaptively detecting and removing regressors to an inactive set so that the computational cost of the algorithm is significantly reduced illustrative examples are included to demonstrate the effectiveness of this new l1 pofr approach"
cs.DS|cs.LG,"we study the worst case error of kernel density estimates via subset approximation a kernel density estimate of a distribution is the convolution of that distribution with a fixed kernel \( e g gaussian kernel \) given a subset \( i e a point set \) of the input distribution , we can compare the kernel density estimates of the input distribution with that of the subset and bound the worst case error if the maximum error is eps , then this subset can be thought of as an eps sample \( aka an eps approximation \) of the range space defined with the input distribution as the ground set and the fixed kernel representing the family of ranges interestingly , in this case the ranges are not binary , but have a continuous range \( for simplicity we focus on kernels with range of 0 , 1 \) these allow for smoother notions of range spaces it turns out , the use of this smoother family of range spaces has an added benefit of greatly decreasing the size required for eps samples for instance , in the plane the size is o \( \( 1 eps 4 3 \) log 2 3 \( 1 eps \) \) for disks \( based on vc dimension arguments \) but is only o \( \( 1 eps \) sqrt log \( 1 eps \) \) for gaussian kernels and for kernels with bounded slope that only affect a bounded domain these bounds are accomplished by studying the discrepancy of these kernel range spaces , and here the improvement in bounds are even more pronounced in the plane , we show the discrepancy is o \( sqrt log n \) for these kernels , whereas for balls there is a lower bound of omega \( n 1 4 \)"
cs.DM,"we consider a game in which a cop searches for a moving robber on a graph using distance probes , studied by carragher , choi , delcourt , erickson and west , which is a slight variation on one introduced by seager carragher , choi , delcourt , erickson and west show that for any fixed graph g there is a winning strategy for the cop on the graph g 1 m , obtained by replacing each edge of g by a path of length m , if m is sufficiently large they conjecture that the cop does not have a winning strategy on k n 1 m if m n we show that in fact the cop wins if and only if m geqslant n 2 , for all but a few small values of n they also show that the robber can avoid capture on any graph of girth 3 , 4 or 5 , and ask whether there is any graph of girth 6 on which the cop wins we show that there is , but that no such graph can be bipartite in the process we give a counterexample for their conjecture that the set of graphs on which the cop wins is closed under the operation of subdividing edges we also give a complete answer to the question of when the cop has a winning strategy on k a , b 1 m"
cs.DS,"discrete tomography deals with reconstructing finite spatial objects from lower dimensional projections and has applications for example in timetable design in this paper we consider the problem of reconstructing a tile packing from its row and column projections it consists of disjoint copies of a fixed tile , all contained in some rectangular grid the projections tell how many cells are covered by a tile in each row and column how difficult is it to construct a tile packing satisfying given projections \? it was known to be solvable by a greedy algorithm for bars \( tiles of width or height 1 \) , and np hardness results were known for some specific tiles this paper shows that the problem is np hard whenever the tile is not a bar"
cs.LG|stat.ML,"supervised \( linear \) embedding models like wsabie and psi have proven successful at ranking , recommendation and annotation tasks however , despite being scalable to large datasets they do not take full advantage of the extra data due to their linear nature , and typically underfit we propose a new class of models which aim to provide improved performance while retaining many of the benefits of the existing class of embedding models our new approach works by iteratively learning a linear embedding model where the next iteration 's features and labels are reweighted as a function of the previous iteration we describe several variants of the family , and give some initial results"
cs.LG|stat.ML,"modern information processing relies on the axiom that high dimensional data lie near low dimensional geometric structures this paper revisits the problem of data driven learning of these geometric structures and puts forth two new nonlinear geometric models for data describing related objects phenomena the first one of these models suited for mildly nonlinear data is termed the metric constrained union of subspaces \( mc uos \) model , which straddles the two extremes of the subspace model and the union of subspaces model the second one of these models suited for highly nonlinear data is termed the metric constrained kernel union of subspaces \( mc kuos \) model , which generalizes the kernel subspace model the main contributions of this paper in this regard include the following first , it motivates and formalizes the problems of mc uos and mc kuos learning second , it presents algorithms that efficiently learn an mc uos or an mc kuos underlying data of interest third , it extends these algorithms to the case when parts of the data are missing last , but not least , it reports the outcomes of a series of numerical experiments involving both synthetic and real data that demonstrate the superiority of the proposed geometric models and learning algorithms over existing approaches in the literature these experiments also help clarify the connections between this work and the literature on \( subspace and kernel k means \) clustering"
cs.AI,"within the possibilistic approach to uncertainty modeling , the paper presents a modal logical system to reason about qualitative \( comparative \) statements of the possibility \( and necessity \) of fuzzy propositions we relate this qualitative modal logic to the many valued analogues mvs5 and mvkd45 of the well known modal logics of knowledge and belief s5 and kd45 respectively completeness results are obtained for such logics and therefore , they extend previous existing results for qualitative possibilistic logics in the classical non fuzzy setting"
cs.AI,"sometime in the future we will have to deal with the impact of ai 's being mistaken for humans for this reason , i propose that any autonomous system should be designed so that it is unlikely to be mistaken for anything besides an autonomous sysem , and should identify itself at the start of any interaction with another agent"
cs.LG|stat.ML,"the aim of this short note is to draw attention to a method by which the partition function and marginal probabilities for a certain class of random fields on complete graphs can be computed in polynomial time this class includes ising models with homogeneous pairwise potentials but arbitrary \( inhomogeneous \) unary potentials similarly , the partition function and marginal probabilities can be computed in polynomial time for random fields on complete bipartite graphs , provided they have homogeneous pairwise potentials we expect that these tractable classes of large scale random fields can be very useful for the evaluation of approximation algorithms by providing exact error estimates"
cs.DM|cs.DS,"different techniques have been used to prove several transference theorems of the form nontrivial algorithms for a circuit class c yield circuit lower bounds against c in this survey we revisit many of these results we discuss how circuit lower bounds can be obtained from derandomization , compression , learning , and satisfiability algorithms we also cover the connection between circuit lower bounds and useful properties , a notion that turns out to be fundamental in the context of these transference theorems along the way , we obtain a few new results , simplify several proofs , and show connections involving different frameworks we hope that our presentation will serve as a self contained introduction for those interested in pursuing research in this area"
cs.DM,"given a graph g with positive integer weights on the vertices , and a token placed on some current vertex u , two players alternately remove a positive integer weight from u and then move the token to a new current vertex adjacent to u when the weight of a vertex is set to 0 , it is removed and its neighborhood becomes a clique the player making the last move wins this adaptation of nim on graphs is called vertexnim , and slightly differs from the game vertex nimg introduced by stockman in 2004 vertexnim can be played on both directed or undirected graphs in this paper , we study the complexity of deciding whether a given game position of vertexnim is winning for the first or second player in particular , we show that for undirected graphs , this problem can be solved in quadratic time our algorithm is also available for the game vertex nimg , thus improving stockman 's exptime algorithm in the directed case , we are able to compute the winning strategy in polynomial time for several instances , including circuits or digraphs with self loops"
cs.AI,"the data mining process consists of a series of steps ranging from data cleaning , data selection and transformation , to pattern evaluation and visualization one of the central problems in data mining is to make the mined patterns or knowledge actionable here , the term actionable refers to the mined patterns suggest concrete and profitable actions to the decision maker that is , the user can do something to bring direct benefits \( increase in profits , reduction in cost , improvement in efficiency , etc \) to the organization 's advantage however , there has been written no comprehensive survey available on this topic the goal of this paper is to fill the void in this paper , we first present two frameworks for mining actionable knowledge that are inexplicitly adopted by existing research methods then we try to situate some of the research on this topic from two different viewpoints 1 \) data mining tasks and 2 \) adopted framework finally , we specify issues that are either not addressed or insufficiently studied yet and conclude the paper"
cs.DM,"we consider the problem of computing identifying codes of graphs and its fractional relaxation the ratio between the size of optimal integer and fractional solutions is between 1 and 2 ln \( v \) 1 where v is the set of vertices of the graph we focus on vertex transitive graphs for which we can compute the exact fractional solution there are known examples of vertex transitive graphs that reach both bounds we exhibit infinite families of vertex transitive graphs with integer and fractional identifying codes of order v a with a in 1 4 , 1 3 , 2 5 these families are generalized quadrangles \( strongly regular graphs based on finite geometries \) they also provide examples for metric dimension of graphs"
cs.DM,"in this short paper we study the game of cops and robbers , played on the vertices of some fixed graph g of order n the minimum number of cops required to capture a robber is called the cop number of g we show that the cop number of graphs of diameter 2 is at most sqrt 2n , improving a recent result of lu and peng by a constant factor we conjecture that this bound is still not optimal , and obtain some partial results towards the optimal bound"
cs.AI|cs.LG|stat.ML,"it is known that fixed points of loopy belief propagation \( bp \) correspond to stationary points of the bethe variational problem , where we minimize the bethe free energy subject to normalization and marginalization constraints unfortunately , this does not entirely explain bp because bp is a dual rather than primal algorithm to solve the bethe variational problem beliefs are infeasible before convergence thus , we have no better understanding of bp than as an algorithm to seek for a common zero of a system of non linear functions , not explicitly related to each other in this theoretical paper , we show that these functions are in fact explicitly related they are the partial derivatives of a single function of reparameterizations that means , bp seeks for a stationary point of a single function , without any constraints this function has a very natural form it is a linear combination of local log partition functions , exactly as the bethe entropy is the same linear combination of local entropies"
cs.AI,"in cognitive sciences it is not uncommon to use various games effectively for example , in artificial intelligence , the robocup initiative was to set up to catalyse research on the field of autonomous agent technology in this paper , we introduce a similar soccer simulation initiative to try to investigate a model of human consciousness and a notion of reality in the form of a cognitive problem in addition , for example , the home pitch advantage and the objective role of the supporters could be naturally described and discussed in terms of this new soccer simulation model"
cs.LG,"data clustering is a technique for clustering set of objects into known number of groups several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other k means , is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence however , k means algorithm suffers from initialization , hence trapped in local optima flower pollination algorithm \( fpa \) is the global optimization technique , which avoids trapping in local optimum solution in this paper , a novel hybrid data clustering approach using flower pollination algorithm and k means \( fpakm \) is proposed the proposed algorithm results are compared with k means and fpa on eight datasets from the experimental results , fpakm is better than fpa and k means"
cs.LG,"recent methods for learning vector space representations of words have succeeded in capturing fine grained semantic and syntactic regularities using vector arithmetic however , these vector space representations \( created through large scale text analysis \) are typically stored verbatim , since their internal structure is opaque using word analogy tests to monitor the level of detail stored in compressed re representations of the same vector space , the trade offs between the reduction in memory usage and expressiveness are investigated a simple scheme is outlined that can reduce the memory footprint of a state of the art embedding by a factor of 10 , with only minimal impact on performance then , using the same 'bit budget' , a binary \( approximate \) factorisation of the same space is also explored , with the aim of creating an equivalent representation with better interpretability"
cs.DS,"many resource allocation problems can be formulated as an optimization problem whose constraints contain sensitive information about participating users this paper concerns solving this kind of optimization problem in a distributed manner while protecting the privacy of user information without privacy considerations , existing distributed algorithms normally consist in a central entity computing and broadcasting certain public coordination signals to participating users however , the coordination signals often depend on user information , so that an adversary who has access to the coordination signals can potentially decode information on individual users and put user privacy at risk we present a distributed optimization algorithm that preserves differential privacy , which is a strong notion that guarantees user privacy regardless of any auxiliary information an adversary may have the algorithm achieves privacy by perturbing the public signals with additive noise , whose magnitude is determined by the sensitivity of the projection operation onto user specified constraints by viewing the differentially private algorithm as an implementation of stochastic gradient descent , we are able to derive a bound for the suboptimality of the algorithm we illustrate the implementation of our algorithm via a case study of electric vehicle charging specifically , we derive the sensitivity and present numerical simulations for the algorithm through numerical simulations , we are able to investigate various aspects of the algorithm when being used in practice , including the choice of step size , number of iterations , and the trade off between privacy level and suboptimality"
cs.DM,"in this note we describe a new method of counting the number of unordered factorizations of a natural number by means of a generating function and a recurrence relation arising from it , which improves an earlier result in this direction"
cs.SI,"many networks display community structure which identifies groups of nodes within which connections are denser than between them detecting and characterizing such community structure , which is known as community detection , is one of the fundamental issues in the study of network systems it has received a considerable attention in the last years numerous techniques have been developed for both efficient and effective community detection among them , the most efficient algorithm is the label propagation algorithm whose computational complexity is o \( e \) although it is linear in the number of edges , the running time is still too long for very large networks , creating the need for parallel community detection also , computing community quality metrics for community structure is computationally expensive both with and without ground truth however , to date we are not aware of any effort to introduce parallelism for this problem in this paper , we provide a parallel toolkit to calculate the values of such metrics we evaluate the parallel algorithms on both distributed memory machine and shared memory machine the experimental results show that they yield a significant performance gain over sequential execution in terms of total running time , speedup , and efficiency"
cs.SI,"many of the systems that are traditionally analyzed as complex networks have natural interpretations as multiplex structures while these formulations retain more information than standard network models , there is not yet a fully developed theory for computing network metrics and statistics on these objects as many of the structural representations associated to these models can distort the underlying characteristics of dynamical process , we introduce an algebraic method for modeling arbitrary dynamics on multiplex networks since several network metrics are based on generalized notions of dynamical transfer , we can use this framework to extend many of the standard network metrics to multiplex structures in a consistent fashion"
cs.SI,"we investigate the effect of a specific edge weighting scheme sim \( k i k j \) beta on distributed flow efficiency and robustness to cascading failures in scale free networks in particular , we analyze a simple , yet fundamental distributed flow model current flow in random resistor networks by the tuning of control parameter beta and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth , we show the dependence of transport efficiency upon the correlations between the topology and weights by studying the severity of cascades for different control parameter beta , we find that network resilience to cascading overloads and network throughput is optimal for the same value of beta over the range of node capacities and available bandwidth"
cs.SI,"preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources social network provides information about activities of humans and social events thus , with the help of social network , we can extract which humans will attend a particular event \( in near time \) and can estimate flow of traffic based on it this opens up a wide area of research which poses need to have a framework for traffic management that can capture essential parameters of real life behaviour and provide a way to iterate upon and evaluate new ideas in this paper , we present building blocks of a framework and a system to simulate a city with its transport system , humans and their social network we emphasize on relevant parameters selected and modular design of the framework our framework defines metrics to evaluate congestion avoidance strategies to show utility of the framework , we present experimental studies of few strategies on a public transport system"
cs.DM,"in this note , we show that a complete k partite graph is the only graph with clique number k among all degree equivalent simple graphs this result gives a lower bound on the clique number , which is sharper than existing bounds on a large family of graphs"
cs.IT|cs.LG,"motivated by sensor networks and other distributed settings , several models for distributed learning are presented the models differ from classical works in statistical pattern recognition by allocating observations of an independent and identically distributed \( i i d \) sampling process amongst members of a network of simple learning agents the agents are limited in their ability to communicate to a central fusion center and thus , the amount of information available for use in classification or regression is constrained for several basic communication models in both the binary classification and regression frameworks , we question the existence of agent decision rules and fusion rules that result in a universally consistent ensemble the answers to this question present new issues to consider with regard to universal consistency insofar as these models present a useful picture of distributed scenarios , this paper addresses the issue of whether or not the guarantees provided by stone 's theorem in centralized environments hold in distributed settings"
cs.DM|cs.DS|cs.IT,"the subset sum problem asks whether a given set of n positive integers contains a subset of elements that sum up to a given target t it is an outstanding open question whether the o \( 2 n 2 \) time algorithm for subset sum by horowitz and sahni j acm 1974 can be beaten in the worst case setting by a truly faster , o \( 2 \( 0 5 delta \) n \) time algorithm , with some constant delta 0 continuing an earlier work stacs 2015 , we study subset sum parameterized by the maximum bin size beta , defined as the largest number of subsets of the n input integers that yield the same sum for every epsilon 0 we give a truly faster algorithm for instances with beta leq 2 \( 0 5 epsilon \) n , as well as instances with beta geq 2 0 661n consequently , we also obtain a characterization in terms of the popular density parameter n log 2 t if all instances of density at least 1 003 admit a truly faster algorithm , then so does every instance this goes against the current intuition that instances of density 1 are the hardest , and therefore is a step toward answering the open question in the affirmative our results stem from novel combinations of earlier algorithms for subset sum and a study of an extremal question in additive combinatorics connected to the problem of uniquely decodable code pairs in information theory"
cs.LG,"while analyzing vehicular sensor data , we found that frequently occurring waveforms could serve as features for further analysis , such as rule mining , classification , and anomaly detection the discovery of waveform patterns , also known as time series motifs , has been studied extensively however , available techniques for discovering frequently occurring time series motifs were found lacking in either efficiency or quality standard subsequence clustering results in poor quality , to the extent that it has even been termed 'meaningless' variants of hierarchical clustering using techniques for efficient discovery of 'exact pair motifs' find high quality frequent motifs , but at the cost of high computational complexity , making such techniques unusable for our voluminous vehicular sensor data we show that good quality frequent motifs can be discovered using bounded spherical clustering of time series subsequences , which we refer to as coin clustering , with near linear complexity in time series size coin clustering addresses many of the challenges that previously led to subsequence clustering being viewed as meaningless we describe an end to end motif discovery procedure using a sequence of pre and post processing techniques that remove trivial matches and shifted motifs , which also plagued previous subsequence clustering approaches we demonstrate that our technique efficiently discovers frequent motifs in voluminous vehicular sensor data as well as in publicly available data sets"
cs.DS,"the subtree isomorphism problem asks whether a given tree is contained in another given tree the problem is of fundamental importance and has been studied since the 1960s for some variants , e g , ordered trees , near linear time algorithms are known , but for the general case truly subquadratic algorithms remain elusive our first result is a reduction from the orthogonal vectors problem to subtree isomorphism , showing that a truly subquadratic algorithm for the latter refutes the strong exponential time hypothesis \( seth \) in light of this conditional lower bound , we focus on natural special cases for which no truly subquadratic algorithms are known we classify these cases against the quadratic barrier , showing in particular that even for binary , rooted trees , a truly subquadratic algorithm refutes seth even for rooted trees of depth o \( log log n \) , where n is the total number of vertices , a truly subquadratic algorithm refutes seth for every constant d , there is a constant epsilon d 0 and a randomized , truly subquadratic algorithm for degree d rooted trees of depth at most \( 1 epsilon d \) log d n in particular , there is an o \( min 2 85 h , n 2 \) algorithm for binary trees of depth h our reductions utilize new tree gadgets that are likely useful for future seth based lower bounds for problems on trees our upper bounds apply a folklore result from randomized decision tree complexity"
cs.LG,"we present a neural network architecture and training method designed to enable very rapid training and low implementation complexity due to its training speed and very few tunable parameters , the method has strong potential for embedded hardware applications requiring frequent retraining or online training the approach is characterized by \( a \) convolutional filters based on biologically inspired visual processing filters , \( b \) randomly valued classifier stage input weights , \( c \) use of least squares regression to train the classifier output weights in a single batch , and \( d \) linear classifier stage output units we demonstrate the efficacy of the method as an image classifier , obtaining state of the art results on the mnist \( 0 37 error \) and norb small \( 2 2 \) image classification databases , with very fast training times compared to standard deep network approaches the network 's performance on the google street view house number \( svhn \) \( 4 \) database is also competitive with state of the art methods"
cs.SI,"people are able to detect up to 1 trillion odors yet , city planning is concerned only with a few bad odors , mainly because odors are currently captured only through complaints made by urban dwellers to capture both good and bad odors , we resort to a methodology that has been recently proposed and relies on tagging information of geo referenced pictures in doing so for the cities of london and barcelona , this work makes three new contributions we study 1 \) how the urban smellscape changes in time and space 2 \) which emotions people share at places with specific smells and 3 \) what is the color of a smell , if it exists without social media data , insights about those three aspects have been difficult to produce in the past , further delaying the creation of urban restorative experiences"
stat.ML,"we consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function in particular , centroidal voronoi partitions have to be computed the difficulty of the task is that the sensory function is unknown and has to be reconstructed on line from noisy measurements hence , estimation and coverage needs to be performed at the same time we cast the problem in a bayesian regression framework , where the sensory function is seen as a gaussian random field then , we design a set of control inputs which try to well balance coverage and estimation , also discussing convergence properties of the algorithm numerical experiments show the effectivness of the new approach"
cs.SI,"observational learning is an important information aggregation mechanism however , it occasionally leads to a state in which an entire population chooses a sub optimal option when it occurs and whether it is a phase transition remain unanswered to address these questions , we performed a voting experiment in which subjects answered a two choice quiz sequentially with and without information about the prior subjects' choices the subjects who could copy others are called herders we obtained a microscopic rule regarding how herders copy others varying the ratio of herders led to qualitative changes in the macroscopic behavior in the experiment of about 50 subjects if the ratio is small , the sequence of choices rapidly converges to the true one as the ratio approaches 100 , convergence becomes extremely slow and information aggregation almost terminates a simulation study of a stochastic model for 10 6 subjects based on the herder 's microscopic rule showed a phase transition to the two peaks phase , where the convergence completely terminates , as the ratio exceeds some critical value"
cs.SI,"we investigate social networks of characters found in cultural works such as novels and films these character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure , but may be of relatively small order with a high multiplicity of edges building on recent work of beveridge , we consider graph extraction , visualization , and network statistics for three novels twilight by stephanie meyer , steven king 's the stand , and j k rowling 's harry potter and the goblet of fire coupling with 800 character networks from films found in the http moviegalaxies com database , we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees \( also known as the chung lu model \) , the configuration model , and the preferential attachment model using machine learning techniques based on motif \( or small subgraph \) counts , we determine that the chung lu model best fits character networks and we conjecture why this may be the case"
cs.SI,"the ways in which an innovation \( e g , new behaviour , idea , technology , product \) diffuses among people can determine its success or failure in this paper , we address the problem of diffusion of innovations over multiplex social networks where the neighbours of a person belong to one or multiple networks \( or layers \) such as friends , families , or colleagues to this end , we generalise one of the basic game theoretic diffusion models , called networked coordination game , for multiplex networks we present analytical results for this extended model and validate them through a simulation study , finding among other properties a lower bound for the success of an innovation while simple and leading to intuitively understandable results , to the best of our knowledge this is the first extension of a game theoretic innovation diffusion model for multiplex networks and as such it provides a basic framework to study more sophisticated innovation dynamics"
cs.DS,"group search optimizer \( gso \) is one of the best algorithms , is very new in the field of evolutionary computing it is very robust and efficient algorithm , which is inspired by animal searching behaviour the paper describes an application of gso to clustering of networks we have tested gso against five standard benchmark datasets , gso algorithm is proved very competitive in terms of accuracy and convergence speed"
cs.DM|cs.DS,"the width measure emph treedepth , also known as vertex ranking , centered coloring and elimination tree height , is a well established notion which has recently seen a resurgence of interest we present an algorithm which given as input an n vertex graph , a tree decomposition of the graph of width w , and an integer t decides treedepth , i e whether the treedepth of the graph is at most t , in time 2 o \( wt \) cdot n if necessary , a witness structure for the treedepth can be constructed in the same running time in conjunction with previous results we provide a simple algorithm and a fast algorithm which decide treedepth in time 2 2 o \( t \) cdot n and 2 o \( t 2 \) cdot n , respectively , which do not require a tree decomposition as part of their input the former answers an open question posed by ossona de mendez and nesetril as to whether deciding treedepth admits an algorithm with a linear running time \( for every fixed t \) that does not rely on courcelle 's theorem or other heavy machinery for chordal graphs we can prove a running time of 2 o \( t log t \) cdot n for the same algorithm"
cs.DM,"the number of holes in a connected component in 2d images is a basic invariant in this note , a simple formula was proven using our previous results in digital topology \( chen 2004 , chen and rong \( 2010 \) the new is h 1 \( c 4 c 2 \) 4 , where h is the number of holes , and c i indicate the set of corner points having i direct adjacent points in the component"
cs.AI,"this paper initiates the use of vector fields to design , optimize , and implement reactive schedules for safe cooperative robot patterns on planar graphs we consider automated guided vehicles \( agv 's \) operating upon a predefined network of pathways in contrast to the case of locally euclidean configuration spaces , regularization of collisions is no longer a local procedure , and issues concerning the global topology of configuration spaces must be addressed the focus of the present inquiry is the achievement of safe , efficient , cooperative patterns in the simplest nontrivial example \( a pair of robots on a y network \) by means of a state event heirarchical controller"
cs.AI,"empty taxi cruising represents a wastage of resources in the context of urban taxi services in this work , we seek to minimize such wastage an analysis of a large trace of taxi operations reveals that the services' inefficiency is caused by drivers' greedy cruising behavior we model the existing system as a continuous time markov chain to address the problem , we propose that each taxi be equipped with an intelligent agent that will guide the driver when cruising for passengers then , drawing from ai literature on multiagent planning , we explore two possible ways to compute such guidance the first formulation assumes fully cooperative drivers this allows us , in principle , to compute systemwide optimal cruising policy this is modeled as a markov decision process the second formulation assumes rational drivers , seeking to maximize their own profit this is modeled as a stochastic congestion game , a specialization of stochastic games nash equilibrium policy is proposed as the solution to the game , where no driver has the incentive to singly deviate from it empirical result shows that both formulations improve the efficiency of the service significantly"
cs.SI,"spatial evolution game has traditionally assumed that players interact with neighbors on a single network , which is isolated and not influenced by other systems we introduce the simple game model into the interdependent networks composed of two networks , and show that when the interdependent factor alpha is smaller than a particular value alpha c , homogeneous cooperation can be guaranteed however , as interdependent factor exceeds alpha c , spontaneous symmetry breaking of fraction of cooperators presents itself between different networks in addition , our results can be well predicted by the strategy couple pair approximation method"
cs.AI|cs.LG|stat.ML,"this paper addresses the estimation of parameters of a bayesian network from incomplete data the task is usually tackled by running the expectation maximization \( em \) algorithm several times in order to obtain a high log likelihood estimate we argue that choosing the maximum log likelihood estimate \( as well as the maximum penalized log likelihood and the maximum a posteriori estimate \) has severe drawbacks , being affected both by overfitting and model uncertainty two ideas are discussed to overcome these issues a maximum entropy approach and a bayesian model averaging approach both ideas can be easily applied on top of em , while the entropy idea can be also implemented in a more sophisticated way , through a dedicated non linear solver a vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum \( penalized \) log likelihood and maximum a posteriori estimates in particular , if em is adopted as optimization engine , the model averaging approach is the best performing one its performance is matched by the entropy approach when implemented using the non linear solver the results suggest that the applicability of these ideas is immediate \( they are easy to implement and to integrate in currently available inference engines \) and that they constitute a better way to learn bayesian network parameters"
cs.SI,"crowd algorithms often assume workers are inexperienced and thus fail to adapt as workers in the crowd learn a task these assumptions fundamentally limit the types of tasks that systems based on such algorithms can handle this paper explores how the crowd learns and remembers over time in the context of human computation , and how more realistic assumptions of worker experience may be used when designing new systems we first demonstrate that the crowd can recall information over time and discuss possible implications of crowd memory in the design of crowd algorithms we then explore crowd learning during a continuous control task recent systems are able to disguise dynamic groups of workers as crowd agents to support continuous tasks , but have not yet considered how such agents are able to learn over time we show , using a real time gaming setting , that crowd agents can learn over time , and `remember' by passing strategies from one generation of workers to the next , despite high turnover rates in the workers comprising them we conclude with a discussion of future research directions for crowd memory and learning"
cs.LG|stat.ML,"we propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data unlike most anomaly detection \( ad \) methods , which detect individual anomalies , our proposed method detects groups \( clusters \) of anomalies i e sets of points which collectively exhibit abnormal patterns in many applications this can lead to better understanding of the nature of the atypical behavior and to identifying the sources of the anomalies moreover , we consider the case where the atypical patterns exhibit on only a small \( salient \) subset of the very high dimensional feature space individual ad techniques and techniques that detect anomalies using all the features typically fail to detect such anomalies , but our method can detect such instances collectively , discover the shared anomalous patterns exhibited by them , and identify the subsets of salient features in this paper , we focus on detecting anomalous topics in a batch of text documents , developing our algorithm based on topic models results of our experiments show that our method can accurately detect anomalous topics and salient features \( words \) under each such topic in a synthetic data set and two real world text corpora and achieves better performance compared to both standard group ad and individual ad techniques all required code to reproduce our experiments is available from https github com hsoleimani atd"
cs.LG,"this paper develops a two layer neural network in which the neuron model computes a user defined similarity function between inputs and weights the neuron transfer function is formed by composition of an adapted logistic function with the mean of the partial input weight similarities the resulting neuron model is capable of dealing directly with variables of potentially different nature \( continuous , fuzzy , ordinal , categorical \) there is also provision for missing values the network is trained using a two stage procedure very similar to that used to train a radial basis function \( rbf \) neural network the network is compared to two types of rbf networks in a non trivial dataset the horse colic problem , taken as a case study and analyzed in detail"
cs.AI|cs.LG,"in this paper we address the problem of discretization in the context of learning bayesian networks \( bns \) from data containing both continuous and discrete variables we describe a new technique for em multivariate em discretization , whereby each continuous variable is discretized while taking into account its interaction with the other variables the technique is based on the use of a bayesian scoring metric that scores the discretization policy for a continuous variable given a bn structure and the observed data since the metric is relative to the bn structure currently being evaluated , the discretization of a variable needs to be dynamically adjusted as the bn structure changes"
cs.DM,"we consider an agent on a fixed but arbitrary node of a known threshold network , with the task of detecting an unknown missing link node we obtain analytic formulas for the probability of success , when the agent 's tool is the free evolution of a single excitation on an xx spin system paired with the network we completely characterize the parameters allowing for an advantageous solution from the results emerges an optimal \( deterministic \) algorithm for quantum search , therefore gaining a quadratic speed up with respect to the optimal classical analogue , and in line with well known results in quantum computation when attempting to detect a faulty node , the chosen setting appears to be very fragile and the probability of success too small to be of any direct use"
cs.DS,"we consider the problem of mapping digital data encoded on a quantum register to analog amplitudes in parallel it is shown to be unlikely that a fully unitary polynomial time quantum algorithm exists for this problem np becomes a subset of bqp if it exists in the practical point of view , we propose a non unitary linear time algorithm using quantum decoherence it tacitly uses an exponentially large physical resource , which is typically a huge number of identical molecules quantumness of correlation appearing in the process of the algorithm is also discussed"
cs.LG|stat.ML,"most traditional online learning algorithms are based on variants of mirror descent or follow the leader in this paper , we present an online algorithm based on a completely different approach , tailored for transductive settings , which combines random playout and randomized rounding of loss subgradients as an application of our approach , we present the first computationally efficient online algorithm for collaborative filtering with trace norm constrained matrices as a second application , we solve an open question linking batch learning and transductive online learning"
cs.SI,"we provide a method for the ranking of the reliability of two networks with the same connectance our method is based on the cheeger constant linking the topological property of a network with its spectrum we first analyze a set of twisted rings with the same connectance and degree distribution , and obtain the ranking of their reliability using their eigenvalue gaps the results are generalized to general networks using the method of rewiring the success of our ranking method is verified numerically for the ieee57 , the erd h o s r 'enyi , and the small world networks"
cs.DS,the increasing use of networked sensor systems and networked databases has led to an increased interest in incorporating encryption directly into sensor algorithms and database analytics matlab is the dominant tool for rapid prototyping of sensor algorithms and has extensive database analytics capabilities the advent of high level and high performance galois field mathematical environments allows encryption algorithms to be expressed succinctly and efficiently this work leverages the galois field primitives found the matlab communication toolbox to implement a mode of the advanced encrypted standard \( aes \) based on first principals mathematics the resulting implementation requires 100x less code than standard aes implementations and delivers speed that is effective for many design purposes the parallel version achieves speed comparable to native openssl on a single node and is sufficient for real time prototyping of many sensor processing algorithms and database analytics
cs.SI,"inferring latent attributes of people online is an important social computing task , but requires integrating the many heterogeneous sources of information available on the web we propose to learn individual representations of people using neural nets to integrate information from social media the algorithm is able to combine any kind of cues , such as the text a person writes , the person 's attributes \( e g gender , employer , school , location \) and social relations to other people \( e g , friendship , marriage \) , using global inference to infer missing attributes from noisy cues the resulting latent representations capture homophily people who have similar attributes , are related socially , or write similar text are closer in vector space we show that these learned representations offer good performance at solving four important tasks in social media inference on twitter predicting \( 1 \) gender , \( 2 \) occupation , \( 3 \) location , and \( 4 \) friendships for users , and that we achieve the best performance by integrating all these signals our approach scales to large datasets , using parallel stochastic gradient descent for learning the resulting representations can be used as general features in and have the potential to benefit a large number of downstream tasks like link prediction , community detection , or reasoning over social networks , discovering for example the high probability that a new york city resident is a fan of the new york knicks , or the greater preference for iphones by computer professionals than legal professionals"
cs.LG|stat.ML,"the problem of optimizing unknown costly to evaluate functions has been studied for a long time in the context of bayesian optimization algorithms in this field aim to find the optimizer of the function by asking only a few function evaluations at locations carefully selected based on a posterior model in this paper , we assume the unknown function is lipschitz continuous leveraging the lipschitz property , we propose an algorithm with a distinct exploration phase followed by an exploitation phase the exploration phase aims to select samples that shrink the search space as much as possible the exploitation phase then focuses on the reduced search space and selects samples closest to the optimizer considering the expected improvement \( ei \) as a baseline , we empirically show that the proposed algorithm significantly outperforms ei"
cs.DM,"the main result of the paper is motivated by the following two , apparently unrelated graph optimization problems \( a \) as an extension of edmonds' disjoint branchings theorem , characterize digraphs comprising k disjoint branchings b i each having a specified number mu i of arcs , \( b \) as an extension of ryser 's maximum term rank formula , determine the largest possible matching number of simple bipartite graphs complying with degree constraints the solutions to these problems and to their generalizations will be obtained from a new min max theorem on covering a supermodular function by a simple degree constrained bipartite graph a specific feature of the result is that its minimum cost extension is already bf np complete therefore classic polyhedral tools themselves definitely cannot be sufficient for solving the problem , even though they make some good service in our approach"
cs.DS,"these are the proceedings of the third workshop on graph inspection and traversal engineering \( graphite 2014 \) , which took place on april 5 , 2014 in grenoble , france , as a satellite event of the 17th european joint conferences on theory and practice of software \( etaps 2014 \) the aim of graphite is to foster the convergence on research interests from several communities dealing with graph analysis in all its forms in computer science , with a particular attention to software development and analysis graphs are used to represent data and processes in many application areas , and they are subjected to various computational algorithms in order to analyze them just restricting the attention to the analysis of software , graph analysis algorithms are used , for instance , to verify properties using model checking techniques that explore the system 's state space graph or static analysis techniques based on control flow graphs further application domains include games , planning , and network analysis very often , graph problems and their algorithmic solutions have common characteristics , independent of their application domain the goal of this event is to gather scientists from different communities , who do research on graph analysis algorithms , such that awareness of each others' work is increased more information can be found at http sysma imtlucca it graphite"
cs.LG|stat.ML,"we consider the problem of learning soft assignments of n items to k categories given two sources of information an item category similarity matrix , which encourages items to be assigned to categories they are similar to \( and to not be assigned to categories they are dissimilar to \) , and an item item similarity matrix , which encourages similar items to have similar assignments we propose a simple quadratic programming model that captures this intuition we give necessary conditions for its solution to be unique , define an out of sample mapping , and derive a simple , effective training algorithm based on the alternating direction method of multipliers the model predicts reasonable assignments from even a few similarity values , and can be seen as a generalization of semisupervised learning it is particularly useful when items naturally belong to multiple categories , as for example when annotating documents with keywords or pictures with tags , with partially tagged items , or when the categories have complex interrelations \( e g hierarchical \) that are unknown"
cs.AI|cs.LG|stat.ML,"an important task in data analysis is the discovery of causal relationships between observed variables for continuous valued data , linear acyclic causal models are commonly used to model the data generating process , and the inference of such models is a well studied problem however , existing methods have significant limitations methods based on conditional independencies \( spirtes et al 1993 pearl 2000 \) cannot distinguish between independence equivalent models , whereas approaches purely based on independent component analysis \( shimizu et al 2006 \) are inapplicable to data which is partially gaussian in this paper , we generalize and combine the two approaches , to yield a method able to learn the model structure in many cases for which the previous methods provide answers that are either incorrect or are not as informative as possible we give exact graphical conditions for when two distinct models represent the same family of distributions , and empirically demonstrate the power of our method through thorough simulations"
cs.DM,"the core of a cooperative game on a set of players n is one of the most popular concept of solution when cooperation is restricted \( feasible coalitions form a subcollection cf of 2 n \) , the core may become unbounded , which makes it usage questionable in practice our proposal is to make the core bounded by turning some of the inequalities defining the core into equalities \( additional efficiency constraints \) we address the following mathematical problem can we find a minimal set of inequalities in the core such that , if turned into equalities , the core becomes bounded \? the new core obtained is called the restricted core we completely solve the question when cf is a distributive lattice , introducing also the notion of restricted weber set we show that the case of regular set systems amounts more or less to the case of distributive lattices we also study the case of weakly union closed systems and give some results for the general case"
cs.LG|stat.ML,"we propose a new problem formulation which is similar to , but more informative than , the binary multiple instance learning problem in this setting , we are given groups of instances \( described by feature vectors \) along with estimates of the fraction of positively labeled instances per group the task is to learn an instance level classifier from this information that is , we are trying to estimate the unknown binary labels of individuals from knowledge of group statistics we propose a principled probabilistic model to solve this problem that accounts for uncertainty in the parameters and in the unknown individual labels this model is trained with an efficient mcmc algorithm its performance is demonstrated on both synthetic and real world data arising in general object recognition"
cs.LG,"diagnosis of liver infection at preliminary stage is important for better treatment in todays scenario devices like sensors are used for detection of infections accurate classification techniques are required for automatic identification of disease samples in this context , this study utilizes data mining approaches for classification of liver patients from healthy individuals four algorithms \( naive bayes , bagging , random forest and svm \) were implemented for classification using r platform further to improve the accuracy of classification a hybrid neurosvm model was developed using svm and feed forward artificial neural network \( ann \) the hybrid model was tested for its performance using statistical parameters like root mean square error \( rmse \) and mean absolute percentage error \( mape \) the model resulted in a prediction accuracy of 98 83 the results suggested that development of hybrid model improved the accuracy of prediction to serve the medicinal community for prediction of liver disease among patients , a graphical user interface \( gui \) has been developed using r the gui is deployed as a package in local repository of r platform for users to perform prediction"
cs.SI,"recently , we have developed a software able of gathering information on social networks from written texts this software , the characters and places interaction network \( chaplin \) tool , is implemented in visual basic by means of it , characters and places of a literary work can be extracted from a list of raw words the software interface helps users to select their names out of this list setting some parameters , chaplin creates a network where nodes represent characters places and edges give their interactions nodes and edges are labelled by performances in this paper , we propose to use chaplin for the analysis a william shakespeare 's play , the famous 'tragedy of hamlet , prince of denmark' performances of characters in the play as a whole and in each act of it are given by graphs"
cs.SI,"in this paper , we use evidence from a remixing community to evaluate two pieces of common wisdom about collaboration first , we test the theory that jointly produced works tend to be of higher quality than individually authored products second , we test the theory that collaboration improves the quality of functional works like code , but that it works less well for artistic works like images and sounds we use data from scratch , a large online community where hundreds of thousands of young users share and remix millions of animations and interactive games using peer ratings as a measure of quality , we estimate a series of fitted regression models and find that collaborative scratch projects tend to receive ratings that are lower than individually authored works we also find that code intensive collaborations are rated higher than media intensive efforts we conclude by discussing the limitations and implications of these findings"
cs.LG,"over the past decade , data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia , business , and government in this paper , we introduce the concept of tree based pipeline optimization for automating one of the most tedious parts of machine learning pipeline design we implement a tree based pipeline optimization tool \( tpot \) and demonstrate its effectiveness on a series of simulated and real world genetic data sets in particular , we show that tpot can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators such as synthetic feature constructors that significantly improve classification accuracy on these data sets we also highlight the current challenges to pipeline optimization , such as the tendency to produce pipelines that overfit the data , and suggest future research paths to overcome these challenges as such , this work represents an early step toward fully automating machine learning pipeline design"
cs.LG|stat.ML,"we consider the problem of learning associative mixtures for classification and regression problems , where the output is modeled as a mixture of conditional distributions , conditioned on the input in contrast to approaches such as expectation maximization \( em \) or variational bayes , which can get stuck in bad local optima , we present a tensor decomposition method which is guaranteed to correctly recover the parameters the key insight is to learn score function features of the input , and employ them in a moment based approach for learning associative mixtures specifically , we construct the cross moment tensor between the label and higher order score functions of the input we establish that the decomposition of this tensor consistently recovers the components of the associative mixture under some simple non degeneracy assumptions thus , we establish that feature learning is the critical ingredient for consistent estimation of associative mixtures using tensor decomposition approaches"
cs.SI,"community detection in networks has drawn much attention in diverse fields , especially social sciences given its significance , there has been a large body of literature with approaches from many fields here we present a statistical framework that is representative , extensible , and that yields an estimator with good properties our proposed approach considers a stochastic blockmodel based on a logistic regression formulation with node correction terms we follow a bayesian approach that explicitly captures the community behavior via prior specification we further adopt a data augmentation strategy with latent polya gamma variables to obtain posterior samples we conduct inference based on a principled , canonically mapped centroid estimator that formally addresses label non identifiability and captures representative community assignments we demonstrate the proposed model and estimation on real world as well as simulated benchmark networks and show that the proposed model and estimator are more flexible , representative , and yield smaller error rates when compared to the map estimator from classical degree corrected stochastic blockmodels"
cs.DM|cs.DS,"in his 1987 paper entitled generalized string matching , abrahamson introduced em pattern matching with character classes and provided the first efficient algorithm to solve it the best known solution to date is due to linhart and shamir \( 2009 \) another broad yet comparatively less studied class of string matching problems is that of numerical string searching , such as , e g , the `less than' or l 1 norm string searching the best known solutions for problems in this class are based on fft convolution after some suitable re encoding the present paper introduces em modulated string searching as a unified framework for string matching problems where the numerical conditions can be combined with some boolean numerical decision conditions on the character classes one example problem in this class is the em locally bounded l 1 norm matching problem on character classes here the match between a character at some position in the text and a set of characters at some position in the pattern is assessed based on the smallest l 1 distance between the text character and one of those pattern characters the two positions match if the \( absolute value of the \) difference between the two characters does not exceed a predefined constant the pattern has an occurrence in an alignment with the text if the sum of all such differences does not exceed a second predefined constant value this problem requires a pointwise evaluation of the quality of each match and has no known solution based on the previously mentioned algorithms"
cs.LG|stat.ML,"the human face constantly conveys information , both consciously and subconsciously however , as basic as it is for humans to visually interpret this information , it is quite a big challenge for machines conventional semantic facial feature recognition and analysis techniques are already in use and are based on physiological heuristics , but they suffer from lack of robustness and high computation time this thesis aims to explore ways for machines to learn to interpret semantic information available in faces in an automated manner without requiring manual design of feature detectors , using the approach of deep learning this thesis provides a study of the effects of various factors and hyper parameters of deep neural networks in the process of determining an optimal network configuration for the task of semantic facial feature recognition this thesis explores the effectiveness of the system to recognize the various semantic features \( like emotions , age , gender , ethnicity etc \) present in faces furthermore , the relation between the effect of high level concepts on low level features is explored through an analysis of the similarities in low level descriptors of different semantic features this thesis also demonstrates a novel idea of using a deep network to generate 3 d active appearance models of faces from real world 2 d images"
cs.SI,"the topological \( graph \) structure of complex networks often provides valuable information about the performance and vulnerability of the network however , there are multiple ways to represent a given network as a graph electric power transmission and distribution networks have a topological structure that is straightforward to represent and analyze as a graph however , simple graph models neglect the comprehensive connections between components that result from ohm 's and kirchhoff 's laws this paper describes the structure of the three north american electric power interconnections , from the perspective of both topological and electrical connectivity we compare the simple topology of these networks with that of random \( erdos and renyi , 1959 \) , preferential attachment \( barabasi and albert , 1999 \) and small world \( watts and strogatz , 1998 \) networks of equivalent sizes and find that power grids differ substantially from these abstract models in degree distribution , clustering , diameter and assortativity , and thus conclude that these topological forms may be misleading as models of power systems to study the electrical connectivity of power systems , we propose a new method for representing electrical structure using electrical distances rather than geographic connections comparisons of these two representations of the north american power networks reveal notable differences between the electrical and topological structure of electric power networks"
cs.DS,"in the correlation clustering , also known as cluster editing , we are given an undirected n vertex graph g and a positive integer k the task is to decide if g can be transformed into a cluster graph , i e , a disjoint union of cliques , by changing at most k adjacencies , i e by adding deleting at most k edges we give a subexponential algorithm that , in time 2 o \( sqrt \( pk \) \) n o \( 1 \) decides whether g can be transformed into a cluster graph with p cliques by changing at most k adjacencies we complement our algorithmic findings by the following tight lower bounds on the asymptotic behaviour of our algorithm we show that , unless eth fails , for any constant 0 s 1 , there is p theta \( k s \) such that there is no algorithm deciding in time 2 o \( sqrt \( pk \) \) n o \( 1 \) whether g can be transformed into a cluster graph with p cliques by changing at most k adjacencies"
cs.AI|cs.DM,"a lattice theoretic framework is introduced that permits the study of the conditional independence \( ci \) implication problem relative to the class of discrete probability measures semi lattices are associated with ci statements and a finite , sound and complete inference system relative to semi lattice inclusions is presented this system is shown to be \( 1 \) sound and complete for saturated ci statements , \( 2 \) complete for general ci statements , and \( 3 \) sound and complete for stable ci statements these results yield a criterion that can be used to falsify instances of the implication problem and several heuristics are derived that approximate this lattice exclusion criterion in polynomial time finally , we provide experimental results that relate our work to results obtained from other existing inference algorithms"
cs.DM|stat.ML,"we consider clustering problems where the goal is to determine an optimal partition of a given point set in euclidean space in terms of a collection of affine subspaces while there is vast literature on heuristics for this kind of problem , such approaches are known to be susceptible to poor initializations and getting trapped in bad local optima we alleviate these issues by introducing a semidefinite relaxation based on lasserre 's method of moments while a similiar approach is known for classical euclidean clustering problems , a generalization to our more general subspace scenario is not straightforward , due to the high symmetry of the objective function that weakens any convex relaxation we therefore introduce a new mechanism for symmetry breaking based on covering the feasible region with polytopes additionally , we introduce and analyze a deterministic rounding heuristic"
cs.AI,"subjective well being \( swb \) , which refers to how people experience the quality of their lives , is of great use to public policy makers as well as economic , sociological research , etc traditionally , the measurement of swb relies on time consuming and costly self report questionnaires nowadays , people are motivated to share their experiences and feelings on social media , so we propose to sense swb from the vast user generated data on social media by utilizing 1785 users' social media data with swb labels , we train machine learning models that are able to sense individual swb from users' social media our model , which attains the state by art prediction accuracy , can then be used to identify swb of large population of social media users in time with very low cost"
cs.SI,"mobile on body sensing has distinct advantages for the analysis and understanding of crowd dynamics sensing is not geographically restricted to a specific instrumented area , mobile phones offer on body sensing and they are already deployed on a large scale , and the rich sets of sensors they contain allows one to characterize the behavior of users through pattern recognition techniques in this paper we present a methodological framework for the machine recognition of crowd behavior from on body sensors , such as those in mobile phones the recognition of crowd behaviors opens the way to the acquisition of large scale datasets for the analysis and understanding of crowd dynamics it has also practical safety applications by providing improved crowd situational awareness in cases of emergency the framework comprises behavioral recognition with the user 's mobile device , pairwise analyses of the activity relatedness of two users , and graph clustering in order to uncover globally , which users participate in a given crowd behavior we illustrate this framework for the identification of groups of persons walking , using empirically collected data we discuss the challenges and research avenues for theoretical and applied mathematics arising from the mobile sensing of crowd behaviors"
cs.AI,"this paper discusses scalability of standard genetic programming \( gp \) and the probabilistic incremental program evolution \( pipe \) to investigate the need for both effective mixing and linkage learning , two test problems are considered order problem , which is rather easy for any recombination based gp , and trap or the deceptive trap problem , which requires the algorithm to learn interactions among subsets of terminals the scalability results show that both gp and pipe scale up polynomially with problem size on the simple order problem , but they both scale up exponentially on the deceptive problem this indicates that while standard recombination is sufficient when no interactions need to be considered , for some problems linkage learning is necessary these results are in agreement with the lessons learned in the domain of binary string genetic algorithms \( gas \) furthermore , the paper investigates the effects of introducing utnnecessary and irrelevant primitives on the performance of gp and pipe"
cs.DM|cs.IT,"in this paper we focus our attention on a family of finite geometry codes , called type i projective geometry low density parity check \( pg ldpc \) codes , that are constructed based on the projective planes pg 2 , q \) in particular , we study their minimal codewords and pseudo codewords , as it is known that these vectors characterize completely the code performance under maximum likelihood decoding and linear programming decoding , respectively the main results of this paper consist of upper and lower bounds on the pseudo weight of the minimal pseudo codewords of type i pg ldpc codes"
cs.AI,"in this paper we introduce temporal action graph games \( taggs \) , a novel graphical representation of imperfect information extensive form games we show that when a game involves anonymity or context specific utility independencies , its encoding as a tagg can be much more compact than its direct encoding as a multiagent influence diagram \( maid \) we also show that taggs can be understood as indirect maid encodings in which many deterministic chance nodes are introduced we provide an algorithm for computing with taggs , and show both theoretically and empirically that our approach improves significantly on the previous state of the art"
cs.LG,"the preservation of our cultural heritage is of paramount importance thanks to recent developments in digital acquisition techniques , powerful image analysis algorithms are developed which can be useful non invasive tools to assist in the restoration and preservation of art in this paper we propose a semi supervised crack detection method that can be used for high dimensional acquisitions of paintings coming from different modalities our dataset consists of a recently acquired collection of images of the ghent altarpiece \( 1432 \) , one of northern europe 's most important art masterpieces our goal is to build a classifier that is able to discern crack pixels from the background consisting of non crack pixels , making optimal use of the information that is provided by each modality to accomplish this we employ a recently developed non parametric bayesian classifier , that uses tensor factorizations to characterize any conditional probability a prior is placed on the parameters of the factorization such that every possible interaction between predictors is allowed while still identifying a sparse subset among these predictors the proposed bayesian classifier , which we will refer to as conditional bayesian tensor factorization or cbtf , is assessed by visually comparing classification results with the random forest \( rf \) algorithm"
cs.DM|cs.DS,"the tree cut width of a graph is a graph parameter defined by wollan j comb theory , ser b , 110 47 66 , 2015 with the help of tree cut decompositions in certain cases , tree cut width appears to be more adequate than treewidth as an invariant that , when bounded , can accelerate the resolution of intractable problems while designing algorithms for problems with bounded tree cut width , it is important to have a parametrically tractable way to compute the exact value of this parameter or , at least , some constant approximation of it in this paper we give a parameterized 2 approximation algorithm for the computation of tree cut width for an input n vertex graph g and an integer w , our algorithm either confirms that the tree cut width of g is more than w or returns a tree cut decomposition of g certifying that its tree cut width is at most 2w , in time 2 o \( w 2 log w \) cdot n 2 prior to this work , no constructive parameterized algorithms , even approximated ones , existed for computing the tree cut width of a graph as a consequence of the graph minors series by robertson and seymour , only the existence of a decision algorithm was known"
cs.AI,the production system is a theoretical model of computation relevant to the artificial intelligence field allowing for problem solving procedures such as hierarchical tree search in this work we explore some of the connections between artificial intelligence and quantum computation by presenting a model for a quantum production system our approach focuses on initially developing a model for a reversible production system which is a simple mapping of bennett 's reversible turing machine we then expand on this result in order to accommodate for the requirements of quantum computation we present the details of how our proposition can be used alongside grover 's algorithm in order to yield a speedup comparatively to its classical counterpart we discuss the requirements associated with such a speedup and how it compares against a similar quantum hierarchical search approach
cs.DS,"in this paper , we propose a theoretical framework to construct matching algorithms for any biometric authentication systems conventional matching algorithms are not necessarily secure against strong intentional impersonation attacks such as wolf attacks the wolf attack is an attempt to impersonate a genuine user by presenting a wolf to a biometric authentication system without the knowledge of a genuine user 's biometric sample a wolf is a sample which can be accepted as a match with multiple templates the wolf attack probability \( wap \) is the maximum success probability of the wolf attack , which was proposed by une , otsuka , imai as a measure for evaluating security of biometric authentication systems we present a principle for construction of secure matching algorithms against the wolf attack for any biometric authentication systems the ideal matching algorithm determines a threshold for each input value depending on the entropy of the probability distribution of the \( hamming \) distances then we show that if the information about the probability distribution for each input value is perfectly given , then our matching algorithm is secure against the wolf attack our generalized matching algorithm gives a theoretical framework to construct secure matching algorithms how lower wap is achievable depends on how accurately the entropy is estimated then there is a trade off between the efficiency and the achievable wap almost every conventional matching algorithm employs a fixed threshold and hence it can be regarded as an efficient but insecure instance of our theoretical framework daugman 's iriscode recognition algorithm proposed can also be regarded as a non optimal instance of our framework"
cs.SI,"navigability , an ability to find a short path between elements using only local information is one of the most fascinating properties of real life networks however the exact mechanism responsible for the formation of navigation properties remained unknown we show that navigability can be achieved by using only two ingredients present in majority of networks network growth and local homophily , giving the answer why the navigation feature appears in real life networks incremental insertion of metric elements in random order by adding connections to the closest neighbors produces a self similar optimally wired navigable small world network with an exponential degree distribution adding a preferential attachment produces a scale free network which has shorter greedy paths , but worse \( polynomial \) scaling of the information extraction locality several features of the proposed model are observed in real life networks , in particular in the brain neural networks , supporting earlier suggestions that they are navigable"
cs.LG|stat.ML,"deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data recently , such techniques have yielded record breaking results on a diverse set of difficult machine learning tasks in computer vision , speech recognition , and natural language processing despite the enormous success of deep learning , relatively little is understood theoretically about why these techniques are so successful at feature learning and compression here , we show that deep learning is intimately related to one of the most important and successful techniques in theoretical physics , the renormalization group \( rg \) rg is an iterative coarse graining scheme that allows for the extraction of relevant features \( i e operators \) as a physical system is examined at different length scales we construct an exact mapping from the variational renormalization group , first introduced by kadanoff , and deep learning architectures based on restricted boltzmann machines \( rbms \) we illustrate these ideas using the nearest neighbor ising model in one and two dimensions our results suggests that deep learning algorithms may be employing a generalized rg like scheme to learn relevant features from data"
cs.DM|cs.DS,"we are given n base elements and a finite collection of subsets of them the size of any subset varies between p to k \( p k \) in addition , we assume that the input contains all possible subsets of size p our objective is to find a subcollection of minimum cardinality which covers all the elements this problem is known to be np hard we provide two approximation algorithms for it , one for the generic case , and an improved one for the special case of \( p , k \) \( 2 , 4 \) the algorithm for the generic case is a greedy one , based on packing phases at each phase we pick a collection of disjoint subsets covering i new elements , starting from i k down to i p 1 at a final step we cover the remaining base elements by the subsets of size p we derive the exact performance guarantee of this algorithm for all values of k and p , which is less than hk , where hk is the k'th harmonic number however , the algorithm exhibits the known improvement methods over the greedy one for the unweighted k set cover problem \( in which subset sizes are only restricted not to exceed k \) , and hence it serves as a benchmark for our improved algorithm the improved algorithm for the special case of \( p , k \) \( 2 , 4 \) is based on non oblivious local search it starts with a feasible cover , and then repeatedly tries to replace sets of size 3 and 4 so as to maximize an objective function which prefers big sets over small ones for this case , our generic algorithm achieves an asymptotic approximation ratio of 1 5 epsilon , and the local search algorithm achieves a better ratio , which is bounded by 1 458333 epsilon"
cs.DM|cs.DS,"a set x of vertices of an acyclic digraph d is convex if x neq emptyset and there is no directed path between vertices of x which contains a vertex not in x a set x is connected if x neq emptyset and the underlying undirected graph of the subgraph of d induced by x is connected connected convex sets and convex sets of acyclic digraphs are of interest in the area of modern embedded processor technology we construct an algorithm cal a for enumeration of all connected convex sets of an acyclic digraph d of order n the time complexity of cal a is o \( n cdot cc \( d \) \) , where cc \( d \) is the number of connected convex sets in d we also give an optimal algorithm for enumeration of all \( not just connected \) convex sets of an acyclic digraph d of order n in computational experiments we demonstrate that our algorithms outperform the best algorithms in the literature using the same approach as for cal a , we design an algorithm for generating all connected sets of a connected undirected graph g the complexity of the algorithm is o \( n cdot c \( g \) \) , where n is the order of g and c \( g \) is the number of connected sets of g the previously reported algorithm for connected set enumeration is of running time o \( mn cdot c \( g \) \) , where m is the number of edges in g"
cs.SI,"we introduce the notion of restricted sensitivity as an alternative to global and smooth sensitivity to improve accuracy in differentially private data analysis the definition of restricted sensitivity is similar to that of global sensitivity except that instead of quantifying over all possible datasets , we take advantage of any beliefs about the dataset that a querier may have , to quantify over a restricted class of datasets specifically , given a query f and a hypothesis h about the structure of a dataset d , we show generically how to transform f into a new query f h whose global sensitivity \( over all datasets including those that do not satisfy h \) matches the restricted sensitivity of the query f moreover , if the belief of the querier is correct \( i e , d is in h \) then f h \( d \) f \( d \) if the belief is incorrect , then f h \( d \) may be inaccurate we demonstrate the usefulness of this notion by considering the task of answering queries regarding social networks , which we model as a combination of a graph and a labeling of its vertices in particular , while our generic procedure is computationally inefficient , for the specific definition of h as graphs of bounded degree , we exhibit efficient ways of constructing f h using different projection based techniques we then analyze two important query classes subgraph counting queries \( e g , number of triangles \) and local profile queries \( e g , number of people who know a spy and a computer scientist who know each other \) we demonstrate that the restricted sensitivity of such queries can be significantly lower than their smooth sensitivity thus , using restricted sensitivity we can maintain privacy whether or not d is in h , while providing more accurate results in the event that h holds true"
stat.ML,"this paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings we propose a frequent sequential pattern mining algorithm suitable for real life smart home event data the performance of the proposed algorithm is compared to existing algorithms regarding completeness correctness of the results , run times as well as memory consumption and elaborates on the shortcomings of the different solutions we also present a recommender system based on the developed algorithm that provides recommendations to the users to reduce their energy consumption the recommender system was deployed to a set of test homes the test participants rated the impact of the recommendations on their comfort we used this feedback to adjust the system parameters and make it more accurate during a second test phase"
cs.AI,"standard models of multi agent modal logic do not capture the fact that information is often emph ambiguous , and may be interpreted in different ways by different agents we propose a framework that can model this , and consider different semantics that capture different assumptions about the agents' beliefs regarding whether or not there is ambiguity we examine the expressive power of logics of ambiguity compared to logics that cannot model ambiguity , with respect to the different semantics that we propose"
cs.IT|cs.LG|stat.ML,"subspace recovery from corrupted and missing data is crucial for various applications in signal processing and information theory to complete missing values and detect column corruptions , existing robust matrix completion \( mc \) methods mostly concentrate on recovering a low rank matrix from few corrupted coefficients w r t the standard basis , which , however , does not apply to more general basis , e g , the fourier basis in this paper , we prove that the range space of an m times n matrix with rank r can be exactly recovered from few coefficients w r t general basis , though the rank r and the number of corrupted samples are both as high as o \( min m , n log 3 \( m n \) \) thus our results cover previous work as special cases , and robust mc can recover the intrinsic matrix with a higher rank moreover , we suggest a universal choice of the regularization parameter , which is lambda 1 sqrt log n by our ell 2 , 1 filtering algorithm , which has theoretical guarantees , we can further reduce the computational cost of our model as an application , we also find that the solutions to extended robust low rank representation and to our extended robust mc are mutually expressible , so both our theory and algorithm can be immediately applied to the subspace clustering problem with missing values experiments verify our theories"
cs.DS,"this paper presents the differences between two np problems it focuses in the euclidian 2 dimension travelling salesman problems and general assign problems the main results are the triangle reduction to verify the solution in polynomial time for the former and for the later the solution to the noted conjecture of the np class , np is not p"
cs.AI|cs.LG,"everyday activities performed by artificial assistants can potentially be executed naively and dangerously given their lack of common sense knowledge this paper presents conceptual work towards obtaining prior knowledge on the usual modality \( passive or active \) of any given entity , and their affordance estimates , by extracting high confidence ability modality semantic relations \( x can y relationship \) from non figurative texts , by analyzing co occurrence of grammatical instances of subjects and verbs , and verbs and objects the discussion includes an outline of the concept , potential and limitations , and possible feature and learning framework adoption"
cs.IT|cs.SI,"in this work , we suggest a parameterized statistical model \( the gamma distribution \) for the frequency of word occurrences in long strings of english text and use this model to build a corresponding thermodynamic picture by constructing the partition function we then use our partition function to compute thermodynamic quantities such as the free energy and the specific heat in this approach , the parameters of the word frequency model vary from word to word so that each word has a different corresponding thermodynamics and we suggest that differences in the specific heat reflect differences in how the words are used in language , differentiating keywords from common and function words finally , we apply our thermodynamic picture to the problem of retrieval of texts based on keywords and suggest some advantages over traditional information retrieval methods"
cs.IT|cs.LG|cs.SI,"most recent developments on the stochastic block model \( sbm \) rely on the knowledge of the model parameters , or at least on the number of communities this paper introduces efficient algorithms that do not require such knowledge and yet achieve the optimal information theoretic tradeoffs identified in as15 for linear size communities the results are three fold \( i \) in the constant degree regime , an algorithm is developed that requires only a lower bound on the relative sizes of the communities and detects communities with an optimal accuracy scaling for large degrees \( ii \) in the regime where degrees are scaled by omega \( 1 \) \( diverging degrees \) , this is enhanced into a fully agnostic algorithm that only takes the graph in question and simultaneously learns the model parameters \( including the number of communities \) and detects communities with accuracy 1 o \( 1 \) , with an overall quasi linear complexity \( iii \) in the logarithmic degree regime , an agnostic algorithm is developed that learns the parameters and achieves the optimal ch limit for exact recovery , in quasi linear time these provide the first algorithms affording efficiency , universality and information theoretic optimality for strong and weak consistency in the general sbm with linear size communities"
cs.DM,"for each subset of edges of a \( directed multi \) graph , one may determine whether the edges can be represented as a trail for each graph , the fraction of trail representable subsets out of the total number of subsets of edges how large can this fraction be as a function of the number edges m \? i give an upper bound of o \( sqrt log m sqrt m \) and show by example that the upper bound is nearly tight"
cs.DS|cs.LG,"there has been a lot of recent interest in mining patterns from graphs often , the exact structure of the patterns of interest is not known this happens , for example , when molecular structures are mined to discover fragments useful as features in chemical compound classification task , or when web sites are mined to discover sets of web pages representing logical documents such patterns are often generated from a few small subgraphs \( cores \) , according to certain generalization rules \( grs \) we call such patterns generalized patterns \( gps \) while being structurally different , gps often perform the same function in the network previously proposed approaches to mining gps either assumed that the cores and the grs are given , or that all interesting gps are frequent these are strong assumptions , which often do not hold in practical applications in this paper , we propose an approach to mining gps that is free from the above assumptions given a small number of gps selected by the user , our algorithm discovers all gps similar to the user examples first , a machine learning style approach is used to find the cores second , generalizations of the cores in the graph are computed to identify gps evaluation on synthetic data , generated using real cores and grs from biological and web domains , demonstrates effectiveness of our approach"
cs.AI|stat.ML,"gaussian processes \( gp \) are powerful tools for probabilistic modeling purposes they can be used to define prior distributions over latent functions in hierarchical bayesian models the prior over functions is defined implicitly by the mean and covariance function , which determine the smoothness and variability of the function the inference can then be conducted directly in the function space by evaluating or approximating the posterior process despite their attractive theoretical properties gps provide practical challenges in their implementation gpstuff is a versatile collection of computational tools for gp models compatible with linux and windows matlab and octave it includes , among others , various inference methods , sparse approximations and tools for model assessment in this work , we review these tools and demonstrate the use of gpstuff in several models"
cs.DS,"we consider quantum algorithms for the unique sink orientation problem on cubes this problem is widely considered to be of intermediate computational complexity this is because there no known polynomial algorithm \( classical or quantum \) from the problem and yet it arrises as part of a series of problems for which it being intractable would imply complexity theoretic collapses we give a reduction which proves that if one can efficiently evaluate the kth power of the unique sink orientation outmap , then there exists a polynomial time quantum algorithm for the unique sink orientation problem on cubes"
cs.DS,a permutation pi contains a permutation sigma as a pattern if it contains a subsequence of length sigma whose elements are in the same relative order as in the permutation sigma this notion plays a major role in enumerative combinatorics we prove that the problem does not have a polynomial kernel \( under the widely believed complexity assumption mbox np not subseteq mbox co np mbox poly \) by introducing a new polynomial reduction from the clique problem to permutation pattern matching
cs.DM,"network applications , such as multimedia streaming and video conferencing , impose growing requirements over quality of service \( qos \) , including bandwidth , delay , jitter , etc meanwhile , networks are expected to be load balanced , energy efficient , and resilient to some degree of failures it is observed that the above requirements could be better met with multiple disjoint qos paths than a single one let g \( v , , e \) be a digraph with nonnegative integral cost and delay on every edge , s , , t in v be two specified vertices , and d in mathbb z 0 be a delay bound \( or some other constraint \) , the emph k disjoint restricted shortest path \( k emph rsp \) emph problem is computing k disjoint paths between s and t with total cost minimized and total delay bounded by d few efficient algorithms have been developed because of the hardness of the problem in this paper , we propose efficient algorithms with provable performance guarantees for the k rsp problem we first present a pseudo polynomial time approximation algorithm with a bifactor approximation ratio of \( 1 , , 2 \) , then improve the algorithm to polynomial time with a bifactor ratio of \( 1 epsilon , , 2 epsilon \) for any fixed epsilon 0 , which is better than the current best approximation ratio \( o \( 1 gamma \) , , o \( 1 frac 1 gamma \) \) for any fixed gamma 0 cite orda2004efficient to the best of our knowledge , this is the first constant factor algorithm that almost strictly obeys the constraint for the k rsp problem"
cs.AI,"this dissertation presents several new methods of supervised and unsupervised learning of word sense disambiguation models the supervised methods focus on performing model searches through a space of probabilistic models , and the unsupervised methods rely on the use of gibbs sampling and the expectation maximization \( em \) algorithm in both the supervised and unsupervised case , the naive bayesian model is found to perform well an explanation for this success is presented in terms of learning rates and bias variance decompositions"
cs.DM,"we consider lattices of regular sets of non negative integers , i e of sets definable in presbuger arithmetic we prove that if such a lattice is closed under decrement then it is also closed under many other functions quotients by an integer , roots , etc"
cs.AI|cs.DS,"given a collection of objects and an associated similarity measure , the all pairs similarity search problem asks us to find all pairs of objects with similarity greater than a certain user specified threshold locality sensitive hashing \( lsh \) based methods have become a very popular approach for this problem however , most such methods only use lsh for the first phase of similarity search i e efficient indexing for candidate generation in this paper , we present bayeslsh , a principled bayesian algorithm for the subsequent phase of similarity search performing candidate pruning and similarity estimation using lsh a simpler variant , bayeslsh lite , which calculates similarities exactly , is also presented bayeslsh is able to quickly prune away a large majority of the false positive candidate pairs , leading to significant speedups over baseline approaches for bayeslsh , we also provide probabilistic guarantees on the quality of the output , both in terms of accuracy and recall finally , the quality of bayeslsh 's output can be easily tuned and does not require any manual setting of the number of hashes to use for similarity estimation , unlike standard approaches for two state of the art candidate generation algorithms , allpairs and lsh , bayeslsh enables significant speedups , typically in the range 2x 20x for a wide variety of datasets"
cs.LG,"there are at least two kinds of similarity relational similarity is correspondence between relations , in contrast with attributional similarity , which is correspondence between attributes when two words have a high degree of attributional similarity , we call them synonyms when two pairs of words have a high degree of relational similarity , we say that their relations are analogous for example , the word pair mason stone is analogous to the pair carpenter wood this paper introduces latent relational analysis \( lra \) , a method for measuring relational similarity lra has potential applications in many areas , including information extraction , word sense disambiguation , and information retrieval recently the vector space model \( vsm \) of information retrieval has been adapted to measuring relational similarity , achieving a score of 47 on a collection of 374 college level multiple choice word analogy questions in the vsm approach , the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus lra extends the vsm approach in three ways \( 1 \) the patterns are derived automatically from the corpus , \( 2 \) the singular value decomposition \( svd \) is used to smooth the frequency data , and \( 3 \) automatically generated synonyms are used to explore variations of the word pairs lra achieves 56 on the 374 analogy questions , statistically equivalent to the average human score of 57 on the related problem of classifying semantic relations , lra achieves similar gains over the vsm"
cs.AI,"this paper proposes new formulas for the probabilities of causation difined by pearl \( 2000 \) tian and pearl \( 2000a , 2000b \) showed how to bound the quantities of the probabilities of causation from experimental and observational data , under the minimal assumptions about the data generating process we derive narrower bounds than tian pearl bounds by making use of the covariate information measured in experimental and observational studies in addition , we provide identifiable case under no prevention assumption and discuss the covariate selection problem from the viewpoint of estimation accuracy these results are helpful in providing more evidence for public policy assessment and dicision making problems"
cs.LG,"bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods the machine learning methods used in bioinformatics are iterative and parallel these methods can be scaled to handle big data using the distributed and parallel computing technologies usually big data tools perform computation in batch mode and are not optimized for iterative processing and high data dependency among operations in the recent years , parallel , incremental , and multi view machine learning algorithms have been proposed similarly , graph based architectures and in memory big data tools have been developed to minimize i o cost and optimize iterative processing however , there lack standard big data architectures and tools for many important bioinformatics problems , such as fast construction of co expression and regulatory networks and salient module identification , detection of complexes over growing protein protein interaction data , fast analysis of massive dna , rna , and protein sequence data , and fast querying on incremental and heterogeneous disease networks this paper addresses the issues and challenges posed by several big data problems in bioinformatics , and gives an overview of the state of the art and the future research opportunities"
cs.DM,"when applied on some particular quantum entangled states , measurements are universal for quantum computing in particular , despite the fondamental probabilistic evolution of quantum measurements , any unitary evolution can be simulated by a measurement based quantum computer \( mbqc \) we consider the extended version of the mbqc where each measurement can occur not only in the \( x , y \) plane of the bloch sphere but also in the \( x , z \) and \( y , z \) planes the existence of a gflow in the underlying graph of the computation is a necessary and sufficient condition for a certain kind of determinism we extend the focused gflow \( a gflow in a particular normal form \) defined for the \( x , y \) plane to the extended case , and we provide necessary and sufficient conditions for the existence of such normal forms"
cs.LG|stat.ML,"rejoinder to latent variable graphical model selection via convex optimization by venkat chandrasekaran , pablo a parrilo and alan s willsky arxiv 1008 1290"
cs.DM,"a linear coloring of a graph is a proper coloring of the vertices of the graph so that each pair of color classes induce a union of disjoint paths in this paper , we prove that for every connected graph with maximum degree at most three and every assignment of lists of size four to the vertices of the graph , there exists a linear coloring such that the color of each vertex belongs to the list assigned to that vertex and the neighbors of every degree two vertex receive different colors , unless the graph is c 5 or k 3 , 3 this confirms a conjecture raised by esperet , montassier , and raspaud our proof is constructive and yields a linear time algorithm to find such a coloring"
cs.SI,"this chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns thanks to an unprecedented collection of counter data recording the number of calls , sms , and data transfers resolved both in time and space , we confirm the connection between temporal activity profile and land usage in three global cities new york , london and hong kong by comparing whole cities typical patterns , we provide insights on how cultural , technological and economical factors shape human dynamics at a more local scale , we use clustering analysis to identify locations with similar patterns within a city our research reveals a universal structure of cities , with core financial centers all sharing similar activity patterns and commercial or residential areas with more city specific patterns these findings hint that as the economy becomes more global , common patterns emerge in business areas of different cities across the globe , while the impact of local conditions still remains recognizable on the level of routine people activity"
cs.SI,"in this paper , we tackle a challenging problem inherent in many important applications tracking influential nodes in dynamic networks specifically , we model a dynamic network as a stream of edge weight updates this general model embraces many practical scenarios as special cases , such as edge and node insertions , deletions as well as evolving weighted graphs under the popularly adopted linear threshold model , we consider two essential versions of the problem finding the nodes whose influences passing a user specified threshold and finding the top k most influential nodes our key idea is to use the polling based methods and maintain a sample of random paths so that we can approximate the influence of nodes with provable quality guarantees we develop an efficient algorithm that incrementally updates the sample random paths against network changes we also design the methods determining the proper sample sizes for the two versions of the problem so that we can provide strong quality guarantees and , at the same time , be efficient in both space and time in addition to the thorough theoretical results , our experimental results on three real network data sets clearly demonstrate the effectiveness and efficiency of our algorithms"
cs.AI,"in this paper we propose the construction of linguistic descriptions of images this is achieved through the extraction of scene description graphs \( sdgs \) from visual scenes using an automatically constructed knowledge base sdgs are constructed using both vision and reasoning specifically , commonsense reasoning is applied on \( a \) detections obtained from existing perception methods on given images , \( b \) a commonsense knowledge base constructed using natural language processing of image annotations and \( c \) lexical ontological knowledge from resources such as wordnet amazon mechanical turk \( amt \) based evaluations on flickr8k , flickr30k and ms coco datasets show that in most cases , sentences auto constructed from sdgs obtained by our method give a more relevant and thorough description of an image than a recent state of the art image caption based approach our image sentence alignment evaluation results are also comparable to that of the recent state of the art approaches"
cs.SI,"culturomics was recently introduced as the application of high throughput data collection and analysis to the study of human culture here we make use of this data by investigating fluctuations in yearly usage frequencies of specific words that describe social and natural phenomena , as derived from books that were published over the course of the past two centuries we show that the determination of the hurst parameter by means of fractal analysis provides fundamental insights into the nature of long range correlations contained in the culturomic trajectories , and by doing so , offers new interpretations as to what might be the main driving forces behind the examined phenomena quite remarkably , we find that social and natural phenomena are governed by fundamentally different processes while natural phenomena have properties that are typical for processes with persistent long range correlations , social phenomena are better described as nonstationary , on off intermittent , or levy walk processes"
cs.DM|cs.SI,"with the growing importance of corporate viral marketing campaigns on online social networks , the interest in studies of influence propagation through networks is higher than ever in a viral marketing campaign , a firm initially targets a small set of pioneers and hopes that they would influence a sizeable fraction of the population by diffusion of influence through the network in general , any marketing campaign might fail to go viral in the first try as such , it would be useful to have some guide to evaluate the effectiveness of the campaign and judge whether it is worthy of further resources , and in case the campaign has potential , how to hit upon a good pioneer who can make the campaign go viral in this paper , we present a diffusion model developed by enriching the generalized random graph \( a k a configuration model \) to provide insight into these questions we offer the intuition behind the results on this model , rigorously proved in blaszczyszyn gaurav \( 2013 \) , and illustrate them here by taking examples of random networks having prototypical degree distributions poisson degree distribution , which is commonly used as a kind of benchmark , and power law degree distribution , which is normally used to approximate the real world networks on these networks , the members are assumed to have varying attitudes towards propagating the information we analyze three cases , in particular \( 1 \) bernoulli transmissions , when a member influences each of its friend with probability p \( 2 \) node percolation , when a member influences all its friends with probability p and none with probability 1 p \( 3 \) coupon collector transmissions , when a member randomly selects one of his friends k times with replacement we assume that the configuration model is the closest approximation of a large online social network , when the information available about the network is very limited the key insight offered by this study from a firm 's perspective is regarding how to evaluate the effectiveness of a marketing campaign and do cost benefit analysis by collecting relevant statistical data from the pioneers it selects the campaign evaluation criterion is informed by the observation that if the parameters of the underlying network and the campaign effectiveness are such that the campaign can indeed reach a significant fraction of the population , then the set of good pioneers also forms a significant fraction of the population therefore , in such a case , the firms can even adopt the naive strategy of repeatedly picking and targeting some number of pioneers at random from the population with this strategy , the probability of them picking a good pioneer will increase geometrically fast with the number of tries"
cs.AI|cs.LG|stat.ML,"we consider deep neural networks , in which the output of each node is a quadratic function of its inputs similar to other deep architectures , these networks can compactly represent any function on a finite training set the main goal of this paper is the derivation of an efficient layer by layer algorithm for training such networks , which we denote as the emph basis learner the algorithm is a universal learner in the sense that the training error is guaranteed to decrease at every iteration , and can eventually reach zero under mild conditions we present practical implementations of this algorithm , as well as preliminary experimental results we also compare our deep architecture to other shallow architectures for learning polynomials , in particular kernel learning"
cs.DS,"it is well known that the static caching algorithm that keeps the most frequently requested documents in the cache is optimal in case when documents are of the same size and requests are independent and equally distributed however , it is hard to develop explicit and provably optimal caching algorithms when requests are statistically correlated in this paper , we show that keeping the most frequently requested documents in the cache is still optimal for large cache sizes even if the requests are strongly correlated"
cs.DS,"we study search by quantum walk on a finite two dimensional grid the algorithm of ambainis , kempe , rivosh \( quant ph 0402107 \) takes o \( sqrt n log n \) steps and finds a marked location with probability o \( 1 log n \) for grid of size sqrt n sqrt n this probability is small , thus amplitude amplification is needed to achieve theta \( 1 \) success probability the amplitude amplification adds an additional o \( sqrt log n \) factor to the number of steps , making it o \( sqrt n log n \) in this paper , we show that despite a small probability to find a marked location , the probability to be within an o \( sqrt n \) neighbourhood \( at an o \( sqrt 4 n \) distance \) of the marked location is theta \( 1 \) this allows to skip amplitude amplification step and leads to an o \( sqrt log n \) speed up we describe the results of numerical experiments supporting this idea , and we prove this fact analytically"
cs.AI,"feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models the idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the complexity and time required to reach to an estimation using a particular modelling technique this work investigates the appropriateness of attributes , obtained from empirical project databases and aims to reduce the cost drivers used while preserving performance finding suitable subset selections that may cater improved predictions may be considered as a pre processing step of a particular technique employed for cost estimation \( filter or wrapper \) or an internal \( embedded \) step to minimise the fitting error this paper compares nine relatively popular feature selection methods and uses the empirical values of selected attributes recorded in the isbsg and desharnais datasets to estimate software development effort"
cs.IT|cs.LG|stat.ML,"the nonnegative matrix factorization \( nmf \) is widely used in signal and image processing , including bio informatics , blind source separation and hyperspectral image analysis in remote sensing a great challenge arises when dealing with a nonlinear formulation of the nmf within the framework of kernel machines , the models suggested in the literature do not allow the representation of the factorization matrices , which is a fallout of the curse of the pre image in this paper , we propose a novel kernel based model for the nmf that does not suffer from the pre image problem , by investigating the estimation of the factorization matrices directly in the input space for different kernel functions , we describe two schemes for iterative algorithms an additive update rule based on a gradient descent scheme and a multiplicative update rule in the same spirit as in the lee and seung algorithm within the proposed framework , we develop several extensions to incorporate constraints , including sparseness , smoothness , and spatial regularization with a total variation like penalty the effectiveness of the proposed method is demonstrated with the problem of unmixing hyperspectral images , using well known real images and results with state of the art techniques"
cs.LG|stat.ML,"approximate inference in high dimensional , discrete probabilistic models is a central problem in computational statistics and machine learning this paper describes discrete particle variational inference \( dpvi \) , a new approach that combines key strengths of monte carlo , variational and search based techniques dpvi is based on a novel family of particle based variational approximations that can be fit using simple , fast , deterministic search techniques like monte carlo , dpvi can handle multiple modes , and yields exact results in a well defined limit like unstructured mean field , dpvi is based on optimizing a lower bound on the partition function when this quantity is not of intrinsic interest , it facilitates convergence assessment and debugging like both monte carlo and combinatorial search , dpvi can take advantage of factorization , sequential structure , and custom search operators this paper defines dpvi particle based approximation family and partition function lower bounds , along with the sequential dpvi and local dpvi algorithm templates for optimizing them dpvi is illustrated and evaluated via experiments on lattice markov random fields , nonparametric bayesian mixtures and block models , and parametric as well as non parametric hidden markov models results include applications to real world spike sorting and relational modeling problems , and show that dpvi can offer appealing time accuracy trade offs as compared to multiple alternatives"
cs.AI,"we consider the design of private prediction markets , financial markets designed to elicit predictions about uncertain events without revealing too much information about market participants' actions or beliefs our goal is to design market mechanisms in which participants' trades or wagers influence the market 's behavior in a way that leads to accurate predictions , yet no single participant has too much influence over what others are able to observe we study the possibilities and limitations of such mechanisms using tools from differential privacy we begin by designing a private one shot wagering mechanism in which bettors specify a belief about the likelihood of a future event and a corresponding monetary wager wagers are redistributed among bettors in a way that more highly rewards those with accurate predictions we provide a class of wagering mechanisms that are guaranteed to satisfy truthfulness , budget balance in expectation , and other desirable properties while additionally guaranteeing epsilon joint differential privacy in the bettors' reported beliefs , and analyze the trade off between the achievable level of privacy and the sensitivity of a bettor 's payment to her own report we then ask whether it is possible to obtain privacy in dynamic prediction markets , focusing our attention on the popular cost function framework in which securities with payments linked to future events are bought and sold by an automated market maker we show that under general conditions , it is impossible for such a market maker to simultaneously achieve bounded worst case loss and epsilon differential privacy without allowing the privacy guarantee to degrade extremely quickly as the number of trades grows , making such markets impractical in settings in which privacy is valued we conclude by suggesting several avenues for potentially circumventing this lower bound"
cs.AI,"recent years have seen significant market penetration for voice based personal assistants such as apple 's siri however , despite this success , user take up is frustratingly low this position paper argues that there is a habitability gap caused by the inevitable mismatch between the capabilities and expectations of human users and the features and benefits provided by contemporary technology suggestions are made as to how such problems might be mitigated , but a more worrisome question emerges is spoken language all or nothing \? the answer , based on contemporary views on the special nature of \( spoken \) language , is that there may indeed be a fundamental limit to the interaction that can take place between mismatched interlocutors \( such as humans and machines \) however , it is concluded that interactions between native and non native speakers , or between adults and children , or even between humans and dogs , might provide critical inspiration for the design of future speech based human machine interaction"
cs.LG,"we propose a new first order optimisation algorithm to solve high dimensional non smooth composite minimisation problems typical examples of such problems have an objective that decomposes into a non smooth empirical risk part and a non smooth regularisation penalty the proposed algorithm , called semi proximal mirror prox , leverages the fenchel type representation of one part of the objective while handling the other part of the objective via linear minimization over the domain the algorithm stands in contrast with more classical proximal gradient algorithms with smoothing , which require the computation of proximal operators at each iteration and can therefore be impractical for high dimensional problems we establish the theoretical convergence rate of semi proximal mirror prox , which exhibits the optimal complexity bounds , i e o \( 1 epsilon 2 \) , for the number of calls to linear minimization oracle we present promising experimental results showing the interest of the approach in comparison to competing methods"
cs.AI,"the constraint satisfaction problem \( csp \) on a relational structure b is to decide , given a set of constraints on variables where the relations come from b , whether or not there is a assignment to the variables satisfying all of the constraints the surjective csp is the variant where one decides the existence of a surjective satisfying assignment onto the universe of b we present an algebraic condition on the polymorphism clone of b and prove that it is sufficient for the hardness of the surjective csp on a finite structure b , in the sense that this problem admits a reduction from a certain fixed structure csp to our knowledge , this is the first result that allows one to use algebraic information from a relational structure b to infer information on the complexity hardness of surjective constraint satisfaction on b a corollary of our result is that , on any finite non trivial structure having only essentially unary polymorphisms , surjective constraint satisfaction is np complete"
cs.LG,"the idea of opposition based learning was introduced 10 years ago since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms among others , evolutionary algorithms , reinforcement agents , and neural networks have been reportedly extended into their opposition based version to become faster and or more accurate however , most works still use a simple notion of opposites , namely linear \( or type i \) opposition , that for each x in a , b assigns its opposite as breve x i a b x this , of course , is a very naive estimate of the actual or true \( non linear \) opposite breve x ii , which has been called type ii opposite in literature in absence of any knowledge about a function y f \( mathbf x \) that we need to approximate , there seems to be no alternative to the naivety of type i opposition if one intents to utilize oppositional concepts but the question is if we can receive some level of accuracy increase and time savings by using the naive opposite estimate breve x i according to all reports in literature , what would we be able to gain , in terms of even higher accuracies and more reduction in computational complexity , if we would generate and employ true opposites \? this work introduces an approach to approximate type ii opposites using evolving fuzzy rules when we first perform opposition mining we show with multiple examples that learning true opposites is possible when we mine the opposites from the training data to subsequently approximate breve x ii f \( mathbf x , y \)"
cs.LG,"in the classic problem of sequence prediction , a predictor receives a sequence of values from an emitter and tries to guess the next value before it appears the predictor masters the emitter if there is a point after which all of the predictor 's guesses are correct in this paper we consider the case in which the predictor is an automaton and the emitted values are drawn from a finite set i e , the emitted sequence is an infinite word we examine the predictive capabilities of finite automata , pushdown automata , stack automata \( a generalization of pushdown automata \) , and multihead finite automata we relate our predicting automata to purely periodic words , ultimately periodic words , and multilinear words , describing novel prediction algorithms for mastering these sequences"
cs.DS,"we present a theoretical study of a problem arising in database query optimization , which we call as the common prefix problem we present a \( 1 o \( 1 \) \) factor approximation algorithm for this problem , when the underlying graph is a binary tree we then use a result of feige and kogan to show that even on stars , the problem is hard to approximate"
cs.LG,"we propose a novel topic discovery algorithm for unlabeled images based on the bag of words \( bow \) framework we first extract a dictionary of visual words and subsequently for each image compute a visual word occurrence histogram we view these histograms as rows of a large matrix from which we extract sparse principal components \( pcs \) each pc identifies a sparse combination of visual words which co occur frequently in some images but seldom appear in others each sparse pc corresponds to a topic , and images whose interference with the pc is high belong to that topic , revealing the common parts possessed by the images we propose to solve the associated sparse pca problems using an alternating maximization \( am \) method , which we modify for purpose of efficiently extracting multiple pcs in a deflation scheme our approach attacks the maximization problem in sparse pca directly and is scalable to high dimensional data experiments on automatic topic discovery and category prediction demonstrate encouraging performance of our approach"
cs.DS|cs.LG,"the diameter k clustering problem is the problem of partitioning a finite subset of mathbb r d into k subsets called clusters such that the maximum diameter of the clusters is minimized one early clustering algorithm that computes a hierarchy of approximate solutions to this problem \( for all values of k \) is the agglomerative clustering algorithm with the complete linkage strategy for decades , this algorithm has been widely used by practitioners however , it is not well studied theoretically in this paper , we analyze the agglomerative complete linkage clustering algorithm assuming that the dimension d is a constant , we show that for any k the solution computed by this algorithm is an o \( log k \) approximation to the diameter k clustering problem our analysis does not only hold for the euclidean distance but for any metric that is based on a norm furthermore , we analyze the closely related k center and discrete k center problem for the corresponding agglomerative algorithms , we deduce an approximation factor of o \( log k \) as well"
cs.DM,"an independent set in a graph g is a set of vertices no two of which are joined by an edge a vertex weighted graph associates a weight with every vertex in the graph a vertex weighted graph g is called a unique independence vertex weighted graph if it has a unique independent set with maximum sum of weights although , in this paper we observe that the problem of recognizing unique independence vertex weighted graphs is np hard in general and therefore no efficient characterization can be expected in general we give , however , some combinatorial characterizations of unique independence vertex weighted graphs this paper introduces a motivating application of this problem in the area of combinatorial auctions , as well"
cs.LG|stat.ML,"this paper presents a new classification methods for event related potentials \( erp \) based on an information geometry framework through a new estimation of covariance matrices , this work extend the use of riemannian geometry , which was previously limited to smr based bci , to the problem of classification of erps as compared to the state of the art , this new method increases performance , reduces the number of data needed for the calibration and features good generalisation across sessions and subjects this method is illustrated on data recorded with the p300 based game brain invaders finally , an online and adaptive implementation is described , where the bci is initialized with generic parameters derived from a database and continuously adapt to the individual , allowing the user to play the game without any calibration while keeping a high accuracy"
cs.IT|cs.LG,"sparse learning is an important topic in many areas such as machine learning , statistical estimation , signal processing , etc recently , there emerges a growing interest on structured sparse learning in this paper we focus on the ell q analysis optimization problem for structured sparse learning \( 0 q leq 1 \) compared to previous work , we establish weaker conditions for exact recovery in noiseless case and a tighter non asymptotic upper bound of estimate error in noisy case we further prove that the nonconvex ell q analysis optimization can do recovery with a lower sample complexity and in a wider range of cosparsity than its convex counterpart in addition , we develop an iteratively reweighted method to solve the optimization problem under the variational framework theoretical analysis shows that our method is capable of pursuing a local minima close to the global minima also , empirical results of preliminary computational experiments illustrate that our nonconvex method outperforms both its convex counterpart and other state of the art methods"
cs.SI,"social networks have become a part in the daily life of millions of users , which offer wide range of interests and practices the main characteristic of social networks is its ability to gather different individuals around a common point of view or collective beliefs among the current social networking sites , facebook is the most popular , which has the highest number of users however , in facebook , the existence of communities \( groups \) is a critical question thus , many researchers focus on potential communities by using techniques like data mining and web mining in this work , we present four approaches based on link analysis techniques to detect prospective groups and their members"
cs.IT|stat.ML,"many problems can be formulated as recovering a low rank tensor although an increasingly common task , tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors to overcome these difficulties , existing approaches often proceed by unfolding tensors into matrices and then apply techniques for matrix completion we show here that such matricization fails to exploit the tensor structure and may lead to suboptimal procedure more specifically , we investigate a convex optimization approach to tensor completion by directly minimizing a tensor nuclear norm and prove that this leads to an improved sample size requirement to establish our results , we develop a series of algebraic and probabilistic techniques such as characterization of subdifferetial for tensor nuclear norm and concentration inequalities for tensor martingales , which may be of independent interests and could be useful in other tensor related problems"
cs.SI,"this study investigates factors that may determine satisfaction in customer service operations we utilized more than 170 , 000 online chat sessions between customers and agents to identify characteristics of chat sessions that incurred dissatisfying experience quantitative data analysis suggests that sentiments or moods conveyed in online conversation are the most predictive factor of perceived satisfaction conversely , other session related meta data \( such as that length , time of day , and response time \) has a weaker correlation with user satisfaction knowing in advance what can predict satisfaction allows customer service staffs to identify potential weaknesses and improve the quality of service for better customer experience"
cs.LG,"this letter is a response to the comments of serang \( 2012 \) on huang and he \( 2012 \) in bioinformatics serang \( 2012 \) claimed that the parameters for the fido algorithm should be specified using the grid search method in serang et al \( 2010 \) so as to generate a deserved accuracy in performance comparison it seems that it is an argument on parameter tuning however , it is indeed the issue of how to conduct an unbiased performance evaluation for comparing different protein inference algorithms in this letter , we would explain why we do n't use the grid search for parameter selection in huang and he \( 2012 \) and show that this procedure may result in an over estimated performance that is unfair to competing algorithms in fact , this issue has also been pointed out by li and radivojac \( 2012 \)"
cs.AI,"in the study of human learning , there is broad evidence that our ability to retain a piece of information improves with repeated exposure , and that it decays with delay since the last exposure this plays a crucial role in the design of educational software , leading to a trade off between teaching new material and reviewing what has already been taught a common way to balance this trade off is via spaced repetition using periodic review of content to improve long term retention though widely used in practice , there is little formal understanding of the design of these systems this paper addresses this gap first , we mine log data from a spaced repetition system to establish the functional dependence of retention on reinforcement and delay second , based on this memory model , we develop a mathematical framework for spaced repetition systems using a queueing network approach this model formalizes the popular leitner heuristic for spaced repetition , providing the first rigorous and computationally tractable means of optimizing the review schedule finally , we empirically confirm the validity of our formal model via a mechanical turk experiment in particular , we verify a key qualitative insight and prediction of our model the existence of a sharp phase transition in learning outcomes upon increasing the rate of new item introductions"
cs.LG,"we consider the problem of removing and replacing clouds in satellite image sequences , which has a wide range of applications in remote sensing our approach first detects and removes the cloud contaminated part of the image sequences it then recovers the missing scenes from the clean parts using the proposed tecromac \( temporally contiguous robust matrix completion \) objective the objective function balances temporal smoothness with a low rank solution while staying close to the original observations the matrix whose the rows are pixels and columnsare days corresponding to the image , has low rank because the pixels reflect land types such as vegetation , roads and lakes and there are relatively few variations as a result we provide efficient optimization algorithms for tecromac , so we can exploit images containing millions of pixels empirical results on real satellite image sequences , as well as simulated data , demonstrate that our approach is able to recover underlying images from heavily cloud contaminated observations"
cs.SI,"networks of interconnected nodes have long played a key role in cognitive science , from artificial neural net works to spreading activation models of semantic mem ory recently , however , a new network science has been developed , providing insights into the emergence of global , system scale properties in contexts as diverse as the internet , metabolic reactions , and collaborations among scientists today , the inclusion of network theory into cognitive sciences , and the expansion of complex systems science , promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood in this paper , we review recent contributions of network theory at different levels and domains within the cognitive sciences"
cs.DS,"we show that if one can solve 3sum on a set of size n in time n 1 e then one can list t triangles in a graph with m edges in time o \( m 1 e t 1 3 e 3 \) this is a reversal of patrascu 's reduction from 3sum to listing triangles \( stoc '10 \) our result builds on and extends works by the paghs \( pods '06 \) and by vassilevska and williams \( focs '10 \) we make our reductions deterministic using tools from pseudorandomness we then re execute both patrascu 's reduction and our reversal for the variant 3xor of 3sum where integer summation is replaced by bit wise xor as a corollary we obtain that if 3xor is solvable in linear time but 3sum requires quadratic randomized time , or vice versa , then the randomized time complexity of listing m triangles in a graph with m edges is m 4 3 up to a factor m alpha for any alpha 0"
cs.DS,"motivated by an application in kidney exchange , we study the following query commit problem we are given the set of vertices of a non bipartite graph g the set of edges in this graph are not known ahead of time we can query any pair of vertices to determine if they are adjacent if the queried edge exists , we are committed to match the two endpoints our objective is to maximize the size of the matching this restriction in the amount of information available to the algorithm constraints us to implement myopic , greedy like algorithms a simple deterministic greedy algorithm achieves a factor 1 2 which is tight for deterministic algorithms an important open question in this direction is to give a randomized greedy algorithm that has a significantly better approximation factor this question was first asked almost 20 years ago by dyer and frieze 9 where they showed that a natural randomized strategy of picking edges uniformly at random does n't help and has an approximation factor of 1 2 o \( 1 \) they left it as an open question to devise a better randomized greedy algorithm in subsequent work , aronson , dyer , frieze , and suen 2 gave a different randomized greedy algorithm and showed that it attains a factor 0 5 epsilon where epsilon is 0 0000025 in this paper we propose and analyze a new randomized greedy algorithm for finding a large matching in a general graph and use it to solve the query commit problem mentioned above we show that our algorithm attains a factor of at least 0 56 , a significant improvement over 0 50000025 we also show that no randomized algorithm can have an approximation factor better than 0 7916 for the query commit problem for another large and interesting class of randomized algorithms that we call vertex iterative algorithms , we show that no vertex iterative algorithm can have an approximation factor better than 0 75"
cs.AI|stat.ML,"the causal discovery of bayesian networks is an active and important research area , and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data however , some of those dependencies are generated by causal structures involving variables which have not been measured , i e , latent variables some such patterns of dependency reveal themselves , in that no model based solely upon the observed variables can explain them as well as a model using a latent variable that is what latent variable discovery is based upon here we did a search for finding them systematically , so that they may be applied in latent variable discovery in a more rigorous fashion"
cs.DS,"recently , cygan , kowalik , and wykurz ipl 2009 gave sub exponential time approximation algorithms for the set cover problem with approximation ratios better than ln \( n \) in light of this result , it is natural to ask whether such improvements can be achieved using lift and project methods we present a simpler combinatorial algorithm which has nearly the same time approximation tradeoff as the algorithm of cygan et al , and which lends itself naturally to a lift and project based approach at a high level , our approach is similar to the recent work of karlin , mathieu , and nguyen ipco 2011 , who examined a known ptas for knapsack \( similar to our combinatorial set cover algorithm \) and its connection to hierarchies of lp and sdp relaxations for knapsack for set cover , we show that , indeed , using the trick of lifting the objective function , we can match the performance of our combinatorial algorithm using the lp hierarchy of lovasz and schrijver we also show that this trick is essential even in the stronger lp hierarchy of sherali and adams , the integrality gap remains at least \( 1 eps \) ln \( n \) at level omega \( n \) \( when the objective function is not lifted \) as shown by aleknovich , arora , and tourlakis stoc 2005 , set cover relaxations stemming from sdp hierarchies \( specifically , ls \) have similarly large integrality gaps this stands in contrast to knapsack , where karlin et al showed that the \( much stronger \) lasserre sdp hierarchy reduces the integrality gap to \( 1 eps \) at level o \( 1 \) for completeness , we show that ls also reduces the integrality gap for knapsack to \( 1 eps \) this result may be of independent interest , as our ls based rounding and analysis are rather different from those of karlin et al , and to the best of our knowledge this is the first explicit demonstration of such a reduction in the integrality gap of ls relaxations after few rounds"
cs.LG|stat.ML,"a number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a markov random field belief propagation , an iterative message passing algorithm , computes exactly such marginals when the underlying graph is a tree but it has gained its popularity as an efficient way to approximate them in the more general case , even if it can exhibits multiple fixed points and is not guaranteed to converge in this paper , we express a new sufficient condition for local stability of a belief propagation fixed point in terms of the graph structure and the beliefs values at the fixed point this gives credence to the usual understanding that belief propagation performs better on sparse graphs"
cs.DS|cs.LG,"many problems in machine learning can be modeled as submodular optimization problems recent work has focused on stochastic or adaptive versions of these problems we consider the scenario submodular cover problem , which is a counterpart to the stochastic submodular cover problem studied by golovin and krause in scenario submodular cover , the goal is to produce a cover with minimum expected cost , where the expectation is with respect to an empirical joint distribution , given as input by a weighted sample of realizations in contrast , in stochastic submodular cover , the variables of the input distribution are assumed to be independent , and the distribution of each variable is given as input building on algorithms developed by cicalese et al and golovin and krause for related problems , we give two approximation algorithms for scenario submodular cover over discrete distributions the first achieves an approximation factor of o \( log qm \) , where m is the size of the sample and q is the goal utility the second , simpler algorithm achieves an approximation bound of o \( log qw \) , where q is the goal utility and w is the sum of the integer weights \( both bounds assume an integer valued utility function \) our results yield approximation bounds for other problems involving non independent distributions that are explicitly specified by their support"
cs.SI,"this work extends the set of works which deal with the popular problem of sentiment analysis in twitter it investigates the most popular document \( tweet \) representation methods which feed sentiment evaluation mechanisms in particular , we study the bag of words , n grams and n gram graphs approaches and for each of them we evaluate the performance of a lexicon based and 7 learning based classification algorithms \( namely svm , na ive bayesian networks , logistic regression , multilayer perceptrons , best first trees , functional trees and c4 5 \) as well as their combinations , using a set of 4451 manually annotated tweets the results demonstrate the superiority of learning based methods and in particular of n gram graphs approaches for predicting the sentiment of tweets they also show that the combinatory approach has impressive effects on n grams , raising the confidence up to 83 15 on the 5 grams , using majority vote and a balanced dataset \( equal number of positive , negative and neutral tweets for training \) in the n gram graph cases the improvement was small to none , reaching 94 52 on the 4 gram graphs , using orthodromic distance and a threshold of 0 001"
cs.SI,"the smallest eigenvalues and the associated eigenvectors \( i e , eigenpairs \) of a graph laplacian matrix have been widely used for spectral clustering and community detection however , the majority of applications require computation of a large number k max of eigenpairs , where k max is an upper bound on the number k of clusters , called the order of the clustering algorithms in this work , we propose an incremental method for constructing the eigenspectrum of the graph laplacian matrix this method leverages the eigenstructure of graph laplacian matrix to obtain the k th eigenpairs of the laplacian matrix given a collection of all the k 1 smallest eigenpairs our proposed method adapts the laplacian matrix such that the batch eigenvalue decomposition problem transforms into an efficient sequential leading eigenpair computation problem as a practical application of our proposed method , we consider user guided spectral clustering specifically , we demonstrate that users can utilize the proposed incremental method for effective eigenpair computation and determining the desired number of clusters based on multiple clustering metrics"
cs.LG|stat.ML,"this paper introduces a novel approach for learning to rank \( letor \) based on the notion of monotone retargeting it involves minimizing a divergence between all monotonic increasing transformations of the training scores and a parameterized prediction function the minimization is both over the transformations as well as over the parameters it is applied to bregman divergences , a large class of distance like functions that were recently shown to be the unique class that is statistically consistent with the normalized discounted gain \( ndcg \) criterion 19 the algorithm uses alternating projection style updates , in which one set of simultaneous projections can be computed independent of the bregman divergence and the other reduces to parameter estimation of a generalized linear model this results in easily implemented , efficiently parallelizable algorithm for the letor task that enjoys global optimum guarantees under mild conditions we present empirical results on benchmark datasets showing that this approach can outperform the state of the art ndcg consistent techniques"
cs.DM,"we describe a deterministic algorithm for finding a generating element of the multiplicative group of the finite field mathbb f p n where p is a prime in time polynomial in p and n , the algorithm either outputs an element that is provably a generator or declares that it has failed in finding one the algorithm relies on a relation generation technique in joux 's heuristically l \( 1 4 \) method for discrete logarithm computation based on a heuristic assumption , the algorithm does succeed in finding a generator for the special case when the order of p in \( mathbb z n mathbb z \) times is small \( that is \( log p \( n \) \) mathcal o \( 1 \) \) , we present a modification with greater guarantee of success while making weaker heuristic assumptions"
cs.AI,"there are two main objectives of this paper the first is to present a statistical framework for models with context specific independence structures , i e , conditional independences holding only for sepcific values of the conditioning variables this framework is constituted by the class of split models split models are extension of graphical models for contigency tables and allow for a more sophisticiated modelling than graphical models the treatment of split models include estimation , representation and a markov property for reading off those independencies holding in a specific context the second objective is to present a software package named yggdrasil which is designed for statistical inference in split models , i e , for learning such models on the basis of data"
cs.DS,"many types of dynamic networks are made up of durable entities whose links evolve over time when considered from a em global and em discrete standpoint , these networks are often modelled as evolving graphs , i e a sequence of static graphs cal g g 1 , g 2 , , g delta such that g i \( v , e i \) represents the network topology at time step i such a sequence is said to be t interval connected if for any t in 1 , delta t 1 all graphs in g t , g t 1 , , g t t 1 share a common connected spanning subgraph in this paper , we consider the problem of deciding whether a given sequence cal g is t interval connected for a given t we also consider the related problem of finding the largest t for which a given cal g is t interval connected we assume that the changes between two consecutive graphs are arbitrary , and that two operations , em binary intersection and em connectivity testing , are available to solve the problems we show that omega \( delta \) such operations are required to solve both problems , and we present optimal o \( delta \) online algorithms for both problems"
cs.SI,"we propose a novel approach framed in terms of information theory and entropy to tackle the issue of conspiracy theories propagation we start with the report of an event \( such as 9 11 terroristic attack \) represented as a series of individual strings of information denoted respectively by two state variable ei 1 , i 1 , , n assigning ei value to all strings , the initial order parameter and entropy are determined conspiracy theorists comment on the report , focusing repeatedly on several strings ek and changing their meaning \( from 1 to 1 \) the reading of the event is turned fuzzy with an increased entropy value beyond some threshold value of entropy , chosen by simplicity to its maximum value , meaning n 2 variables with ei 1 , doubt prevails in the reading of the event and the chance is created that an alternative theory might prevail therefore , the evolution of the associated entropy is a way to measure the degree of penetration of a conspiracy theory our general framework relies on online content made voluntarily available by crowds of people , in response to some news or blog articles published by official news agencies we apply different aggregation levels \( comment , person , discussion thread \) and discuss the associated patterns of entropy change"
cs.SI,"the increasing demands in security and reliability of infrastructures call for the optimal design of their embedded complex networks topologies the following question then arises what is the optimal layout to fulfill best all the demands \? here we present a general solution for this problem with scale free networks , like the internet and airline networks precisely , we disclose a way to systematically construct networks which are 100 robust against random failures as well as to malicious attacks furthermore , as the sizes of these networks increase , their shortest paths become asymptotically invariant and densities of links go to zero , making them ultra small worlds and highly sparse , respectively the first property is ideal for communication and navigation purposes , while the second is interesting economically"
cs.AI,"many exact and approximate solution methods for markov decision processes \( mdps \) attempt to exploit structure in the problem and are based on factorization of the value function especially multiagent settings , however , are known to suffer from an exponential increase in value component sizes as interactions become denser , meaning that approximation architectures are restricted in the problem sizes and types they can handle we present an approach to mitigate this limitation for certain types of multiagent systems , exploiting a property that can be thought of as anonymous influence in the factored mdp anonymous influence summarizes joint variable effects efficiently whenever the explicit representation of variable identity in the problem can be avoided we show how representational benefits from anonymity translate into computational efficiencies , both for general variable elimination in a factor graph but in particular also for the approximate linear programming solution to factored mdps the latter allows to scale linear programming to factored mdps that were previously unsolvable our results are shown for the control of a stochastic disease process over a densely connected graph with 50 nodes and 25 agents"
cs.AI,"this paper studies peek arc consistency , a reasoning technique that extends the well known arc consistency technique for constraint satisfaction in contrast to other more costly extensions of arc consistency that have been studied in the literature , peek arc consistency requires only linear space and quadratic time and can be parallelized in a straightforward way such that it runs in linear time with a linear number of processors we demonstrate that for various constraint languages , peek arc consistency gives a polynomial time decision procedure for the constraint satisfaction problem we also present an algebraic characterization of those constraint languages that can be solved by peek arc consistency , and study the robustness of the algorithm"
cs.DM|cs.DS|cs.SI,"the h index is one of the most important bibliographic measures used to assess the performance of researchers van bevern et al ijcai 2015 showed that , despite computational worst case hardness results , substantial manipulation of the h index of google scholar author profiles is possible by merging articles complementing previous work , we study the opposite operation , the splitting of articles , which is arguably the more natural operation for manipulation and which is also allowed within google scholar we present numerous results on computational complexity \( from linear time algorithms to parameterized hardness results \) and empirically indicate that at least small improvements of the h index by splitting merged articles are easily achievable"
cs.DS|cs.SI,"given a task mathcal t , a set of experts v with multiple skills and a social network g \( v , w \) reflecting the compatibility among the experts , team formation is the problem of identifying a team c subseteq v that is both competent in performing the task mathcal t and compatible in working together existing methods for this problem make too restrictive assumptions and thus cannot model practical scenarios the goal of this paper is to consider the team formation problem in a realistic setting and present a novel formulation based on densest subgraphs our formulation allows modeling of many natural requirements such as \( i \) inclusion of a designated team leader and or a group of given experts , \( ii \) restriction of the size or more generally cost of the team \( iii \) enforcing locality of the team , e g , in a geographical sense or social sense , etc the proposed formulation leads to a generalized version of the classical densest subgraph problem with cardinality constraints \( dsp \) , which is an np hard problem and has many applications in social network analysis in this paper , we present a new method for \( approximately \) solving the generalized dsp \( gdsp \) our method , forte , is based on solving an equivalent continuous relaxation of gdsp the solution found by our method has a quality guarantee and always satisfies the constraints of gdsp experiments show that the proposed formulation \( gdsp \) is useful in modeling a broader range of team formation problems and that our method produces more coherent and compact teams of high quality we also show , with the help of an lp relaxation of gdsp , that our method gives close to optimal solutions to gdsp"
cs.DM,a schnyder wood is an orientation and coloring of the edges of a planar map satisfying a simple local property we propose a generalization of schnyder woods to graphs embedded on the torus with application to graph drawing we prove several properties on this new object among all we prove that a graph embedded on the torus admits such a schnyder wood if and only if it is an essentially 3 connected toroidal map we show that these schnyder woods can be used to embed the universal cover of an essentially 3 connected toroidal map on an infinite and periodic orthogonal surface finally we use this embedding to obtain a straight line flat torus representation of any toroidal map in a polynomial size grid
cs.DS,"this paper refutes the validity of the polynomial time algorithm for solving satisfiability proposed by sergey gubin gubin introduces the algorithm using 3 sat and eventually expands it to accept a broad range of forms of the boolean satisfiability problem because 3 sat is np complete , the algorithm would have implied p np , had it been correct additionally , this paper refutes the correctness of his polynomial time reduction of sat to 2 sat"
cs.DM,"we describe computer algorithms that produce the complete set of isohedral tilings by n omino or n iamond tiles in which the tiles are fundamental domains and the tilings have 3 , 4 , or 6 fold rotational symmetry the symmetry groups of such tilings are of types p3 , p31m , p4 , p4g , and p6 there are no isohedral tilings with symmetry groups p3m1 , p4m , or p6m that have polyominoes or polyiamonds as fundamental domains we display the algorithms' output and give enumeration tables for small values of n this expands on our earlier works \( fukuda et al 2006 , 2008 \)"
cs.DM|cs.DS,"although largely unnoticed by the online algorithms community , t theory , a field of discrete mathematics , has contributed to the development of several online algorithms for the k server problem a brief summary of the k server problem , and some important application concepts of t theory , are given additionally , a number of known k server results are restated using the established terminology of t theory lastly , a previously unpublished 3 competitiveness proof , using t theory , for the harmonic algorithm for two servers is presented"
cs.SI|stat.ML,"mobile sensing is an emerging technology that utilizes agent participatory data for decision making or state estimation , including multimedia applications this article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing inspired by social network , one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing a prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose numerical experiments on real world datasets show improved performance of mobile sensing via crowdsourcing challenges for mobile sensing with respect to internet layers are discussed"
cs.DM,the aim of this paper is to present the recently proposed fluid diffusion based algorithm in the general context of the matrix inversion problem associated to the gauss seidel method we explain the simple intuitions that are behind this diffusion method and how it can outperform existing methods then we present some theoretical problems that are associated to this representation as open research problems we also illustrate some connected problems such as the graph transformation and the pagerank problem
cs.SI,"many real world complex networks are best modeled as bipartite \( or 2 mode \) graphs , where nodes are divided into two sets with links connecting one side to the other however , there is currently a lack of methods to analyze properly such graphs as most existing measures and methods are suited to classical graphs a usual but limited approach consists in deriving 1 mode graphs \( called projections \) from the underlying bipartite structure , though it causes important loss of information and data storage issues we introduce here internal links and pairs as a new notion useful for such analysis it gives insights on the information lost by projecting the bipartite graph we illustrate the relevance of theses concepts on several real world instances illustrating how it enables to discriminate behaviors among various cases when we compare them to a benchmark of random networks then , we show that we can draw benefit from this concept for both modeling complex networks and storing them in a compact format"
cs.DS,"we describe an asynchronous algorithm to solve secure multiparty computation \( mpc \) over n players , when strictly less than a 1 8 fraction of the players are controlled by a static adversary for any function f over a field that can be computed by a circuit with m gates , our algorithm requires each player to send a number of field elements and perform an amount of computation that is o \( m n sqrt n \) this significantly improves over traditional algorithms , which require each player to both send a number of messages and perform computation that is omega \( nm \) additionally , we define the threshold counting problem and present a distributed algorithm to solve it in the asynchronous communication model our algorithm is load balanced , with computation , communication and latency complexity of o \( log n \) , and may be of independent interest to other applications with a load balancing goal in mind"
cs.LG|stat.ML,finding the most effective way to aggregate multi subject fmri data is a long standing and challenging problem it is of increasing interest in contemporary fmri studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects recent work on latent factor models shows promising results in this task but this approach does not preserve spatial locality in the brain we examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi subject fmri data while preserving spatial locality we first do this directly by combining a recent factor method known as a shared response model with searchlight analysis then we design a multi view convolutional autoencoder for the same task both approaches preserve spatial locality and have competitive or better performance compared with standard searchlight analysis and the shared response model applied across the whole brain we also report a system design to handle the computational challenge of training the convolutional autoencoder
cs.SI,"recently bramoulle and kranton presented a model for the provision of public goods over a network and showed the existence of a class of nash equilibria called specialized equilibria wherein some agents exert maximum effort while other agents free ride we examine the efficiency , effort and cost of specialized equilibria in comparison to other equilibria our main results show that the welfare of a particular specialized equilibrium approaches the maximum welfare amongst all equilibria as the concavity of the benefit function tends to unity for forest networks a similar result also holds as the concavity approaches zero moreover , without any such concavity conditions , there exists for any network a specialized equilibrium that requires the maximum weighted effort amongst all equilibria when the network is a forest , a specialized equilibrium also incurs the minimum total cost amongst all equilibria for well covered forest networks we show that all welfare maximizing equilibria are specialized and all equilibria incur the same total cost thus we argue that specialized equilibria may be considered as a refinement of the equilibrium of the public goods game we show several results on the structure and efficiency of equilibria that highlight the role of dependants in the network"
cs.DS|stat.ML,"accelerated coordinate descent methods are widely used in optimization and machine learning by taking cheap to compute coordinate gradients in each iteration , they are usually faster than accelerated full gradient descent , and thus suitable for large scale optimization problems in this paper , we improve the running time of accelerated coordinate descent , using a clean and novel non uniform sampling method in particular , if a function f is coordinate wise smooth with parameters l 1 , l 2 , dots , l n , we obtain an algorithm that converges in o \( sum i sqrt l i sqrt varepsilon \) iterations for convex f and in tilde o \( sum i sqrt l i sqrt sigma \) iterations for sigma strongly convex f the best known result was tilde o \( sqrt n sum i l i sqrt varepsilon \) for convex f and tilde o \( sqrt n sum i l i sqrt sigma \) for sigma strongly convex f , due to lee and sidford thus , our algorithm improves the running time by a factor up to sqrt n our speed up naturally applies to important problems such as empirical risk minimizations and solving linear systems"
cs.DS|cs.LG|stat.ML,"we consider the problem of search through comparisons , where a user is presented with two candidate objects and reveals which is closer to her intended target we study adaptive strategies for finding the target , that require knowledge of rank relationships but not actual distances between objects we propose a new strategy based on rank nets , and show that for target distributions with a bounded doubling constant , it finds the target in a number of comparisons close to the entropy of the target distribution and , hence , of the optimum we extend these results to the case of noisy oracles , and compare this strategy to prior art over multiple datasets"
cs.AI|cs.LG|stat.ML,"one way to solve lasso problems when the dictionary does not fit into available memory is to first screen the dictionary to remove unneeded features prior research has shown that sequential screening methods offer the greatest promise in this endeavor most existing work on sequential screening targets the context of tuning parameter selection , where one screens and solves a sequence of n lasso problems with a fixed grid of geometrically spaced regularization parameters in contrast , we focus on the scenario where a target regularization parameter has already been chosen via cross validated model selection , and we then need to solve many lasso instances using this fixed value in this context , we propose and explore a feedback controlled sequential screening scheme feedback is used at each iteration to select the next problem to be solved this allows the sequence of problems to be adapted to the instance presented and the number of intermediate problems to be automatically selected we demonstrate our feedback scheme using several datasets including a dictionary of approximate size 100 , 000 by 300 , 000"
cs.DS|cs.LG,"this version is superseded by a full version that can be found at http www itu dk people pagh papers mining jour pdf , which contains stronger theoretical results and fixes a mistake in the reporting of experiments abstract sampling based methods have previously been proposed for the problem of finding interesting associations in data , even for low support items while these methods do not guarantee precise results , they can be vastly more efficient than approaches that rely on exact counting however , for many similarity measures no such methods have been known in this paper we show how a wide variety of measures can be supported by a simple biased sampling method the method also extends to find high confidence association rules we demonstrate theoretically that our method is superior to exact methods when the threshold for interesting similarity confidence is above the average pairwise similarity confidence , and the average support is not too low our method is particularly good when transactions contain many items we confirm in experiments on standard association mining benchmarks that this gives a significant speedup on real data sets \( sometimes much larger than the theoretical guarantees \) reductions in computation time of over an order of magnitude , and significant savings in space , are observed"
cs.LG|stat.ML,"this paper characterizes hierarchical clustering methods that abide by two previously introduced axioms thus , denominated admissible methods and proposes tractable algorithms for their implementation we leverage the fact that , for asymmetric networks , every admissible method must be contained between reciprocal and nonreciprocal clustering , and describe three families of intermediate methods grafting methods exchange branches between dendrograms generated by different admissible methods the convex combination family combines admissible methods through a convex operation in the space of dendrograms , and thirdly , the semi reciprocal family clusters nodes that are related by strong cyclic influences in the network algorithms for the computation of hierarchical clusters generated by reciprocal and nonreciprocal clustering as well as the grafting , convex combination , and semi reciprocal families are derived using matrix operations in a dioid algebra finally , the introduced clustering methods and algorithms are exemplified through their application to a network describing the interrelation between sectors of the united states \( u s \) economy"
cs.AI,"speech repairs occur often in spontaneous spoken dialogues the ability to detect and correct those repairs is necessary for any spoken language system we present a framework to detect and correct speech repairs where all relevant levels of information , i e , acoustics , lexis , syntax and semantics can be integrated the basic idea is to reduce the search space for repairs as soon as possible by cascading filters that involve more and more features at first an acoustic module generates hypotheses about the existence of a repair second a stochastic model suggests a correction for every hypothesis well scored corrections are inserted as new paths in the word lattice finally a lattice parser decides on accepting the rep air"
cs.SI,"our aim here is to address the problem of decomposing a whole network into a minimal number of ego centered subnetworks for this purpose , the network egos are picked out as the members of a minimum dominating set of the network however , to find such an efficient dominating ego centered construction , we need to be able to detect all the minimum dominating sets and to compare all the corresponding dominating ego centered decompositions of the network to find all the minimum dominating sets of the network , we are developing a computational heuristic , which is based on the partition of the set of nodes of a graph into three subsets , the always dominant vertices , the possible dominant vertices and the never dominant vertices , when the domination number of the network is known to compare the ensuing dominating ego centered decompositions of the network , we are introducing a number of structural measures that count the number of nodes and links inside and across the ego centered subnetworks furthermore , we are applying the techniques of graph domination and ego centered decomposition for six empirical social networks"
cs.LG,"gradient descent training techniques are remarkably successful in training analog valued artificial neural networks \( anns \) such training techniques , however , do not transfer easily to spiking networks due to the spike generation hard non linearity and the discrete nature of spike communication we show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates , the network input output relation is differentiable almost everywhere moreover , this relation is locally linear after a transformation of variables methods for training anns thus carry directly to the training of such spiking networks as we show when training on the permutation invariant mnist task in contrast to rate based spiking networks that are often used to approximate the behavior of anns , the networks we present spike much more sparsely and their behavior can not be directly approximated by conventional anns our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics , opening up the potential for using these networks to process spike patterns with complex temporal information"
cs.DS,"this paper presents an improved analysis of a structured dimension reduction map called the subsampled randomized hadamard transform this argument demonstrates that the map preserves the euclidean geometry of an entire subspace of vectors the new proof is much simpler than previous approaches , and it offers for the first time optimal constants in the estimate on the number of dimensions required for the embedding"
cs.AI,"in this article a tool for the analysis of population based eas is used to derive asymptotic upper bounds on the optimization time of the algorithm solving royal roads problem , a test function with plateaus of fitness in addition to this , limiting distribution of a certain subset of the population is approximated"
cs.SI,"research on information diffusion generally assumes complete knowledge of the underlying network however , in the presence of factors such as increasing privacy awareness , restrictions on application programming interfaces \( apis \) and sampling strategies , this assumption rarely holds in the real world which in turn leads to an underestimation of the size of information cascades in this work we study the effect of hidden network structure on information diffusion processes we characterise information cascades through activation paths traversing visible and hidden parts of the network we quantify diffusion estimation error while varying the amount of hidden structure in five empirical and synthetic network datasets and demonstrate the effect of topological properties on this error finally , we suggest practical recommendations for practitioners and propose a model to predict the cascade size with minimal information regarding the underlying network"
cs.SI,"generally , social network analysis has often focused on the topology of the network without considering the characteristics of individuals involved in them less attention is given to study the behavior of individuals , considering they are the basic entity of a graph given a mobile social network graph , what are good features to extract key information from the nodes \? how many distinct neighborhood patterns exist for ego nodes \? what clues does such information provide to study nodes over a long period of time \? in this report , we develop an automated system in order to discover the occurrences of prototypical ego centric patterns from data we aim to provide a data driven instrument to be used in behavioral sciences for graph interpretations we analyze social networks derived from real world data collected with smart phones we select 13 well known network measures , especially those concerned with ego graphs we form eight feature subsets and then assess their performance using unsupervised clustering techniques to discover distinguishing ego centric patterns from clustering analysis , we discover that eight distinct neighborhood patterns have emerged this categorization allows concise analysis of users' data as they change over time the results provide a fine grained analysis for the contribution of different feature sets to detect unique clustering patterns last , as a case study , two datasets are studied over long periods to demonstrate the utility of this method the study shows the effectiveness of the proposed approach in discovering important trends from data"
cs.SI,"how an information spreads throughout a social network is a valuable knowledge sought by many groups such as marketing enterprises and political parties if they can somehow predict the impact of a given message or manipulate it in order to amplify how long it will spread , it would give them a huge advantage over their competitors intuitively , it is expected that two factors contribute to make an information becoming viral how influential the person who spreads is inside its network and the content of the message the former should have a more important role , since people will not just blindly share any content , or will they \? in this work it is found that the degree of a node alone is capable of accurately predicting how many followers of the seed user will spread the information through a simple linear regression the analysis was performed with five different messages from twitter network that was shared with different degrees along the users the results show evidences that no matter the content , the number of affected neighbors is predictable the role of the content of the messages of a user is likely to influence the network formation and the path the message will follow through the network"
cs.LG|stat.ML,"we propose to prune a random forest \( rf \) for resource constrained prediction we first construct a rf and then prune it to optimize expected feature cost accuracy we pose pruning rfs as a novel 0 1 integer program with linear constraints that encourages feature re use we establish total unimodularity of the constraint set to prove that the corresponding lp relaxation solves the original integer program we then exploit connections to combinatorial optimization and develop an efficient primal dual algorithm , scalable to large datasets in contrast to our bottom up approach , which benefits from good rf initialization , conventional methods are top down acquiring features based on their utility value and is generally intractable , requiring heuristics empirically , our pruning algorithm outperforms existing state of the art resource constrained algorithms"
cs.LG|stat.ML,"empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization however , classical convex surrogates can only tightly bound modular loss functions , sub modular functions or supermodular functions separately while maintaining polynomial time computation in this work , a novel generic convex surrogate for general non modular loss functions is introduced , which provides for the first time a tractable solution for loss functions that are neither super modular nor submodular this convex surro gate is based on a submodular supermodular decomposition for which the existence and uniqueness is proven in this paper it takes the sum of two convex surrogates that separately bound the supermodular component and the submodular component using slack rescaling and the lov 'a sz hinge , respectively it is further proven that this surrogate is convex , piecewise linear , an extension of the loss function , and for which subgradient computation is polynomial time empirical results are reported on a non submodular loss based on the s o rensen dice difference function , and a real world face track dataset with tens of thousands of frames , demonstrating the improved performance , efficiency , and scalabil ity of the novel convex surrogate"
cs.DM,"an \( r , s \) formation is a concatenation of s permutations of r letters if u is a sequence with r distinct letters , then let mathit ex \( u , n \) be the maximum length of any r sparse sequence with n distinct letters which has no subsequence isomorphic to u for every sequence u define mathit fw \( u \) , the formation width of u , to be the minimum s for which there exists r such that there is a subsequence isomorphic to u in every \( r , s \) formation we use mathit fw \( u \) to prove upper bounds on mathit ex \( u , n \) for sequences u such that u contains an alternation with the same formation width as u we generalize nivasch 's bounds on mathit ex \( \( ab \) t , n \) by showing that mathit fw \( \( 12 ldots l \) t \) 2t 1 and mathit ex \( \( 12 ldots l \) t , n \) n2 frac 1 \( t 2 \) ! alpha \( n \) t 2 pm o \( alpha \( n \) t 3 \) for every l geq 2 and t geq 3 , such that alpha \( n \) denotes the inverse ackermann function upper bounds on mathit ex \( \( 12 ldots l \) t , n \) have been used in other papers to bound the maximum number of edges in k quasiplanar graphs on n vertices with no pair of edges intersecting in more than o \( 1 \) points if u is any sequence of the form a v a v' a such that a is a letter , v is a nonempty sequence excluding a with no repeated letters and v' is obtained from v by only moving the first letter of v to another place in v , then we show that mathit fw \( u \) 4 and mathit ex \( u , n \) theta \( n alpha \( n \) \) furthermore we prove that mathit fw \( abc \( acb \) t \) 2t 1 and mathit ex \( abc \( acb \) t , n \) n2 frac 1 \( t 1 \) ! alpha \( n \) t 1 pm o \( alpha \( n \) t 2 \) for every t geq 2"
cs.DS,covering a bounded region with minimum number of homogeneous sensor nodes is a np complete problem cite li09 in this paper we have proposed an it id based distributed algorithm for optimal coverage in an unbounded region the proposed algorithm guarantees maximum spreading in o \( sqrt n \) rounds without creating any coverage hole the algorithm executes in synchronous rounds without exchanging any message we have also explained how our proposed algorithm can achieve optimal energy consumption and handle random sensor node deployment for optimal spreading
cs.DS,"rna seq technology offers new high throughput ways for transcript identification and quantification based on short reads , and has recently attracted great interest the problem is usually modeled by a weighted splicing graph whose nodes stand for exons and whose edges stand for split alignments to the exons the task consists of finding a number of paths , together with their expression levels , which optimally explain the coverages of the graph under various fitness functions , such least sum of squares in \( tomescu et al recomb seq 2013 \) we showed that under general fitness functions , if we allow a polynomially bounded number of paths in an optimal solution , this problem can be solved in polynomial time by a reduction to a min cost flow program in this paper we further refine this problem by asking for a bounded number k of paths that optimally explain the splicing graph this problem becomes np hard in the strong sense , but we give a fast combinatorial algorithm based on dynamic programming for it in order to obtain a practical tool , we implement three optimizations and heuristics , which achieve better performance on real data , and similar or better performance on simulated data , than state of the art tools cufflinks , isolasso and slide our tool , called traph , is available at http www cs helsinki fi gsa traph"
cs.AI|cs.LG,"this report presents giraffe , a chess engine that uses self play to discover all its domain specific knowledge , with minimal hand crafted knowledge given by the programmer unlike previous attempts using machine learning only to perform parameter tuning on hand crafted evaluation functions , giraffe 's learning system also performs automatic feature extraction and pattern recognition the trained evaluation function performs comparably to the evaluation functions of state of the art chess engines all of which containing thousands of lines of carefully hand crafted pattern recognizers , tuned over many years by both computer chess experts and human chess masters giraffe is the most successful attempt thus far at using end to end machine learning to play chess"
stat.ML,"this paper considers the problem of detection in distributed networks in the presence of data falsification \( byzantine \) attacks detection approaches considered in the paper are based on fully distributed consensus algorithms , where all of the nodes exchange information only with their neighbors in the absence of a fusion center in such networks , we characterize the negative effect of byzantines on the steady state and transient detection performance of the conventional consensus based detection algorithms to address this issue , we study the problem from the network designer 's perspective more specifically , we first propose a distributed weighted average consensus algorithm that is robust to byzantine attacks we show that , under reasonable assumptions , the global test statistic for detection can be computed locally at each node using our proposed consensus algorithm we exploit the statistical distribution of the nodes' data to devise techniques for mitigating the influence of data falsifying byzantines on the distributed detection system since some parameters of the statistical distribution of the nodes' data might not be known a priori , we propose learning based techniques to enable an adaptive design of the local fusion or update rules"
cs.DS,"identical products being sold at different prices in different locations is a common phenomenon price differences might occur due to various reasons such as shipping costs , trade restrictions and price discrimination to model such scenarios , we supplement the classical fisher model of a market by introducing em transaction costs for every buyer i and every good j , there is a transaction cost of cij if the price of good j is p j , then the cost to the buyer i em per unit of j is p j cij this allows the same good to be sold at different \( effective \) prices to different buyers we provide a combinatorial algorithm that computes epsilon approximate equilibrium prices and allocations in o left \( frac 1 epsilon \( n log m \) mn log \( b epsilon \) right \) operations where m is the number goods , n is the number of buyers and b is the sum of the budgets of all the buyers"
cs.LG|stat.ML,"in this note , we introduce a new algorithm to deal with finite dimensional clustering with errors in variables the design of this algorithm is based on recent theoretical advances \( see loustau \( 2013a , b \) \) in statistical learning with errors in variables as the previous mentioned papers , the algorithm mixes different tools from the inverse problem literature and the machine learning community coarsely , it is based on a two step procedure \( 1 \) a deconvolution step to deal with noisy inputs and \( 2 \) newton 's iterations as the popular k means"
cs.SI,"social networks allow users to self present by sharing personal contents with others which may add comments recent studies highlighted how the emotions expressed in a post affect others' posts , eliciting a congruent emotion so far , no studies have yet investigated the emotional coherence between wall posts and its comments this research evaluated posts and comments mood of facebook profiles , analyzing their linguistic features , and a measure to assess an excessive self presentation was introduced two new experimental measures were built , describing the emotional loading \( positive and negative \) of posts and comments , and the mood correspondence between them was evaluated the profiles empathy , the mood coherence between post and comments , was used to investigate the relation between an excessive self presentation and the emotional coherence of a profile participants publish a higher average number of posts with positive mood to publish an emotional post corresponds to get more likes , comments and receive a coherent mood of comments , confirming the emotional contagion effect reported in literature finally , the more empathetic profiles are characterized by an excessive self presentation , having more posts , and receiving more comments and likes to publish emotional contents appears to be functional to receive more comments and likes , fulfilling needs of attention seeking"
cs.LG,"this paper aims to compare different regularization strategies to address a common phenomenon , severe overfitting , in embedding based neural networks for nlp we chose two widely studied neural models and tasks as our testbed we tried several frequently applied or newly proposed regularization strategies , including penalizing weights \( embeddings excluded \) , penalizing embeddings , re embedding words , and dropout we also emphasized on incremental hyperparameter tuning , and combining different regularizations the results provide a picture on tuning hyperparameters for neural nlp models"
cs.AI,"the xapagy cognitive architecture had been designed to perform narrative reasoning to model and mimic the activities performed by humans when witnessing , reading , recalling , narrating and talking about stories xapagy communicates with the outside world using xapi , a simplified , pidgin language which is strongly tied to the internal representation model \( instances , scenes and verb instances \) and reasoning techniques \( shadows and headless shadows \) while not fully a semantic equivalent of natural language , xapi can represent a wide range of complex stories we illustrate the representation technique used in xapi through examples taken from folk physics , folk psychology as well as some more unusual literary examples we argue that while the xapi model represents a conceptual shift from the english representation , the mapping is logical and consistent , and a trained knowledge engineer can translate between english and xapi at near native speed"
cs.LG|cs.SI,"influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized topic aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic dependent and information , ideas , innovations etc being propagated in networks \( referred collectively as items in this paper \) are typically mixtures of topics in this paper , we focus on the topic aware influence maximization task in particular , we study preprocessing methods for these topics to avoid redoing influence maximization for each item from scratch we explore two preprocessing algorithms with theoretical justifications our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread , with reasonable preprocessing effort"
cs.DM,"pattern avoidance is a central topic in graph theory and combinatorics pattern avoidance in matrices has applications in computer science and engineering , such as robot motion planning and vlsi circuit design a d dimensional zero one matrix a avoids another d dimensional zero one matrix p if no submatrix of a can be transformed to p by changing some ones to zeros a fundamental problem is to study the maximum number of nonzero entries in a d dimensional n times cdots times n matrix that avoids p this maximum number , denoted by f \( n , p , d \) , is called the extremal function we advance the extremal theory of matrices in two directions the methods that we use come from combinatorics , probability , and analysis firstly , we obtain non trivial lower and upper bounds on f \( n , p , d \) when n is large for every d dimensional block permutation matrix p we establish the tight bound theta \( n d 1 \) on f \( n , p , d \) for every d dimensional tuple permutation matrix p this tight bound has the lowest possible order that an extremal function of a nontrivial matrix can ever achieve secondly , we show that f \( n , p , d \) is super homogeneous for a class of matrices p we use this super homogeneity to show that the limit inferior of the sequence f \( n , p , d \) over n d 1 has a lower bound 2 omega \( k 1 d \) for a family of k times cdots times k permutation matrices p we also improve the upper bound on the limit superior from 2 o \( k log k \) to 2 o \( k \) for all k times cdots times k permutation matrices and show that the new upper bound also holds for tuple permutation matrices"
cs.DM,"many combinatorial problems can be formulated as can i transform configuration 1 into configuration 2 , if certain transformations only are allowed \? an example of such a question is given two k colourings of a graph , can i transform the first k colouring into the second one , by recolouring one vertex at a time , and always maintaining a proper k colouring \? another example is given two solutions of a sat instance , can i transform the first solution into the second one , by changing the truth value one variable at a time , and always maintaining a solution of the sat instance \? other examples can be found in many classical puzzles , such as the 15 puzzle and rubik 's cube in this survey we shall give an overview of some older and more recent work on this type of problem the emphasis will be on the computational complexity of the problems how hard is it to decide if a certain transformation is possible or not \?"
cs.AI,"we present the ciff proof procedure for abductive logic programming with constraints , and we prove its correctness ciff is an extension of the iff proof procedure for abductive logic programming , relaxing the original restrictions over variable quantification \( allowedness conditions \) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming finally , we describe the ciff system , comparing it with state of the art abductive systems and answer set solvers and showing how to use it to program some applications \( to appear in theory and practice of logic programming tplp \)"
cs.LG,"data representation is an important pre processing step in many machine learning algorithms there are a number of methods used for this task such as deep belief networks \( dbns \) and discrete fourier transforms \( dfts \) since some of the features extracted using automated feature extraction methods may not always be related to a specific machine learning task , in this paper we propose two methods in order to make a distinction between extracted features based on their relevancy to the task we applied these two methods to a deep belief network trained for a face recognition task"
cs.AI,"we introduce a new framework to evaluate and improve first order \( fo \) ontologies using automated theorem provers \( atps \) on the basis of competency questions \( cqs \) our framework includes both the adaptation of a methodology for evaluating ontologies to the framework of first order logic and a new set of non trivial cqs designed to evaluate fo versions of sumo , which significantly extends the very small set of cqs proposed in the literature most of these new cqs have been automatically generated from a small set of patterns and the mapping of wordnet to sumo applying our framework , we demonstrate that adimen sumo v2 2 outperforms tptp sumo in addition , using the feedback provided by atps we have set an improved version of adimen sumo \( v2 4 \) this new version outperforms the previous ones in terms of competency for instance , humans can reason is automatically inferred from adimen sumo v2 4 , while it is neither deducible from tptp sumo nor adimen sumo v2 2"
cs.DS,"using three coupled harmonic oscillators , we present an amplitude amplification method for factorization of an integer we generalize the method in arxiv 1007 4338 by employing non orthogonal measurements on the harmonic oscillator this method can increase the probability of obtaining the factors by repeatedly using the nonlinear interactions between the oscillators and non orthogonal measurements however , this approach requires an exponential amount of resources for implementation thus , this method cannot provide a speed up over classical algorithms unless its limitations are resolved"
cs.SI,"we present results of opinion dynamics simulations based on the emotion information opinion \( e i o \) model , applied to a strongly polarized society under certain conditions the model leads to metastable coexistence of two subcommunities \( supporting each of the opinions \) of comparable size which corresponds to bipartisan split found in many real world communities spurred by the recent breakdown of such system , which existed in poland for over 9 years , we extend the model by allowing a third opinion we show that if the propaganda messages of the two incumbent parties differ in emotional tone , the system may be invaded by a newcomer third party very quickly in qualitative agreement with the actual political situation in poland in 2015"
cs.LG,"recommender systems apply data mining techniques and prediction algorithms to predict users' interest on information , products and services among the tremendous amount of available items the vast growth of information on the internet as well as number of visitors to websites add some key challenges to recommender systems these are producing accurate recommendation , handling many recommendations efficiently and coping with the vast growth of number of participants in the system therefore , new recommender system technologies are needed that can quickly produce high quality recommendations even for huge data sets to address these issues we have explored several collaborative filtering techniques such as the item based approach , which identify relationship between items and indirectly compute recommendations for users based on these relationships the user based approach was also studied , it identifies relationships between users of similar tastes and computes recommendations based on these relationships in this paper , we introduce the topic of recommender system it provides ways to evaluate e \? ciency , scalability and accuracy of recommender system the paper also analyzes different algorithms of user based and item based techniques for recommendation generation moreover , a simple experiment was conducted using a data mining application weka to apply data mining algorithms to recommender system we conclude by proposing our approach that might enhance the quality of recommender systems"
cs.DS,"resource allocation for cloud services is a complex task due to the diversity of the services and the dynamic workloads one way to address this is by overprovisioning which results in high cost due to the unutilized resources a much more economical approach , relying on the stochastic nature of the demand , is to allocate just the right amount of resources and use additional more expensive mechanisms in case of overflow situations where demand exceeds the capacity in this paper we study this approach and show both by comprehensive analysis for independent normal distributed demands and simulation on synthetic data that it is significantly better than currently deployed methods"
cs.AI,"artificial immune systems \( aiss \) to date have generally been inspired by naive biological metaphors this has limited the effectiveness of these systems in this position paper two ways in which aiss could be made more biologically realistic are discussed we propose that aiss should draw their inspiration from organisms which possess only innate immune systems , and that aiss should employ systemic models of the immune system to structure their overall design an outline of plant and invertebrate immune systems is presented , and a number of contemporary research that more biologically realistic aiss could have is also discussed"
cs.LG|stat.ML,"asynchronous distributed stochastic gradient descent methods have trouble converging because of stale gradients a gradient update sent to a parameter server by a client is stale if the parameters used to calculate that gradient have since been updated on the server approaches have been proposed to circumvent this problem that quantify staleness in terms of the number of elapsed updates in this work , we propose a novel method that quantifies staleness in terms of moving averages of gradient statistics we show that this method outperforms previous methods with respect to convergence speed and scalability to many clients we also discuss how an extension to this method can be used to dramatically reduce bandwidth costs in a distributed training context in particular , our method allows reduction of total bandwidth usage by a factor of 5 with little impact on cost convergence we also describe \( and link to \) a software library that we have used to simulate these algorithms deterministically on a single machine"
cs.IT|cs.LG,"in this paper we consider general l0 norm minimization problems , that is , the problems with l0 norm appearing in either objective function or constraint in particular , we first reformulate the l0 norm constrained problem as an equivalent rank minimization problem and then apply the penalty decomposition \( pd \) method proposed in 33 to solve the latter problem by utilizing the special structures , we then transform all matrix operations of this method to vector operations and obtain a pd method that only involves vector operations under some suitable assumptions , we establish that any accumulation point of the sequence generated by the pd method satisfies a first order optimality condition that is generally stronger than one natural optimality condition we further extend the pd method to solve the problem with the l0 norm appearing in objective function finally , we test the performance of our pd methods by applying them to compressed sensing , sparse logistic regression and sparse inverse covariance selection the computational results demonstrate that our methods generally outperform the existing methods in terms of solution quality and or speed"
cs.IT|cs.SI,"this paper introduces the novel concept of proactive resource allocation in which the predictability of user behavior is exploited to balance the wireless traffic over time , and hence , significantly reduce the bandwidth required to achieve a given blocking outage probability we start with a simple model in which the smart wireless devices are assumed to predict the arrival of new requests and submit them to the network t time slots in advance using tools from large deviation theory , we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases linearly with the prediction duration t this model is then generalized to incorporate the effect of prediction errors and the randomness in the prediction lookahead time t remarkably , we also show that , in the cognitive networking scenario , the appropriate use of proactive resource allocation by the primary users results in more spectral opportunities for the secondary users at a marginal , or no , cost in the primary network outage finally , we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive \( non causal \) wireless networking framework"
cs.SI,"the mechanism of the online user preference evolution is of great significance for understanding the online user behaviors and improving the quality of online services since users are allowed to rate on objects in many online systems , ratings can well reflect the users' preference with two benchmark datasets from online systems , we uncover the memory effect in users' selecting behavior which is the sequence of qualities of selected objects and the rating behavior which is the sequence of ratings delivered by each user furthermore , the memory duration is presented to describe the length of a memory , which exhibits the power law distribution , i e , the probability of the occurring of long duration memory is much higher than that of the random case which follows the exponential distribution we present a preference model in which a markovian process is utilized to describe the users' selecting behavior , and the rating behavior depends on the selecting behavior with only one parameter for each of the user 's selecting and rating behavior , the preference model could regenerate any duration distribution ranging from the power law form \( strong memory \) to the exponential form \( weak memory \)"
cs.DM,"looking at incidence matrices of t \( v , k , lambda \) designs as v times b matrices with 2 possible entries , each of which indicates incidences of a t design , we introduce the notion of a c mosaic of designs , having the same number of points and blocks , as a matrix with c different entries , such that each entry defines incidences of a design in fact , a v times b matrix is decomposed in c incidence matrices of designs , each denoted by a different colour , hence this decomposition might be seen as a tiling of a matrix with incidence matrices of designs as well these mosaics have applications in experiment design when considering a simultaneous run of several different experiments we have constructed infinite series of examples of mosaics and state some probably non trivial open problems"
cs.LG,"an educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics this helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades as a solution , we have developed a system which can predict the performance of students from their previous performances using concepts of data mining techniques under classification we have analyzed the data set containing information about students , such as gender , marks scored in the board examinations of classes x and xii , marks and rank in entrance examinations and results in first year of the previous batch of students by applying the id3 \( iterative dichotomiser 3 \) and c4 5 classification algorithms on this data , we have predicted the general and individual performance of freshly admitted students in future examinations"
cs.LG|stat.ML,"nonparametric estimation of the conditional distribution of a response given high dimensional features is a challenging problem it is important to allow not only the mean but also the variance and shape of the response density to change flexibly with features , which are massive dimensional we propose a multiscale dictionary learning model , which expresses the conditional response density as a convex combination of dictionary densities , with the densities used and their weights dependent on the path through a tree decomposition of the feature space a fast graph partitioning algorithm is applied to obtain the tree decomposition , with bayesian methods then used to adaptively prune and average over different sub trees in a soft probabilistic manner the algorithm scales efficiently to approximately one million features state of the art predictive performance is demonstrated for toy examples and two neuroscience applications including up to a million features"
cs.AI,"this paper presents experiments on common knowledge logic , conducted with the help of the proof assistant coq the main feature of common knowledge logic is the eponymous modality that says that a group of agents shares a knowledge about a certain proposition in a inductive way this modality is specified by using a fixpoint approach furthermore , from these experiments , we discuss and compare the structure of theorems that can be proved in specific theories that use common knowledge logic those structures manifests the interplay between the theory \( as implemented in the proof assistant coq \) and the metatheory"
cs.AI,"the sonar images provide a rapid view of the seabed in order to characterize it however , in such as uncertain environment , real seabed is unknown and the only information we can obtain , is the interpretation of different human experts , sometimes in conflict in this paper , we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms the classification is conducted by a multilayer perceptron , taking into account the uncertainty of the reality in the learning stage the results of this seabed characterization are presented on real sonar images"
cs.AI|cs.LG,"in this paper , we extend the deep long short term memory \( dlstm \) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers these direct links , called highway connections , enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper lstms we further introduce the latency controlled bidirectional lstms \( blstms \) which can exploit the whole history while keeping the latency under control efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria experiments on the ami distant speech recognition \( dsr \) task indicate that we can train deeper lstms and achieve better improvement from sequence training with highway lstms \( hlstms \) our novel model obtains 43 9 47 7 wer on ami \( sdm \) dev and eval sets , outperforming all previous works it beats the strong dnn and dlstm baselines with 15 7 and 5 3 relative improvement respectively"
cs.AI|cs.LG,"in this paper we propose a non metric ranking based representation of semantic similarity that allows natural aggregation of semantic information from multiple heterogeneous sources we apply the ranking based representation to zero shot learning problems , and present deterministic and probabilistic zero shot classifiers which can be built from pre trained classifiers without retraining we demonstrate their the advantages on two large real world image datasets in particular , we show that aggregating different sources of semantic information , including crowd sourcing , leads to more accurate classification"
cs.AI|cs.LG,"we discuss a variant of thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments these environments can be non markov , non ergodic , and partially observable we show that thompson sampling learns the environment class in the sense that \( 1 \) asymptotically its value converges to the optimal value in mean and \( 2 \) given a recoverability assumption regret is sublinear"
cs.AI,"understanding how users tailor their sparql queries is crucial when designing query evaluation engines or fine tuning rdf stores with performance in mind in this paper we analyze 3 million real world sparql queries extracted from logs of the dbpedia and swdf public endpoints we aim at finding which are the most used language elements both from syntactical and structural perspectives , paying special attention to triple patterns and joins , since they are indeed some of the most expensive sparql operations at evaluation phase we have determined that most of the queries are simple and include few triple patterns and joins , being subject subject , subject object and object object the most common join types the graph patterns are usually star shaped and despite triple pattern chains exist , they are generally short"
cs.LG,"we study a class of neural nets gibbs machines which are a type of variational auto encoders , designed for gradual learning they offer an universal platform for incrementally adding newly learned features , including physical symmetries in space time combining them with classifiers gives rise to a brand of universal generative neural nets stochastic auto classifier encoders \( ace \) ace preserve the non gaussian and clustering nature of real life data and have state of the art performance , both for classification and density estimation for the mnist data set"
cs.LG|stat.ML,"in this note , we present a version of the thompson sampling algorithm for the problem of online linear generalization with full information \( i e , the experts setting \) , studied by kalai and vempala , 2005 the algorithm uses a gaussian prior and time varying gaussian likelihoods , and we show that it essentially reduces to kalai and vempala 's follow the perturbed leader strategy , with exponentially distributed noise replaced by gaussian noise this implies sqrt \( t \) regret bounds for thompson sampling \( with time varying likelihood \) for online learning with full information"
cs.DS,in 2009 grzegorz czajkowski from google 's system infrastructure team has published an article which did n't get much attention in the seo community at the time it was titled large scale graph computing at google and gave an excellent insight into the future of google 's search this article highlights some of the little known facts which lead to transformation of google 's algorithm in the last two years
cs.DM,"we introduce and study a complexity function on words c omega \( n \) , we call emph cyclic complexity , which counts the number of conjugacy classes of factors of length n of an infinite word omega we extend the well known morse hedlund theorem to the setting of cyclic complexity by showing that a word is ultimately periodic if and only if it has bounded cyclic complexity unlike most complexity functions , cyclic complexity distinguishes between sturmian words of different slopes we prove that if x is a sturmian word and y is a word having the same cyclic complexity of x , then up to renaming letters , x and y have the same set of factors in particular , y is also sturmian of slope equal to that of x since c omega \( n \) 1 for some n geq 1 implies omega is periodic , it is natural the consider the liminf n rightarrow infty c omega \( n \) we show that if omega is a sturmian word , then liminf n rightarrow infty c omega \( n \) 2 we prove however that this is not a characterization of sturmian words by exhibiting a restricted class of toeplitz words , including the period doubling word , which also verify this same condition on the limit infimum in contrast we show that for the thue morse word the liminf n rightarrow infty c omega \( n \) infty"
cs.LG,"in this paper , we propose to employ the convolutional neural network \( cnn \) for learning to answer questions from the image our proposed cnn provides an end to end framework for learning not only the image representation , the composition model for question , but also the inter modal interaction between the image and question , for the generation of answer more specifically , the proposed model consists of three components an image cnn to extract the image representation , one sentence cnn to encode the question , and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words we demonstrate the efficacy of our proposed model on daquar and coco qa datasets , two datasets recently created for the image question answering \( qa \) , with performance substantially outperforming the state of the arts"
cs.LG,"we present datagrad , a general back propagation style training procedure for deep neural architectures that uses regularization of a deep jacobian based penalty it can be viewed as a deep extension of the layerwise contractive auto encoder penalty more importantly , it unifies previous proposals for adversarial training of deep neural nets this list includes directly modifying the gradient , training on a mix of original and adversarial examples , using contractive penalties , and approximately optimizing constrained adversarial objective functions in an experiment using a deep sparse rectifier network , we find that the deep jacobian regularization of datagrad \( which also has l1 and l2 flavors of regularization \) outperforms traditional l1 and l2 regularization both on the original dataset as well as on adversarial examples"
cs.IT|stat.ML,"we study the localization of a cluster of activated vertices in a graph , from adaptively designed compressive measurements we propose a hierarchical partitioning of the graph that groups the activated vertices into few partitions , so that a top down sensing procedure can identify these partitions , and hence the activations , using few measurements by exploiting the cluster structure , we are able to provide localization guarantees at weaker signal to noise ratios than in the unstructured setting we complement this performance guarantee with an information theoretic lower bound , providing a necessary signal to noise ratio for any algorithm to successfully localize the cluster we verify our analysis with some simulations , demonstrating the practicality of our algorithm"
cs.LG|stat.ML,"the random cluster model is used to define an upper bound on a distance measure as a function of the number of data points to be classified and the expected value of the number of classes to form in a hybrid k means and regression classification methodology , with the intent of detecting anomalies conditions are given for the identification of classes which contain anomalies and individual anomalies within identified classes a neural network model describes the decision region separating surface for offline storage and recall in any new anomaly detection"
cs.DM,"in this work we improve on a result from cite grykoszma15 in particular , we investigate the situation where a word is constructed jointly by two players who alternately append letters to the end of an existing word one of the players \( ann \) tries to avoid \( non trivial \) repetitions , while the other one \( ben \) tries to enforce them we show a construction that is closer to the lower bound showed in cite grykozmic13 using entropy compression , and building on the probabilistic arguments based on a version of the lov 'asz local lemma from cite peg11 we provide an explicit strategy for ann to avoid \( non trivial \) repetitions over a 7 letter alphabet"
cs.LG|stat.ML,"this paper describes a pattern recognition approach aiming to estimate fuel cell duration time from electrochemical impedance spectroscopy measurements it consists in first extracting features from both real and imaginary parts of the impedance spectrum a parametric model is considered in the case of the real part , whereas regression model with latent variables is used in the latter case then , a linear regression model using different subsets of extracted features is used fo r the estimation of fuel cell time duration the performances of the proposed approach are evaluated on experimental data set to show its feasibility this could lead to interesting perspectives for predictive maintenance policy of fuel cell"
cs.SI,"we study the dynamical properties of human communication through different channels , i e , short messages , phone calls , and emails , adopting techniques from neuronal spike train analysis in order to characterize the temporal fluctuations of successive inter event times we first measure the so called local variation \( lv \) of incoming and outgoing event sequences of users , and find that these in and out lv values are positively correlated for short messages , and uncorrelated for phone calls and emails second , we analyze the response time distribution after receiving a message to focus on the input output relationship in each of these channels we find that the time scales and amplitudes of response are different between the three channels to understand the impacts of the response time distribution on the correlations between the lv values , we develop a point process model whose activity rate is modulated by incoming and outgoing events numerical simulations of the model indicate that a quick response to incoming events and a refractory effect after outgoing events are key factors to reproduce the positive lv correlations finally , we also find that the lv value is mostly uncorrelated with conventional centrality measures of nodes in the aggregate network , suggesting that this type of analysis reveals a new dimension of social networks , associated to their temporal properties"
cs.SI,"recent work has shown that different theoretical approaches to the dynamics of the susceptible infected susceptible \( sis \) model for epidemics lead to qualitatively different estimates for the position of the epidemic threshold in networks here we present large scale numerical simulations of the sis dynamics on various types of networks , allowing the precise determination of the effective threshold for systems of finite size n we compare quantitatively the numerical thresholds with theoretical predictions of the heterogeneous mean field theory and of the quenched mean field theory we show that the latter is in general more accurate , scaling with n with the correct exponent , but often failing to capture the correct prefactor"
cs.LG,"recommendation systems are an important units in today 's e commerce applications , such as targeted advertising , personalized marketing and information retrieval in recent years , the importance of contextual information has motivated generation of personalized recommendations according to the available contextual information of users compared to the traditional systems which mainly utilize users' rating history , review based recommendation hopefully provide more relevant results to users we introduce a review based recommendation approach that obtains contextual information by mining user reviews the proposed approach relate to features obtained by analyzing textual reviews using methods developed in natural language processing \( nlp \) and information retrieval discipline to compute a utility function over a given item an item utility is a measure that shows how much it is preferred according to user 's current context in our system , the context inference is modeled as similarity between the users reviews history and the item reviews history as an example application , we used our method to mine contextual data from customers' reviews of movies and use it to produce review based rating prediction the predicted ratings can generate recommendations that are item based and should appear at the recommended items list in the product page our evaluations \( surprisingly \) suggest that our system can help produce better prediction rating scores in comparison to the standard prediction methods"
cs.DM,"we present quantitative analysis of various \( syntactic and behavioral \) properties of random lambda terms our main results are that asymptotically all the terms are strongly normalizing and that any fixed closed term almost never appears in a random term surprisingly , in combinatory logic \( the translation of the lambda calculus into combinators \) , the result is exactly opposite we show that almost all terms are not strongly normalizing this is due to the fact that any fixed combinator almost always appears in a random combinator"
cs.SI,"we introduce a new matrix function for studying graphs and real world networks based on a double factorial penalization of walks between nodes in a graph this new matrix function is based on the matrix error function we find a very good approximation of this function using a matrix hyperbolic tangent function we derive a communicability function , a subgraph centrality and a double factorial estrada index based on this new matrix function we obtain upper and lower bounds for the double factorial estrada index of graphs , showing that they are similar to those of the single factorial estrada index we then compare these indices with the single factorial one for simple graphs and real world networks in particular , we study the problem of identification of essential proteins in yeast we conclude that for this practical problem the structural information contained in the longer walks on graphs is not as relevant as the one contained in the shorter ones for characterizing network structure more practical applications of the new indices are needed to extract more definitive conclusions on their use for analyzing complex networks"
cs.AI|cs.LG,"there exists a theory of a single general purpose learning al gorithm which could explain the principles of its operation this theory assumes that the brain has some initial rough architecture , a small li brary of simple innate circuits which are prewired at birth and proposes that all significant mental algorithms can be learned given current un derstanding and observations , this paper reviews and lists the ingredients of such an algorithm from both architectural and functional perspectives"
cs.DS,"the workflow satisfiability problem is concerned with determining whether it is possible to find an allocation of authorized users to the steps in a workflow in such a way that all constraints are satisfied the problem is np hard in general , but is known to be fixed parameter tractable for certain classes of constraints the known results on fixed parameter tractability rely on the symmetry \( in some sense \) of the constraints in this paper , we provide the first results that establish fixed parameter tractability of the satisfiability problem when the constraints are asymmetric in particular , we introduce the notion of seniority constraints , in which the execution of steps is determined , in part , by the relative seniority of the users that perform them our results require new techniques , which make use of tree decompositions of the graph of the binary relation defining the constraint finally , we establish a lower bound for the hardness of the workflow satisfiability problem"
cs.AI,"this article introduces the idea that information compression by multiple alignment , unification and search \( icmaus \) provides a framework within which natural language syntax may be represented in a simple format and the parsing and production of natural language may be performed in a transparent manner the icmaus concepts are embodied in a software model , sp61 the organisation and operation of the model are described and a simple example is presented showing how the model can achieve parsing of natural language notwithstanding the apparent paradox of 'decompression by compression' , the icmaus framework , without any modification , can produce a sentence by decoding a compressed code for the sentence this is illustrated with output from the sp61 model the article includes four other examples one of the parsing of a sentence in french and three from the domain of english auxiliary verbs these examples show how the icmaus framework and the sp61 model can accommodate 'context sensitive' features of syntax in a relatively simple and direct manner"
cs.AI,"in this article we study a problem within dempster shafer theory where 2 n 1 pieces of evidence are clustered by a neural structure into n clusters the clustering is done by minimizing a metaconflict function previously we developed a method based on iterative optimization however , for large scale problems we need a method with lower computational complexity the neural structure was found to be effective and much faster than iterative optimization for larger problems while the growth in metaconflict was faster for the neural structure compared with iterative optimization in medium sized problems , the metaconflict per cluster and evidence was moderate the neural structure was able to find a global minimum over ten runs for problem sizes up to six clusters"
cs.DM,automata operating on general graphs have been introduced by virtue of graphoids in this paper we construct a graph automaton that recognizes k colorable graphs
cs.LG|stat.ML,"we study the problem of recovering the subspace spanned by the first k principal components of d dimensional data under the streaming setting , with a memory bound of mathcal o \( kd \) two families of algorithms are known for this problem the first family is based on stochastic gradient descent , but no convergence proof of its existing algorithms was previously known when k 1 the second family is based on the power method over blocks of data , but setting the block size for its existing algorithms is not an easy task in this paper , we analyze the convergence rate of a representative algorithm in the first family cite oja for the general k 1 case moreover , we propose a novel algorithm for the second family that sets the block sizes automatically and dynamically with faster convergence rate we then conduct empirical studies that fairly compare the two families on real world data the studies reveal the advantages and disadvantages of these two families"
cs.DM,"the core of a game v on n , which is the set of additive games phi dominating v such that phi \( n \) v \( n \) , is a central notion in cooperative game theory , decision making and in combinatorics , where it is related to submodular functions , matroids and the greedy algorithm in many cases however , the core is empty , and alternative solutions have to be found we define the k additive core by replacing additive games by k additive games in the definition of the core , where k additive games are those games whose m obius transform vanishes for subsets of more than k elements for a sufficiently high value of k , the k additive core is nonempty , and is a convex closed polyhedron our aim is to establish results similar to the classical results of shapley and ichiishi on the core of convex games \( corresponds to edmonds' theorem for the greedy algorithm \) , which characterize the vertices of the core"
cs.AI,"the intention of the present study is to establish the mathematical fundamentals for automated problem solving essentially targeted for robotics by approaching the task universal algebraically introducing knowledge as realizations of generalized free algebra based nets , graphs with gluing forms connecting in and out edges to nodes nets are caused to undergo transformations in conceptual level by type wise differentiated intervening net rewriting systems dispersing problems to abstract parts , matching being determined by substitution relations achieved sets of conceptual nets constitute congruent classes new results are obtained within construction of problem solving systems where solution algorithms are derived parallel with other candidates applied to the same net classes by applying parallel transducer paths consisting of net rewriting systems to net classes congruent quotient algebras are established and the manifested class rewriting comprises all solution candidates whenever produced nets are in anticipated languages liable to acceptance of net automata furthermore new solutions will be added to the set of already known ones thus expanding the solving power in the forthcoming moreover special attention is set on universal abstraction , thereof generation by net block homomorphism , consequently multiple order solving systems and the overall decidability of the set of the solutions by overlapping presentation of nets new abstraction relation among nets is formulated alongside with consequent alphabetical net block renetting system proportional to normal forms of renetting systems regarding the operational power a new structure in self evolving problem solving is established via saturation by groups of equivalence relations and iterative closures of generated quotient transducer algebras over the whole evolution"
cs.SI|stat.ML,"community detection is a fundamental task in social network analysis in this paper , first we develop an endorsement filtered user connectivity network by utilizing heider 's structural balance theory and certain twitter triad patterns next , we develop three nonnegative matrix factorization frameworks to investigate the contributions of different types of user connectivity and content information in community detection we show that user content and endorsement filtered connectivity information are complementary to each other in clustering politically motivated users into pure political communities word usage is the strongest indicator of users' political orientation among all content categories incorporating user word matrix and word similarity regularizer provides the missing link in connectivity only methods which suffer from detection of artificially large number of clusters for twitter networks"
cs.LG|stat.ML,"recurrent neural networks \( rnns \) are rich models for the processing of sequential data recent work on advancing the state of the art has been focused on the optimization or modelling of rnns , mostly motivated by adressing the problems of the vanishing and exploding gradients the control of overfitting has seen considerably less attention this paper contributes to that by analyzing fast dropout , a recent regularization method for generalized linear models and neural networks from a back propagation inspired perspective we show that fast dropout implements a quadratic form of an adaptive , per parameter regularizer , which rewards large weights in the light of underfitting , penalizes them for overconfident predictions and vanishes at minima of an unregularized training loss the derivatives of that regularizer are exclusively based on the training error signal one consequence of this is the absense of a global weight attractor , which is particularly appealing for rnns , since the dynamics are not biased towards a certain regime we positively test the hypothesis that this improves the performance of rnns on four musical data sets"
cs.LG,"we consider synthesis of control policies that maximize the probability of satisfying given temporal logic specifications in unknown , stochastic environments we model the interaction between the system and its environment as a markov decision process \( mdp \) with initially unknown transition probabilities the solution we develop builds on the so called model based probably approximately correct markov decision process \( pac mdp \) methodology the algorithm attains an varepsilon approximately optimal policy with probability 1 delta using samples \( i e observations \) , time and space that grow polynomially with the size of the mdp , the size of the automaton expressing the temporal logic specification , frac 1 varepsilon , frac 1 delta and a finite time horizon in this approach , the system maintains a model of the initially unknown mdp , and constructs a product mdp based on its learned model and the specification automaton that expresses the temporal logic constraints during execution , the policy is iteratively updated using observation of the transitions taken by the system the iteration terminates in finitely many steps with high probability , the resulting policy is such that , for any state , the difference between the probability of satisfying the specification under this policy and the optimal one is within a predefined bound"
cs.SI,"many real world complex systems are best modeled by multiplex networks of interacting network layers the multiplex network study is one of the newest and hottest themes in the statistical physics of complex networks pioneering studies have proven that the multiplexity has broad impact on the system 's structure and function in this colloquium paper , we present an organized review of the growing body of current literature on multiplex networks by categorizing existing studies broadly according to the type of layer coupling in the problem major recent advances in the \? field are surveyed and some outstanding open challenges and future perspectives will be proposed"
cs.LG|stat.ML,"we present a new analysis of the problem of learning with drifting distributions in the batch setting using the notion of discrepancy we prove learning bounds based on the rademacher complexity of the hypothesis set and the discrepancy of distributions both for a drifting pac scenario and a tracking scenario our bounds are always tighter and in some cases substantially improve upon previous ones based on the l 1 distance we also present a generalization of the standard on line to batch conversion to the drifting scenario in terms of the discrepancy and arbitrary convex combinations of hypotheses we introduce a new algorithm exploiting these learning guarantees , which we show can be formulated as a simple qp finally , we report the results of preliminary experiments demonstrating the benefits of this algorithm"
cs.AI,"the public sector comprises government agencies , ministries , education institutions , health providers and other types of government , commercial and not for profit organisations unlike commercial enterprises , this environment is highly heterogeneous in all aspects this forms a complex network which is not always optimised a lack of optimisation and communication hinders information sharing between the network nodes limiting the flow of information another limiting aspect is privacy of personal information and security of operations of some nodes or segments of the network attempts to reorganise the network or improve communications to make more information available for sharing and analysis may be hindered or completely halted by public concerns over privacy , political agendas , social and technological barriers this paper discusses a technical solution for information sharing while addressing the privacy concerns with no need for reorganisation of the existing public sector infrastructure the solution is based on imposing an additional layer of intelligent software agents and knowledge bases for data mining and analysis"
cs.AI,"this paper demonstrates the use of neural networks for developing a system that can recognize hand written english alphabets in this system , each english alphabet is represented by binary values that are used as input to a simple feature extraction system , whose output is fed to our neural network system"
cs.LG|stat.ML,"we consider the partial observability model for multi armed bandits , introduced by mannor and shamir our main result is a characterization of regret in the directed observability model in terms of the dominating and independence numbers of the observability graph we also show that in the undirected case , the learner can achieve optimal regret without even accessing the observability graph before selecting an action both results are shown using variants of the exp3 algorithm operating on the observability graph in a time efficient manner"
cs.DS,"in this paper we construct quantum algorithms for matrix products over several algebraic structures called semirings , including the \( max , min \) matrix product , the distance matrix product and the boolean matrix product in particular , we obtain the following results we construct a quantum algorithm computing the product of two n x n matrices over the \( max , min \) semiring with time complexity o \( n 2 473 \) in comparison , the best known classical algorithm for the same problem , by duan and pettie , has complexity o \( n 2 687 \) as an application , we obtain a o \( n 2 473 \) time quantum algorithm for computing the all pairs bottleneck paths of a graph with n vertices , while classically the best upper bound for this task is o \( n 2 687 \) , again by duan and pettie we construct a quantum algorithm computing the l most significant bits of each entry of the distance product of two n x n matrices in time o \( 2 0 64l n 2 46 \) in comparison , prior to the present work , the best known classical algorithm for the same problem , by vassilevska and williams and yuster , had complexity o \( 2 l n 2 69 \) our techniques lead to further improvements for classical algorithms as well , reducing the classical complexity to o \( 2 0 96l n 2 69 \) , which gives a sublinear dependency on 2 l the above two algorithms are the first quantum algorithms that perform better than the tilde o \( n 5 2 \) time straightforward quantum algorithm based on quantum search for matrix multiplication over these semirings we also consider the boolean semiring , and construct a quantum algorithm computing the product of two n x n boolean matrices that outperforms the best known classical algorithms for sparse matrices for instance , if the input matrices have o \( n 1 686 \) non zero entries , then our algorithm has time complexity o \( n 2 277 \) , while the best classical algorithm has complexity o \( n 2 373 \)"
cs.IT|cs.SI,"in previous work , i have developed an information theoretic complexity measure of networks when applied to several real world food webs , there is a distinct difference in complexity between the real food web , and randomised control networks obtained by shuffling the network links one hypothesis is that this complexity surplus represents information captured by the evolutionary process that generated the network in this paper , i test this idea by applying the same complexity measure to several well known artificial life models that exhibit ecological networks tierra , ecolab and webworld contrary to what was found in real networks , the artificial life generated foodwebs had little information difference between itself and randomly shuffled versions"
cs.SI,"an increasing amount of geo referenced mobile phone data enables the identification of behavioral patterns , habits and movements of people with this data , we can extract the knowledge potentially useful for many applications including the one tackled in this study understanding spatial variation of epidemics we explored the datasets collected by a cell phone service provider and linked them to spatial hiv prevalence rates estimated from publicly available surveys for that purpose , 224 features were extracted from mobility and connectivity traces and related to the level of hiv epidemic in 50 ivory coast departments by means of regression models , we evaluated predictive ability of extracted features several models predicted hiv prevalence that are highly correlated \( 0 7 \) with actual values through contribution analysis we identified key elements that impact the rate of infections our findings indicate that night connectivity and activity , spatial area covered by users and overall migrations are strongly linked to hiv by visualizing the communication and mobility flows , we strived to explain the spatial structure of epidemics we discovered that strong ties and hubs in communication and mobility align with hiv hot spots"
cs.DS|cs.SI,"in the context of designing a scalable overlay network to support decentralized topic based pub sub communication , the minimum topic connected overlay problem \( min tco in short \) has been investigated given a set of t topics and a collection of n users together with the lists of topics they are interested in , the aim is to connect these users to a network by a minimum number of edges such that every graph induced by users interested in a common topic is connected it is known that min tco is np hard and approximable within o \( log t \) in polynomial time in this paper , we further investigate the problem and some of its special instances we give various hardness results for instances where the number of topics in which an user is interested in is bounded by a constant , and also for the instances where the number of users interested in a common topic is constant for the latter case , we present a \? rst constant approximation algorithm we also present some polynomial time algorithms for very restricted instances of min tco"
cs.AI,"recent work introduced generalized first order decision diagrams \( gfodd \) as a knowledge representation that is useful in mechanizing decision theoretic planning in relational domains gfodds generalize function free first order logic and include numerical values and numerical generalizations of existential and universal quantification previous work presented heuristic inference algorithms for gfodds and implemented these heuristics in systems for decision theoretic planning in this paper , we study the complexity of the computational problems addressed by such implementations in particular , we study the evaluation problem , the satisfiability problem , and the equivalence problem for gfodds under the assumption that the size of the intended model is given with the problem , a restriction that guarantees decidability our results provide a complete characterization placing these problems within the polynomial hierarchy the same characterization applies to the corresponding restriction of problems in first order logic , giving an interesting new avenue for efficient inference when the number of objects is bounded our results show that for sigma k formulas , and for corresponding gfodds , evaluation and satisfiability are sigma k p complete , and equivalence is pi k 1 p complete for pi k formulas evaluation is pi k p complete , satisfiability is one level higher and is sigma k 1 p complete , and equivalence is pi k 1 p complete"
cs.LG|stat.ML,"we propose a new class of convex penalty functions , called variational gram functions \( vgfs \) , that can promote pairwise relations , such as orthogonality , among a set of vectors in a vector space when used as regularizers in convex optimization problems , these functions find application in hierarchical classification , multitask learning , and estimation of vectors with disjoint supports , among other applications we describe a general condition for convexity , which is then used to prove the convexity of a few known functions as well as some new ones we give a characterization of the associated subdifferential and the proximal operator , and discuss efficient optimization algorithms for some structured regularized loss minimization problems using vgfs numerical experiments on a hierarchical classification problem are also presented that demonstrate the effectiveness of vgfs and the associated optimization algorithms in practice"
cs.DM,"we present a new polynomially solvable case of the quadratic assignment problem in koopmans beckman form qap \( a , b \) , by showing that the identity permutation is optimal when a and b are respectively a robinson similarity and dissimilarity matrix and one of a or b is a toeplitz matrix a robinson \( dis \) similarity matrix is a symmetric matrix whose entries \( increase \) decrease monotonically along rows and columns when moving away from the diagonal , and such matrices arise in the classical seriation problem"
cs.DS,"distance queries are a basic tool in data analysis they are used for detection and localization of change for the purpose of anomaly detection , monitoring , or planning distance queries are particularly useful when data sets such as measurements , snapshots of a system , content , traffic matrices , and activity logs are collected repeatedly random sampling , which can be efficiently performed over streamed or distributed data , is an important tool for scalable data analysis the sample constitutes an extremely flexible summary , which naturally supports domain queries and scalable estimation of statistics , which can be specified after the sample is generated the effectiveness of a sample as a summary , however , hinges on the estimators we have we derive novel estimators for estimating l p distance from sampled data our estimators apply with the most common weighted sampling schemes poisson probability proportional to size \( pps \) and its fixed sample size variants they also apply when the samples of different data sets are independent or coordinated our estimators are admissible \( pareto optimal in terms of variance \) and have compelling properties we study the performance of our manhattan and euclidean distance \( p 1 , 2 \) estimators on diverse datasets , demonstrating scalability and accuracy even when a small fraction of the data is sampled our work , for the first time , facilitates effective distance estimation over sampled data"
cs.AI,"we describe cfw , a computationally efficient algorithm for collaborative filtering that uses posteriors over weights of evidence in experiments on real data , we show that this method predicts as well or better than other methods in situations where the size of the user query is small the new approach works particularly well when the user s query contains low frequency \( unpopular \) items the approach complements that of dependency networks which perform well when the size of the query is large also in this paper , we argue that the use of posteriors over weights of evidence is a natural way to recommend similar items collaborative filtering task"
cs.DS,"this paper connects multi agent path planning on graphs \( roadmaps \) to network flow problems , showing that the former can be reduced to the latter , therefore enabling the application of combinatorial network flow algorithms , as well as general linear program techniques , to multi agent path planning problems on graphs exploiting this connection , we show that when the goals are permutation invariant , the problem always has a feasible solution path set with a longest finish time of no more than n v 1 steps , in which n is the number of agents and v is the number of vertices of the underlying graph we then give a complete algorithm that finds such a solution in o \( nve \) time , with e being the number of edges of the graph taking a further step , we study time and distance optimality of the feasible solutions , show that they have a pairwise pareto optimal structure , and again provide efficient algorithms for optimizing two of these practical objectives"
cs.AI,"understanding user instructions in natural language is an active research topic in ai and robotics typically , natural user instructions are high level and can be reduced into low level tasks expressed in common verbs \( e g , `take' , `get' , `put' \) for robots understanding such instructions , one of the key challenges is to process high level user instructions and achieve the specified tasks with robots' primitive actions to address this , we propose novel algorithms by utilizing semantic roles of common verbs defined in semantic dictionaries and integrating multiple open knowledge to generate task plans specifically , we present a new method for matching and recovering semantics of user instructions and a novel task planner that exploits functional knowledge of robot 's action model to verify and evaluate our approach , we implemented a prototype system using knowledge from several open resources experiments on our system confirmed the correctness and efficiency of our algorithms notably , our system has been deployed in the kejia robot , which participated the annual robocup home competitions in the past three years and achieved encouragingly high scores in the benchmark tests"
cs.LG|stat.ML,"in statistical learning theory , convex surrogates of the 0 1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in this is of more importance if we consider smooth surrogates as witnessed by the fact that the smoothness is further beneficial both computationally by attaining an it optimal convergence rate for optimization , and in a statistical sense by providing an improved it optimistic rate for generalization bound in this paper we investigate the smoothness property from the viewpoint of statistical consistency and show how it affects the binary excess risk we show that in contrast to optimization and generalization errors that favor the choice of smooth surrogate loss , the smoothness of loss function may degrade the binary excess risk motivated by this negative result , we provide a unified analysis that integrates optimization error , generalization bound , and the error in translating convex excess risk into a binary excess risk when examining the impact of smoothness on the binary excess risk we show that under favorable conditions appropriate choice of smooth convex loss will result in a binary excess risk that is better than o \( 1 sqrt n \)"
cs.LG|stat.ML,"when eliciting judgements from humans for an unknown quantity , one often has the choice of making direct scoring \( cardinal \) or comparative \( ordinal \) measurements in this paper we study the relative merits of either choice , providing empirical and theoretical guidelines for the selection of a measurement scheme we provide empirical evidence based on experiments on amazon mechanical turk that in a variety of tasks , \( pairwise comparative \) ordinal measurements have lower per sample noise and are typically faster to elicit than cardinal ones ordinal measurements however typically provide less information we then consider the popular thurstone and bradley terry luce \( btl \) models for ordinal measurements and characterize the minimax error rates for estimating the unknown quantity we compare these minimax error rates to those under cardinal measurement models and quantify for what noise levels ordinal measurements are better finally , we revisit the data collected from our experiments and show that fitting these models confirms this prediction for tasks where the noise in ordinal measurements is sufficiently low , the ordinal approach results in smaller errors in the estimation"
cs.DS,"assembling a gene from candidate exons is an important problem in computational biology among the most successful approaches to this problem is emph spliced alignment , proposed by gelfand et al , which scores different candidate exon chains within a dna sequence of length m by comparing them to a known related gene sequence of length n , m theta \( n \) gelfand et al gave an algorithm for spliced alignment running in time o \( n 3 \) kent et al considered sparse spliced alignment , where the number of candidate exons is o \( n \) , and proposed an algorithm for this problem running in time o \( n 2 5 \) we improve on this result , by proposing an algorithm for sparse spliced alignment running in time o \( n 2 25 \) our approach is based on a new framework of emph quasi local string comparison"
cs.AI|cs.DS,"this paper addresses the problem of finding multiple near optimal , spatially dissimilar paths that can be considered as alternatives in the decision making process , for finding optimal corridors in which to construct a new road we further consider combinations of techniques for reducing the costs associated with the computation and increasing the accuracy of the cost formulation numerical results for five algorithms to solve the dissimilar multipath problem show that a bidirectional approach yields the fastest running times and the most robust algorithm further modifications of the algorithms to reduce the running time were tested and it is shown that running time can be reduced by an average of 56 percent without compromising the quality of the results"
cs.DS,"we study the problem of implementing equilibria of complete information games in settings of incomplete information , and address this problem using recommender mechanisms a recommender mechanism is one that does not have the power to enforce outcomes or to force participation , rather it only has the power to suggestion outcomes on the basis of voluntary participation we show that despite these restrictions , recommender mechanisms can implement equilibria of complete information games in settings of incomplete information under the condition that the game is large i e that there are a large number of players , and any player 's action affects any other 's payoff by at most a small amount our result follows from a novel application of differential privacy we show that any algorithm that computes a correlated equilibrium of a complete information game while satisfying a variant of differential privacy which we call joint differential privacy can be used as a recommender mechanism while satisfying our desired incentive properties our main technical result is an algorithm for computing a correlated equilibrium of a large game while satisfying joint differential privacy although our recommender mechanisms are designed to satisfy game theoretic properties , our solution ends up satisfying a strong privacy property as well no group of players can learn much about the type of any player outside the group from the recommendations of the mechanism , even if these players collude in an arbitrary way as such , our algorithm is able to implement equilibria of complete information games , without revealing information about the realized types"
cs.LG|stat.ML,"we present an extension of sparse canonical correlation analysis \( cca \) designed for finding multiple to multiple linear correlations within a single set of variables unlike cca , which finds correlations between two sets of data where the rows are matched exactly but the columns represent separate sets of variables , the method proposed here , canonical autocorrelation analysis \( caa \) , finds multivariate correlations within just one set of variables this can be useful when we look for hidden parsimonious structures in data , each involving only a small subset of all features in addition , the discovered correlations are highly interpretable as they are formed by pairs of sparse linear combinations of the original features we show how caa can be of use as a tool for anomaly detection when the expected structure of correlations is not followed by anomalous data we illustrate the utility of caa in two application domains where single class and unsupervised learning of correlation structures are particularly relevant breast cancer diagnosis and radiation threat detection when applied to the wisconsin breast cancer data , single class caa is competitive with supervised methods used in literature on the radiation threat detection task , unsupervised caa performs significantly better than an unsupervised alternative prevalent in the domain , while providing valuable additional insights for threat analysis"
cs.DS,"meta kernelization theorems are general results that provide polynomial kernels for large classes of parameterized problems the known meta kernelization theorems , in particular the results of bodlaender et al \( focs'09 \) and of fomin et al \( focs'10 \) , apply to optimization problems parameterized by solution size we present the first meta kernelization theorems that use a structural parameters of the input and not the solution size let c be a graph class we define the c cover number of a graph to be a the smallest number of modules the vertex set can be partitioned into , such that each module induces a subgraph that belongs to the class c we show that each graph problem that can be expressed in monadic second order \( mso \) logic has a polynomial kernel with a linear number of vertices when parameterized by the c cover number for any fixed class c of bounded rank width \( or equivalently , of bounded clique width , or bounded boolean width \) many graph problems such as independent dominating set , c coloring , and c domatic number are covered by this meta kernelization result our second result applies to mso expressible optimization problems , such as minimum vertex cover , minimum dominating set , and maximum clique we show that these problems admit a polynomial annotated kernel with a linear number of vertices"
cs.SI,"we introduce a reshuffled approach to empirical analyze signs' organization in real directed signed social networks of epinions and slashdots from the global viewpoint in the reshuffled approach , each negative link has probability p rs to exchange its sign with another positive link chosen randomly through calculating the entropies of social status \( s in and s out \) of and mimicking opinion formation of the majority rule model on each reshuffled signed network , we find that s in and s out reach their own minimum values as well as the magnetization m reaches its maximum value at p rs 0 namely , individuals share the homogeneous properties of social status and dynamic status in real directed signed social networks our present work provides some interesting tools and perspective to understand the signs' organization in signed social networks"
cs.SI,"one of the challenges in fighting cybercrime is to understand the dynamics of message propagation on botnets , networks of infected computers used to send viruses , unsolicited commercial emails \( spam \) or denial of service attacks we map this problem to the propagation of multiple random walkers on directed networks and we evaluate the inter arrival time distribution between successive walkers arriving at a target we show that the temporal organization of this process , which models information propagation on unstructured peer to peer networks , has the same features as spam arriving to a single user we study the behavior of the message inter arrival time distribution on three different network topologies using two different rules for sending messages in all networks the propagation is not a pure poisson process it shows universal features on poissonian networks and a more complex behavior on scale free networks results open the possibility to indirectly learn about the process of sending messages on networks with unknown topologies , by studying inter arrival times at any node of the network"
cs.DS,"the problem of publishing personal data without giving up privacy is becoming increasingly important an interesting formalization recently proposed is the k anonymity this approach requires that the rows in a table are clustered in sets of size at least k and that all the rows in a cluster become the same tuple , after the suppression of some records the natural optimization problem , where the goal is to minimize the number of suppressed entries , is known to be np hard when the values are over a ternary alphabet , k 3 and the rows length is unbounded in this paper we give a lower bound on the approximation factor that any polynomial time algorithm can achive on two restrictions of the problem , namely \( i \) when the records values are over a binary alphabet and k 3 , and \( ii \) when the records have length at most 8 and k 4 , showing that these restrictions of the problem are apx hard"
cs.AI,"inspired by the hierarchical cognitive architecture and the perception action model \( pam \) , we propose that the internal status acts as a kind of common coding representation which affects , mediates and even regulates the sensorimotor behaviours these regulation can be depicted in the bayesian framework , that is why cognitive agents are able to generate behaviours with subtle differences according to their emotion or recognize the emotion by perception a novel recurrent neural network called recurrent neural network with parametric bias units \( rnnpb \) runs in three modes , constructing a two level emotion regulated learning model , was further applied to testify this theory in two different cases"
cs.LG|stat.ML,"we define a generalized likelihood function based on uncertainty measures and show that maximizing such a likelihood function for different measures induces different types of classifiers in the probabilistic framework , we obtain classifiers that optimize the cross entropy function in the possibilistic framework , we obtain classifiers that maximize the interclass margin furthermore , we show that the support vector machine is a sub class of these maximum margin classifiers"
cs.DM,"let g be a simple undirected graph the regular number of g is defined to be the minimum number of subsets into which the edge set of g can be partitioned so that the subgraph induced by each subset is regular in this work , we obtain the regular number of some families of graphs and discuss some general bounds on this parameter also , some of the lower or upper bounds proved in cite kulli janakiram iyer 2001 are shown here to hold with equality"
cs.DS,"let a be a matrix , c be any linear objective function and x be a fractional vector , say an lp solution to some discrete optimization problem then a recurring task in theoretical computer science \( and in approximation algorithms in particular \) is to obtain an integral vector y such that ax is roughly ay and c y exceeds c x by only a moderate factor we give a new randomized rounding procedure for this task , provided that a has bounded delta approximate entropy this property means that for uniformly chosen random signs chi \( j \) in 1 , 1 on any subset of the columns , the outcome a chi can be approximately described using a sub linear number of bits in expectation to achieve this result , we modify well known techniques from the field of discrepancy theory , especially we rely on beck 's entropy method , which to the best of our knowledge has never been used before in the context of approximation algorithms our result can be made constructive using the bansal framework based on semidefinite programming we demonstrate the versatility of our procedure by rounding fractional solutions to column based linear programs for some generalizations of bin packing for example we obtain a polynomial time opt o \( log 2 opt \) approximation for bin packing with rejection and the first afptas for the train delivery problem"
cs.LG,"convolutional neural networks \( cnns \) have recently emerged as the dominant model in computer vision if provided with enough training data , they predict almost any visual quantity in a discrete setting , such as classification , cnns are not only able to predict a label but often predict a confidence in the form of a probability distribution over the output space in continuous regression tasks , such a probability estimate is often lacking we present a regression framework which models the output distribution of neural networks this output distribution allows us to infer the most likely labeling following a set of physical or modeling constraints these constraints capture the intricate interplay between different input and output variables , and complement the output of a cnn however , they may not hold everywhere our setup further allows to learn a confidence with which a constraint holds , in the form of a distribution of the constrain satisfaction we evaluate our approach on the problem of intrinsic image decomposition , and show that constrained structured regression significantly increases the state of the art"
cs.SI,"we developed a website for kids where they can share new as well as remixed animations and games , e g , interactive music videos , which they created on their smartphones or tablets using a visual lego style programming environment called catroid online communities for children like our website have unique requirements , and keeping the commitment of kids on a high level is a continuous challenge for instance , one key motivator for kids is the ability to entertain their friends another success factor is the ability to learn from and cooperate with other children in this short position paper we attempt at identifying the requirements for the success of such an online community , both from the point of view of the kids as well as of their parents , and at finding ways to make it attractive for both"
cs.AI,"we introduce gotchas \( generating panoptic turing tests to tell computers and humans apart \) as a way of preventing automated offline dictionary attacks against user selected passwords a gotcha is a randomized puzzle generation protocol , which involves interaction between a computer and a human informally , a gotcha should satisfy two key properties \( 1 \) the puzzles are easy for the human to solve \( 2 \) the puzzles are hard for a computer to solve even if it has the random bits used by the computer to generate the final puzzle unlike a captcha our main theorem demonstrates that gotchas can be used to mitigate the threat of offline dictionary attacks against passwords by ensuring that a password cracker must receive constant feedback from a human being while mounting an attack finally , we provide a candidate construction of gotchas based on inkblot images our construction relies on the usability assumption that users can recognize the phrases that they originally used to describe each inkblot image a much weaker usability assumption than previous password systems based on inkblots which required users to recall their phrase exactly we conduct a user study to evaluate the usability of our gotcha construction we also generate a gotcha challenge where we encourage artificial intelligence and security researchers to try to crack several passwords protected with our scheme"
cs.DS,d krieger and j shallit have proved that every real number greater than 1 is a critical exponent of some sequence we show how this result can be derived from some general statements about sequences whose subsequences have \( almost \) maximal kolmogorov complexity in this way one can also construct a sequence that has no approximate fractional powers with exponent that exceeds a given value
cs.DM,"sampling algorithms , hypergraph degree sequences , and polytopes play a crucial role in statistical analysis of network data this article offers a brief overview of open problems in this area of discrete mathematics from the point of view of a particular family of statistical models for networks called exponential random graph models the problems and underlying constructions are also related to well known concepts in commutative algebra and graph theoretic concepts in computer science we outline a few lines of recent work that highlight the natural connection between these fields and unify them into some open problems while these problems are often relevant in discrete mathematics in their own right , the emphasis here is on statistical relevance with the hope that these lines of research do not remain disjoint suggested specific open problems and general research questions should advance algebraic statistics theory as well as applied statistical tools for rigorous statistical analysis of networks"
cs.SI,"for a given graph , g , let a be the adjacency matrix , d is the diagonal matrix of degrees , l' d a is the combinatorial laplacian , and l d 1 2 l'd 1 2 is the normalized laplacian recently , the eigenvectors corresponding to the smallest eigenvalues of l and l' have been of great interest because of their application to community detection , which is a nebulously defined problem that essentially seeks to find a vertex set s such that there are few edges incident with exactly one vertex of s the connection between community detection and the second smallest eigenvalue \( and the corresponding eigenvector \) is well known the k smallest eigenvalues have been used heuristically to find multiple communities in the same graph , and a justification with theoretical rigor for the use of k geq 3 eigenpairs has only been found very recently the largest eigenpair of l has been used more classically to solve the max cut problem , which seeks to find a vertex set s that maximizes the number of edges incident with exactly one vertex of s very recently trevisan presented a connection between the largest eigenvalue of l and a recursive approach to the max cut problem that seeks to find a bipartite community at each stage this is related to kleinberg 's hits algorithm that finds the largest eigenvalue of a ta we will provide a justification with theoretical rigor for looking at the k largest eigenvalues of l to find multiple bipartite communities in the same graph , and then provide a heuristic algorithm to find strong bipartite communities that is based on the intuition developed by the theoretical methods finally , we will present the results of applying our algorithm to various data mining problems"
cs.AI|cs.SI,"a major problem in road network analysis is discovery of important crossroads , which can provide useful information for transport planning however , none of existing approaches addresses the problem of identifying network wide important crossroads in real road network in this paper , we propose a novel data driven based approach named crrank to rank important crossroads our key innovation is that we model the trip network reflecting real travel demands with a tripartite graph , instead of solely analysis on the topology of road network to compute the importance scores of crossroads accurately , we propose a hits like ranking algorithm , in which a procedure of score propagation on our tripartite graph is performed we conduct experiments on crrank using a real world dataset of taxi trajectories experiments verify the utility of crrank"
cs.AI,"the singleton property \( sp \) approach has been proposed for representing and querying metadata about rdf triples such as provenance , time , location , and evidence in this approach , one singleton property is created to uniquely represent a relationship in a particular context , and in general , generates a large property hierarchy in the schema it has become the subject of important questions from semantic web practitioners can an existing reasoner recognize the singleton property triples \? and how \? if the singleton property triples describe a data triple , then how can a reasoner infer this data triple from the singleton property triples \? or would the large property hierarchy affect the reasoners in some way \? we address these questions in this paper and present our study about the reasoning aspects of the singleton properties we propose a simple mechanism to enable existing reasoners to recognize the singleton property triples , as well as to infer the data triples described by the singleton property triples we evaluate the effect of the singleton property triples in the reasoning processes by comparing the performance on rdf datasets with and without singleton properties our evaluation uses as benchmark the lubm datasets and the lubm sp datasets derived from lubm with temporal information added through singleton properties"
cs.LG,"current state of the art classification and detection algorithms rely on supervised training in this work we study unsupervised feature learning in the context of temporally coherent video data we focus on feature learning from unlabeled video data , using the assumption that adjacent video frames contain semantically similar information this assumption is exploited to train a convolutional pooling auto encoder regularized by slowness and sparsity we establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric"
cs.AI,"an answer to a query has a well defined lineage expression \( alternatively called how provenance \) that explains how the answer was derived recent work has also shown how to compute the lineage of a non answer to a query however , the cause of an answer or non answer is a more subtle notion and consists , in general , of only a fragment of the lineage in this paper , we adapt halpern , pearl , and chockler 's recent definitions of causality and responsibility to define the causes of answers and non answers to queries , and their degree of responsibility responsibility captures the notion of degree of causality and serves to rank potentially many causes by their relative contributions to the effect then , we study the complexity of computing causes and responsibilities for conjunctive queries it is known that computing causes is np complete in general our first main result shows that all causes to conjunctive queries can be computed by a relational query which may involve negation thus , causality can be computed in ptime , and very efficiently so next , we study computing responsibility here , we prove that the complexity depends on the conjunctive query and demonstrate a dichotomy between ptime and np complete cases for the ptime cases , we give a non trivial algorithm , consisting of a reduction to the max flow computation problem finally , we prove that , even when it is in ptime , responsibility is complete for logspace , implying that , unlike causality , it cannot be computed by a relational query"
cs.DM|cs.DS,"we present a novel algorithm for calculating fix points the algorithm calculates fix points of an endo function f on a distributive lattice , by performing reachability computation a graph derived from the dual of f this is in comparison to traditional algorithms that are based on iterated application of f until a fix point is reached"
cs.DS,"given an underlying network , the em averaging dynamics is the following distributed process initially , each node sets its local state to an element of 1 , 1 , uniformly at random and independently of other nodes then , in each consecutive round , every node updates its state to the average of its neighbors we show that when the graph is organized into two equal sized communities separated by a sparse cut and certain additional conditions hold , a global state is reached within a logarithmic number of rounds , in which states of nodes belonging to the same community lie within a small range , while states of members of different communities are separated by a large gap depending on our assumptions on the underlying graph , this can either hold for all or for most nodes either way , this behavior allows nodes to locally identify \( exactly or approximately \) the structure of the two communities we show that the conditions under which the above happens are satisfied with high probability in graphs sampled according to the stochastic block model mathcal g n , p , q , provided that min \( p q \) n , qn gg sqrt \( p q \) n log n , in which case the nodes are able to perform approximate reconstruction of the community structure in mathcal o \( log n \) time we further prove that , with respect to an analog of the stochastic block model for regular graphs , an algorithm based on the averaging dynamics performs exact reconstruction in a logarithmic number of rounds for the full range of parameters for which exact reconstruction is known to be doable in polynomial time"
cs.LG|stat.ML,"we consider the problem of modeling discrete valued vector time series data using extensions of chow liu tree models to capture both dependencies across time and dependencies across variables conditional chow liu tree models are introduced , as an extension to standard chow liu trees , for modeling conditional rather than joint densities we describe learning algorithms for such models and show how they can be used to learn parsimonious representations for the output distributions in hidden markov models these models are applied to the important problem of simulating and forecasting daily precipitation occurrence for networks of rain stations to demonstrate the effectiveness of the models , we compare their performance versus a number of alternatives using historical precipitation data from southwestern australia and the western united states we illustrate how the structure and parameters of the models can be used to provide an improved meteorological interpretation of such data"
cs.DS,we prove that every yes instance of balanced st connectivity has a balanced path of polynomial length
cs.DM|cs.DS,"network alignment refers to the problem of finding a bijective mapping across vertices of two graphs to maximize the number of overlapping edges and or to minimize the number of mismatched interactions across networks this problem is often cast as an expensive quadratic assignment problem \( qap \) although spectral methods have received significant attention in different network science problems such as network clustering , the use of spectral techniques in the network alignment problem has been limited partially owing to the lack of principled connections between spectral methods and relaxations of the network alignment optimization in this paper , we propose a network alignment framework that uses an orthogonal relaxation of the underlying qap in a maximum weight bipartite matching optimization our method takes into account the ellipsoidal level sets of the quadratic objective function by exploiting eigenvalues and eigenvectors of \( transformations of \) adjacency graphs our framework not only can be employed to provide a theoretical justification for existing heuristic spectral network alignment methods , but it also leads to a new scalable network alignment algorithm which outperforms existing ones over various synthetic and real networks moreover , we generalize the objective function of the network alignment problem to consider both matched and mismatched interactions in a standard qap formulation this can be critical in applications where networks have low similarity and therefore we expect more mismatches than matches we assess the effectiveness of our proposed method theoretically for certain classes of networks , through simulations over various synthetic network models , and in two real data applications in comparative analysis of gene regulatory networks across human , fly and worm , and in user de anonymization over twitter follower subgraphs"
cs.DM|cs.DS,"given a convex polyhedron p of n vertices inside a sphere q , we give an o \( n 3 \) time algorithm that cuts p out of q by using guillotine cuts and has cutting cost o \( \( log n \) 2 \) times the optimal"
cs.AI,"we present a comprehensive study of the use of value precedence constraints to break value symmetry we first give a simple encoding of value precedence into ternary constraints that is both efficient and effective at breaking symmetry we then extend value precedence to deal with a number of generalizations like wreath value and partial interchangeability we also show that value precedence is closely related to lexicographical ordering finally , we consider the interaction between value precedence and symmetry breaking constraints for variable symmetries"
cs.DM,"we introduce a concept of similarity between vertices of directed graphs let g a and g b be two directed graphs we define a similarity matrix whose \( i , j \) th real entry expresses how similar vertex j \( in g a \) is to vertex i \( in g b the similarity matrix can be obtained as the limit of the normalized even iterates of a linear transformation in the special case where g a g b g , the matrix is square and the \( i , j \) th entry is the similarity score between the vertices i and j of g we point out that kleinberg 's hub and authority method to identify web pages relevant to a given query can be viewed as a special case of our definition in the case where one of the graphs has two vertices and a unique directed edge between them in analogy to kleinberg , we show that our similarity scores are given by the components of a dominant eigenvector of a non negative matrix potential applications of our similarity concept are numerous we illustrate an application for the automatic extraction of synonyms in a monolingual dictionary"
cs.DS,"motivated by applications to sensor networks , as well as to many other areas , this paper studies the construction of minimum degree spanning trees we consider the classical node register state model , with a weakly fair scheduler , and we present a space optimal emph silent self stabilizing construction of minimum degree spanning trees in this model computing a spanning tree with minimum degree is np hard therefore , we actually focus on constructing a spanning tree whose degree is within one from the optimal our algorithm uses registers on o \( log n \) bits , converges in a polynomial number of rounds , and performs polynomial time computation at each node specifically , the algorithm constructs and stabilizes on a special class of spanning trees , with degree at most opt 1 indeed , we prove that , unless np conp , there are no proof labeling schemes involving polynomial time computation at each node for the whole family of spanning trees with degree at most opt 1 up to our knowledge , this is the first example of the design of a compact silent self stabilizing algorithm constructing , and stabilizing on a subset of optimal solutions to a natural problem for which there are no time efficient proof labeling schemes on our way to design our algorithm , we establish a set of independent results that may have interest on their own in particular , we describe a new space optimal silent self stabilizing spanning tree construction , stabilizing on emph any spanning tree , in o \( n \) rounds , and using just emph one additional bit compared to the size of the labels used to certify trees we also design a silent loop free self stabilizing algorithm for transforming a tree into another tree last but not least , we provide a silent self stabilizing algorithm for computing and certifying the labels of a nca labeling scheme"
cs.AI,"this paper presents a system which creates and visualizes probabilistic semantic links between concepts in a thesaurus and classes in a classification system for creating the links , we build on the polylingual labeled topic model \( pll tm \) pll tm identifies probable thesaurus descriptors for each class in the classification system by using information from the natural language text of documents , their assigned thesaurus descriptors and their designated classes the links are then presented to users of the system in an interactive visualization , providing them with an automatically generated overview of the relations between the thesaurus and the classification system"
cs.SI,"temporal networks come with a wide variety of heterogeneities , from burstiness of event sequences to correlations between timingsof node and link activations in this paper , we set to explore the latter by using greedy walks as probes of temporal network structure given a temporal network \( a sequence of contacts \) , greedy walks proceed from node to node by always following the first available contact because of this , their structure is particularly sensitive to temporal topological patterns involving repeated contacts between sets of nodes this becomes evident in their small coverage per step as compared to a temporal reference model in empirical temporal networks , greedy walks often get stuck within small sets of nodes because of correlated contact patterns while this may also happen in static networks that have pronounced community structure , the use of the temporal reference model takes the underlying static network structure out of the equation and indicates that there is a purely temporal reason for the observations further analysis of the structure of greedy walks indicates that burst trains , sequences of repeated contacts between node pairs , are the dominant factor however , there are larger patterns too , as shown with non backtracking greedy walks we proceed further to study the entropy rates of greedy walks , and show that the sequences of visited nodes are more structured and predictable in original data as compared to temporally uncorrelated references taken together , these results indicate a richness of correlated temporal topological patterns in temporal networks"
cs.LG|stat.ML,"we present a polynomial time noise robust margin based active learning algorithm to find homogeneous \( passing the origin \) linear separators and analyze its statistical rate of error convergence when labels are corrupted by noise we show that when the imposed noise satisfies the tsybakov low noise condition mt 99 , tsy04 the algorithm is able to adapt to unknown level of noise and achieves optimal statistical rate up to polylogarithmic factors in addition , the presented algorithm is simple and does not require prior knowledge of the amount of noise in the label distribution we also derive lower bounds for margin based active learning algorithms under tsybakov noise conditions \( tnc \) for the membership query synthesis scenario ang88 our result implies lower bounds for the stream based selective sampling scenario coh90 under tnc for some fairly simple data distributions quite surprisingly , we show that the sample complexity cannot be improved even if the underlying data distribution is as simple as the uniform distribution on the unit ball our proof involves the construction of a well separated hypothesis set on the d dimensional unit ball along with carefully designed label distributions for the tsybakov noise condition our analysis might provide insights for other forms of lower bounds as well"
cs.AI,"ondex is a data integration and visualization platform developed to support systems biology research at its core is a data model based on two main principles first , all information can be represented as a graph and , second , all elements of the graph can be annotated with ontologies this data model is conformant to the semantic web framework , in particular to rdf , and therefore ondex is ideally positioned as a platform that can exploit the semantic web"
cs.LG|stat.ML,"kernel methods are successful approaches for different machine learning problems this success is mainly rooted in using feature maps and kernel matrices some methods rely on the eigenvalues eigenvectors of the kernel matrix , while for other methods the spectral information can be used to estimate the excess risk an important question remains on how close the sample eigenvalues eigenvectors are to the population values in this paper , we improve earlier results on concentration bounds for eigenvalues of general kernel matrices for distance and inner product kernel functions , e g radial basis functions , we provide new concentration bounds , which are characterized by the eigenvalues of the sample covariance matrix meanwhile , the obstacles for sharper bounds are accounted for and partially addressed as a case study , we derive a concentration inequality for sample kernel target alignment"
cs.DM|cs.DS,"we consider the np hard problem of finding a spanning tree with a maximum number of internal vertices this problem is a generalization of the famous hamiltonian path problem our dynamic programming algorithms for general and degree bounded graphs have running times of the form o \( c n \) \( c 3 \) the main result , however , is a branching algorithm for graphs with maximum degree three it only needs polynomial space and has a running time of o \( 1 8669 n \) when analyzed with respect to the number of vertices we also show that its running time is 2 1364 k n o \( 1 \) when the goal is to find a spanning tree with at least k internal vertices both running time bounds are obtained via a measure conquer analysis , the latter one being a novel use of this kind of analyses for parameterized algorithms"
cs.DM,the summation formula within pascalian triangle resulting in the fibonacci sequence is extended to the q binomial coefficients q gaussian triangles
cs.DM,"in a properly edge colored graph , a subgraph using every color at most once is called rainbow in this thesis , we study rainbow cycles and paths in proper edge colorings of complete graphs , and we prove that in every proper edge coloring of k n , there is a rainbow path on \( 3 4 o \( 1 \) \) n vertices , improving on the previously best bound of \( 2n 1 \) 3 from gyarfas and mhalla similarly , a k rainbow path in a proper edge coloring of k n is a path using no color more than k times we prove that in every proper edge coloring of k n , there is a k rainbow path on \( 1 2 \( k 1 \) ! \) n vertices"
cs.SI,"an innovative approach to analyze the complexity of translating novel molecular entities and nanomaterials into pharmaceutical alternatives \( i e , knowledge translation , kt \) is discussed first , some key concepts on the organization and translation of the biomedical knowledge \( paradigms , homophily , power law distributions , hierarchy , modularity , and research fronts \) are reviewed then , we propose a model for the knowledge translation \( kt \) in drug discovery that considers the complexity of interdisciplinary communication specifically , we address two highly relevant aspects 1 \) a successful kt requires the emergence of organized bodies of inter and transdisciplinary research , and 2 \) the hierarchical and modular topological organization of these bodies of knowledge we focused on a set of previously published studies on kt which rely on a combination of network analysis and computer assisted analysis of the contents of scientific literature and patents the selected studies provide a duo of complementary perspectives the demand of knowledge \( cervical cancer and ebola hemorrhagic fever \) and the supply of knowledge \( liposomes and nanoparticles to treat cancer and the paradigmatic doxil , the first nanodrug to be approved \)"
cs.AI,"we introduce the concept of continuous transportation task to the context of multi agent systems a continuous transportation task is one in which a multi agent team visits a number of fixed locations , picks up objects , and delivers them to a final destination the goal is to maximize the rate of transportation while the objects are replenished over time examples of problems that need continuous transportation are foraging , area sweeping , and first last mile problem previous approaches typically neglect the interference and are highly dependent on communications among agents some also incorporate an additional reconnaissance agent to gather information in this paper , we present a hybrid of centralized and distributed approaches that minimize the interference and communications in the multi agent team without the need for a reconnaissance agent we contribute two partitioning transportation algorithms inspired by existing algorithms , and contribute one novel online partitioning transportation algorithm with information gathering in the multi agent team our algorithms have been implemented and tested extensively in the simulation the results presented in this paper demonstrate the effectiveness of our algorithms that outperform the existing algorithms , even without any communications between the agents and without the presence of a reconnaissance agent"
cs.SI,"our primary objective in this paper is to study the distribution of the maximal clique size of the vertices in complex networks we define the maximal clique size for a vertex as the maximum size of the clique that the vertex is part of and such a clique need not be the maximum size clique for the entire network we determine the maximal clique size of the vertices using a modified version of a branch and bound based exact algorithm that has been originally proposed to determine the maximum size clique for an entire network graph we then run this algorithm on two categories of complex networks one category of networks capture the evolution of small world networks from regular network \( according to the wellknown watts strogatz model \) and their subsequent evolution to random networks we show that the distribution of the maximal clique size of the vertices follows a poisson style distribution at different stages of the evolution of the small world network to a random network on the other hand , the maximal clique size of the vertices is observed to be in variant and to be very close to that of the maximum clique size for the entire network graph as the regular network is transformed to a small world network the second category of complex networks studied are real world networks \( ranging from random networks to scale free networks \) and we observe the maximal clique size of the vertices in five of the six real world networks to follow a poisson style distribution in addition to the above case studies , we also analyze the correlation between the maximal clique size and clustering coefficient as well as analyze the assortativity index of the vertices with respect to maximal clique size and node degree"
cs.IT|cs.LG,"based on alpha stable random projections with small alpha , we develop a simple algorithm for compressed sensing \( sparse signal recovery \) by using only 1 bit \( i e , the sign \) of the measurements the method of alpha stable random projections has become popular in data stream computations using only 1 bit of the measurements results in substantial cost reduction in collection , storage , communication , and decoding for compressed sensing the proposed algorithm is efficient in that the decoding procedure requires only one scan of the coordinates for a k sparse signal of length n , a conservative version of our algorithm requires 12 3k log n measurements to recover the support and the signs of the signal a more practical version needs fewer measurements , as validated by experiments a closely related issue is the estimation of k , i e , the size of the support it turns out that , the harmonic mean estimator developed in the prior work for alpha stable random projections already provides a very accurate estimate of k for the task of sparse recovery , using merely \( e g , \) 5 or 10 measurements since this is an important practical problem , a separate technical note is provided to introduce very efficient estimators based on 1 bit or multi bit measurements , for estimating k as well as the scale parameter of the alpha stable distribution family for 0 alpha leq2"
cs.DS|cs.IT,"we give a general unified method that can be used for l 1 em closeness testing of a wide range of univariate structured distribution families more specifically , we design a sample optimal and computationally efficient algorithm for testing the equivalence of two unknown \( potentially arbitrary \) univariate distributions under the mathcal a k distance metric given sample access to distributions with density functions p , q i to mathbb r , we want to distinguish between the cases that p q and p q mathcal a k ge epsilon with probability at least 2 3 we show that for any k ge 2 , epsilon 0 , the em optimal sample complexity of the mathcal a k closeness testing problem is theta \( max k 4 5 epsilon 6 5 , k 1 2 epsilon 2 \) this is the first o \( k \) sample algorithm for this problem , and yields new , simple l 1 closeness testers , in most cases with optimal sample complexity , for broad classes of structured distributions"
cs.SI,"current tests for contagion in social network studies are vulnerable to the confounding effects of latent homophily \( i e , ties form preferentially between individuals with similar hidden traits \) we demonstrate a general method to lower bound the strength of causal effects in observational social network studies , even in the presence of arbitrary , unobserved individual traits our tests require no parametric assumptions and each test is associated with an algebraic proof we demonstrate the effectiveness of our approach by correctly deducing the causal effects for examples previously shown to expose defects in existing methodology finally , we discuss preliminary results on data taken from the framingham heart study"
stat.ML,"it has been observed in a variety of contexts that gradient descent methods have great success in solving low rank matrix factorization problems , despite the relevant problem formulation being non convex we tackle a particular instance of this scenario , where we seek the d dimensional subspace spanned by a streaming data matrix we apply the natural first order incremental gradient descent method , constraining the gradient method to the grassmannian we show that this method converges from any random initialization to the global minimum of the problem , which is also given by the span of the top d left singular vectors further , we give bounds on the expected convergence rate and a high probability convergence rate in a local neighborhood of the global minimizer , our results match the linear rate of convergence in 7"
cs.LG|cs.SI|stat.ML,"this paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types , where each type may play a distinct role while common link analysis models are limited to single type link prediction , we attempt here to capture the correlations among different relation types and reveal the impact of various relation types on performance quality for that , we define the overall relations between object pairs as a textit link pattern which consists in interaction pattern and connection structure in the network , and then use tensor formalization to jointly model and predict the link patterns , which we refer to as textit link pattern prediction \( lpp \) problem to address the issue , we propose a probabilistic latent tensor factorization \( pltf \) model by introducing another latent factor for multiple relation types and furnish the hierarchical bayesian treatment of the proposed probabilistic model to avoid overfitting for solving the lpp problem to learn the proposed model we develop an efficient markov chain monte carlo sampling method extensive experiments are conducted on several real world datasets and demonstrate significant improvements over several existing state of the art methods"
cs.LG,longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects algorithms mining longitudinal observation databases are not restricted by many of the limitations associated with the more conventional methods that have been developed for spontaneous reporting system databases in this paper we investigate the robustness of four recently developed algorithms that mine longitudinal observational databases by applying them to the health improvement network \( thin \) for six drugs with well document known negative side effects our results show that none of the existing algorithms was able to consistently identify known adverse drug reactions above events related to the cause of the drug and no algorithm was superior
cs.AI|cs.LG,"support vector machines \( svms \) and fuzzy rule systems are functionally equivalent under some conditions therefore , the learning algorithms developed in the field of support vector machines can be used to adapt the parameters of fuzzy systems extracting fuzzy models from support vector machines has the inherent advantage that the model does not need to determine the number of rules in advance however , after the support vector machine learning , the complexity is usually high , and interpretability is also impaired this paper not only proposes a complete framework for extracting interpretable svm based fuzzy modeling , but also provides optimization issues of the models simulations examples are given to embody the idea of this paper"
cs.LG|stat.ML,"we present the bayesian case model \( bcm \) , a general framework for bayesian case based reasoning \( cbr \) and prototype classification and clustering bcm brings the intuitive power of cbr to a bayesian generative framework the bcm learns prototypes , the quintessential observations that best represent clusters in a dataset , by performing joint inference on cluster labels , prototypes and important features simultaneously , bcm pursues sparsity by learning subspaces , the sets of features that play important roles in the characterization of the prototypes the prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by bcm , compared to those given by prior art"
cs.SI,"we study binary state dynamics on a network where each node acts in response to the average state of its neighborhood allowing varying amounts of stochasticity in both the network and node responses , we find different outcomes in random and deterministic versions of the model in the limit of a large , dense network , however , we show that these dynamics coincide we construct a general mean field theory for random networks and show this predicts that the dynamics on the network are a smoothed version of the average response function dynamics thus , the behavior of the system can range from steady state to chaotic depending on the response functions , network connectivity , and update synchronicity as a specific example , we model the competing tendencies of imitation and non conformity by incorporating an off threshold into standard threshold models of social contagion in this way we attempt to capture important aspects of fashions and societal trends we compare our theory to extensive simulations of this limited imitation contagion model on poisson random graphs , finding agreement between the mean field theory and stochastic simulations"
cs.LG|stat.ML,"although nonnegative matrix factorization \( nmf \) is np hard in general , it has been shown very recently that it is tractable under the assumption that the input nonnegative data matrix is close to being separable \( separability requires that all columns of the input matrix belongs to the cone spanned by a small subset of these columns \) since then , several algorithms have been designed to handle this subclass of nmf problems in particular , bittorf , recht , r 'e and tropp \( `factoring nonnegative matrices with linear programs' , nips 2012 \) proposed a linear programming model , referred to as hottopixx in this paper , we provide a new and more general robustness analysis of their method in particular , we design a provably more robust variant using a post processing strategy which allows us to deal with duplicates and near duplicates in the dataset"
cs.LG,"we describe a open domain information extraction method for extracting concept instance pairs from an html corpus most earlier approaches to this problem rely on combining clusters of distributionally similar terms and concept instance pairs obtained with hearst patterns in contrast , our method relies on a novel approach for clustering terms found in html tables , and then assigning concept names to these clusters using hearst patterns the method can be efficiently applied to a large corpus , and experimental results on several datasets show that our method can accurately extract large numbers of concept instance pairs"
cs.LG|stat.ML,we present a hybrid algorithm for bayesian topic models that combines the efficiency of sparse gibbs sampling with the scalability of online stochastic inference we used our algorithm to analyze a corpus of 1 2 million books \( 33 billion words \) with thousands of topics our approach reduces the bias of variational inference and generalizes to many bayesian hidden variable models
cs.DM,"in j combin theory ser b 70 \( 1997 \) , 2 44 we gave a simplified proof of the four color theorem the proof is computer assisted in the sense that for two lemmas in the article we did not give proofs , and instead asserted that we have verified those statements using a computer here we give additional details for one of those lemmas , and we include the original computer programs and data as ancillary files accompanying this submission"
cs.DM,"in 5 , holroyd , levine , m 'esz 'aros , peres , propp and wilson characterize recurrent chip and rotor configurations for strongly connected digraphs however , the number of steps needed to recur , and the number of orbits is left open for general digraphs recently , these questions were ans wered by pham 6 , using linear algebraic methods we give new , purely combinatorial proofs for these formulas we also relate rotor router orbits to the chip firing game the number of recurrent rotor router unicycle orbits equals the order of the picard group of the graph , defined in the sense of 1 , and during a period , the same chip moves happen , as during firing the period vector in the chip firing game"
cs.SI,"the problem of knowing who knows what is multi faceted knowledge and expertise lie on a spectrum and one 's expertise in one topic area may have little bearing on one 's knowledge in a disparate topic area in addition , we continue to learn new things over time each of us see but a sliver of our acquaintances' and co workers' areas of expertise by making explicit and visible many individual perceptions of cognitive authority , this work shows that a group can know what its members know about in a relatively efficient and inexpensive manner"
cs.DS,"two important metrics for measuring the quality of routing paths are the maximum edge congestion c and maximum path length d here , we study bicriteria in routing games where each player i selfishly selects a path that simultaneously minimizes its maximum edge congestion c i and path length d i we study the stability and price of anarchy of two bicriteria games em max games , where the social cost is max \( c , d \) and the player cost is max \( c i , d i \) we prove that max games are stable and convergent under best response dynamics , and that the price of anarchy is bounded above by the maximum path length in the players' strategy sets we also show that this bound is tight in worst case scenarios em sum games , where the social cost is c d and the player cost is c i d i for sum games , we first show the negative result that there are game instances that have no nash equilibria therefore , we examine an approximate game called the em sum bucket game that is always convergent \( and therefore stable \) we show that the price of anarchy in sum bucket games is bounded above by c cdot d \( c d \) \( with a poly log factor \) , where c and d are the optimal coordinated congestion and path length thus , the sum bucket game has typically superior price of anarchy bounds than the max game in fact , when either c or d is small \( e g constant \) the social cost of the nash equilibria is very close to the coordinated optimal c d \( within a poly log factor \) we also show that the price of anarchy bound is tight for cases where both c and d are large"
cs.SI,"we argue that when a social convergence mechanism exists and is strong enough , one should expect the emergence of a well defined field , i e a slowly evolving , local quantity around which individual attributes fluctuate in a finite range this condensation phenomenon is well illustrated by the deffuant weisbuch opinion model for which we provide a natural extension to allow for spatial heterogeneities we show analytically and numerically that the resulting dynamics of the emergent field is a noisy diffusion equation that has a slow dynamics this random diffusion equation reproduces the long ranged , logarithmic decrease of the correlation of spatial voting patterns empirically found in 1 , 2 interestingly enough , we find that when the social cohesion mechanism becomes too weak , cultural cohesion breaks down completely , in the sense that the distribution of intentions opinions becomes infinitely broad no emerging field exists in this case all these analytical findings are confirmed by numerical simulations of an agent based model"
cs.LG|stat.ML,"big data provides big opportunities for statistical inference , but perhaps even bigger challenges , often related to differences in volume , variety , velocity , and veracity of information when compared to smaller carefully collected datasets from january to june , 2015 , the canadian institute of statistical sciences organized a thematic program on statistical inference , learning and models in big data this paper arose from presentations and discussions that took place during the thematic program"
cs.SI,"widespread interest in the diffusion of information through social networks has produced a large number of social dynamics models a majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different content our empirical research tracking the step by step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features the topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message their disconnected , low transitivity , tree like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants' involvement as the propagation paths length grows such patterns not described before , nor replicated by any of the existing models of information diffusion , can be explained if participants make their pass along decisions based uniquely on local knowledge of their network neighbors affinity with the message content we prove the plausibility of such mechanism through a stylized , agent based model that replicates the emph affinity paths observed in real information diffusion cascades"
cs.DS,"wifi offloading , where mobile device users \( e g , smart phone users \) transmit packets through wifi networks rather than cellular networks , is a promising solution to alleviating the heavy traffic burden of cellular networks due to data explosion however , since wifi networks are intermittently available , a mobile device user in wifi offloading usually needs to wait for wifi connection and thus experiences longer delay of packet transmission to motivate users to participate in wifi offloading , cellular network operators give incentives \( rewards like coupons , e coins \) to users who wait for wifi connection and transmit packets through wifi networks in this paper , we aim at maximizing users' rewards while meeting constraints on queue stability and energy consumption however , we face scheduling challenges from random packet arrivals , intermittent wifi connection and time varying wireless link states to address these challenges , we first formulate the problem as a stochastic optimization problem we then propose an optimal scheduling policy , named optimal scheduling policy under energy constraint \( opec \) , which makes online decisions as to when to delay packet transmission to wait for wifi connection and which wireless link \( wifi link or cellular link \) to transmit packets on opec automatically adapts to random packet arrivals and time varying wireless link states , not requiring a priori knowledge of packet arrival and wireless link probabilities as verified by simulations , opec scheduling policy can achieve the maximum rewards while keeping queue stable and meeting energy consumption constraint"
cs.LG|stat.ML,"in this paper , we will investigate the efficacy of imat \( iterative method of adaptive thresholding \) in recovering the sparse signal \( parameters \) for linear models with missing data sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction methods specifically when we work with big data this paper will focus on comparing the power of imat in reconstruction of the desired sparse signal with lasso additionally , we will assume the model has random missing information missing data has been recently of interest in big data and machine learning problems since they appear in many cases including but not limited to medical imaging datasets , hospital datasets , and massive mimo the dominance of imat over the well known lasso will be taken into account in different scenarios simulations and numerical results are also provided to verify the arguments"
cs.LG|stat.ML,"apache spark is a popular open source platform for large scale data processing that is well suited for iterative machine learning tasks in this paper we present mllib , spark 's open source distributed machine learning library mllib provides efficient functionality for a wide range of learning settings and includes several underlying statistical , optimization , and linear algebra primitives shipped with spark , mllib supports several languages and provides a high level api that leverages spark 's rich ecosystem to simplify the development of end to end machine learning pipelines mllib has experienced a rapid growth due to its vibrant open source community of over 140 contributors , and includes extensive documentation to support further growth and to let users quickly get up to speed"
cs.LG,"microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions it is used to identify the co expressed genes in specific cells or tissues that are actively used to make proteins this method is used to analysis the gene expression , an important task in bioinformatics research cluster analysis of gene expression data has proved to be a useful tool for identifying co expressed genes , biologically relevant groupings of genes and samples in this paper we applied k means with automatic generations of merge factor for isodata agmfi though agmfi has been applied for clustering of gene expression data , this proposed enhanced automatic generations of merge factor for isodata eagmfi algorithms overcome the drawbacks of agmfi in terms of specifying the optimal number of clusters and initialization of good cluster centroids experimental results on gene expression data show that the proposed eagmfi algorithms could identify compact clusters with perform well in terms of the silhouette coefficients cluster measure"
cs.DM|cs.DS,"we consider the problem of hitting sets online the hypergraph \( i e , range space consisting of points and ranges \) is known in advance , and the ranges to be stabbed are input one by one in an online fashion the online algorithm must stab each range upon arrival an online algorithm may add points to the hitting set but may not remove already chosen points the goal is to use the smallest number of points the best known competitive ratio for hitting sets online by alon et al cite alon2009online is o \( log n cdot log m \) for general hypergraphs , where n and m denote the number of points and the number of ranges , respectively we consider hypergraphs in which the union of two intersecting ranges is also a range our main result for such hypergraphs is as follows the competitive ratio of the online hitting set problem is at most the unique max number and at least this number minus one"
cs.LG,"this paper investigates distributed cooperative learning algorithms for data processing in a network setting specifically , the extreme learning machine \( elm \) is introduced to train a set of data distributed across several components , and each component runs a program on a subset of the entire data in this scheme , there is no requirement for a fusion center in the network due to e g , practical limitations , security , or privacy reasons we first reformulate the centralized elm training problem into a separable form among nodes with consensus constraints then , we solve the equivalent problem using distributed optimization tools a new distributed cooperative learning algorithm based on elm , called dc elm , is proposed the architecture of this algorithm differs from that of some existing parallel distributed elms based on mapreduce or cloud computing we also present an online version of the proposed algorithm that can learn data sequentially in a one by one or chunk by chunk mode the novel algorithm is well suited for potential applications such as artificial intelligence , computational biology , finance , wireless sensor networks , and so on , involving datasets that are often extremely large , high dimensional and located on distributed data sources we show simulation results on both synthetic and real world data sets"
cs.LG,"in this work we study the binary transfer learning problem involving 10 2 10 3 sources we focus on how to select sources from the large pool and how to combine them to yield a good performance on a target task in particular , we consider the transfer learning setting where one does not have direct access to the source data , but rather employs the source hypotheses trained from them building on results on greedy algorithms , we propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously on three computer vision datasets we achieve state of the art results , substantially outperforming both popular feature selection and transfer learning baselines when transferring in a small sample setting our experiments involve up to 1000 classes , totalling 1 2 million examples , with only 11 to 20 training examples from the target domain we corroborate our findings showing theoretically that , under reasonable assumptions on the source hypotheses , our algorithm can learn effectively from few examples"
cs.LG|cs.SI,"social network platforms can archive data produced by their users and re use the data to serve the users better one of the services that these platforms provide is the recommendation service recommendation systems can predict the future preferences of the users using various different techniques one of the most popular technique for recommendation is matrix factorization , which uses low rank approximation of input data similarly word embedding methods from natural language processing literature learn low dimensional vector space representation of input elements noticing the similarities among word embedding and matrix factorization techniques and based on the previous works that apply techniques from text processing for recommendation , word2vec 's skip gram technique is employed to make recommendations unlike previous works that use word2vec for recommendation , non textual features are used the aim of this work is to make recommendation on next check in venues and a foursquare check in dataset is used for this purpose the results showed that use of vector space representations of items modelled by skip gram technique is promising for making recommendations"
cs.DS|cs.LG|stat.ML,"minwise hashing \( minhash \) is a widely popular indexing scheme in practice minhash is designed for estimating set resemblance and is known to be suboptimal in many applications where the desired measure is set overlap \( i e , inner product between binary vectors \) or set containment minhash has inherent bias towards smaller sets , which adversely affects its performance in applications where such a penalization is not desirable in this paper , we propose asymmetric minwise hashing \( mh alsh \) , to provide a solution to this problem the new scheme utilizes asymmetric transformations to cancel the bias of traditional minhash towards smaller sets , making the final collision probability monotonic in the inner product our theoretical comparisons show that for the task of retrieving with binary inner products asymmetric minhash is provably better than traditional minhash and other recently proposed hashing algorithms for general inner products thus , we obtain an algorithmic improvement over existing approaches in the literature experimental evaluations on four publicly available high dimensional datasets validate our claims and the proposed scheme outperforms , often significantly , other hashing algorithms on the task of near neighbor retrieval with set containment our proposal is simple and easy to implement in practice"
cs.LG|stat.ML,"we study large scale kernel methods for acoustic modeling and compare to dnns on performance metrics related to both acoustic modeling and recognition measuring perplexity and frame level classification accuracy , kernel based acoustic models are as effective as their dnn counterparts however , on token error rates dnn models can be significantly better we have discovered that this might be attributed to dnn 's unique strength in reducing both the perplexity and the entropy of the predicted posterior probabilities motivated by our findings , we propose a new technique , entropy regularized perplexity , for model selection this technique can noticeably improve the recognition performance of both types of models , and reduces the gap between them while effective on broadcast news , this technique could be also applicable to other tasks"
cs.LG|stat.ML,"we consider the problem of textit best arm identification with a textit fixed budget t , in the k armed stochastic bandit setting , with arms distribution defined on 0 , 1 we prove that any bandit strategy , for at least one bandit problem characterized by a complexity h , will misidentify the best arm with probability lower bounded by exp big \( frac t log \( k \) h big \) , where h is the sum for all sub optimal arms of the inverse of the squared gaps our result disproves formally the general belief coming from results in the fixed confidence setting that there must exist an algorithm for this problem whose probability of error is upper bounded by exp \( t h \) this also proves that some existing strategies based on the successive rejection of the arms are optimal closing therefore the current gap between upper and lower bounds for the fixed budget best arm identification problem"
cs.DM,"the cutoff phenomenon for an ergodic markov chain describes a sharp transition in the convergence to its stationary distribution , over a negligible period of time , known as cutoff window we study the cutoff phenomenon for simple random walks on kneser graphs , which is a family of ergodic markov chains given two integers n and k , the kneser graph k \( 2n k , n \) is defined as the graph with vertex set being all subsets of 1 , ldots , 2n k of size n and two vertices a and b being connected by an edge if a cap b emptyset we show that for any k o \( n \) , the random walk on k \( 2n k , n \) exhibits a cutoff at frac 1 2 log 1 k n \( 2n k \) with a window of size o \( frac n k \)"
cs.LG,"distance metric learning aims to learn from the given training data a valid distance metric , with which the similarity between data samples can be more effectively evaluated for classification metric learning is often formulated as a convex or nonconvex optimization problem , while many existing metric learning algorithms become inefficient for large scale problems in this paper , we formulate metric learning as a kernel classification problem , and solve it by iterated training of support vector machines \( svm \) the new formulation is easy to implement , efficient in training , and tractable for large scale problems two novel metric learning models , namely positive semidefinite constrained metric learning \( pcml \) and nonnegative coefficient constrained metric learning \( ncml \) , are developed both pcml and ncml can guarantee the global optimality of their solutions experimental results on uci dataset classification , handwritten digit recognition , face verification and person re identification demonstrate that the proposed metric learning methods achieve higher classification accuracy than state of the art methods and they are significantly more efficient in training"
cs.LG|stat.ML,"we present new findings in regard to data analysis in very high dimensional spaces we use dimensionalities up to around one million a particular benefit of correspondence analysis is its suitability for carrying out an orthonormal mapping , or scaling , of power law distributed data power law distributed data are found in many domains correspondence factor analysis provides a latent semantic or principal axes mapping our experiments use data from digital chemistry and finance , and other statistically generated data"
cs.LG,"canonical correlation analysis is a technique to extract common features from a pair of multivariate data in complex situations , however , it does not extract useful features because of its linearity on the other hand , kernel method used in support vector machine is an efficient approach to improve such a linear method in this paper , we investigate the effectiveness of applying kernel method to canonical correlation analysis"
cs.LG,"in this paper , we propose a malware categorization method that models malware behavior in terms of instructions using pagerank pagerank computes ranks of web pages based on structural information and can also compute ranks of instructions that represent the structural information of the instructions in malware analysis methods our malware categorization method uses the computed ranks as features in machine learning algorithms in the evaluation , we compare the effectiveness of different pagerank algorithms and also investigate bagging and boosting algorithms to improve the categorization accuracy"
cs.DS,"this paper presents a convergence analysis on distributed alternating direction method of multipliers \( admm \) for online convex optimization problems under linear constraints the goal is to distributively optimize a global objective function over a network of decision makers the global objective function is composed of convex cost functions associated with each agent the local cost functions , on the other hand , are assumed to have been broken down into two convex functions , one of which is revealed over time to the decision makers and one known a priori in addition , the agents must achieve consensus on a global variable which is associated with a private local variable through a linear constraint we extend an online admm algorithm to a distributed setting based on dual averaging we then propose a new performance metric for such distributed algorithms and explore the rate of convergence of the performance of the sequence of decisions generated by the algorithm as compared with the best fixed decision in hindsight this performance metric is called the regret a sub linear upper bound on regret of the proposed algorithm is presented as a function of the underlying network topology and linear constraints the online distributed admm algorithm is then applied to a formation acquisition problem followed by simulation results depicting its o \( sqrt t \) regret bound"
cs.LG,"the optimization of deep neural networks can be more challenging than traditional convex optimization problems due to the highly non convex nature of the loss function , e g it can involve pathological landscapes such as saddle surfaces that can be difficult to escape for algorithms based on simple gradient descent in this paper , we attack the problem of optimization of highly non convex neural networks by starting with a smoothed or textit mollified objective function that gradually has a more non convex energy landscape during the training our proposition is inspired by the recent studies in continuation methods similar to curriculum methods , we begin learning an easier \( possibly convex \) objective function and let it evolve during the training , until it eventually goes back to being the original , difficult to optimize , objective function the complexity of the mollified networks is controlled by a single hyperparameter which is annealed during the training we show improvements on various difficult optimization tasks and establish a relationship with recent works on continuation methods for neural networks and mollifiers"
cs.DS,"many websites with an underlying database containing structured data provide the richest and most dense source of information relevant for topical data integration the real data integration requires sustainable and reliable pattern discovery to enable accurate content retrieval and to recognize pattern changes from time to time yet , extracting the structured data from web documents is still lacking from its accuracy this paper proposes the bar tree representation to describe the whole pattern of web pages in an efficient way based on the reverse algorithm while previous algorithms always trace the pattern and extract the region of interest from textit top root , the reverse algorithm recognizes the pattern from the region of interest to both top and bottom roots simultaneously the attributes are then extracted and labeled reversely from the region of interest of targeted contents since using conventional representations for the algorithm should require more computational power , the bar tree method is developed to represent the generated patterns using bar graphs characterized by the depths and widths from the document roots we show that this representation is suitable for extracting the data from the semi structured web sources , and for detecting the template changes of targeted pages the experimental results show perfect recognition rate for template changes in several web targets"
cs.DM,"we show that graph theoretic thickness and geometric thickness are not asymptotically equivalent for every t , there exists a graph with thickness three and geometric thickness t"
cs.SI,"it is estimated that italian mafias registered 135 billion euros in profits only in 2010 part of this huge amount of money , coming mostly from the drugs , prostitution and arms illicit markets , is often used to invest into legitimate private economies as a consequence , the affected economies destabilise , become entrenched with violent forms of competition and are bound to stagnation nonetheless , few are the attempts to uncover the patterns followed by criminal organisations in their business ventures the reason lays mostly in the poor availability of data on criminal activity , or in the highly risky task of gather it this paper partially fills this gap thanks to access to information about the sicilian mafia in a city more specifically , it tries to analyse the nature and extent of criminal infiltration into the legitimate private economy of the case study using network techniques the research demonstrates that sectors with a high degree of centrality and comprising fewer firms are the most vulnerable to this kind of security threat it also shows that centrality is also the key criterion that makes a firm sensitive to infiltration , provided it belongs to a susceptible economic sector"
cs.LG,"stacked denoising auto encoders \( daes \) are well known to learn useful deep representations , which can be used to improve supervised training by initializing a deep network we investigate a training scheme of a deep dae , where dae layers are gradually added and keep adapting as additional layers are added we show that in the regime of mid sized datasets , this gradual training provides a small but consistent improvement over stacked training in both reconstruction quality and classification error over stacked training on mnist and cifar datasets"
cs.LG|stat.ML,"the output scores of a neural network classifier are converted to probabilities via normalizing over the scores of all competing categories computing this partition function , z , is then linear in the number of categories , which is problematic as real world problem sets continue to grow in categorical types , such as in visual object recognition or discriminative language modeling we propose three approaches for sublinear estimation of the partition function , based on approximate nearest neighbor search and kernel feature maps and compare the performance of the proposed approaches empirically"
cs.SI,"the identification of communities in large scale networks is a challenging task to existing searching schemes when the communities overlap significantly among their members , as often the case in large scale social networks the strong overlaps render many algorithms invalid we propose a detection scheme based on properly merging the partial communities revealed by the ego networks of each vertex the general principle , merger criteria , and post processing procedures are discussed this partial community merger algorithm \( pcma \) is tested on two modern benchmark models it shows a linear time complexity and it performs accurately and efficiently when compared with two widely used algorithms pcma is then applied to a huge social network and millions of communities are identified a detected community can be visualized with all its members as well as the number of different communities that each member belongs to the multiple memberships of a vertex , in turn , illustrates the significant overlaps between communities that calls for the need of a novel and efficient algorithm such as pcma"
cs.DS,"mapreduce is a popular parallel computing paradigm for big data processing in clusters and data centers it is observed that different job execution orders and mapreduce slot configurations for a mapreduce workload have significantly different performance with regarding to the makespan , total completion time , system utilization and other performance metrics there are quite a few algorithms on minimizing makespan of multiple mapreduce jobs however , these algorithms are heuristic or suboptimal the best known algorithm for minimizing the makespan is 3 approximation by applying johnson rule in this paper , we propose an approach called uaas algorithm to meet the conditions of classical johnson model then we can still use johnson model for an optimal solution we explain how to adapt to johnson model and provide a few key features of our proposed method"
cs.DS|cs.LG|stat.ML,"in this paper we are going to introduce a new nearest neighbours based approach to clustering , and compare it with previous solutions the resulting algorithm , which takes inspiration from both dbscan and minimum spanning tree approaches , is deterministic but proves simpler , faster and doesnt require to set in advance a value for k , the number of clusters"
cs.AI,"we present a novel technique for automatic program correction in moocs , capable of fixing both syntactic and semantic errors without manual , problem specific correction strategies given an incorrect student program , it generates candidate programs from a distribution of likely corrections , and checks each candidate for correctness against a test suite the key observation is that in moocs many programs share similar code fragments , and the seq2seq neural network model , used in the natural language processing task of machine translation , can be modified and trained to recover these fragments experiment shows our scheme can correct 29 of all incorrect submissions and out performs state of the art approach which requires manual , problem specific correction strategies"
cs.DM,"we determine the exact freezing threshold , r f , for a family of models of random boolean constraint satisfaction problems , including nae sat and hypergraph 2 colouring , when the constraint size is sufficiently large if the constraint density of a random csp , f , in our family is greater than r f then for almost every solution of f , a linear number of variables are frozen , meaning that their colours cannot be changed by a sequence of alterations in which we change o \( n \) variables at a time , always switching to another solution if the constraint density is less than r f , then almost every solution has o \( n \) frozen variables freezing is a key part of the clustering phenomenon that is hypothesized by non rigorous techniques from statistical physics the understanding of clustering has led to the development of advanced heuristics such as survey propogation it has been suggested that the freezing threshold is a precise algorithmic barrier that for densities below r f the random csps can be solved using very simple algorithms , while for densities above r f one requires more sophisticated techniques in order to deal with frozen clusters"
cs.SI,"latent space network models have been used successfully in many applications in network science and other disciplines , yet the probability distributions in the random graph ensembles that these models define tend to be highly intractable therefore it is usually impossible to tell if a given real network is a typical element in a graph ensemble defined by a given model , so that the model can be used for reliable predictions it is thus desirable to identify structural properties of networks such that random graphs having these properties are guaranteed to be latent geometric , that is , to be typical elements in a latent space ensemble here we show that random graphs in which expected degrees and clustering of every node are fixed to the same values , are equivalent to random geometric graphs on the real line if clustering is sufficiently strong large numbers of triangles , homogeneously distributed across all nodes as in real networks , are thus a signature of their latent geometry the methods we use to prove this are quite general and applicable to other network ensembles , geometric or not , and to certain problems in quantum gravity"
cs.DS,"key value data sets of the form \( x , w x \) where w x 0 are prevalent common queries over such data are em segment f statistics q \( f , h \) sum x in h f \( w x \) , specified for a segment h of the keys and a function f different choices of f correspond to count , sum , moments , capping , and threshold statistics when the data set is large , we can compute a smaller sample from which we can quickly estimate statistics a weighted sample of keys taken with respect to f \( w x \) provides estimates with statistically guaranteed quality for f statistics such a sample s \( f \) can be used to estimate g statistics for g not f , but quality degrades with the disparity between g and f in this paper we address applications that require quality estimates for a set f of different functions a naive solution is to compute and work with a different sample s \( f \) for each f in f instead , this can be achieved more effectively and seamlessly using a single em multi objective sample s \( f \) of a much smaller size we review multi objective sampling schemes and place them in our context of estimating f statistics we show that a multi objective sample for f provides quality estimates for any f that is a positive linear combination of functions from f we then establish a surprising and powerful result when the target set m is em all monotone non decreasing functions , noting that m includes most natural statistics we provide efficient multi objective sampling algorithms for m and show that a sample size of k ln n \( where n is the number of active keys \) provides the same estimation quality , for any f in m , as a dedicated weighted sample of size k for f"
cs.AI,"the problem of similarity search is one of the main problems in computer science this problem has many applications in text retrieval , web search , computational biology , bioinformatics and others similarity between two data objects can be depicted using a similarity measure or a distance metric there are numerous distance metrics in the literature , some are used for a particular data type , and others are more general in this paper we present a new distance metric for sequential data which is based on the sum of n grams the novelty of our distance is that these n grams are weighted using artificial bee colony a recent optimization algorithm based on the collective intelligence of a swarm of bees on their search for nectar this algorithm has been used in optimizing a large number of numerical problems we validate the new distance experimentally"
cs.AI|cs.DS|cs.LG,"genetic fitness optimization using small populations or small population updates across generations generally suffers from randomly diverging evolutions we propose a notion of highly probable fitness optimization through feasible evolutionary computing runs on small size populations based on rapidly mixing markov chains , the approach pertains to most types of evolutionary genetic algorithms , genetic programming and the like we establish that for systems having associated rapidly mixing markov chains and appropriate stationary distributions the new method finds optimal programs \( individuals \) with probability almost 1 to make the method useful would require a structured design methodology where the development of the program and the guarantee of the rapidly mixing property go hand in hand we analyze a simple example to show that the method is implementable more significant examples require theoretical advances , for example with respect to the metropolis filter"
cs.DS,"in the parameterized problem textsc maxlin2 aa k , we are given a system with variables x 1 , , x n consisting of equations of the form prod i in i x i b , where x i , b in 1 , 1 and i subseteq n , each equation has a positive integral weight , and we are to decide whether it is possible to simultaneously satisfy equations of total weight at least w 2 k , where w is the total weight of all equations and k is the parameter \( if k 0 , the possibility is assured \) we show that textsc maxlin2 aa k has a kernel with at most o \( k 2 log k \) variables and can be solved in time 2 o \( k log k \) \( nm \) o \( 1 \) this solves an open problem of mahajan et al \( 2006 \) the problem textsc max r lin2 aa k , r is the same as textsc maxlin2 aa k with two differences each equation has at most r variables and r is the second parameter we prove a theorem on textsc max r lin2 aa k , r which implies that textsc max r lin2 aa k , r has a kernel with at most \( 2k 1 \) r variables improving a number of results including one by kim and williams \( 2010 \) the theorem also implies a lower bound on the maximum of a function f 1 , 1 n rightarrow mathbb r of degree r we show applicability of the lower bound by giving a new proof of the edwards erd h o s bound \( each connected graph on n vertices and m edges has a bipartite subgraph with at least m 2 \( n 1 \) 4 edges \) and obtaining a generalization"
cs.DM|cs.IT,"to reduce computational complexity and delay in randomized network coded content distribution , and for some other practical reasons , coding is not performed simultaneously over all content blocks , but over much smaller , possibly overlapping subsets of these blocks , known as generations a penalty of this strategy is throughput reduction to analyze the throughput loss , we model coding over generations with random generation scheduling as a coupon collector 's brotherhood problem this model enables us to derive the expected number of coded packets needed for successful decoding of the entire content as well as the probability of decoding failure \( the latter only when generations do not overlap \) and further , to quantify the tradeoff between computational complexity and throughput interestingly , with a moderate increase in the generation size , throughput quickly approaches link capacity overlaps between generations can further improve throughput substantially for relatively small generation sizes"
cs.AI,"in this paper we study the complexity of solving a problem when a solution of a similar instance is known this problem is relevant whenever instances may change from time to time , and known solutions may not remain valid after the change we consider two scenarios in the first one , what is known is only a solution of the problem before the change in the second case , we assume that some additional information , found during the search for this solution , is also known in the first setting , the techniques from the theory of np completeness suffice to show complexity results in the second case , negative results can only be proved using the techniques of compilability , and are often related to the size of considered changes"
cs.SI,"nowadays , sustained development of different social media can be observed worldwide one of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains these evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods stable group changes identification \( sgci \) and group evolution discovery \( ged \) based on the observed evolution chains of various length , structural network features are extracted , validated and selected as well as used to learn classification models the experimental studies were performed on three real datasets with different profile dblp , facebook and polish blogosphere the process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length the results revealed that , in general , the longer evolution chains the better predictive abilities of the classification models however , chains of length 3 to 7 enabled the ged based method to almost reach its maximum possible prediction quality for sgci , this value was at the level of 3 to 5 last periods"
cs.SI,"we study a prototypical model of a parliament with two parties or two political coalitions and we show how the introduction of a variable percentage of randomly selected independent legislators can increase the global efficiency of a legislature , in terms of both the number of laws passed and the average social welfare obtained we also analytically find an efficiency golden rule which allows to fix the optimal number of legislators to be selected at random after that regular elections have established the relative proportion of the two parties or coalitions these results are in line with both the ancient greek democratic system and the recent discovery that the adoption of random strategies can improve the efficiency of hierarchical organizations"
cs.DM,"we show that edge clique graphs of cocktail party graphs have unbounded rankwidth this , and other observations lead us to conjecture that the edge clique cover problem is np complete for cographs we show that the independent set problem on edge clique graphs of cographs we show that the independent set problem on edge clique graphs of graphs without odd wheels remains np complete we present a ptas for planar graphs and show that the problem is polynomial for planar graphs without triangle separators"
cs.DM|cs.SI|stat.ML,"we study the problem of identifying the source of a diffusion spreading over a regular tree when the degree of each node is at least three , we show that it is possible to construct confidence sets for the diffusion source with size independent of the number of infected nodes our estimators are motivated by analogous results in the literature concerning identification of the root node in preferential attachment and uniform attachment trees at the core of our proofs is a probabilistic analysis of p ' o lya urns corresponding to the number of uninfected neighbors in specific subtrees of the infection tree we also provide an example illustrating the shortcomings of source estimation techniques in settings where the underlying graph is asymmetric"
cs.AI,"for robots to be accommodated in human spaces and in humans daily activities , robots should be able to understand messages from the human conversation partner in the same light , humans must also understand the messages that are being communicated by robots , including the non verbal ones we conducted a web based video study wherein participants gave interpretations on the iconic gestures and emblems that were produced by an anthropomorphic robot out of the 15 gestures presented , we found 6 robotic gestures that can be accurately recognized by the human observer these were nodding , clapping , hugging , expressing anger , walking , and flying we reviewed these gestures for their meaning from literatures in human and animal behavior we conclude by discussing the possible implications of these gestures for the design of social robots that are aimed to have engaging interactions with humans"
cs.AI|cs.DS,"discovering patterns from data is an important task in data mining there exist techniques to find large collections of many kinds of patterns from data very efficiently a collection of patterns can be regarded as a summary of the data a major difficulty with patterns is that pattern collections summarizing the data well are often very large in this dissertation we describe methods for summarizing pattern collections in order to make them also more understandable more specifically , we focus on the following themes 1 \) quality value simplifications 2 \) pattern orderings 3 \) pattern chains and antichains 4 \) change profiles 5 \) inverse pattern discovery"
cs.LG|stat.ML,"this paper discusses the effect of hubness in zero shot learning , when ridge regression is used to find a mapping between the example space to the label space contrary to the existing approach , which attempts to find a mapping from the example space to the label space , we show that mapping labels into the example space is desirable to suppress the emergence of hubs in the subsequent nearest neighbor search step assuming a simple data model , we prove that the proposed approach indeed reduces hubness this was verified empirically on the tasks of bilingual lexicon extraction and image labeling hubness was reduced with both of these tasks and the accuracy was improved accordingly"
cs.LG|stat.ML,"we propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample this method achieves robustness by combining a traditional kernel density estimator \( kde \) with ideas from classical m estimation we interpret the kde based on a radial , positive semi definite kernel as a sample mean in the associated reproducing kernel hilbert space since the sample mean is sensitive to outliers , we estimate it robustly via m estimation , yielding a robust kernel density estimator \( rkde \) an rkde can be computed efficiently via a kernelized iteratively re weighted least squares \( irwls \) algorithm necessary and sufficient conditions are given for kernelized irwls to converge to the global minimizer of the m estimator objective function the robustness of the rkde is demonstrated with a representer theorem , the influence function , and experimental results for density estimation and anomaly detection"
cs.DM,after identifying the reduced incidence algebra of an arbitrary cobweb poset the very first properties of these algebras are being disclosed
cs.LG|cs.SI,"in this work , we analyze the generalization ability of distributed online learning algorithms under stationary and non stationary environments we derive bounds for the excess risk attained by each node in a connected network of learners and study the performance advantage that diffusion strategies have over individual non cooperative processing we conduct extensive simulations to illustrate the results"
cs.DS,"we study a quantum algorithm that consists of a simple quantum markov process , and we analyze its behavior on restricted versions of quantum 2 sat we prove that the algorithm solves this decision problem with high probability for n qubits , l clauses , and promise gap c in time o \( n 2 l 2 c 2 \) if the hamiltonian is additionally polynomially gapped , our algorithm efficiently produces a state that has high overlap with the satisfying subspace the markov process we study is a quantum analogue of sch oning 's probabilistic algorithm for k sat"
cs.LG,"model predictive control \( mpc \) is an effective method for controlling robotic systems , particularly autonomous aerial vehicles such as quadcopters however , application of mpc can be computationally demanding , and typically requires estimating the state of the system , which can be challenging in complex , unstructured environments reinforcement learning can in principle forego the need for explicit state estimation and acquire a policy that directly maps sensor readings to actions , but is difficult to apply to underactuated systems that are liable to fail catastrophically during training , before an effective policy has been found we propose to combine mpc with reinforcement learning in the framework of guided policy search , where mpc is used to generate data at training time , under full state observations provided by an instrumented training environment this data is used to train a deep neural network policy , which is allowed to access only the raw observations from the vehicle 's onboard sensors after training , the neural network policy can successfully control the robot without knowledge of the full state , and at a fraction of the computational cost of mpc we evaluate our method by learning obstacle avoidance policies for a simulated quadrotor , using simulated onboard sensors and no explicit state estimation at test time"
cs.AI|cs.LG|stat.ML,"many interesting real world domains involve reinforcement learning \( rl \) in partially observable environments efficient learning in such domains is important , but existing sample complexity bounds for partially observable rl are at least exponential in the episode length we give , to our knowledge , the first partially observable rl algorithm with a polynomial bound on the number of episodes on which the algorithm may not achieve near optimal performance our algorithm is suitable for an important class of episodic pomdps our approach builds on recent advances in method of moments for latent variable model estimation"
cs.SI,"real complex systems are not rigidly structured no clear rules or blueprints exist for their construction yet , amidst their apparent randomness , complex structural properties universally emerge we propose that an important class of complex systems can be modeled as an organization of many embedded levels \( potentially infinite in number \) , all of them following the same universal growth principle known as preferential attachment we give examples of such hierarchy in real systems , for instance in the pyramid of production entities of the film industry more importantly , we show how real complex networks can be interpreted as a projection of our model , from which their scale independence , their clustering , their hierarchy , their fractality and their navigability naturally emerge our results suggest that complex networks , viewed as growing systems , can be quite simple , and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature"
cs.DS,"in this work , we focus on several completion problems for subclasses of chordal graphs minimum fill in , interval completion , proper interval completion , threshold completion , and trivially perfect completion in these problems , the task is to add at most k edges to a given graph in order to obtain a chordal , interval , proper interval , threshold , or trivially perfect graph , respectively we prove the following lower bounds for all these problems , as well as for the related chain completion problem assuming the exponential time hypothesis , none of these problems can be solved in time 2 o \( n \( 1 2 \) log c n \) or 2 o \( k \( 1 4 \) log c k \) n o \( 1 \) , for some integer c assuming the non existence of a subexponential time approximation scheme for min bisection on d regular graphs , for some constant d , none of these problems can be solved in time 2 o \( n \) or 2 o \( sqrt \( k \) \) n o \( 1 \) for all the aforementioned completion problems , apart from proper interval completion , fpt algorithms with running time of the form 2 o \( sqrt \( k \) log k \) n o \( 1 \) are known thus , the second result proves that a significant improvement of any of these algorithms would lead to a surprising breakthrough in the design of approximation algorithms for min bisection to prove our results , we propose a new reduction methodology based on combining the classic approach of starting with a sparse instance of 3 sat , prepared using the sparsification lemma , with the existence of almost linear size probabilistically checkable proofs \( pcps \) apart from our main results , we also obtain lower bounds excluding the existence of subexponential algorithms for the optimum linear arrangement problem , as well as improved , yet still not tight , lower bounds for feedback arc set in tournaments"
cs.AI,"the vocabulary of a continuous speech recognition \( csr \) system is a significant factor in determining its performance in this paper , we present three principled approaches to select the target vocabulary for a particular domain by trading off between the target out of vocabulary \( oov \) rate and vocabulary size we evaluate these approaches against an ad hoc baseline strategy results are presented in the form of oov rate graphs plotted against increasing vocabulary size for each technique"
cs.LG|cs.SI,"the average distance from a node to all other nodes in a graph , or from a query point in a metric space to a set of points , is a fundamental quantity in data analysis the inverse of the average distance , known as the \( classic \) closeness centrality of a node , is a popular importance measure in social network analysis we show that the average distance \( and hence centrality \) of all nodes in a network can be estimated in time o \( epsilon 2 m log n \) , where n and m are the number of nodes and edges in a graph for a set of n point in a general metric space , we show that using preprocessing that takes epsilon 2 n distance queries we can compute a weighted sample of only o \( epsilon 2 \) points such that the average distance from any query point to our points can be estimated using o \( epsilon 2 \) distance computations our estimates are unbiased with nrmse of at most epsilon another log n factor on sample size ensures a very high probability of relative error at most epsilon for all nodes"
cs.DS,"we present a series of almost settled inapproximability results for three fundamental problems the first in our series is the subexponential time inapproximability of the maximum independent set problem , a question studied in the area of parameterized complexity the second is the hardness of approximating the maximum induced matching problem on bounded degree bipartite graphs the last in our series is the tight hardness of approximating the k hypergraph pricing problem , a fundamental problem arising from the area of algorithmic game theory in particular , assuming the exponential time hypothesis , our two main results are for any r larger than some constant , any r approximation algorithm for the maximum independent set problem must run in at least 2 n 1 epsilon r 1 epsilon time this nearly matches the upper bound of 2 n r \( cygan et al , 2008 \) it also improves some hardness results in the domain of parameterized complexity \( e g , escoffier et al , 2012 and chitnis et al , 2013 \) for any k larger than some constant , there is no polynomial time min \( k 1 epsilon , n 1 2 epsilon \) approximation algorithm for the k hypergraph pricing problem , where n is the number of vertices in an input graph this almost matches the upper bound of min \( o \( k \) , tilde o \( sqrt n \) \) \( by balcan and blum , 2007 and an algorithm in this paper \) we note an interesting fact that , in contrast to n 1 2 epsilon hardness for polynomial time algorithms , the k hypergraph pricing problem admits n delta approximation for any delta 0 in quasi polynomial time this puts this problem in a rare approximability class in which approximability thresholds can be improved significantly by allowing algorithms to run in quasi polynomial time"
cs.AI|cs.LG,"a useful method for representing bayesian classifiers is through emph discriminant functions here , using copula functions , we propose a new model for discriminants this model provides a rich and generalized class of decision boundaries these decision boundaries significantly boost the classification accuracy especially for high dimensional feature spaces we strengthen our analysis through simulation results"
cs.DS,"the problem of counting occurrences of query graphs in a large data graph , known as subgraph counting , is fundamental to several domains such as genomics and social network analysis many important special cases \( e g triangle counting \) have received significant attention color coding is a very general and powerful algorithmic technique for subgraph counting color coding has been shown to be effective in several applications , but scalable implementations are only known for the special case of em tree queries \( i e queries of treewidth one \) in this paper we present the first efficient distributed implementation for color coding that goes beyond tree queries our algorithm applies to any query graph of treewidth 2 since tree queries can be solved in time linear in the size of the data graph , our contribution is the first step into the realm of colour coding for queries that require superlinear running time in the worst case this superlinear complexity leads to significant load balancing problems on graphs with heavy tailed degree distributions our algorithm structures the computation to work around high degree nodes in the data graph , and achieves very good runtime and scalability on a diverse collection of data and query graph pairs as a result we also provide theoretical analysis of our algorithmic techniques , showing asymptotic improvements in runtime on random graphs with power law degree distributions , a popular model for real world graphs"
cs.DM|cs.SI,"network robustness research aims at finding a measure to quantify network robustness once such a measure has been established , we will be able to compare networks , to improve existing networks and to design new networks that are able to continue to perform well when it is subject to failures or attacks in this paper we survey a large amount of robustness measures on simple , undirected and unweighted graphs , in order to offer a tool for network administrators to evaluate and improve the robustness of their network the measures discussed in this paper are based on the concepts of connectivity \( including reliability polynomials \) , distance , betweenness and clustering some other measures are notions from spectral graph theory , more precisely , they are functions of the laplacian eigenvalues in addition to surveying these graph measures , the paper also contains a discussion of their functionality as a measure for topological network robustness"
cs.SI,"animals in groups often exchange calls , in patterns whose temporal structure may be influenced by contextual factors such as physical location and the social network structure of the group we introduce a model based analysis for temporal patterns of animal call timing , originally developed for networks of firing neurons this has advantages over cross correlation analysis in that it can correctly handle common cause confounds and provides a generative model of call patterns with explicit parameters for the influences between individuals it also has advantages over standard markovian analysis in that it incorporates detailed temporal interactions which affect timing as well as sequencing of calls further , a fitted model can be used to generate novel synthetic call sequences we apply the method to calls recorded from groups of domesticated zebra finch \( taenopyggia guttata \) individuals we find that the communication network in these groups has stable structure that persists from one day to the next , and that kernels reflecting the temporal range of influence have a characteristic structure for a calling individual 's effect on itself , its partner , and on others in the group we further find characteristic patterns of influences by call type as well as by individual"
cs.DS,"we consider the problem of maximizing a submodular set function that can be expressed as the expected value of a function of an n size collection of independent random variables with given prior distributions this is a combinatorial optimization problem that arises in many applications , including the team selection problem that arises in the context of online labour platforms we consider a class of approximation algorithms that are restricted to use some statistics of the prior distributions to estimate the outputs of value oracle calls , which can be interpreted as test scores we establish that the existence of test scores that provide an approximation guarantee is equivalent to that a special type of test scores , so called replication test scores , provide this approximation guarantee we also identify sufficient conditions for replication test scores to provide this approximation guarantee these sufficient conditions are shown to hold for a large number of models of production , including a monotone concave function of total production , best shot , and constant elasticity of substitution production function we also study a more general submodular welfare maximization problem , which accommodates the case of multiple projects , and derive an omega \( 1 log \( k \) \) approximation , algorithm where k is the maximum number of participants in a project we evaluate the quality of approximation provided by test scores in a case study using data from a popular online labour platform for software development"
cs.IT|cs.LG,"least squares \( ls \) fitting is one of the most fundamental techniques in science and engineering it is used to estimate parameters from multiple noisy observations in many problems the parameters are known a priori to be bounded integer valued , or they come from a finite set of values on an arbitrary finite lattice in this case finding the closest vector becomes np hard problem in this paper we propose a novel algorithm , the tomographic least squares decoder \( tlsd \) , that not only solves the ils problem , better than other sub optimal techniques , but also is capable of providing the a posteriori probability distribution for each element in the solution vector the algorithm is based on reconstruction of the vector from multiple two dimensional projections the projections are carefully chosen to provide low computational complexity unlike other iterative techniques , such as the belief propagation , the proposed algorithm has ensured convergence we also provide simulated experiments comparing the algorithm to other sub optimal algorithms"
cs.LG,"because of their performance , deep neural networks are increasingly used for object recognition they are particularly attractive because of their ability to 'absorb' great quantities of labeled data through millions of parameters however , as the accuracy and the model sizes increase , so does the memory requirements of the classifiers this prohibits their usage on resource limited hardware , including cell phones or other embedded devices we introduce a novel compression method for deep neural networks that performs during the learning phase it consists in adding an extra regularization term to the cost function of fully connected layers we combine this method with product quantization \( pq \) of the trained weights for higher savings in memory and storage consumption we evaluate our method on two data sets \( mnist and cifar10 \) , on which we achieve significantly larger compression than state of the art methods"
cs.DS,"given an implicit n times n matrix a with oracle access x ta x for any x in mathbb r n , we study the query complexity of randomized algorithms for estimating the trace of the matrix this problem has many applications in quantum physics , machine learning , and pattern matching two metrics are commonly used for evaluating the estimators i \) variance ii \) a high probability multiplicative approximation guarantee almost all the known estimators are of the form frac 1 k sum i 1 k x i t a x i for x i in mathbb r n being i i d for some special distribution our main results are summarized as follows we give an exact characterization of the minimum variance unbiased estimator in the broad class of linear nonadaptive estimators \( which subsumes all the existing known estimators \) we also consider the query complexity lower bounds for any \( possibly nonlinear and adaptive \) estimators \( 1 \) we show that any estimator requires omega \( 1 epsilon \) queries to have a guarantee of variance at most epsilon \( 2 \) we show that any estimator requires omega \( frac 1 epsilon 2 log frac 1 delta \) queries to achieve a \( 1 pm epsilon \) multiplicative approximation guarantee with probability at least 1 delta both above lower bounds are asymptotically tight as a corollary , we also resolve a conjecture in the seminal work of avron and toledo \( journal of the acm 2011 \) regarding the sample complexity of the gaussian estimator"
cs.AI,"big data analytics applications drive the convergence of data management and machine learning but there is no conceptual language available that is spoken in both worlds the main contribution of the paper is a method to translate bayesian networks , a main conceptual language for probabilistic graphical models , into usable entity relationship models the transformed representation of a bayesian network leaves out mathematical details about probabilistic relationships but unfolds all information relevant for data management tasks as a real world example , we present the topicexplorer system that uses bayesian topic models as a core component in an interactive , database supported web application last , we sketch a conceptual framework that eases machine learning specific development tasks while building big data analytics applications"
cs.DM,"we consider d dimensional simplicial complexes which can be pl embedded in the 2d dimensional euclidean space in short , we show that in any such complex , for any three vertices , the intersection of link complexes of the vertices is linklessly embeddable in \( 2d 1 \) dimensional euclidean space from this observation , we derive a new upper bound on the total number of d simplices in an embeddable complex in 2d space with n vertices , improving known upper bounds moreover , the bound is also true for the size of d complexes linklessly embeddable in \( 2d 1 \) dimensional space"
cs.SI,"communication networks are vulnerable to natural disasters , such as earthquakes or floods , as well as to physical attacks , such as an electromagnetic pulse \( emp \) attack such real world events happen at specific geographical locations and disrupt specific parts of the network therefore , the geographical layout of the network determines the impact of such events on the network 's physical topology in terms of capacity , connectivity , and flow recent works focused on assessing the vulnerability of a deterministic network to such events in this work , we focus on assessing the vulnerability of \( geographical \) random networks to such disasters we consider stochastic graph models in which nodes and links are probabilistically distributed on a plane , and model the disaster event as a circular cut that destroys any node or link within or intersecting the circle we develop algorithms for assessing the damage of both targeted and non targeted \( random \) attacks and determining which attack locations have the expected most disruptive impact on the network then , we provide experimental results for assessing the impact of circular disasters to communications networks in the usa , where the network 's geographical layout was modeled probabilistically , relying on demographic information only our results demonstrates the applicability of our algorithms to real world scenarios our algorithms allows to examine how valuable is public information about the network 's geographical area \( e g , demography , topography , economy \) to an attacker 's destruction assessment capabilities in the case the network 's physical topology is hidden or examine the affect of hiding the actual physical location of the fibers on the attack strategy thereby , our schemes can be used as a tool for policy makers and engineers to design more robust networks and identifying locations which require additional protection efforts"
cs.AI|cs.DS,"in many combinatorial problems one may need to model the diversity or similarity of assignments in a solution for example , one may wish to maximise or minimise the number of distinct values in a solution to formulate problems of this type , we can use soft variants of the well known alldifferent and allequal constraints we present a taxonomy of six soft global constraints , generated by combining the two latter ones and the two standard cost functions , which are either maximised or minimised we characterise the complexity of achieving arc and bounds consistency on these constraints , resolving those cases for which np hardness was neither proven nor disproven in particular , we explore in depth the constraint ensuring that at least k pairs of variables have a common value we show that achieving arc consistency is np hard , however achieving bounds consistency can be done in polynomial time through dynamic programming moreover , we show that the maximum number of pairs of equal variables can be approximated by a factor 1 2 with a linear time greedy algorithm finally , we provide a fixed parameter tractable algorithm with respect to the number of values appearing in more than two distinct domains interestingly , this taxonomy shows that enforcing equality is harder than enforcing difference"
cs.AI,"the main purpose of this article is to describe potential benefits and applications of the sp theory , a unique attempt to simplify and integrate ideas across artificial intelligence , mainstream computing and human cognition , with information compression as a unifying theme the theory , including a concept of multiple alignment , combines conceptual simplicity with descriptive and explanatory power in several areas including representation of knowledge , natural language processing , pattern recognition , several kinds of reasoning , the storage and retrieval of information , planning and problem solving , unsupervised learning , information compression , and human perception and cognition in the sp machine an expression of the sp theory which is currently realised in the form of computer models there is potential for an overall simplification of computing systems , including software as a theory with a broad base of support , the sp theory promises useful insights in many areas and the integration of structures and functions , both within a given area and amongst different areas there are potential benefits in natural language processing \( with potential for the understanding and translation of natural languages \) , the need for a versatile intelligence in autonomous robots , computer vision , intelligent databases , maintaining multiple versions of documents or web pages , software engineering , criminal investigations , the management of big data and gaining benefits from it , the semantic web , medical diagnosis , the detection of computer viruses , the economical transmission of data , and data fusion further development of these ideas would be facilitated by the creation of a high parallel , web based , open source version of the sp machine , with a good user interface this would provide a means for researchers to explore what can be done with the system and to refine it"
cs.AI,"this paper presents a multilayered architecture that enhances the capabilities of current qa systems and allows different types of complex questions or queries to be processed the answers to these questions need to be gathered from factual information scattered throughout different documents specifically , we designed a specialized layer to process the different types of temporal questions complex temporal questions are first decomposed into simple questions , according to the temporal relations expressed in the original question in the same way , the answers to the resulting simple questions are recomposed , fulfilling the temporal restrictions of the original complex question a novel aspect of this approach resides in the decomposition which uses a minimal quantity of resources , with the final aim of obtaining a portable platform that is easily extensible to other languages in this paper we also present a methodology for evaluation of the decomposition of the questions as well as the ability of the implemented temporal layer to perform at a multilingual level the temporal layer was first performed for english , then evaluated and compared with a \) a general purpose qa system \( f measure 65 47 for qa plus english temporal layer vs 38 01 for the general qa system \) , and b \) a well known qa system much better results were obtained for temporal questions with the multilayered system this system was therefore extended to spanish and very good results were again obtained in the evaluation \( f measure 40 36 for qa plus spanish temporal layer vs 22 94 for the general qa system \)"
cs.LG,"one of the most challenging recommendation tasks is recommending to a new , previously unseen user this is known as the 'user cold start' problem assuming certain features or attributes of users are known , one approach for handling new users is to initially model them based on their features motivated by an ad targeting application , this paper describes an extreme online recommendation setting where the cold start problem is perpetual every user is encountered by the system just once , receives a recommendation , and either consumes or ignores it , registering a binary reward we introduce one pass factorization of feature sets , off set , a novel recommendation algorithm based on latent factor analysis , which models users by mapping their features to a latent space furthermore , off set is able to model non linear interactions between pairs of features off set is designed for purely online recommendation , performing lightweight updates of its model per each recommendation reward observation we evaluate off set against several state of the art baselines , and demonstrate its superiority on real ad targeting data"
cs.DM,"a multi relational graph maintains two or more relations over a vertex set this article defines an algebra for traversing such graphs that is based on an n ary relational algebra , a concatenative single relational path algebra , and a tensor based multi relational algebra the presented algebra provides a monoid , automata , and formal language theoretic foundation for the construction of a multi relational graph traversal engine"
cs.LG|stat.ML,we study the problem of finding the most mutually correlated arms among many arms we show that adaptive arms sampling strategies can have significant advantages over the non adaptive uniform sampling strategy our proposed algorithms rely on a novel correlation estimator the use of this accurate estimator allows us to get improved results for a wide range of problem instances
cs.DS,"in the context of load balancing , lu et al introduced the distributed join idle queue algorithm , where a group of dispatchers distribute jobs to a cluster of parallel servers each dispatcher maintains a queue of idle servers when a job arrives to a dispatcher , it sends it to a server on its queue , or to a random server if the queue is empty in turn , when a server has no jobs , it requests to be placed on the idle queue of a randomly chosen dispatcher although this algorithm was shown to be quite effective , the original asymptotic analysis makes simplifying assumptions that become increasingly inaccurate as the system load increases further , the analysis does not naturally generalize to interesting variations , such as having a server request to be placed on the idle queue of a dispatcher before it has completed all jobs , which can be beneficial under high loads we provide a new asymptotic analysis of join idle queue systems based on mean field fluid limit methods , deriving families of differential equations that describe these systems our analysis avoids previous simplifying assumptions , is empirically more accurate , and generalizes naturally to the variation described above , as well as other simple variations our theoretical and empirical analyses shed further light on the performance of join idle queue , including potential performance pitfalls under high load"
cs.DS,"we study three representations of hierarchies of partitions dendrograms \( direct representations \) , saliency maps , and minimum spanning trees we provide a new bijection between saliency maps and hierarchies based on quasi flat zones as used in image processing and characterize saliency maps and minimum spanning trees as solutions to constrained minimization problems where the constraint is quasi flat zones preservation in practice , these results form a toolkit for new hierarchical methods where one can choose the most convenient representation they also invite us to process non image data with morphological hierarchies"
cs.SI,this paper presents the research results that have identified a set of characteristic parameters of propagandistic messages later these parameters can be used in the algorithm creating special user oriented propagandistic messages to improve distribution and assimilation of information by users
cs.SI,"most previous works study the evolution of cooperation in a structured population by commonly employing an isolated single network however , realistic systems are composed of many interdependent networks coupled with each other , rather than the isolated single one in this paper , we consider a system including two interacting networks with the same size , entangled with each other by the introduction of probabilistic interconnections we introduce the public goods game into such system , and study how the probabilistic interconnection influences the evolution of cooperation of the whole system and the coupling effect between two layers of interdependent networks simulation results show that there exists an intermediate region of interconnection probability leading to the maximum cooperation level in the whole system interestingly , we find that at the optimal interconnection probability the fraction of internal links between cooperators in two layers is maximal also , even if initially there are no cooperators in one layer of interdependent networks , cooperation can still be promoted by probabilistic interconnection , and the cooperation levels in both layers can more easily reach an agreement at the intermediate interconnection probability our results may be helpful in understanding the cooperative behavior in some realistic interdependent networks and thus highlight the importance of probabilistic interconnection on the evolution of cooperation"
cs.LG|cs.SI,"the abundance of user generated data in social media has incentivized the development of methods to infer the latent attributes of users , which are crucially useful for personalization , advertising and recommendation however , the current user profiling approaches have limited success , due to the lack of a principled way to integrate different types of social relationships of a user , and the reliance on scarcely available labeled data in building a prediction model in this paper , we present a novel solution termed collective semi supervised learning \( csl \) , which provides a principled means to integrate different types of social relationship and unlabeled data under a unified computational framework the joint learning from multiple relationships and unlabeled data yields a computationally sound and accurate approach to model user attributes in social media extensive experiments using twitter data have demonstrated the efficacy of our csl approach in inferring user attributes such as account type and marital status we also show how csl can be used to determine important user features , and to make inference on a larger user population"
cs.LG,"one major bottleneck in the practical implementation of received signal strength \( rss \) based indoor localization systems is the extensive deployment efforts required to construct the radio maps through fingerprinting in this paper , we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment by accumulating the information of localized rsss , this scheme can also simultaneously construct the radio map with limited calibration to design this scheme , we employ a source data set that possesses the same spatial correlation of the rsss in the indoor environment under study the knowledge of this data set is then transferred to a limited number of calibration fingerprints and one or several rss observations with unknown locations , in order to perform direct localization of these observations using manifold alignment we test two different source data sets , namely a simulated radio propagation map and the environments plan coordinates for moving users , we exploit the correlation of their observations to improve the localization accuracy the online testing in two indoor environments shows that the plan coordinates achieve better results than the simulated radio maps , and a negligible degradation with 70 85 reduction in calibration load"
cs.AI,"we report our initial investigations into reliability and path finding based models and propose future areas of interest inspired by broken sidewalks during on campus construction projects , we develop two models for navigating this unreliable network these are based on a concept of accumulating risk backward from the destination , and both operate on directed acyclic graphs with a probability of failure associated with each edge the first serves to introduce and has faults addressed by the second , more conservative model next , we show a paradox when these models are used to construct polynomials on conceptual networks , such as design processes and software development life cycles when the risk of a network increases uniformly , the most reliable path changes from wider and longer to shorter and narrower if we let professional inexperience such as with entry level cooks and software developers represent probability of edge failure , does this change in path imply that the novice should follow instructions with fewer back up plans , yet those with alternative routes should be followed by the expert \?"
cs.LG,"we consider unsupervised crowdsourcing performance based on the model wherein the responses of end users are essentially rated according to how their responses correlate with the majority of other responses to the same subtasks questions in one setting , we consider an independent sequence of identically distributed crowdsourcing assignments \( meta tasks \) , while in the other we consider a single assignment with a large number of component subtasks both problems yield intuitive results in which the overall reliability of the crowd is a factor"
cs.DS,"we present an efficient fixed parameter algorithm for sat parameterized by the incidence treewidth , i e , the treewidth of the bipartite graph whose vertices are the variables and clauses of the given cnf formula a variable and a clause are joined by an edge if and only if the variable occurs in the clause our algorithm runs in time o \( 4 k k l n \) , where k denotes the incidence treewidth , l denotes the size of a largest clause , and n denotes the number of nodes of the tree decomposition"
cs.DM,a homogeneous set of a graph g is a set x of vertices such that 2 le lvert x rvert lvert v \( g \) rvert and no vertex in v \( g \) x has both a neighbor and a non neighbor in x a graph is prime if it has no homogeneous set we present an algorithm to decide whether a class of graphs given by a finite set of forbidden induced subgraphs contains infinitely many non isomorphic prime graphs
cs.LG,"an algorithm is described that adaptively learns a non linear mutation distribution it works by training a denoising autoencoder \( da \) online at each generation of a genetic algorithm to reconstruct a slowly decaying memory of the best genotypes so far a compressed hidden layer forces the autoencoder to learn hidden features in the training set that can be used to accelerate search on novel problems with similar structure its output neurons define a probability distribution that we sample from to produce offspring solutions the algorithm outperforms a canonical genetic algorithm on several combinatorial optimisation problems , e g multidimensional 0 1 knapsack problem , maxsat , hiff , and on parameter optimisation problems , e g rastrigin and rosenbrock functions"
cs.LG,"we present bl wolf , a framework for learnability in repeated zero sum games where the cost of learning is measured by the losses the learning agent accrues \( rather than the number of rounds \) the game is adversarially chosen from some family that the learner knows the opponent knows the game and the learner 's learning strategy the learner tries to either not accrue losses , or to quickly learn about the game so as to avoid future losses \( this is consistent with the win or learn fast \( wolf \) principle bl stands for ``bounded loss'' \) our framework allows for both probabilistic and approximate learning the resultant notion of em bl wolf learnability can be applied to any class of games , and allows us to measure the inherent disadvantage to a player that does not know which game in the class it is in we present em guaranteed bl wolf learnability results for families of games with deterministic payoffs and families of games with stochastic payoffs we demonstrate that these families are em guaranteed approximately bl wolf learnable with lower cost we then demonstrate families of games \( both stochastic and deterministic \) that are not guaranteed bl wolf learnable we show that those families , nevertheless , are em bl wolf learnable to prove these results , we use a key lemma which we derive"
cs.LG|stat.ML,"variance reduction stochastic gradient descent methods enable minimization of model fitting problems involving big datasets with low iteration complexity and fast asymptotic convergence rates however , they scale poorly in distributed settings in this paper , we propose a highly parallel variance reduction method , centralvr , with performance that scales linearly with the number of worker nodes we also propose distributed versions of popular variance reduction methods that support a high degree of parallelization unlike existing distributed stochastic gradient schemes , centralvr exhibits linear performance gains up to thousands of cores for massive datasets"
cs.AI,"in multiobjective combinatorial optimization , there exists two main classes of metaheuristics , based either on multiple aggregations , or on a dominance relation as in the single objective case , the structure of the search space can explain the difficulty for multiobjective metaheuristics , and guide the design of such methods in this work we analyze the properties of multiobjective combinatorial search spaces in particular , we focus on the features related the efficient set , and we pay a particular attention to the correlation between objectives few benchmark takes such objective correlation into account here , we define a general method to design multiobjective problems with correlation as an example , we extend the well known multiobjective nk landscapes by measuring different properties of the search space , we show the importance of considering the objective correlation on the design of metaheuristics"
cs.LG,"we present a method for gesture detection and localisation based on multi scale and multi modal deep learning each visual modality captures spatial information at a particular spatial scale \( such as motion of the upper body or a hand \) , and the whole system operates at three temporal scales key to our technique is a training strategy which exploits i \) careful initialization of individual modalities and ii \) gradual fusion involving random dropping of separate channels \( dubbed moddrop \) for learning cross modality correlations while preserving uniqueness of each modality specific representation we present experiments on the chalearn 2014 looking at people challenge gesture recognition track , in which we placed first out of 17 teams fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates , allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels futhermore , the proposed moddrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities in addition , we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio"
cs.SI,"microblogging services such as twitter are an increasingly important way to communicate , both for individuals and for groups through the use of hashtags that denote topics of conversation however , groups can be easily blocked from communicating through blocking of posts with the given hashtags we propose h00t , a system for censorship resistant microblogging h00t presents an interface that is much like twitter , except that hashtags are replaced with very short hashes \( e g , 24 bits \) of the group identifier naturally , with such short hashes , hashtags from different groups may collide and h00t users will actually seek to create collisions by encrypting all posts with keys derived from the group identifiers , h00t client software can filter out other groups' posts while making such filtering difficult for the adversary in essence , by leveraging collisions , groups can tunnel their posts in other groups' posts a censor could not block a given group without also blocking the other groups with colliding hashtags we evaluate the feasibility of h00t through traces collected from twitter , showing that a single modern computer has enough computational throughput to encrypt every tweet sent through twitter in real time we also use these traces to analyze the bandwidth and anonymity tradeoffs that would come with different variations on how group identifiers are encoded and hashtags are selected to purposefully collide with one another"
cs.LG|stat.ML,in this paper we introduce deep gaussian process \( gp \) models deep gps are a deep belief network based on gaussian process mappings the data is modeled as the output of a multivariate gp the inputs to that gaussian process are then governed by another gp a single layer model is equivalent to a standard gp or the gp latent variable model \( gp lvm \) we perform inference in the model by approximate variational marginalization this results in a strict lower bound on the marginal likelihood of the model which we use for model selection \( number of layers and nodes per layer \) deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization our fully bayesian treatment allows for the application of deep models even when data is scarce model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples
cs.DM|cs.IT,"consider a linear n , k , d q code c we say that that i th coordinate of c has locality r , if the value at this coordinate can be recovered from accessing some other r coordinates of c data storage applications require codes with small redundancy , low locality for information coordinates , large distance , and low locality for parity coordinates in this paper we carry out an in depth study of the relations between these parameters we establish a tight bound for the redundancy n k in terms of the message length , the distance , and the locality of information coordinates we refer to codes attaining the bound as optimal we prove some structure theorems about optimal codes , which are particularly strong for small distances this gives a fairly complete picture of the tradeoffs between codewords length , worst case distance and locality of information symbols we then consider the locality of parity check symbols and erasure correction beyond worst case distance for optimal codes using our structure theorem , we obtain a tight bound for the locality of parity symbols possible in such codes for a broad class of parameter settings we prove that there is a tradeoff between having good locality for parity checks and the ability to correct erasures beyond the minimum distance"
cs.DS,"we give two results for multicommodity flows in the d dimensional hypercube q d with independent random edge capacities distributed like c where pr c 0 1 2 firstly , with high probability as d rightarrow infty , the network can support simultaneous multicommodity flows of volume close to e c between all antipodal vertex pairs secondly , with high probability , the network can support simultaneous multicommodity flows of volume close to 2 1 d e c between all vertex pairs both results are best possible"
cs.DS,"courcelle 's theorem states that every problem definable in monadic second order logic can be solved in linear time on structures of bounded treewidth , for example , by constructing a tree automaton that recognizes or rejects a tree decomposition of the structure existing , optimized software like the mona tool can be used to build the corresponding tree automata , which for bounded treewidth are of constant size unfortunately , the constants involved can become extremely large every quantifier alternation requires a power set construction for the automaton here , the required space can become a problem in practical applications in this paper , we present a novel , direct approach based on model checking games , which avoids the expensive power set construction experiments with an implementation are promising , and we can solve problems on graphs where the automata theoretic approach fails in practice"
cs.LG,"we propose the neural programmer interpreter \( npi \) a recurrent and compositional neural network that learns to represent and execute programs npi has three learnable components a task agnostic recurrent core , a persistent key value program memory , and domain specific encoders that enable a single npi to operate in multiple perceptually diverse environments with distinct affordances by learning to compose lower level programs to express higher level programs , npi reduces sample complexity and increases generalization ability compared to sequence to sequence lstms the program memory allows efficient learning of additional tasks by building on existing programs npi can also harness the environment \( e g a scratch pad with read write pointers \) to cache intermediate results of computation , lessening the long term memory burden on recurrent hidden units in this work we train the npi with fully supervised execution traces each program has example sequences of calls to the immediate subprograms conditioned on the input rather than training on a huge number of relatively weak labels , npi learns from a small number of rich examples we demonstrate the capability of our model to learn several types of compositional programs addition , sorting , and canonicalizing 3d models furthermore , a single npi learns to execute these programs and all 21 associated subprograms"
cs.DM,"we consider integer programming and the semi group membership problem we provide the following theorem of the alternative the system ax b has no nonnegative integral solution x if and only if p \( b \) 0 for some given polynomial p whose vector of coefficients lies in a convex cone that we characterize we also provide a hierarchy of linear programming relaxations , where the continuous case ax b with x real and nonnegative , describes the first relaxation in the hierarchy"
cs.DS,"in this paper new algorithm for calculating power indices is described the complexity class of the problem is p complete and even calculating power index of the biggest player is np hard task constructed algorithm is a mix of ideas of two algorithms klinz woeginger partitioning algorithm and mann shapley generating functions algorithm time and space complexities of the algorithm are analysed and compared with other known algorithms for the problem constructed algorithm has pessimistic time complexity o \( n 2 \( n 2 \) \) and pseudopolynomial complexity o \( nq \) , where q is quota of the voting game this paper also solves open problem stated by h aziz and m paterson existence of the algorithm for calculating banzhaf power indices of all players with time complexity lower than o \( n 2 2 \( n 2 \) \) not only is the answer positive but this can be done keeping the pseudopolynomial complexity of generating functions algorithm in case weights are integers new open problems are stated"
cs.LG,"place classification is a fundamental ability that a robot should possess to carry out effective human robot interactions it is a nontrivial classification problem which has attracted many research in recent years , there is a high exploitation of artificial intelligent algorithms in robotics applications inspired by the recent successes of deep learning methods , we propose an end to end learning approach for the place classification problem with the deep architectures , this methodology automatically discovers features and contributes in general to higher classification accuracies the pipeline of our approach is composed of three parts firstly , we construct multiple layers of laser range data to represent the environment information in different levels of granularity secondly , each layer of data is fed into a deep neural network model for classification , where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples finally , the predicted labels obtained from all the layers are fused based on confidence trees to maximize the overall confidence experimental results validate the effective ness of our end to end place classification framework in which both the multi layer structure and the graph regularization promote the classification performance furthermore , results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information"
cs.LG|stat.ML,"submodular functions can be exactly minimized in polynomial time , and the special case that graph cuts solve with max flow cite kz pami04 has had significant impact in computer vision cite bvz pami01 , kwatra siggraph03 , rother grabcut04 in this paper we address the important class of sum of submodular \( sos \) functions cite arora eccv12 , kolmogorov dam12 , which can be efficiently minimized via a variant of max flow called submodular flow cite edmonds adm77 sos functions can naturally express higher order priors involving , e g , local image patches however , it is difficult to fully exploit their expressive power because they have so many parameters rather than trying to formulate existing higher order priors as an sos function , we take a discriminative learning approach , effectively searching the space of sos functions for a higher order prior that performs well on our training set we adopt a structural svm approach cite joachims etal 09a , tsochantaridis etal 04 and formulate the training problem in terms of quadratic programming as a result we can efficiently search the space of sos priors via an extended cutting plane algorithm we also show how the state of the art max flow method for vision problems cite goldberg esa11 can be modified to efficiently solve the submodular flow problem experimental comparisons are made against the opencv implementation of the grabcut interactive segmentation technique cite rother grabcut04 , which uses hand tuned parameters instead of machine learning on a standard dataset cite gulshan cvpr10 our method learns higher order priors with hundreds of parameter values , and produces significantly better segmentations while our focus is on binary labeling problems , we show that our techniques can be naturally generalized to handle more than two labels"
cs.AI,"we introduce a novel latent vector space model that jointly learns the latent representations of words , e commerce products and a mapping between the two without the need for explicit annotations the power of the model lies in its ability to directly model the discriminative relation between products and a particular word we compare our method to existing latent vector space models \( lsi , lda and word2vec \) and evaluate it as a feature in a learning to rank setting our latent vector space model achieves its enhanced performance as it learns better product representations furthermore , the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation we provide an in depth analysis of the performance of our model and analyze the structure of the learned representations"
cs.AI,"unit resolution can simplify a cnf formula or detect an inconsistency by repeatedly assign the variables occurring in unit clauses given any cnf formula sigma , we show that there exists a satisfiable cnf formula psi with size polynomially related to the size of sigma such that applying unit resolution to psi simulates all the effects of applying it to sigma the formula psi is said to be the reified counterpart of sigma this approach can be used to prove that the failed literal rule , which is an inference rule used by some sat solvers , can be entirely simulated by unit resolution more generally , it sheds new light on the expressive power of unit resolution"
cs.AI,"rewriting is widely used to optimise owl sameas reasoning in materialisation based owl 2 rl systems we investigate issues related to both the correctness and efficiency of rewriting , and present an algorithm that guarantees correctness , improves efficiency , and can be effectively parallelised our evaluation shows that our approach can reduce reasoning times on practical data sets by orders of magnitude"
cs.DM,"the number of nonequivalent huffman codes of length r over an alphabet of size t has been studied frequently equivalently , the number of nonequivalent complete t ary trees has been examined we first survey the literature , unifying several independent approaches to the problem then , improving on earlier work we prove a very precise asymptotic result on the counting function , consisting of two main terms and an error term"
cs.SI,"several projects have shown the feasibility to use textual social media data to track public health concerns , such as temporal influenza patterns or geographical obesity patterns in this paper , we look at whether geo tagged images from instagram also provide a viable data source especially for lifestyle diseases , such as obesity , drinking or smoking , images of social gatherings could provide information that is not necessarily shared in , say , tweets in this study , we explore whether \( i \) tags provided by the users and \( ii \) annotations obtained via automatic image tagging are indeed valuable for studying public health we find that both user provided and machine generated tags provide information that can be used to infer a county 's health statistics whereas for most statistics user provided tags are better features , for predicting excessive drinking machine generated tags such as liquid and glass yield better models this hints at the potential of using machine generated tags to study substance abuse"
cs.LG,"dexterous multi fingered hands can accomplish fine manipulation behaviors that are infeasible with simple robotic grippers however , sophisticated multi fingered hands are often expensive and fragile low cost soft hands offer an appealing alternative to more conventional devices , but present considerable challenges in sensing and actuation , making them difficult to apply to more complex manipulation tasks in this paper , we describe an approach to learning from demonstration that can be used to train soft robotic hands to perform dexterous manipulation tasks our method uses object centric demonstrations , where a human demonstrates the desired motion of manipulated objects with their own hands , and the robot autonomously learns to imitate these demonstrations using reinforcement learning we propose a novel algorithm that allows us to blend and select a subset of the most feasible demonstrations to learn to imitate on the hardware , which we use with an extension of the guided policy search framework to use multiple demonstrations to learn generalizable neural network policies we demonstrate our approach on the rbo hand 2 , with learned motor skills for turning a valve , manipulating an abacus , and grasping"
cs.AI,"association rules are among the most widely employed data analysis methods in the field of data mining an association rule is a form of partial implication between two sets of binary variables in the most common approach , association rules are parameterized by a lower bound on their confidence , which is the empirical conditional probability of their consequent given the antecedent , and or by some other parameter bounds such as support or deviation from independence we study here notions of redundancy among association rules from a fundamental perspective we see each transaction in a dataset as an interpretation \( or model \) in the propositional logic sense , and consider existing notions of redundancy , that is , of logical entailment , among association rules , of the form any dataset in which this first rule holds must obey also that second rule , therefore the second is redundant we discuss several existing alternative definitions of redundancy between association rules and provide new characterizations and relationships among them we show that the main alternatives we discuss correspond actually to just two variants , which differ in the treatment of full confidence implications for each of these two notions of redundancy , we provide a sound and complete deduction calculus , and we show how to construct complete bases \( that is , axiomatizations \) of absolutely minimum size in terms of the number of rules we explore finally an approach to redundancy with respect to several association rules , and fully characterize its simplest case of two partial premises"
cs.AI,"counterfactual regret minimization \( cfr \) is an efficient no regret learning algorithm for decision problems modeled as extensive games cfr 's regret bounds depend on the requirement of perfect recall players always remember information that was revealed to them and the order in which it was revealed in games without perfect recall , however , cfr 's guarantees do not apply in this paper , we present the first regret bound for cfr when applied to a general class of games with imperfect recall in addition , we show that cfr applied to any abstraction belonging to our general class results in a regret bound not just for the abstract game , but for the full game as well we verify our theory and show how imperfect recall can be used to trade a small increase in regret for a significant reduction in memory in three domains die roll poker , phantom tic tac toe , and bluff"
cs.AI,"symmetries are intrinsic to many combinatorial problems including boolean satisfiability \( sat \) and constraint programming \( cp \) in sat , the identification of symmetry breaking predicates \( sbps \) is a well known , often effective , technique for solving hard problems the identification of sbps in sat has been the subject of significant improvements in recent years , resulting in more compact sbps and more effective algorithms the identification of sbps has also been applied to pseudo boolean \( pb \) constraints , showing that symmetry breaking can also be an effective technique for pb constraints this paper extends further the application of sbps , and shows that sbps can be identified and used in maximum satisfiability \( maxsat \) , as well as in its most well known variants , including partial maxsat , weighted maxsat and weighted partial maxsat as with sat and pb , symmetry breaking predicates for maxsat and variants are shown to be effective for a representative number of problem domains , allowing solving problem instances that current state of the art maxsat solvers could not otherwise solve"
cs.AI|cs.IT|cs.LG,"we propose a method for improving approximate inference methods that corrects for the influence of loops in the graphical model the method is applicable to arbitrary factor graphs , provided that the size of the markov blankets is not too large it is an alternative implementation of an idea introduced recently by montanari and rizzo \( 2005 \) in its simplest form , which amounts to the assumption that no loops are present , the method reduces to the minimal cluster variation method approximation \( which uses maximal factors as outer clusters \) on the other hand , using estimates of the effect of loops \( obtained by some approximate inference algorithm \) and applying the loop correcting \( lc \) method usually gives significantly better results than applying the approximate inference algorithm directly without loop corrections indeed , we often observe that the loop corrected error is approximately the square of the error of the approximate inference method used to estimate the effect of loops we compare different variants of the loop correcting method with other approximate inference methods on a variety of graphical models , including real world networks , and conclude that the lc approach generally obtains the most accurate results"
cs.AI,"in this paper , we propose causality as a unified framework to explain query answers and non answers , thus generalizing and extending several previously proposed approaches of provenance and missing query result explanations we develop our framework starting from the well studied definition of actual causes by halpern and pearl after identifying some undesirable characteristics of the original definition , we propose functional causes as a refined definition of causality with several desirable properties these properties allow us to apply our notion of causality in a database context and apply it uniformly to define the causes of query results and their individual contributions in several ways \( i \) we can model both provenance as well as non answers , \( ii \) we can define explanations as either data in the input relations or relational operations in a query plan , and \( iii \) we can give graded degrees of responsibility to individual causes , thus allowing us to rank causes in particular , our approach allows us to explain contributions to relational aggregate functions and to rank causes according to their respective responsibilities we give complexity results and describe polynomial algorithms for evaluating causality in tractable cases throughout the paper , we illustrate the applicability of our framework with several examples overall , we develop in this paper the theoretical foundations of causality theory in a database context"
cs.DM|cs.IT,"we present a deterministic channel model which captures several key features of multiuser wireless communication we consider a model for a wireless network with nodes connected by such deterministic channels , and present an exact characterization of the end to end capacity when there is a single source and a single destination and an arbitrary number of relay nodes this result is a natural generalization of the max flow min cut theorem for wireline networks finally to demonstrate the connections between deterministic model and gaussian model , we look at two examples the single relay channel and the diamond network we show that in each of these two examples , the capacity achieving scheme in the corresponding deterministic model naturally suggests a scheme in the gaussian model that is within 1 bit and 2 bit respectively from cut set upper bound , for all values of the channel gains this is the first part of a two part paper the sequel 1 will focus on the proof of the max flow min cut theorem of a class of deterministic networks of which our model is a special case"
cs.AI,"the interaction of two binary variables , assumed to be empirical observations , has three degrees of freedom when expressed as a matrix of frequencies usually , the size of causal influence of one variable on the other is calculated as a single value , as increase in recovery rate for a medical treatment , for example we examine what is lost in this simplification , and propose using two interface constants to represent positive and negative implications separately given certain assumptions about non causal outcomes , the set of resulting epistemologies is a continuum we derive a variety of particular measures and contrast them with the one dimensional index"
cs.DM,"we present a rigorous mathematical framework for analyzing dynamics of a broad class of boolean network models we use this framework to provide the first formal proof of many of the standard critical transition results in boolean network analysis , and offer analogous characterizations for novel classes of random boolean networks we precisely connect the short run dynamic behavior of a boolean network to the average influence of the transfer functions we show that some of the assumptions traditionally made in the more common mean field analysis of boolean networks do not hold in general for example , we offer some evidence that imbalance , or expected internal inhomogeneity , of transfer functions is a crucial feature that tends to drive quiescent behavior far more strongly than previously observed"
cs.DS,"in this paper the problem of information dissemination in mobile ad hoc networks \( manet \) is studied the problem is to disseminate a piece of information , initially held by a distinguished source node , to all nodes in a set defined by some predicate we use a model of manets that is well suited for dynamic networks and opportunistic communication in this model nodes are placed in a plane , in which they can move with bounded speed , and communication between nodes occurs over a collision prone single channel in this setup informed and uninformed nodes can be disconnected for some time \( bounded by a parameter alpha \) , but eventually some uninformed node must become neighbor of an informed node and remain so for some time \( bounded by a parameter beta \) in addition , nodes can start at different times , and they can crash and recover under the above framework , we show negative and positive results for different types of randomized protocols , and we put those results in perspective with respect to previous deterministic results"
cs.LG,"deep learning , in particular convolutional neural network \( cnn \) , has achieved promising results in face recognition recently however , it remains an open question why cnns work well and how to design a 'good' architecture the existing works tend to focus on reporting cnn architectures that work well for face recognition rather than investigate the reason in this work , we conduct an extensive evaluation of cnn based face recognition systems \( cnn frs \) on a common ground to make our work easily reproducible specifically , we use public database lfw \( labeled faces in the wild \) to train cnns , unlike most existing cnns trained on private databases we propose three cnn architectures which are the first reported architectures trained using lfw data this paper quantitatively compares the architectures of cnns and evaluate the effect of different implementation choices we identify several useful properties of cnn frs for instance , the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy in addition , traditional metric learning method exploiting cnn learned features is evaluated experiments show two crucial factors to good cnn frs performance are the fusion of multiple cnns and metric learning to make our work reproducible , source code and models will be made publicly available"
cs.DM,"a proper k coloring of a graph g \( v , e \) is a function c v \( g \) to 1 , ldots , k such that c \( u \) neq c \( v \) , for every uv in e \( g \) the chromatic number chi \( g \) is the minimum k such that there exists a proper k coloring of g given a spanning subgraph h of g , a q backbone k coloring of \( g , h \) is a proper k coloring c of v \( g \) such that lvert c \( u \) c \( v \) rvert ge q , for every edge uv in e \( h \) the q backbone chromatic number bbc q \( g , h \) is the smallest k for which there exists a q backbone k coloring of \( g , h \) in this work , we show that every connected graph g has a generating tree t such that bbc q \( g , t \) max chi \( g \) , left lceil frac chi \( g \) 2 right rceil q , and that this value is the best possible as a direct consequence , we get that every connected graph g has a spanning tree t for which bbc 2 \( g , t \) chi \( g \) , if chi \( g \) ge 4 , or bbc 2 \( g , t \) chi \( g \) 1 , otherwise thus , by applying the four color theorem , we have that every connected nonbipartite planar graph g has a spanning tree t such that bbc 2 \( g , t \) 4 this settles a question by wang , bu , montassier and raspaud \( 2012 \) , and generalizes a number of previous partial results to their question"
cs.DM|cs.DS,"the structure of large networks can be revealed by partitioning them to smaller parts , which are easier to handle one of such decompositions is based on k cores , proposed in 1983 by seidman in the paper an efficient , o \( m \) , m is the number of lines , algorithm for determining the cores decomposition of a given network is presented"
cs.AI,"agents interacting with an incompletely known world need to be able to reason about the effects of their actions , and to gain further information about that world they need to use sensors of some sort unfortunately , both the effects of actions and the information returned from sensors are subject to error to cope with such uncertainties , the agent can maintain probabilistic beliefs about the state of the world with probabilistic beliefs the agent will be able to quantify the likelihood of the various outcomes of its actions and is better able to utilize the information gathered from its error prone actions and sensors in this paper , we present a model in which we can reason about an agent 's probabilistic degrees of belief and the manner in which these beliefs change as various actions are executed we build on a general logical theory of action developed by reiter and others , formalized in the situation calculus we propose a simple axiomatization that captures an agent 's state of belief and the manner in which these beliefs change when actions are executed our model displays a number of intuitively reasonable properties"
cs.AI|cs.LG,"we propose a simple domain adaptation method for neural networks in a supervised setting supervised domain adaptation is a way of improving the generalization performance on the target domain by using the source domain dataset , assuming that both of the datasets are labeled recently , recurrent neural networks have been shown to be successful on a variety of nlp tasks such as caption generation however , the existing domain adaptation techniques are limited to \( 1 \) tune the model parameters by the target dataset after the training by the source dataset , or \( 2 \) design the network to have dual output , one for the source domain and the other for the target domain reformulating the idea of the domain adaptation technique proposed by daume \( 2007 \) , we propose a simple domain adaptation method , which can be applied to neural networks trained with a cross entropy loss on captioning datasets , we show performance improvements over other domain adaptation methods"
cs.LG|stat.ML,"we investigate the use of deep neural networks for the classification of image datasets where texture features are important for generating class conditional discriminative representations to this end , we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of vapnik chervonenkis dimension to show that hand crafted feature extraction creates low dimensional representations which help in reducing the overall excess error rate as a corollary to this analysis , we derive for the first time upper bounds on the vc dimension of convolutional neural network as well as dropout and dropconnect networks and the relation between excess error rate of dropout and dropconnect networks the concept of intrinsic dimension is used to validate the intuition that texture based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks we then derive the mean distance from the centroid to the nearest and farthest sampling points in an n dimensional manifold and show that the relative contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity"
cs.DM,"cayley 's formula states that there are n n 2 spanning trees in the complete graph on n vertices it has been proved in more than a dozen different ways over its 150 year history the complete graphs are a special case of threshold graphs , and using merris' theorem and the matrix tree theorem , there is a strikingly simple formula for counting the number of spanning trees in a threshold graph on n vertices it is simply the product , over i 2 , 3 , , n 1 , of the number of vertices of degree at least i in this manuscript , we provide a direct combinatorial proof for this formula which does not use the matrix tree theorem the proof is an extension of joyal 's proof for cayley 's formula then we apply this methodology to give a formula for the number of spanning trees in any difference graph"
cs.LG,"feature selection \( fs \) has become the focus of much research on decision support systems areas for which data sets with tremendous number of variables are analyzed in this paper we present a new method for the diagnosis of coronary artery diseases \( cad \) founded on genetic algorithm \( ga \) wrapped bayes naive \( bn \) based fs basically , cad dataset contains two classes defined with 13 features in ga bn algorithm , ga generates in each iteration a subset of attributes that will be evaluated using the bn in the second step of the selection procedure the final set of attribute contains the most relevant feature model that increases the accuracy the algorithm in this case produces 85 50 classification accuracy in the diagnosis of cad thus , the asset of the algorithm is then compared with the use of support vector machine \( svm \) , multilayer perceptron \( mlp \) and c4 5 decision tree algorithm the result of classification accuracy for those algorithms are respectively 83 5 , 83 16 and 80 85 consequently , the ga wrapped bn algorithm is correspondingly compared with other fs algorithms the obtained results have shown very promising outcomes for the diagnosis of cad"
cs.LG,"recently , several works have shown that natural modifications of the classical conditional gradient method \( aka frank wolfe algorithm \) for constrained convex optimization , provably converge with a linear rate when i \) the feasible set is a polytope , and ii \) the objective is smooth and strongly convex however , all of these results suffer from two significant shortcomings large memory requirement due to the need to store an explicit convex decomposition of the current iterate , and as a consequence , large running time overhead per iteration , and worst case convergence rate that depends unfavorably on the dimension in this work we present a new conditional gradient variant and a corresponding analysis that improves on both of the above shortcomings in particular both memory and computation overheads are only linear in the dimension moreover , in case the optimal solution is sparse , the new convergence rate replaces a factor which is at least linear in the dimension in previous works , with a linear dependence on the number of non zeros in the optimal solution at the heart of our method , and corresponding analysis , is a novel way to compute decomposition invariant away steps while our theoretical guarantees do not apply to any polytope , they apply to several important structured polytopes that capture central concepts such as paths in graphs , perfect matchings in bipartite graphs , marginal distributions that arise in structured prediction tasks , and more our theoretical findings are complemented by empirical evidence which shows that our method delivers state of the art performance"
cs.DS|cs.LG,"for dimension reduction in l 1 , the method of em cauchy random projections multiplies the original data matrix mathbf a in mathbb r n times d with a random matrix mathbf r in mathbb r d times k \( k ll min \( n , d \) \) whose entries are i i d samples of the standard cauchy c \( 0 , 1 \) because of the impossibility results , one can not hope to recover the pairwise l 1 distances in mathbf a from mathbf b mathbf ar in mathbb r n times k , using linear estimators without incurring large errors however , nonlinear estimators are still useful for certain applications in data stream computation , information retrieval , learning , and data mining we propose three types of nonlinear estimators the bias corrected sample median estimator , the bias corrected geometric mean estimator , and the bias corrected maximum likelihood estimator the sample median estimator and the geometric mean estimator are asymptotically \( as k to infty \) equivalent but the latter is more accurate at small k we derive explicit tail bounds for the geometric mean estimator and establish an analog of the johnson lindenstrauss \( jl \) lemma for dimension reduction in l 1 , which is weaker than the classical jl lemma for dimension reduction in l 2 asymptotically , both the sample median estimator and the geometric mean estimators are about 80 efficient compared to the maximum likelihood estimator \( mle \) we analyze the moments of the mle and propose approximating the distribution of the mle by an inverse gaussian"
cs.DS,"finding nonoverlapping balls with given centers in any metric space , maximizing the sum of radii of the balls , can be expressed as a linear program its dual linear program expresses the problem of finding a minimum weight set of cycles \( allowing 2 cycles \) covering all vertices in a complete geometric graph for points in a euclidean space of any finite dimension d , this graph can be replaced by a sparse subgraph obeying a separator theorem this graph structure leads to an algorithm for finding the optimum set of balls in time o \( n 2 1 d \) , improving the o \( n 3 \) time of a naive cycle cover algorithm"
cs.DM,the aim of this paper is to analyse the gain of the update algorithm associated to the recently proposed d iteration the d iteration is a fluid diffusion based new iterative method it exploits a simple intuitive decomposition of the product matrix vector as elementary operations of fluid diffusion \( forward scheme \) associated to a new algebraic representation we show through experimentations on real datasets how much this approach can improve the computation efficiency in presence of the graph evolution
cs.DM,"two independent sets of a graph are adjacent if they differ on exactly one vertex \( i e we can transform one into the other by adding or deleting a vertex \) let k be an integer we consider the reconfiguration graph tar k \( g \) on the set of independent sets of size at least k in a graph g , with the above notion of adjacency here we provide a cubic time algorithm to decide whether tar k \( g \) is connected when g is a cograph , thus solving an open question of bonsma 2014 as a by product , we also describe a linear time algorithm which decides whether two elements of tar k \( g \) are in the same connected component"
cs.LG|stat.ML,"low rank matrix is desired in many machine learning and computer vision problems most of the recent studies use the nuclear norm as a convex surrogate of the rank operator however , all singular values are simply added together by the nuclear norm , and thus the rank may not be well approximated in practical problems in this paper , we propose to use a log determinant \( logdet \) function as a smooth and closer , though non convex , approximation to rank for obtaining a low rank representation in subspace clustering augmented lagrange multipliers strategy is applied to iteratively optimize the logdet based non convex objective function on potentially large scale data by making use of the angular information of principal directions of the resultant low rank representation , an affinity graph matrix is constructed for spectral clustering experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state of the art subspace clustering algorithms"
cs.LG|stat.ML,"in this letter we borrow from the inference techniques developed for unbounded state cardinality \( nonparametric \) variants of the hmm and use them to develop a tuning parameter free , black box inference procedure for explicit state duration hidden markov models \( edhmm \) edhmms are hmms that have latent states consisting of both discrete state indicator and discrete state duration random variables in contrast to the implicit geometric state duration distribution possessed by the standard hmm , edhmms allow the direct parameterisation and estimation of per state duration distributions as most duration distributions are defined over the positive integers , truncation or other approximations are usually required to perform edhmm inference"
cs.DM,"we show that a nontrivial graph isomorphism problem of two undirected graphs , and more generally , the permutation similarity of two given n times n matrices , is equivalent to equalities of volumes of the induced three convex bounded polytopes intersected with a given sequence of balls , centered at the origin with radii t i in \( 0 , sqrt n 1 \) , where t i is an increasing sequence converging to sqrt n 1 these polytopes are characterized by n 2 inequalities in at most n 2 variables the existence of fpras for computing volumes of convex bodies gives rise to a semi frpas of order o \( n 14 \) at most to find if given two undirected graphs are isomorphic"
cs.AI|cs.LG,"within the framework of probably approximately correct markov decision processes \( pac mdp \) , much theoretical work has focused on methods to attain near optimality after a relatively long period of learning and exploration however , practical concerns require the attainment of satisfactory behavior within a short period of time in this paper , we relax the pac mdp conditions to reconcile theoretically driven exploration methods and practical needs we propose simple algorithms for discrete and continuous state spaces , and illustrate the benefits of our proposed relaxation via theoretical analyses and numerical examples our algorithms also maintain anytime error bounds and average loss bounds our approach accommodates both bayesian and non bayesian methods"
cs.AI|cs.DS,"maximal frequent patterns superset checking plays an important role in the efficient mining of complete maximal frequent itemsets \( mfi \) and maximal search space pruning in this paper we present a new indexing approach , fastlmfi for local maximal frequent patterns \( itemset \) propagation and maximal patterns superset checking experimental results on different sparse and dense datasets show that our work is better than the previous well known progressive focusing technique we have also integrated our superset checking approach with an existing state of the art maximal itemsets algorithm mafia , and compare our results with current best maximal itemsets algorithms afopt max and fp \( zhu \) max our results outperform afopt max and fp \( zhu \) max on dense \( chess and mushroom \) datasets on almost all support thresholds , which shows the effectiveness of our approach"
cs.AI|cs.LG,"many databases store data in relational format , with different types of entities and information about links between the entities the field of statistical relational learning \( srl \) has developed a number of new statistical models for such data in this paper we focus on learning class level or first order dependencies , which model the general database statistics over attributes of linked objects and links \( e g , the percentage of a grades given in computer science classes \) class level statistical relationships are important in themselves , and they support applications like policy making , strategic planning , and query optimization most current srl methods find class level dependencies , but their main task is to support instance level predictions about the attributes or links of specific entities we focus only on class level prediction , and describe algorithms for learning class level models that are orders of magnitude faster for this task our algorithms learn bayes nets with relational structure , leveraging the efficiency of single table nonrelational bayes net learners an evaluation of our methods on three data sets shows that they are computationally feasible for realistic table sizes , and that the learned structures represent the statistical information in the databases well after learning compiles the database statistics into a bayes net , querying these statistics via bayes net inference is faster than with sql queries , and does not depend on the size of the database"
cs.DM,"we introduce a catalog of a catalog of a class of \( 3 , g \) graphs for even girth g , satisfying 6 g 16 our catalog has the following properties firstly , our catalog contains the smallest known \( 3 , g \) graphs we have identified an appropriate class of trivalent graphs for our catalog , such that the \( 3 , g \) graph of minimum order within the class is also the smallest known \( 3 , g \) graph secondly , our catalog contains \( 3 , g \) graphs for more orders than other listings thirdly , the class of graphs have been defined so that a practical algorithm to generate graphs can be created , and have implemented such an algorithm fourthly , our catalog is inifinite , since we extend the results into knowledge about infinitely many graphs our findings are as follows firstly , we have identified hamiltonian bipartite graphs \( hbgs \) , as a promising class of trivalent graphs that can lead to a catalog of \( 3 , g \) graphs for even girth g with graphs for more orders than other listings , that is also expected to contain a \( 3 , g \) graph with minimum order secondly , our catalog of \( 3 , g \) graphs has graphs for more orders compared with other lists , with many graphs outside of the vertex transitive class thirdly , in order to make the computation more tractable , we have introduced the symmetry factor b , which reflects the extent of rotational symmetry we introduce the d3 chord index notation , a concise notation for trivalent hbgs fourthly , results on the minimum order for existence of a \( 3 , g \) hbg , and minimum value of symmetry factor for existence of a \( 3 , g \) hbg are of wider interest from an extremal graph theory perspective lastly from a cage problem perspective , an analysis on upper bounds and lower bounds within subclasses has been done , where proofs of emptiness of several subclasses partially supports the likeliness of the \( 3 , 14 \) record graph to be a cage"
cs.DM|cs.DS,"an exact formula b \( n \) frac n 2 \( lfloor lg n rfloor 1 \) sum k 0 lfloor lg n rfloor 2 k mbox zigzag , \( frac n 2 k 1 \) , where mbox zigzag \( x \) min \( x lfloor x rfloor , lceil x rceil x \) , for the minimal number b \( n \) of comparisons of keys performed by tt mergesort on an n element array is derived and analyzed the said formula is less complex than any other known formula for the same and can be evaluated in o \( log c \) time , where c is a constant it is shown that there is no closed form formula for the above since the recurrence relation for the minimal number of comparisons of keys for tt mergesort is identical with a recurrence relation for the number of 1s in binary expansions of all integers between 0 and n \( exclusively \) , the above results extend to the sum of binary digits problem"
cs.AI,"objective to pilot test an artificial intelligence \( ai \) algorithm that selects peer change agents \( pca \) to disseminate hiv testing messaging in a population of homeless youth methods we recruited and assessed 62 youth at baseline , 1 month \( n 48 \) , and 3 months \( n 38 \) a facebook app collected preliminary social network data eleven pcas selected by ai attended a 1 day training and 7 weekly booster sessions mixed effects models with random effects were used to assess change over time results significant change over time was observed in past 6 month hiv testing \( 57 9 , 82 4 , 76 3 p 05 \) but not condom use \( 63 9 , 65 7 , 65 8 \) most youth reported speaking to a pca about hiv prevention \( 72 0 at 1 month , 61 5 at 3 months \) conclusions ai is a promising avenue for implementing pca models for homeless youth increasing rates of regular hiv testing is critical to hiv prevention and linking homeless youth to treatment"
cs.LG|stat.ML,we show how to train a quantum network of pairwise interacting qubits such that its evolution implements a target quantum algorithm into a given network subset our strategy is inspired by supervised learning and is designed to help the physical construction of a quantum computer which operates with minimal external classical control
cs.LG|stat.ML,"candecomp parafac \( cp \) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors the existing cp algorithms require the tensor rank to be manually specified , however , the determination of tensor rank remains a challenging problem especially for cp rank in addition , existing approaches do not take into account uncertainty information of latent factors , as well as missing entries to address these issues , we formulate cp factorization using a hierarchical probabilistic model and employ a fully bayesian treatment by incorporating a sparsity inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters , resulting in automatic rank determination to learn the model , we develop an efficient deterministic bayesian inference algorithm , which scales linearly with data size our method is characterized as a tuning parameter free approach , which can effectively infer underlying multilinear factors with a low rank constraint , while also providing predictive distributions over missing entries extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground truth of cp rank and prevent the overfitting problem , even when a large amount of entries are missing moreover , the results from real world applications , including image inpainting and facial image synthesis , demonstrate that our method outperforms state of the art approaches for both tensor factorization and tensor completion in terms of predictive performance"
cs.AI,"evolution of belief systems has always been in focus of cognitive research in this paper we delineate a new model describing belief systems as a network of statements considered true testing the model a small number of parameters enabled us to reproduce a variety of well known mechanisms ranging from opinion changes to development of psychological problems the self organizing opinion structure showed a scale free degree distribution the novelty of our work lies in applying a convenient set of definitions allowing us to depict opinion network dynamics in a highly favorable way , which resulted in a scale free belief network as an additional benefit , we listed several conjectural consequences in a number of areas related to thinking and reasoning"
cs.LG|stat.ML,"we investigate the use of sparse coding and dictionary learning in the context of multitask and transfer learning the central assumption of our learning method is that the tasks parameters are well approximated by sparse linear combinations of the atoms of a dictionary on a high or infinite dimensional space this assumption , together with the large quantity of available data in the multitask and transfer learning settings , allows a principled choice of the dictionary we provide bounds on the generalization error of this approach , for both settings numerical experiments on one synthetic and two real datasets show the advantage of our method over single task learning , a previous method based on orthogonal and dense representation of the tasks and a related method learning task grouping"
cs.LG,"with the popularity of mobile devices , personalized speech recognizer becomes more realizable today and highly attractive each mobile device is primarily used by a single user , so it 's possible to have a personalized recognizer well matching to the characteristics of individual user although acoustic model personalization has been investigated for decades , much less work have been reported on personalizing language model , probably because of the difficulties in collecting enough personalized corpora previous work used the corpora collected from social networks to solve the problem , but constructing a personalized model for each user is troublesome in this paper , we propose a universal recurrent neural network language model with user characteristic features , so all users share the same model , except each with different user characteristic features these user characteristic features can be obtained by crowdsouring over social networks , which include huge quantity of texts posted by users with known friend relationships , who may share some subject topics and wording patterns the preliminary experiments on facebook corpus showed that this proposed approach not only drastically reduced the model perplexity , but offered very good improvement in recognition accuracy in n best rescoring tests this approach also mitigated the data sparseness problem for personalized language models"
cs.LG|stat.ML,"item neighbourhood methods for collaborative filtering learn a weighted graph over the set of items , where each item is connected to those it is most similar to the prediction of a user 's rating on an item is then given by that rating of neighbouring items , weighted by their similarity this paper presents a new neighbourhood approach which we call item fields , whereby an undirected graphical model is formed over the item graph the resulting prediction rule is a simple generalization of the classical approaches , which takes into account non local information in the graph , allowing its best results to be obtained when using drastically fewer edges than other neighbourhood approaches a fast approximate maximum entropy training method based on the bethe approximation is presented , which uses a simple gradient ascent procedure when using precomputed sufficient statistics on the movielens datasets , our method is faster than maximum likelihood approaches by two orders of magnitude"
cs.LG,"different types of dynamics have been studied in repeated game play , and one of them which has received much attention recently consists of those based on no regret algorithms from the area of machine learning it is known that dynamics based on generic no regret algorithms may not converge to nash equilibria in general , but to a larger set of outcomes , namely coarse correlated equilibria moreover , convergence results based on generic no regret algorithms typically use a weaker notion of convergence the convergence of the average plays instead of the actual plays some work has been done showing that when using a specific no regret algorithm , the well known multiplicative updates algorithm , convergence of actual plays to equilibria can be shown and better quality of outcomes in terms of the price of anarchy can be reached for atomic congestion games and load balancing games are there more cases of natural no regret dynamics that perform well in suitable classes of games in terms of convergence and quality of outcomes that the dynamics converge to \? we answer this question positively in the bulletin board model by showing that when employing the mirror descent algorithm , a well known generic no regret algorithm , the actual plays converge quickly to equilibria in nonatomic congestion games furthermore , the bandit model considers a probably more realistic and prevalent setting with only partial information , in which at each time step each player only knows the cost of her own currently played strategy , but not any costs of unplayed strategies for the class of atomic congestion games , we propose a family of bandit algorithms based on the mirror descent algorithms previously presented , and show that when each player individually adopts such a bandit algorithm , their joint \( mixed \) strategy profile quickly converges with implications"
cs.LG,"learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons first , one needs to deal with a large number of topics \( typically in the order of thousands \) second , one needs a scalable and efficient way of distributing the computation across multiple machines in this paper we present a novel algorithm f nomad lda which simultaneously tackles both these problems in order to handle large number of topics we use an appropriately modified fenwick tree this data structure allows us to sample from a multinomial distribution over t items in o \( log t \) time moreover , when topic counts change the data structure can be updated in o \( log t \) time in order to distribute the computation across multiple processor we present a novel asynchronous framework inspired by the nomad algorithm of cite yunyuhsietal13 we show that f nomad lda significantly outperform state of the art on massive problems which involve millions of documents , billions of words , and thousands of topics"
cs.AI|stat.ML,"bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty its defining property is the interpretation of probabilities as degrees of belief in propositions about the state of the world relative to an inquiring subject this essay examines the notion of subjectivity by drawing parallels between lacanian theory and bayesian probability theory , and concludes that the latter must be enriched with causal interventions to model agency the central contribution of this work is an abstract model of the subject that accommodates causal interventions in a measure theoretic formalisation that is more general than causal graphs this formalisation is obtained through a game theoretic ansatz based on modelling the inside and outside of the subject as an extended game with imperfect information between two players finally , i illustrate the expressiveness of this model with an example of causal induction"
cs.LG|stat.ML,"while deep networks show to be highly effective in extensive applications , few efforts have been spent on studying its potential in clustering in this paper , we argue that the successful domain expertise of sparse coding in clustering is still valuable , and can be combined with the key ingredients of deep learning a novel feed forward architecture , named tag lista , is constructed from graph regularized sparse coding it is then trained with task specific loss functions from end to end the inner connections of the proposed network to sparse coding leads to more effective training moreover , by introducing auxiliary clustering tasks to the hierarchy of intermediate features , we present dtag lista and obtain a further performance boost we demonstrate extensive experiments on several benchmark datasets , under a wide variety of settings the results verify that the proposed model performs significantly outperforms the generic architectures of the same parameter capacity , and also gains remarkable margins over several state of the art methods"
cs.DS|cs.IT,"given a probability distribution over a set of n words to be transmitted , the huffman coding problem is to find a minimal cost prefix free code for transmitting those words the basic huffman coding problem can be solved in o \( n log n \) time but variations are more difficult one of the standard techniques for solving these variations utilizes a top down dynamic programming approach in this paper we show that this approach is amenable to dynamic programming speedup techniques , permitting a speedup of an order of magnitude for many algorithms in the literature for such variations as mixed radix , reserved length and one ended coding these speedups are immediate implications of a general structural property that permits batching together the calculation of many dp entries"
cs.DM,"extracting structured subgraphs inside large graphs often known as the planted subgraph problem is a fundamental question that arises in a range of application domains this problem is np hard in general , and as a result , significant efforts have been directed towards the development of tractable procedures that succeed on specific families of problem instances we propose a new computationally efficient convex relaxation for solving the planted subgraph problem our approach is based on tractable semidefinite descriptions of majorization inequalities on the spectrum of a symmetric matrix this procedure is effective at finding planted subgraphs that consist of few distinct eigenvalues , and it generalizes previous convex relaxation techniques for finding planted cliques our analysis relies prominently on the notion of spectrally comonotone matrices , which are pairs of symmetric matrices that can be transformed to diagonal matrices with sorted diagonal entries upon conjugation by the same orthogonal matrix"
cs.LG|stat.ML,"we propose a novel sparsity and clustering \( sparc \) regularizer , which is a modified version of the previous octagonal shrinkage and clustering algorithm for regression \( oscar \) , where , the proposed regularizer consists of a k sparse constraint and a pair wise ell infty norm restricted on the k largest components in magnitude the proposed regularizer is able to separably enforce k sparsity and encourage the non zeros to be equal in magnitude moreover , it can accurately group the features without shrinking their magnitude in fact , sparc is closely related to oscar , so that the proximity operator of the former can be efficiently computed based on that of the latter , allowing using proximal splitting algorithms to solve problems with sparc regularization experiments on synthetic data and with benchmark breast cancer data show that sparc is a competitive group sparsity inducing regularizer for regression and classification"
cs.LG,"communication service providers \( csps \) are in a unique position to utilize their vast transactional data assets generated from interactions of subscribers with network elements as well as with other subscribers csps could leverage its data assets for a gamut of applications such as service personalization , predictive offer management , loyalty management , revenue forecasting , network capacity planning , product bundle optimization and churn management to gain significant competitive advantage however , due to the sheer data volume , variety , velocity and veracity of mobile subscriber datasets , sophisticated data analytics techniques and frameworks are necessary to derive actionable insights in a useable timeframe in this paper , we describe our journey from a relational database management system \( rdbms \) based campaign management solution which allowed data scientists and marketers to use hand written rules for service personalization and targeted promotions to a distributed big data analytics platform , capable of performing large scale machine learning and data mining to deliver real time service personalization , predictive modelling and product optimization our work involves a careful blend of technology , processes and best practices , which facilitate man machine collaboration and continuous experimentation to derive measurable economic value from data our platform has a reach of more than 500 million mobile subscribers worldwide , delivering over 1 billion personalized recommendations annually , processing a total data volume of 64 petabytes , corresponding to 8 5 trillion events"
cs.LG|stat.ML,"mixture of experts \( moe \) is a popular framework for modelling heterogeneity in data for regression , classification and clustering for continuous data which we consider here in the context of regression and cluster analysis , moe usually use normal experts , that is , expert components following the gaussian distribution however , for a set of data containing a group or groups of observations with asymmetric behavior , heavy tails or atypical observations , the use of normal experts may be unsuitable and can unduly affect the fit of the moe model in this paper , we propose new mixture of experts which can deal with these issues regarding possibly skewed , heavy tailed data and with outliers the investigated new non normal mixture of experts \( nnmoe \) are the skew normal moe and the robust t moe and skew t moe , respectively named snmoe , tmoe and stmoe we develop dedicated expectation maximization \( em \) and expectation conditional maximization \( ecm \) algorithms to estimate the parameters of the proposed models by monotonically maximizing the observed data log likelihood we describe how they can be used in prediction and in model based clustering of regression data numerical experiments carried out on simulated data show that the effectiveness and the robustness of the proposed models in terms modeling non linear regression functions as well as in model based clustering applications to the real world data of tone perception for musical data analysis , and the one of temperature anomalies for the analysis of climate change , confirm the usefulness of the proposed approaches for practical applications"
cs.AI|cs.IT|cs.LG,"this paper studies sequence prediction based on the monotone kolmogorov complexity km log m , i e based on universal deterministic one part mdl m is extremely close to solomonoff 's prior m , the latter being an excellent predictor in deterministic as well as probabilistic environments , where performance is measured in terms of convergence of posteriors or losses despite this closeness to m , it is difficult to assess the prediction quality of m , since little is known about the closeness of their posteriors , which are the important quantities for prediction we show that for deterministic computable environments , the posterior and losses of m converge , but rapid convergence could only be shown on sequence the off sequence behavior is unclear in probabilistic environments , neither the posterior nor the losses converge , in general"
cs.LG|stat.ML,"relational machine learning studies methods for the statistical analysis of relational , or graph structured , data in this paper , we provide a review of how such statistical models can be trained on large knowledge graphs , and then used to predict new facts about the world \( which is equivalent to predicting new edges in the graph \) in particular , we discuss two different kinds of statistical relational models , both of which can scale to massive datasets the first is based on tensor factorization methods and related latent variable models the second is based on mining observable patterns in the graph we also show how to combine these latent and observable models to get improved modeling power at decreased computational cost finally , we discuss how such statistical models of graphs can be combined with text based information extraction methods for automatically constructing knowledge graphs from the web in particular , we discuss google 's knowledge vault project"
cs.LG|stat.ML,"we give two provably accurate feature selection techniques for the linear svm the algorithms run in deterministic and randomized time respectively our algorithms can be used in an unsupervised or supervised setting the supervised approach is based on sampling features from support vectors we prove that the margin in the feature space is preserved to within epsilon relative error of the margin in the full feature space in the worst case in the unsupervised setting , we also provide worst case guarantees of the radius of the minimum enclosing ball , thereby ensuring comparable generalization as in the full feature space and resolving an open problem posed in dasgupta et al we present extensive experiments on real world datasets to support our theory and to demonstrate that our method is competitive and often better than prior state of the art , for which there are no known provable guarantees"
cs.DM|cs.IT,"motivated by communication scenarios such as timing channels \( in queuing systems , molecular communications , etc \) and bit shift channels \( in magnetic recording systems \) , we study the error control problem in cases where the dominant type of noise are symbol shifts in particular , two channel models are introduced and their zero error capacities determined by an explicit construction of optimal zero error codes model a can be informally described as follows 1 \) the information is stored in an n cell register , where each cell can either be left empty , or can contain a particle of one of p possible types , and 2 \) due to the imperfections of the device every particle is shifted k cells away from its original position over time , where k is drawn from a certain range of integers , without the possibility of reordering particles model b is an abstraction of a single server queue 1 \) the transmitter sends symbols packets from a p ary alphabet through a queuing system with an infinite buffer , and 2 \) each packet is being processed by the server for a number of time slots k in 0 , 1 , ldots , k several variations of the above models are also discussed , e g , with multiple particles per cell , with additional types of noise , and the continuous time case the models are somewhat atypical due to the fact that the length of the channel output in general differs from that of the corresponding input , and that this length depends on the noise \( shift \) pattern as well as on the input itself this will require the notions of a zero error code and the zero error capacity , as introduced by shannon , to be generalized"
cs.LG,"the u curve optimization problem is characterized by a decomposable in u shaped curves cost function over the chains of a boolean lattice this problem can be applied to model the classical feature selection problem in machine learning recently , the u curve algorithm was proposed to give optimal solutions to the u curve problem in this article , we point out that the u curve algorithm is in fact suboptimal , and introduce the u curve search \( ucs \) algorithm , which is actually optimal we also present the results of optimal and suboptimal experiments , in which ucs is compared with the ubb optimal branch and bound algorithm and the sffs heuristic , respectively we show that , in both experiments , proc ucs had a better performance than its competitor finally , we analyze the obtained results and point out improvements on ucs that might enhance the performance of this algorithm"
cs.AI|cs.SI|stat.ML,"dyadic data prediction \( ddp \) is an important problem in many research areas this paper develops a novel fully bayesian nonparametric framework which integrates two popular and complementary approaches , discrete mixed membership modeling and continuous latent factor modeling into a unified heterogeneous matrix factorization \( hemf \) model , which can predict the unobserved dyadics accurately the hemf can determine the number of communities automatically and exploit the latent linear structure for each bicluster efficiently we propose a variational bayesian method to estimate the parameters and missing data we further develop a novel online learning approach for variational inference and use it for the online learning of hemf , which can efficiently cope with the important large scale ddp problem we evaluate the performance of our method on the eachmoive , movielens and netflix prize collaborative filtering datasets the experiment shows that , our model outperforms state of the art methods on all benchmarks compared with stochastic gradient method \( sgd \) , our online learning approach achieves significant improvement on the estimation accuracy and robustness"
cs.DS,"our chapter in the upcoming volume i computer science and software engineering of computing handbook \( third edition \) , allen tucker , teo gonzales and jorge l diaz herrera , editors , covers algebraic algorithms , both symbolic and numerical , for matrix computations and root finding for polynomials and systems of polynomials equations we cover part of these large subjects and include basic bibliography for further study to meet space limitation we cite books , surveys , and comprehensive articles with pointers to further references , rather than including all the original technical papers"
cs.DM,"a word w over an alphabet sigma is a lyndon word if there exists an order defined on sigma for which w is lexicographically smaller than all of its conjugates \( other than itself \) we introduce and study emph universal lyndon words , which are words over an n letter alphabet that have length n ! and such that all the conjugates are lyndon words we show that universal lyndon words exist for every n and exhibit combinatorial and structural properties of these words we then define particular prefix codes , which we call hamiltonian lex codes , and show that every hamiltonian lex code is in bijection with the set of the shortest unrepeated prefixes of the conjugates of a universal lyndon word this allows us to give an algorithm for constructing all the universal lyndon words"
cs.LG|stat.ML,"in crowd labeling , a large amount of unlabeled data instances are outsourced to a crowd of workers workers will be paid for each label they provide , but the labeling requester usually has only a limited amount of the budget since data instances have different levels of labeling difficulty and workers have different reliability , it is desirable to have an optimal policy to allocate the budget among all instance worker pairs such that the overall labeling accuracy is maximized we consider categorical labeling tasks and formulate the budget allocation problem as a bayesian markov decision process \( mdp \) , which simultaneously conducts learning and decision making using the dynamic programming \( dp \) recurrence , one can obtain the optimal allocation policy however , dp quickly becomes computationally intractable when the size of the problem increases to solve this challenge , we propose a computationally efficient approximate policy , called optimistic knowledge gradient policy our mdp is a quite general framework , which applies to both pull crowdsourcing marketplaces with homogeneous workers and push marketplaces with heterogeneous workers it can also incorporate the contextual information of instances when they are available the experiments on both simulated and real data show that the proposed policy achieves a higher labeling accuracy than other existing policies at the same budget level"
cs.LG,"machine learning is used in a number of security related applications such as biometric user authentication , speaker identification etc a type of causative integrity attack against machine learning called poisoning attack works by injecting specially crafted data points in the training data so as to increase the false positive rate of the classifier in the context of the biometric authentication , this means that more intruders will be classified as valid user , and in case of speaker identification system , user a will be classified user b in this paper , we examine poisoning attack against svm and introduce curie a method to protect the svm classifier from the poisoning attack the basic idea of our method is to identify the poisoned data points injected by the adversary and filter them out our method is light weight and can be easily integrated into existing systems experimental results shows that it works very well in filtering out the poisoned data"
cs.LG|stat.ML,"this work addresses the problem of regret minimization in non stochastic multi armed bandit problems , focusing on performance guarantees that hold with high probability such results are rather scarce in the literature since proving them requires a large deal of technical effort and significant modifications to the standard , more intuitive algorithms that come only with guarantees that hold on expectation one of these modifications is forcing the learner to sample the losses of every arm at least omega \( sqrt t \) times over t rounds , which can adversely affect performance if many of the arms are obviously suboptimal while it is widely conjectured that this property is essential for proving high probability regret bounds , we show in this paper that it is possible to achieve such strong results without this undesirable exploration component our result relies on a simple and intuitive loss estimation strategy called emph implicit exploration \( ix \) that allows a remarkably clean analysis to demonstrate the flexibility of our technique , we derive several improved high probability bounds for various extensions of the standard multi armed bandit framework finally , we conduct a simple experiment that illustrates the robustness of our implicit exploration technique"
stat.ML,"there has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs gamarnik and sudan \( 2014 \) showed that local algorithms are weaker than global algorithms for finding large independent sets in sparse random regular graphs montanari \( 2015 \) showed that local algorithms are suboptimal for finding a community with high connectivity in the sparse erd h o s r 'enyi random graphs for the symmetric planted partition problem \( also named community detection for the block models \) on sparse graphs , a simple observation is that local algorithms cannot have non trivial performance in this work we consider the effect of side information on local algorithms for community detection under the binary symmetric stochastic block model in the block model with side information each of the n vertices is labeled or independently and uniformly at random each pair of vertices is connected independently with probability a n if both of them have the same label or b n otherwise the goal is to estimate the underlying vertex labeling given 1 \) the graph structure and 2 \) side information in the form of a vertex labeling positively correlated with the true one assuming that the ratio between in and out degree a b is theta \( 1 \) and the average degree \( a b \) 2 n o \( 1 \) , we characterize three different regimes under which a local algorithm , namely , belief propagation run on the local neighborhoods , maximizes the expected fraction of vertices labeled correctly thus , in contrast to the case of symmetric block models without side information , we show that local algorithms can achieve optimal performance for the block model with side information"
cs.DS,"in this paper , we prove an almost optimal hardness for max k csp r based on khot 's unique games conjecture \( ugc \) in max k csp r , we are given a set of predicates each of which depends on exactly k variables each variable can take any value from 1 , 2 , dots , r the goal is to find an assignment to variables that maximizes the number of satisfied predicates assuming the unique games conjecture , we show that it is np hard to approximate max k csp r to within factor 2 o \( k log k \) \( log r \) k 2 r k 1 for any k , r to the best of our knowledge , this result improves on all the known hardness of approximation results when 3 leq k o \( log r log log r \) in this case , the previous best hardness result was np hardness of approximating within a factor o \( k r k 2 \) by chan when k 2 , our result matches the best known ugc hardness result of khot , kindler , mossel and o'donnell in addition , by extending an algorithm for max 2 csp r by kindler , kolla and trevisan , we provide an omega \( log r r k 1 \) approximation algorithm for max k csp r this algorithm implies that our inapproximability result is tight up to a factor of 2 o \( k log k \) \( log r \) k 2 1 in comparison , when 3 leq k is a constant , the previously known gap was o \( r \) , which is significantly larger than our gap of o \( text polylog r \) finally , we show that we can replace the unique games conjecture assumption with khot 's d to 1 conjecture and still get asymptotically the same hardness of approximation"
stat.ML,"in this paper , we address the general case of a coordinated secondary network willing to exploit communication opportunities left vacant by a licensed primary network since secondary users \( su \) usually have no prior knowledge on the environment , they need to learn the availability of each channel through sensing techniques , which however can be prone to detection errors we argue that cooperation among secondary users can enable efficient learning and coordination mechanisms in order to maximize the spectrum exploitation by sus , while minimizing the impact on the primary network to this goal , we provide three novel contributions in this paper first , we formulate the spectrum selection in secondary networks as an instance of the multi armed bandit \( mab \) problem , and we extend the analysis to the collaboration learning case , in which each su learns the spectrum occupation , and shares this information with other sus we show that collaboration among sus can mitigate the impact of sensing errors on system performance , and improve the convergence of the learning process to the optimal solution second , we integrate the learning algorithms with two collaboration techniques based on modified versions of the hungarian algorithm and of the round robin algorithm that allows reducing the interference among sus third , we derive fundamental limits to the performance of cooperative learning algorithms based on upper confidence bound \( ucb \) policies in a symmetric scenario where all su have the same perception of the quality of the resources extensive simulation results confirm the effectiveness of our joint learning collaboration algorithm in protecting the operations of primary users \( pus \) , while maximizing the performance of sus"
cs.LG|stat.ML,"motivated by clinical trials , we study bandits with observable non compliance at each step , the learner chooses an arm , after , instead of observing only the reward , it also observes the action that took place we show that such noncompliance can be helpful or hurtful to the learner in general unfortunately , naively incorporating compliance information into bandit algorithms loses guarantees on sublinear regret we present hybrid algorithms that maintain regret bounds up to a multiplicative factor and can incorporate compliance information simulations based on real data from the international stoke trial show the practical potential of these algorithms"
cs.AI|cs.LG|stat.ML,"feature extraction has gained increasing attention in the field of machine learning , as in order to detect patterns , extract information , or predict future observations from big data , the urge of informative features is crucial the process of extracting features is highly linked to dimensionality reduction as it implies the transformation of the data from a sparse high dimensional space , to higher level meaningful abstractions this dissertation employs neural networks for distributed paragraph representations , and latent dirichlet allocation to capture higher level features of paragraph vectors although neural networks for distributed paragraph representations are considered the state of the art for extracting paragraph vectors , we show that a quick topic analysis model such as latent dirichlet allocation can provide meaningful features too we evaluate the two methods on the cmu movie summary corpus , a collection of 25 , 203 movie plot summaries extracted from wikipedia finally , for both approaches , we use k nearest neighbors to discover similar movies , and plot the projected representations using t distributed stochastic neighbor embedding to depict the context similarities these similarities , expressed as movie distances , can be used for movies recommendation the recommended movies of this approach are compared with the recommended movies from imdb , which use a collaborative filtering recommendation approach , to show that our two models could constitute either an alternative or a supplementary recommendation approach"
cs.LG|stat.ML,"in this article , the logic rule ensembles approach to supervised learning is applied to the unsupervised or semi supervised clustering logic rules which were obtained by combining simple conjunctive rules are used to partition the input space and an ensemble of these rules is used to define a similarity matrix similarity partitioning is used to partition the data in an hierarchical manner we have used internal and external measures of cluster validity to evaluate the quality of clusterings or to identify the number of clusters"
cs.AI|cs.LG,"the objective of the paper is to design an agent which provides efficient response to the caller when a call goes unanswered in smartphones the agent provides responses through text messages , email etc stating the most likely reason as to why the callee is unable to answer a call responses are composed taking into consideration the importance of the present call and the situation the callee is in at the moment like driving , sleeping , at work etc the agent makes decisons in the compostion of response messages based on the patterns it has come across in the learning environment initially the user helps the agent to compose response messages the agent associates this message to the percept it recieves with respect to the environment the callee is in the user may thereafter either choose to make to response system automatic or choose to recieve suggestions from the agent for responses messages and confirm what is to be sent to the caller"
cs.LG|cs.SI|stat.ML,"in this thesis , we propose several modelling strategies to tackle evolving data in different contexts in the framework of static clustering , we start by introducing a soft kernel spectral clustering \( sksc \) algorithm , which can better deal with overlapping clusters with respect to kernel spectral clustering \( ksc \) and provides more interpretable outcomes afterwards , a whole strategy based upon ksc for community detection of static networks is proposed , where the extraction of a high quality training sub graph , the choice of the kernel function , the model selection and the applicability to large scale data are key aspects this paves the way for the development of a novel clustering algorithm for the analysis of evolving networks called kernel spectral clustering with memory effect \( mksc \) , where the temporal smoothness between clustering results in successive time steps is incorporated at the level of the primal optimization problem , by properly modifying the ksc formulation later on , an application of ksc to fault detection of an industrial machine is presented here , a smart pre processing of the data by means of a proper windowing operation is necessary to catch the ongoing degradation process affecting the machine in this way , in a genuinely unsupervised manner , it is possible to raise an early warning when necessary , in an online fashion finally , we propose a new algorithm called incremental kernel spectral clustering \( iksc \) for online learning of non stationary data this ambitious challenge is faced by taking advantage of the out of sample property of kernel spectral clustering \( ksc \) to adapt the initial model , in order to tackle merging , splitting or drifting of clusters across time real world applications considered in this thesis include image segmentation , time series clustering , community detection of static and evolving networks"
cs.AI|cs.LG,"solomonoff unified occam 's razor and epicurus' principle of multiple explanations to one elegant , formal , universal theory of inductive inference , which initiated the field of algorithmic information theory his central result is that the posterior of his universal semimeasure m converges rapidly to the true sequence generating posterior mu , if the latter is computable hence , m is eligible as a universal predictor in case of unknown mu we investigate the existence and convergence of computable universal \( semi \) measures for a hierarchy of computability classes finitely computable , estimable , enumerable , and approximable for instance , m is known to be enumerable , but not finitely computable , and to dominate all enumerable semimeasures we define seven classes of \( semi \) measures based on these four computability concepts each class may or may not contain a \( semi \) measure which dominates all elements of another class the analysis of these 49 cases can be reduced to four basic cases , two of them being new the results hold for discrete and continuous semimeasures we also investigate more closely the types of convergence , possibly implied by universality in difference and in ratio , with probability 1 , in mean sum , and for martin loef random sequences we introduce a generalized concept of randomness for individual sequences and use it to exhibit difficulties regarding these issues"
cs.DS,"consider an undirected graph g \( vg , eg \) and a set of six emph terminals t set s 1 , s 2 , s 3 , t 1 , t 2 , t 3 subseteq vg the goal is to find a collection calp of three edge disjoint paths p 1 , p 2 , and p 3 , where p i connects nodes s i and t i \( i 1 , 2 , 3 \) results obtained by robertson and seymour by graph minor techniques imply a polynomial time solvability of this problem the time bound of their algorithm is o \( m 3 \) \( hereinafter we assume n abs vg , m abs eg , n o \( m \) \) in this paper we consider a special , emph eulerian case of g and t namely , construct the emph demand graph h \( vg , set s 1t 1 , s 2t 2 , s 3t 3 \) the edges of h correspond to the desired paths in calp in the eulerian case the degrees of all nodes in the \( multi \) graph g h \( \( vg , eg cup eh \) \) are even schrijver showed that , under the assumption of eulerianess , cut conditions provide a criterion for the existence of calp this , in particular , implies that checking for existence of calp can be done in o \( m \) time our result is a combinatorial o \( m \) time algorithm that constructs calp \( if the latter exists \)"
cs.DS,"the intersection graph of a collection of trapezoids with corner points lying on two parallel lines is called a trapezoid graph these graphs and their generalizations were applied in various fields , including modeling channel routing problems in vlsi design and identifying the optimal chain of non overlapping fragments in bioinformatics using modified binary indexed tree data structure , we design an algorithm for calculating the vertex connectivity of trapezoid graph g with time complexity o \( n log n \) , where n is the number of trapezoids furthermore , we establish sufficient and necessary condition for a trapezoid graph g to be bipartite and characterize trees that can be represented as trapezoid graphs"
cs.DS|cs.IT,"compression of integer sets and sequences has been extensively studied for settings where elements follow a uniform probability distribution in addition , methods exist that exploit clustering of elements in order to achieve higher compression performance in this work , we address the case where enumeration of elements may be arbitrary or random , but where statistics is kept in order to estimate probabilities of elements we present a recursive subset size encoding method that is able to benefit from statistics , explore the effects of permuting the enumeration order based on element probabilities , and discuss general properties and possibilities for this class of compression problem"
cs.DM|cs.IT,"the asyptotic number of nonequivalent binary n codes is determined this is also the asymptotic number of nonisomorphic binary n matroids the connection to a result of lefmann , roedl , phelps is explored the latter states that almost all binary n codes have a trivial automorphism group"
cs.LG,"albeit , the implicit feedback based recommendation problem when only the user history is available but there are no ratings is the most typical setting in real world applications , it is much less researched than the explicit feedback case state of the art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained there are few if any implicit feedback benchmark datasets , therefore new ideas are usually experimented on explicit benchmarks in this paper , we propose a generic context aware implicit feedback recommender algorithm , coined itals itals apply a fast , als based tensor factorization learning method that scales linearly with the number of non zero elements in the tensor the method also allows us to incorporate diverse context information into the model while maintaining its computational efficiency in particular , we present two such context aware implementation variants of itals the first incorporates seasonality and enables to distinguish user behavior in different time intervals the other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items , e g to automatically tell apart product types or categories that are typically purchased repetitively \( collectibles , grocery goods \) or once \( household appliances \) experiments performed on three implicit datasets \( two proprietary ones and an implicit variant of the netflix dataset \) show that by integrating context aware information with our factorization framework into the state of the art implicit recommender algorithm the recommendation quality improves significantly"
cs.AI|cs.DS,"bessiere et al \( aaai'08 \) showed that several intractable global constraints can be efficiently propagated when certain natural problem parameters are small in particular , the complete propagation of a global constraint is fixed parameter tractable in k the number of holes in domains whenever bound consistency can be enforced in polynomial time this applies to the global constraints atmost nvalue and extended global cardinality \( egc \) in this paper we extend this line of research and introduce the concept of reduction to a problem kernel , a key concept of parameterized complexity , to the field of global constraints in particular , we show that the consistency problem for atmost nvalue constraints admits a linear time reduction to an equivalent instance on o \( k 2 \) variables and domain values this small kernel can be used to speed up the complete propagation of nvalue constraints we contrast this result by showing that the consistency problem for egc constraints does not admit a reduction to a polynomial problem kernel unless the polynomial hierarchy collapses"
cs.DS,"inferring cluster structure in microarray datasets is a fundamental task for the omic sciences a fundamental question in statistics , data analysis and classification , is the prediction of the number of clusters in a dataset , usually established via internal validation measures despite the wealth of internal measures available in the literature , new ones have been recently proposed , some of them specifically for microarray data in this dissertation , a study of internal validation measures is given , paying particular attention to the stability based ones indeed , this class of measures is particularly prominent and promising in order to have a reliable estimate the number of clusters in a dataset for those measures , a new general algorithmic paradigm is proposed here that highlights the richness of measures in this class and accounts for the ones already available in the literature moreover , some of the most representative validation measures are also considered experiments on 12 benchmark datasets are performed in order to assess both the intrinsic ability of a measure to predict the correct number of clusters in a dataset and its merit relative to the other measures the main result is a hierarchy of internal validation measures in terms of precision and speed , highlighting some of their merits and limitations not reported before in the literature this hierarchy shows that the faster the measure , the less accurate it is in order to reduce the time performance gap between the fastest and the most precise measures , the technique of designing fast approximation algorithms is systematically applied the end result is a speed up of many of the measures studied here that brings the gap between the fastest and the most precise within one order of magnitude in time , with no degradation in their prediction power prior to this work , the time gap was at least two orders of magnitude"
cs.AI|cs.LG,"in this paper we experimentally compare the classification uncertainty of the randomised decision tree \( dt \) ensemble technique and the bayesian dt technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community for quantitative evaluation of classification uncertainty , we use an uncertainty envelope dealing with the class posterior distribution and a given confidence probability counting the classifier outcomes , this technique produces feasible evaluations of the classification uncertainty using this technique in our experiments , we found that the bayesian dt technique is superior to the randomised dt ensemble technique"
cs.DS,"provisioning is a technique for avoiding repeated expensive computations in what if analysis given a query , an analyst formulates k hypotheticals , each retaining some of the tuples of a database instance , possibly overlapping , and she wishes to answer the query under scenarios , where a scenario is defined by a subset of the hypotheticals that are turned on we say that a query admits compact provisioning if given any database instance and any k hypotheticals , one can create a poly size \( in k \) sketch that can then be used to answer the query under any of the 2 k possible scenarios without accessing the original instance in this paper , we focus on provisioning complex queries that combine relational algebra \( the logical component \) , grouping , and statistics analytics \( the numerical component \) we first show that queries that compute quantiles or linear regression \( as well as simpler queries that compute count and sum average of positive values \) can be compactly provisioned to provide \( multiplicative \) approximate answers to an arbitrary precision in contrast , exact provisioning for each of these statistics requires the sketch size to be exponential in k we then establish that for any complex query whose logical component is a positive relational algebra query , as long as the numerical component can be compactly provisioned , the complex query itself can be compactly provisioned on the other hand , introducing negation or recursion in the logical component again requires the sketch size to be exponential in k while our positive results use algorithms that do not access the original instance after a scenario is known , we prove our lower bounds even for the case when , knowing the scenario , limited access to the instance is allowed"
cs.DS,"the k center problem is one of several classic np hard clustering questions for contemporary massive data sets , ram based algorithms become impractical and although there exist good sequential algorithms for k center , they are not easily parallelizable in this paper , we design and implement parallel approximation algorithms for this problem we observe that gonzalez 's greedy algorithm can be efficiently parallelized in several mapreduce rounds in practice , we find that two rounds are sufficient , leading to a 4 approximation we contrast this with an existing parallel algorithm for k center that runs in a constant number of rounds , and offers a 10 approximation in depth runtime analysis reveals that this scheme is often slow , and that its sampling procedure only runs if k is sufficiently small , relative to the input size to trade off runtime for approximation guarantee , we parameterize this sampling algorithm , and find in our experiments that the algorithm is not only faster , but sometimes more effective yet the parallel version of gonzalez is about 100 times faster than both its sequential version and the parallel sampling algorithm , barely compromising solution quality"
cs.DM,"this article analyzes directed lattice paths , when a boundary reflecting or absorbing condition is added to the classical models the lattice paths are characterized by two time independent sets of rules \( also called steps \) which have a privileged direction of increase and are therefore essentially one dimensional objects depending on the spatial coordinate , one of the two sets of rules applies , namely one for altitude 0 and one for altitude bigger than 0 the abscissa y 0 thus acts as a border which either absorbs or reflects steps the absorption model corresponds to the model analyzed by banderier and flajolet \( analytic combinatorics of directed lattice paths \) , while the reflecting model leads to a more complicated situation we show how the generating functions are then modified the kernel method strikes again but here it unfortunately does not give a nice product formula this makes the analysis more challenging , and , in the case of l ukasiewicz walks , we give the asymptotics for the number of excursions , arches and meanders limit laws for the number of returns to 0 of excursions are given we also compute the limit laws of the final altitude of meanders the full analytic situation is more complicated than the banderier flajolet model \( partly because new critical compositions appear , forcing us to introduce new key quantities , like the drift at 0 \) , and we quantify to what extent the global drift , and the drift at 0 play a role in the universal behavior of such walks"
cs.IT|stat.ML,"we study linear models under heavy tailed priors from a probabilistic viewpoint instead of computing a single sparse most probable \( map \) solution as in standard deterministic approaches , the focus in the bayesian compressed sensing framework shifts towards capturing the full posterior distribution on the latent variables , which allows quantifying the estimation uncertainty and learning model parameters using maximum likelihood the exact posterior distribution under the sparse linear model is intractable and we concentrate on variational bayesian techniques to approximate it repeatedly computing gaussian variances turns out to be a key requisite and constitutes the main computational bottleneck in applying variational techniques in large scale problems we leverage on the recently proposed perturb and map algorithm for drawing exact samples from gaussian markov random fields \( gmrf \) the main technical contribution of our paper is to show that estimating gaussian variances using a relatively small number of such efficiently drawn random samples is much more effective than alternative general purpose variance estimation techniques by reducing the problem of variance estimation to standard optimization primitives , the resulting variational algorithms are fully scalable and parallelizable , allowing bayesian computations in extremely large scale problems with the same memory and time complexity requirements as conventional point estimation techniques we illustrate these ideas with experiments in image deblurring"
cs.DM|cs.DS,"we present an algorithm , called bisc , that describes the patterns avoided by a given set of permutations it automatically conjectures the statements of known theorems such as the descriptions of stack sortable \( knuth 1975 \) and west 2 stack sortable permutations \( west 1990 \) , smooth \( lakshmibai and sandhya 1990 \) and forest like permutations \( bousquet melou and butler 2007 \) , and simsun permutations \( branden and claesson 2011 \) the algorithm has also been used to discover new theorems and conjectures related to young tableaux , wilf equivalences and sorting devices we further give algorithms to prove a complete description of preimages of pattern classes under certain sorting devices these generalize an algorithm of claesson and ulfarsson \( 2012 \) and allow us to prove a linear time algorithm for finding occurrences of the pattern 4312"
cs.DS,"auditability is crucial for data outsourcing , facilitating accountability and identifying data loss or corruption incidents in a timely manner , reducing in turn the risks from such losses in recent years , in synch with the growing trend of outsourcing , a lot of progress has been made in designing probabilistic \( for efficiency \) provable data possession \( pdp \) schemes however , even the recent and advanced pdp solutions that do deal with dynamic data , do so in a limited manner , and for only the latest version of the data a naive solution treating different versions in isolation would work , but leads to tremendous overheads , and is undesirable in this paper , we present algorithms to achieve full persistence \( all intermediate configurations are preserved and are modifiable \) for an optimized skip list \( known as flexlist \) so that versioned data can be audited the proposed scheme provides deduplication at the level of logical , variable sized blocks , such that only the altered parts of the different versions are kept , while the persistent data structure facilitates access \( read \) of any arbitrary version with the same storage and process efficiency that state of the art dynamic pdp solutions provide for only the current version , while commit \( write \) operations incur around 5 additional time furthermore , the time overhead for auditing arbitrary versions in addition to the latest version is imperceptible even on a low end server"
cs.DM|cs.DS,"we study graph orientations that minimize the entropy of the in degree sequence the problem of finding such an orientation is an interesting special case of the minimum entropy set cover problem previously studied by halperin and karp theoret comput sci , 2005 and by the current authors algorithmica , to appear we prove that the minimum entropy orientation problem is np hard even if the graph is planar , and that there exists a simple linear time algorithm that returns an approximate solution with an additive error guarantee of 1 bit this improves on the only previously known algorithm which has an additive error guarantee of log 2 e bits \( approx 1 4427 bits \)"
cs.DM|cs.DS,"we consider the single source \( or single sink \) buy at bulk problem with an unknown concave cost function we want to route a set of demands along a graph to or from a designated root node , and the cost of routing x units of flow along an edge is proportional to some concave , non decreasing function f such that f \( 0 \) 0 we present a polynomial time algorithm that finds a distribution over trees such that the expected cost of a tree for any f is within an o \( 1 \) factor of the optimum cost for that f the previous best simultaneous approximation for this problem , even ignoring computation time , was o \( log d \) , where d is the multi set of demand nodes we design a simple algorithmic framework using the ellipsoid method that finds an o \( 1 \) approximation if one exists , and then construct a separation oracle using a novel adaptation of the guha , meyerson , and munagala algorithm for the single sink buy at bulk problem that proves an o \( 1 \) approximation is possible for all f the number of trees in the support of the distribution constructed by our algorithm is at most 1 log d"
cs.LG|stat.ML,"we consider learning on graphs , guided by kernels that encode similarity between vertices our focus is on random walk kernels , the analogues of squared exponential kernels in euclidean spaces we show that on large , locally treelike , graphs these have some counter intuitive properties , specifically in the limit of large kernel lengthscales we consider using these kernels as covariance matrices of e g gaussian processes \( gps \) in this situation one typically scales the prior globally to normalise the average of the prior variance across vertices we demonstrate that , in contrast to the euclidean case , this generically leads to significant variation in the prior variance across vertices , which is undesirable from the probabilistic modelling point of view we suggest the random walk kernel should be normalised locally , so that each vertex has the same prior variance , and analyse the consequences of this by studying learning curves for gaussian process regression numerical calculations as well as novel theoretical predictions for the learning curves using belief propagation make it clear that one obtains distinctly different probabilistic models depending on the choice of normalisation our method for predicting the learning curves using belief propagation is significantly more accurate than previous approximations and should become exact in the limit of large random graphs"
cs.DS,"we show the first dimension preserving search to decision reductions for approximate svp and cvp in particular , for any gamma leq 1 o \( log n n \) and any constant c 0 , we obtain an efficient dimension preserving reduction from gamma c n log n svp to gamma gapsvp and an efficient dimension preserving reduction from gamma cn cvp to gamma gapcvp these results generalize the known equivalence of the search and decision versions of these problems in the exact case when gamma 1 both of these efficient reductions follow from more general dimension preserving reductions whose running times depend on the search and decision approximation factors indeed , the svp result is actually slightly stronger , as we obtain a reduction to gamma unique svp for the same values of gamma , a potentially easier problem we also derive some interesting corollaries for example , we give an improved hardness result for gamma unique svp we show np hardness \( under randomized reductions \) for any gamma leq 1 o \( log n n \) , improving on aggarwal and dubey , who showed hardness for gamma leq 1 1 n c for some large unspecified constant c and , we obtain an efficient dimension preserving reduction from sqrt n cvp to gamma unique svp \( or gamma gapsvp \) , again for any gamma leq 1 o \( log n n \)"
cs.LG,"face representation is a crucial step of face recognition systems an optimal face representation should be discriminative , robust , compact , and very easy to implement while numerous hand crafted and learning based representations have been proposed , considerable room for improvement is still present in this paper , we present a very easy to implement deep learning framework for face representation our method bases on a new structure of deep network \( called pyramid cnn \) the proposed pyramid cnn adopts a greedy filter and down sample operation , which enables the training procedure to be very fast and computation efficient in addition , the structure of pyramid cnn can naturally incorporate feature sharing across multi scale face representations , increasing the discriminative ability of resulting representation our basic network is capable of achieving high recognition accuracy \( 85 8 on lfw benchmark \) with only 8 dimension representation when extended to feature sharing pyramid cnn , our system achieves the state of the art performance \( 97 3 \) on lfw benchmark we also introduce a new benchmark of realistic face images on social network and validate our proposed representation has a good ability of generalization"
cs.DM|cs.IT,"we consider the problem of constructing deletion correcting codes over a binary alphabet and take a graph theoretic view an n bit s deletion correcting code is an independent set in a particular graph we propose constructing such a code by taking the union of many constant hamming weight codes this results in codes that have additional structure searching for codes in constant hamming weight induced subgraphs is computationally easier than searching the original graph we prove a lower bound on size of a codebook constructed this way for any number of deletions and show that it is only a small factor below the corresponding lower bound on unrestricted codes in the single deletion case , we find optimal colorings of the constant hamming weight induced subgraphs we show that the resulting code is asymptotically optimal we discuss the relationship between codes and colorings and observe that the vt codes are optimal in a coloring sense we prove a new lower bound on the chromatic number of the deletion channel graphs colorings of the deletion channel graphs that match this bound do not necessarily produce asymptotically optimal codes"
cs.DM,"we prove that every graph of rank width k is a pivot minor of a graph of tree width at most 2k we also prove that graphs of rank width at most 1 , equivalently distance hereditary graphs , are exactly vertex minors of trees , and graphs of linear rank width at most 1 are precisely vertex minors of paths in addition , we show that bipartite graphs of rank width at most 1 are exactly pivot minors of trees and bipartite graphs of linear rank width at most 1 are precisely pivot minors of paths"
cs.LG|stat.ML,"randomized matrix compression techniques , such as the johnson lindenstrauss transform , have emerged as an effective and practical way for solving large scale problems efficiently with a focus on computational efficiency , however , forsaking solutions quality and accuracy becomes the trade off in this paper , we investigate compressed least squares problems and propose new models and algorithms that address the issue of error and noise introduced by compression while maintaining computational efficiency , our models provide robust solutions that are more accurate relative to solutions of uncompressed least squares than those of classical compressed variants we introduce tools from robust optimization together with a form of partial compression to improve the error time trade offs of compressed least squares solvers we develop an efficient solution algorithm for our robust partially compressed \( rpc \) model based on a reduction to a one dimensional search we also derive the first approximation error bounds for partially compressed least squares solutions empirical results comparing numerous alternatives suggest that robust and partially compressed solutions are effectively insulated against aggressive randomized transforms"
cs.AI|cs.LG,"we introduce se3 nets , which are deep networks designed to model rigid body motion from raw point cloud data based only on pairs of depth images along with an action vector and point wise data associations , se3 nets learn to segment effected object parts and predict their motion resulting from the applied force rather than learning point wise flow vectors , se3 nets predict se3 transformations for different parts of the scene using simulated depth data of a table top scene and a robot manipulator , we show that the structure underlying se3 nets enables them to generate a far more consistent prediction of object motion than traditional flow based networks"
cs.DS,"in this paper , we continue our development of algorithms used for topological network discovery we present native p system versions of two fundamental problems in graph theory finding the maximum number of edge and node disjoint paths between a source node and target node we start from the standard depth first search maximum flow algorithms , but our approach is totally distributed , when initially no structural information is available and each p system cell has to even learn its immediate neighbors for the node disjoint version , our p system rules are designed to enforce node weight capacities \( of one \) , in addition to edge capacities \( of one \) , which are not readily available in the standard network flow algorithms"
cs.LG|stat.ML,"adaptive sampling theory has shown that , with proper assumptions on the signal class , algorithms exist to reconstruct a signal in mathbb r d with an optimal number of samples we generalize this problem to when the cost of sampling is not only the number of samples but also the distance traveled between samples this is motivated by our work studying regions of low oxygen concentration in the great lakes we show that for one dimensional threshold classifiers , a tradeoff between number of samples and distance traveled can be achieved using a generalization of binary search , which we refer to as quantile search we derive the expected total sampling time for noiseless measurements and the expected number of samples for an extension to the noisy case we illustrate our results in simulations relevant to our sampling application"
cs.LG|stat.ML,"nowadays , hyperspectral image classification widely copes with spatial information to improve accuracy one of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation in the classification context , the extracted features are commonly concatenated into a long vector \( also called stacked vector \) , on which is applied a conventional vector based machine learning technique \( e g svm with gaussian kernel \) in this paper , we rather propose to use a sequence structured kernel the spectrum kernel we show that the conventional stacked vector based kernel is actually a special case of this kernel experiments conducted on various publicly available hyperspectral datasets illustrate the improvement of the proposed kernel w r t conventional ones using the same hierarchical spatial features"
cs.LG,"nonnegative matrix factorization \( nmf \) is the problem of decomposing a given nonnegative n times m matrix m into a product of a nonnegative n times d matrix w and a nonnegative d times m matrix h a longstanding open question , posed by cohen and rothblum in 1993 , is whether a rational matrix m always has an nmf of minimal inner dimension d whose factors w and h are also rational we answer this question negatively , by exhibiting a matrix for which w and h require irrational entries"
cs.DM,"erd h o s and hajnal conjectured that , for every graph h , there exists a constant c h such that every graph g on n vertices which does not contain any induced copy of h has a clique or a stable set of size n c h we prove that for every k , there exists c k 0 such that every graph g on n vertices not inducing a cycle of length at least k nor its complement contains a clique or a stable set of size n c k"
cs.DS,"we present a deterministic way of assigning small \( log bit \) weights to the edges of a bipartite planar graph so that the minimum weight perfect matching becomes unique the isolation lemma as described in \( mulmuley et al 1987 \) achieves the same for general graphs using a randomized weighting scheme , whereas we can do it deterministically when restricted to bipartite planar graphs as a consequence , we reduce both decision and construction versions of the matching problem to testing whether a matrix is singular , under the promise that its determinant is 0 or 1 , thus obtaining a highly parallel spl algorithm for bipartite planar graphs this improves the earlier known bounds of non uniform spl by \( allender et al 1999 \) and nc 2 by \( miller and naor 1995 , mahajan and varadarajan 2000 \) it also rekindles the hope of obtaining a deterministic parallel algorithm for constructing a perfect matching in non bipartite planar graphs , which has been open for a long time our techniques are elementary and simple"
cs.DM,"the discriminator of an integer sequence s \( s \( i \) \) i 1 , introduced by arnold , benkoski , and mccabe in 1985 , is the map d s \( n \) that sends n to the least integer m such that the numbers s \( 0 \) , s \( 1 \) , , s \( n 1 \) are pairwise incongruent modulo m in this note we consider the discriminators of a certain class of sequences , the k regular sequences we compute the discriminators of two such sequences , the so called evil and odious numbers , and show they are 2 regular we also give an example of a k regular sequence whose discriminator is not k regular"
cs.DM|cs.DS|cs.IT,"we give two different and simple constructions for dimensionality reduction in ell 2 via linear mappings that are sparse only an o \( varepsilon \) fraction of entries in each column of our embedding matrices are non zero to achieve distortion 1 varepsilon with high probability , while still achieving the asymptotically optimal number of rows these are the first constructions to provide subconstant sparsity for all values of parameters , improving upon previous works of achlioptas \( jcss 2003 \) and dasgupta , kumar , and sarl ' o s \( stoc 2010 \) such distributions can be used to speed up applications where ell 2 dimensionality reduction is used"
cs.AI|cs.LG|stat.ML,"efficient exploration in complex environments remains a major challenge for reinforcement learning we propose bootstrapped dqn , a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions unlike dithering strategies such as epsilon greedy exploration , bootstrapped dqn carries out temporally extended \( or deep \) exploration this can lead to exponentially faster learning we demonstrate these benefits in complex stochastic mdps and in the large scale arcade learning environment bootstrapped dqn substantially improves learning times and performance across most atari games"
cs.IT|cs.SI,"considering electronic implications in the information society \( is \) as a complex system , complexity science tools are used to describe the processes that are seen to be taking place the sometimes troublesome relationship between the information and communication new technologies and e society gives rise to different problems , some of them being unexpected probably , the digital divide \( dd \) and the internet governance \( ig \) are among the most conflictive ones of internationally based e affairs admitting that solutions should be found for these problems , certain international policies are required in this context , data gathering and subsequent analysis , as well as the construction of adequate physical models are extremely important in order to imagine different future scenarios and suggest some subsequent control in the main text , mathematical modelization helps for visualizing how policies could e g influence the individual and collective behavior in an empirical social agent system in order to show how this purpose could be achieved , two approaches , \( i \) the ising model and \( ii \) a generalized lotka volterra model are used for dd and ig considerations respectively it can be concluded that the social modelization of the e information society as a complex system provides insights about how dd can be reduced and how the a large number of weak members of the is could influence the outcomes of the ig"
cs.DS|cs.LG|stat.ML,"we describe the fast greedy sparse subspace clustering \( fgssc \) algorithm providing an efficient method for clustering data belonging to a few low dimensional linear or affine subspaces the main difference of our algorithm from predecessors is its ability to work with noisy data having a high rate of erasures \( missed entries with the known coordinates \) and errors \( corrupted entries with unknown coordinates \) we discuss here how to implement the fast version of the greedy algorithm with the maximum efficiency whose greedy strategy is incorporated into iterations of the basic algorithm we provide numerical evidences that , in the subspace clustering capability , the fast greedy algorithm outperforms not only the existing state of the art ssc algorithm taken by the authors as a basic algorithm but also the recent gssc algorithm at the same time , its computational cost is only slightly higher than the cost of ssc the numerical evidence of the algorithm significant advantage is presented for a few synthetic models as well as for the extended yale b dataset of facial images in particular , the face recognition misclassification rate turned out to be 6 20 times lower than for the ssc algorithm we provide also the numerical evidence that the fgssc algorithm is able to perform clustering of corrupted data efficiently even when the sum of subspace dimensions significantly exceeds the dimension of the ambient space"
cs.DM,"a mixed hypergraph is a triple h \( v , mathcal c , mathcal d \) , where v is a set of vertices , mathcal c and mathcal d are sets of hyperedges a vertex coloring of h is proper if c edges are not totally multicolored and d edges are not monochromatic the feasible set s \( h \) of h is the set of all integers , s , such that h has a proper coloring with s colors bujt 'as and tuza graphs and combinatorics 24 \( 2008 \) , 1 12 gave a characterization of feasible sets for mixed hypergraphs with all c and d edges of the same size r , r geq 3 in this note , we give a short proof of a complete characterization of all possible feasible sets for mixed hypergraphs with all c edges of size ell and all d edges of size m , where ell , m geq 2 moreover , we show that for every sequence \( r \( s \) \) s ell n , n geq ell , of natural numbers there exists such a hypergraph with exactly r \( s \) proper colorings using s colors , s ell , ldots , n , and no proper coloring with more than n colors choosing ell m r this answers a question of bujt 'as and tuza , and generalizes their result with a shorter proof"
cs.LG|stat.ML,"sparse coding is an unsupervised learning algorithm that learns a succinct high level representation of the inputs given only unlabeled data it represents each input as a sparse linear combination of a set of basis functions originally applied to modeling the human visual cortex , sparse coding has also been shown to be useful for self taught learning , in which the goal is to solve a supervised classification task given access to additional unlabeled data drawn from different classes than that in the supervised learning problem shift invariant sparse coding \( sisc \) is an extension of sparse coding which reconstructs a \( usually time series \) input using all of the basis functions in all possible shifts in this paper , we present an efficient algorithm for learning sisc bases our method is based on iteratively solving two large convex optimization problems the first , which computes the linear coefficients , is an l1 regularized linear least squares problem with potentially hundreds of thousands of variables existing methods typically use a heuristic to select a small subset of the variables to optimize , but we present a way to efficiently compute the exact solution the second , which solves for bases , is a constrained linear least squares problem by optimizing over complex valued variables in the fourier domain , we reduce the coupling between the different variables , allowing the problem to be solved efficiently we show that sisc 's learned high level representations of speech and music provide useful features for classification tasks within those domains when applied to classification , under certain conditions the learned features outperform state of the art spectral and cepstral features"
cs.DM,"in bianco , l , giuseppe c , and p reverberi 2001 a network based model for traffic sensor location with implications on o d matrix estimates transportation science 35 \( 1 \) 50 60 , the authors present the sensor location problem that of locating the minimum number of traffic sensors at intersections of a road network such that the traffic flow on the entire network can be determined they offer a necessary and sufficient condition on the set of monitored nodes in order for the flow everywhere to be determined in this paper , we present a counterexample that demonstrates that the condition is not actually sufficient \( though it is still necessary \) we present a stronger necessary condition for flow calculability , and show that it is a sufficient condition in a large class of graphs in which a particular subgraph is a tree many typical road networks are included in this category , and we show how our condition can be used to inform traffic sensor placement"
cs.LG|stat.ML,"we propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem , demonstrating , that such a interpretation leads to new practical methods for the original problem in particular we characterise a novel class of iterative solutions to the stochastic optimal control problem based on a natural relaxation of the exact dual formulation these theoretical insights are applied to the reinforcement learning problem where they lead to new model free , off policy methods for discrete and continuous problems"
cs.LG,"dictionaries are often developed using tools that save to extensible markup language \( xml \) based standards these standards often allow high level repeating elements to represent lexical entries , and utilize descendants of these repeating elements to represent the structure within each lexical entry , in the form of an xml tree in many cases , dictionaries are published that have errors and inconsistencies that are expensive to find manually this paper discusses a method for dictionary writers to quickly audit structural regularity across entries in a dictionary by using statistical language modeling the approach learns the patterns of xml nodes that could occur within an xml tree , and then calculates the probability of each xml tree in the dictionary against these patterns to look for entries that diverge from the norm"
cs.AI|cs.LG,"we provide a solution for elementary science test using instructional materials we posit that there is a hidden structure that explains the correctness of an answer given the question and instructional materials and present a unified max margin framework that learns to find these hidden structures \( given a corpus of question answer pairs and instructional materials \) , and uses what it learns to answer novel elementary science questions our evaluation shows that our framework outperforms several strong baselines"
cs.DS,"we propose a different methodology towards approaching a maze problem we convert the problem into a quantum search problem \( qsp \) , and its solutions are sought for using the iterative grover 's search algorithm though the category of mazes we are looking at are of the np complete class , we have redirected such a np complete problem into a qsp our solution deals with two dimensional perfect mazes with no closed loops we encode all possible individual paths from the starting point of the maze into a quantum register a quantum fitness operator applied on the register encodes each individual with its fitness value we propose an oracle design which marks all the individuals above a certain fitness value and use the grover search algorithm to find one of the marked states iterating over this method , we approach towards the optimum solution"
cs.AI|cs.DS|cs.LG,"we investigate the problem of learning bayesian networks in an agnostic model where an epsilon fraction of the samples are adversarially corrupted our agnostic learning model is similar to in fact , stronger than huber 's contamination model in robust statistics in this work , we study the fully observable bernoulli case where the structure of the network is given even in this basic setting , previous learning algorithms either run in exponential time or lose dimension dependent factors in their error guarantees we provide the first computationally efficient agnostic learning algorithm for this problem with dimension independent error guarantees our algorithm has polynomial sample complexity , runs in polynomial time , and achieves error that scales nearly linearly with the fraction of adversarially corrupted samples"
cs.SI|stat.ML,"one of the longstanding open problems in spectral graph clustering \( sgc \) is the so called model order selection problem automated selection of the correct number of clusters this is equivalent to the problem of finding the number of connected components or communities in an undirected graph we propose a solution to the sgc model selection problem under a random interconnection model \( rim \) using a novel selection criterion that is based on an asymptotic phase transition analysis our solution can more generally be applied to discovering hidden block diagonal structure in symmetric non negative matrices numerical experiments on simulated graphs validate the phase transition analysis , and real world network data is used to validate the performance of the proposed model selection procedure"
cs.AI|cs.SI,"this paper advances a framework for modeling the component interactions between cognitive and social aspects of scientific creativity and technological innovation specifically , it aims to characterize innovation networks those networks that involve the interplay of people , ideas and organizations to create new , technologically feasible , commercially realizable products , processes and organizational structures the tri partite framework captures networks of ideas \( concept level \) , people \( individual level \) and social structures \( social organizational level \) and the interactions between these levels at the concept level , new ideas are the nodes that are created and linked , kept open for further investigation or closed if solved by actors at the individual or organizational levels at the individual level , the nodes are actors linked by shared worldviews \( based on shared professional , educational , experiential backgrounds \) who are the builders of the concept level at the social organizational level , the nodes are organizations linked by common efforts on a given project \( e g , a company university collaboration \) that by virtue of their intellectual property or rules of governance constrain the actions of individuals \( at the individual level \) or ideas \( at the concept level \) after describing this framework and its implications we paint a number of scenarios to flesh out how it can be applied"
cs.DS,"continent scale datasets challenge hydrological algorithms for processing digital elevation models flow accumulation is an important input for many such algorithms here , i parallelize its calculation the new algorithm works on one or many cores , or multiple machines , and can take advantage of large memories or cope with small ones unlike previous algorithms , the new algorithm guarantees a fixed number of memory access and communication events per raster cell in testing , the new algorithm ran faster and used fewer resources than previous algorithms exhibiting 30 strong and weak scaling efficiencies up to 48 cores and linear scaling across datasets ranging over three orders of magnitude the largest dataset tested has two trillion \( 2 10 12 \) cells with 48 cores , processing required 24 minutes wall time \( 14 5 compute hours \) this test is three orders of magnitude larger than any previously performed in the literature complete , well commented source code and correctness tests are available for download from github"
cs.DS,"we present a new secure cloud storage mechanism that combines three previously disjoint security properties obliviousness , secure deletion , and history independence the system maintains strong privacy guarantees against a cloud observation attack , wherein an attacker learns all previous states of , and accesses to , the persistent cloud storage , as well as a catastrophic attack , where the decryption keys from erasable memory are also leaked in the first scenario , the access pattern reveals nothing about the contents \( obliviousness \) , and in both scenarios , no previously deleted data is recoverable \( secure deletion \) , and the structure of the data leaks a bounded amount of information about previous states \( history independence \) to achieve these goals we developed a new oblivious ram with variable size storage blocks \( voram \) and a new history independent , randomized data structure based on b trees \( hirb tree \) stored within the voram we prove that the voram hirb achieves obliviousness and secure deletion we also show that any such system must inevitably leak a bounded amount of history information , and prove that our voram hirb construction matches this lower bound , up to logarithmic factors our system also provides better utilization of the oram buckets and reduces the amount of local storage requirements by o \( log n \) as compared to prior work for storing map data structures finally , we have implemented and measured the performance of our system using amazon web services for a sample password management application that maintains the privacy of login records , addition and removal of accounts , and password changes the empirical performance is comparable to current state of the art in oram technologies and greatly outperforms naive approaches that provide the same security properties"
cs.LG,"deep neural network \( dnn \) models have recently obtained state of the art prediction accuracy for the transcription factor binding \( tfbs \) site classification task however , it remains unclear how these approaches identify meaningful dna sequence signals and give insights as to why tfs bind to certain locations in this paper , we propose a toolkit called the deep genomic dashboard \( deep gdashboard \) which provides a suite of visualization strategies to extract motifs , or sequence patterns from deep neural network models for tfbs classification we demonstrate how to visualize and understand three important dnn models convolutional , recurrent , and convolutional recurrent networks our first visualization method is finding a test sequence 's saliency map which uses first order derivatives to describe the importance of each nucleotide in making the final prediction second , considering recurrent models make predictions in a temporal manner \( from one end of a tfbs sequence to the other \) , we introduce temporal output values , indicating the prediction score of a model over time for a sequential input lastly , a class specific visualization strategy finds the optimal input sequence for a given tfbs positive class via stochastic gradient optimization our experimental results indicate that a convolutional recurrent architecture performs the best among the three architectures the visualization techniques indicate that cnn rnn makes predictions by modeling both motifs as well as dependencies among them"
cs.LG|stat.ML,"the rise of big data has led to new demands for machine learning \( ml \) systems to learn complex models with millions to billions of parameters , that promise adequate capacity to digest massive datasets and offer powerful predictive analytics thereupon in order to run ml algorithms at such scales , on a distributed cluster with 10s to 1000s of machines , it is often the case that significant engineering efforts are required and one might fairly ask if such engineering truly falls within the domain of ml research or not taking the view that big ml systems can benefit greatly from ml rooted statistical and algorithmic insights and that ml researchers should therefore not shy away from such systems design we discuss a series of principles and strategies distilled from our recent efforts on industrial scale ml solutions these principles and strategies span a continuum from application , to engineering , and to theoretical research and development of big ml systems and architectures , with the goal of understanding how to make them efficient , generally applicable , and supported with convergence and scaling guarantees they concern four key questions which traditionally receive little attention in ml research how to distribute an ml program over a cluster \? how to bridge ml computation with inter machine communication \? how to perform such communication \? what should be communicated between machines \? by exposing underlying statistical and algorithmic characteristics unique to ml programs but not typically seen in traditional computer programs , and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high performance distributed ml software as well as general purpose ml frameworks , we present opportunities for ml researchers and practitioners to further shape and grow the area that lies between ml and systems"
cs.DS,"for an undirected n vertex graph g with non negative edge weights , we consider the following type of query given two vertices s and t in g , what is the weight of a minimum st cut in g \? we solve this problem in preprocessing time o \( n log 3 n \) for graphs of bounded genus , giving the first sub quadratic time algorithm for this class of graphs our result also improves by a logarithmic factor a previous algorithm by borradaile , sankowski and wulff nilsen \( focs 2010 \) that applied only to planar graphs our algorithm constructs a gomory hu tree for the given graph , providing a data structure with space o \( n \) that can answer minimum cut queries in constant time the dependence on the genus of the input graph in our preprocessing time is 2 o \( g 2 \)"
cs.LG,"this paper takes a computational learning theory approach to a problem of linear systems identification it is assumed that input signals have only a finite number k of frequency components , and systems to be identified have dimension no greater than n the main result establishes that the sample complexity needed for identification scales polynomially with n and logarithmically with k"
cs.DM,"several variations of hat guessing games have been popularly discussed in recreational mathematics in a typical hat guessing game , after initially coordinating a strategy , each of n players is assigned a hat from a given color set simultaneously , each player tries to guess the color of his her own hat by looking at colors of hats worn by other players in this paper , we consider a new variation of this game , in which we require at least k correct guesses and no wrong guess for the players to win the game , but they can choose to pass a strategy is called em perfect if it can achieve the simple upper bound frac n n k of the winning probability we present sufficient and necessary condition on the parameters n and k for the existence of perfect strategy in the hat guessing games in fact for any fixed parameter k , the existence of perfect strategy can be determined for every sufficiently large n in our construction we introduce a new notion \( d 1 , d 2 \) regular partition of the boolean hypercube , which is worth to study in its own right for example , it is related to the k dominating set of the hypercube it also might be interesting in coding theory the existence of \( d 1 , d 2 \) regular partition is explored in the paper and the existence of perfect k dominating set follows as a corollary"
cs.LG,"simple power electronic drive circuit and fault tolerance of converter are specific advantages of srm drives , but excessive torque ripple has limited its use to special applications it is well known that controlling the current shape adequately can minimize the torque ripple this paper presents a new method for shaping the motor currents to minimize the torque ripple , using a neuro fuzzy compensator in the proposed method , a compensating signal is added to the output of a pi controller , in a current regulated speed control loop numerical results are presented in this paper , with an analysis of the effects of changing the form of the membership function of the neuro fuzzy compensator"
cs.DM|cs.DS,"a tropical graph \( h , c \) consists of a graph h and a \( not necessarily proper \) vertex colouring c of h given two tropical graphs \( g , c 1 \) and \( h , c \) , a homomorphism of \( g , c 1 \) to \( h , c \) is a standard graph homomorphism of g to h that also preserves the vertex colours we initiate the study of the computational complexity of tropical graph homomorphism problems we consider two settings first , when the tropical graph \( h , c \) is fixed this is a problem called \( h , c \) colouring second , when the colouring of h is part of the input the associated decision problem is called h tropical colouring each \( h , c \) colouring problem is a constraint satisfaction problem \( csp \) , and we show that a complexity dichotomy for the class of \( h , c \) colouring problems holds if and only if the feder vardi dichotomy conjecture for csps is true this implies that \( h , c \) colouring problems form a rich class of decision problems on the other hand , we were successful in classifying the complexity of at least certain classes of h tropical colouring problems"
cs.LG,"in this paper , we investigate the problem of learning feature representation from unlabeled data using a single layer k means network a k means network maps the input data into a feature representation by finding the nearest centroid for each input point , which has attracted researchers' great attention recently due to its simplicity , effectiveness , and scalability however , one drawback of this feature mapping is that it tends to be unreliable when the training data contains noise to address this issue , we propose a svdd based feature learning algorithm that describes the density and distribution of each cluster from k means with an svdd ball for more robust feature representation for this purpose , we present a new svdd algorithm called c svdd that centers the svdd ball towards the mode of local density of each cluster , and we show that the objective of c svdd can be solved very efficiently as a linear programming problem additionally , previous single layer networks favor a large number of centroids but a crude pooling size , resulting in a representation that highlights the global aspects of the object here we explore an alternative network architecture with much smaller number of nodes but with much finer pooling size , hence emphasizing the local details of the object the architecture is also extended with multiple receptive field scales and multiple pooling sizes extensive experiments on several popular object recognition benchmarks , such as minst , norb , cifar 10 and stl 10 , shows that the proposed c svddnet method yields comparable or better performance than that of the previous state of the art methods"
cs.LG|stat.ML,"methods to find correlation among variables are of interest to many disciplines , including statistics , machine learning , \( big \) data mining and neurosciences parameters that measure correlation between two variables are of limited utility when used with multiple variables in this work , i propose a simple criterion to measure correlation among an arbitrary number of variables , based on a data set the central idea is to i \) design a function of the variables that can take different forms depending on a set of parameters , ii \) calculate the difference between a statistics associated to the function computed on the data set and the same statistics computed on a randomised version of the data set , called scrambled data set , and iii \) optimise the parameters to maximise this difference many such functions can be organised in layers , which can in turn be stacked one on top of the other , forming a neural network the function parameters are searched with an enhanced genetic algortihm called poet and the resulting method is tested on a cancer gene data set the method may have potential implications for some issues that affect the field of neural networks , such as overfitting , the need to process huge amounts of data for training and the presence of adversarial examples"
cs.DS|stat.ML,"we consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants we use differential privacy to model individuals' privacy losses this immediately poses a problem , as differentially private computation of a linear model necessarily produces a biased estimation , and existing approaches to design mechanisms to elicit data from privacy sensitive individuals do not generalize well to biased estimators we overcome this challenge through an appropriate design of the computation and payment scheme"
cs.AI|cs.LG,"a model of sensory information processing is presented the model assumes that learning of internal \( hidden \) generative models , which can predict the future and evaluate the precision of that prediction , is of central importance for information extraction furthermore , the model makes a bridge to goal oriented systems and builds upon the structural similarity between the architecture of a robust controller and that of the hippocampal entorhinal loop this generative control architecture is mapped to the neocortex and to the hippocampal entorhinal loop implicit memory phenomena priming and prototype learning are emerging features of the model mathematical theorems ensure stability and attractive learning properties of the architecture connections to reinforcement learning are also established both the control network , and the network with a hidden model converge to \( near \) optimal policy under suitable conditions falsifying predictions , including the role of the feedback connections between neocortical areas are made"
cs.DM,"we provide a numerical refutation of the developments of fiorini et al \( 2015 \) for models with disjoint sets of descriptive variables we also provide an insight into the meaning of the existence of a one to one linear map between solutions of such models fiorini , s , s massar , s pokutta , h r tiwary , and r de wolf \( 2015 \) exponential lower bounds for polytopes in combinatorial optimization journal of the acm 62 2 , article no 17"
cs.DM,"in 1995 , brouwer proved that the toughness of a connected k regular graph g is at least k lambda 2 , where lambda is the maximum absolute value of the non trivial eigenvalues of g brouwer conjectured that one can improve this lower bound to k lambda 1 and that many graphs \( especially graphs attaining equality in the hoffman ratio bound for the independence number \) have toughness equal to k lambda in this paper , we improve brouwer 's spectral bound when the toughness is small and we determine the exact value of the toughness for many strongly regular graphs attaining equality in the hoffman ratio bound such as lattice graphs , triangular graphs , complements of triangular graphs and complements of point graphs of generalized quadrangles for all these graphs with the exception of the petersen graph , we confirm brouwer 's intuition by showing that the toughness equals k \( lambda min \) , where lambda min is the smallest eigenvalue of the adjacency matrix of the graph"
cs.LG,"we study the task of cleaning scanned text documents that are strongly corrupted by dirt such as manual line strokes , spilled ink etc we aim at autonomously removing dirt from a single letter size page based only on the information the page contains our approach , therefore , has to learn character representations without supervision and requires a mechanism to distinguish learned representations from irregular patterns to learn character representations , we use a probabilistic generative model parameterizing pattern features , feature variances , the features' planar arrangements , and pattern frequencies the latent variables of the model describe pattern class , pattern position , and the presence or absence of individual pattern features the model parameters are optimized using a novel variational em approximation after learning , the parameters represent , independently of their absolute position , planar feature arrangements and their variances a quality measure defined based on the learned representation then allows for an autonomous discrimination between regular character patterns and the irregular patterns making up the dirt the irregular patterns can thus be removed to clean the document for a full latin alphabet we found that a single page does not contain sufficiently many character examples however , even if heavily corrupted by dirt , we show that a page containing a lower number of character types can efficiently and autonomously be cleaned solely based on the structural regularity of the characters it contains in different examples using characters from different alphabets , we demonstrate generality of the approach and discuss its implications for future developments"
cs.IT|cs.LG|stat.ML,"matrix completion , i e , the exact and provable recovery of a low rank matrix from a small subset of its elements , is currently only known to be possible if the matrix satisfies a restrictive structural constraint known as em incoherence on its row and column spaces in these cases , the subset of elements is sampled uniformly at random in this paper , we show that em any rank r n by n matrix can be exactly recovered from as few as o \( nr log 2 n \) randomly chosen elements , provided this random choice is made according to a em specific biased distribution the probability of any element being sampled should be proportional to the sum of the leverage scores of the corresponding row , and column perhaps equally important , we show that this specific form of sampling is nearly necessary , in a natural precise sense this implies that other perhaps more intuitive sampling schemes fail we further establish three ways to use the above result for the setting when leverage scores are not known textit a priori \( a \) a sampling strategy for the case when only one of the row or column spaces are incoherent , \( b \) a two phase sampling procedure for general matrices that first samples to estimate leverage scores followed by sampling for exact recovery , and \( c \) an analysis showing the advantages of weighted nuclear trace norm minimization over the vanilla un weighted formulation for the case of non uniform sampling"
cs.LG,i present a new way to parallelize the training of convolutional neural networks across multiple gpus the method scales significantly better than all alternatives when applied to modern convolutional neural networks
cs.DM,"the distribution of overlaps of solutions of a random csp is an indicator of the overall geometry of its solution space for random k sat , nonrigorous methods from statistical physics support the validity of the ``one step replica symmetry breaking'' approach some of these predictions were rigorously confirmed in cite cond mat 0504070 prl cite cond mat 0506053 there it is proved that the overlap distribution of random k sat , k geq 9 , has discontinuous support furthermore , achlioptas and ricci tersenghi proved that , for random k sat , k geq 8 and constraint densities close enough to the phase transition there exists an exponential number of clusters of satisfying assignments moreover , the distance between satisfying assignments in different clusters is linear we aim to understand the structural properties of random csp that lead to solution clustering to this end , we prove two results on the cluster structure of solutions for binary csp under the random model from molloy \( stoc 2002 \) 1 for all constraint sets s \( described explicitly in creignou and daude \( 2004 \) , istrate \( 2005 \) \) s t sat \( s \) has a sharp threshold and all q in \( 0 , 1 , q overlap sat \( s \) has a sharp threshold \( i e the first step of the approach in mora et al works in all nontrivial cases \) 2 for any constraint density value c 1 , the set of solutions of a random instance of 2 sat form , w h p , a single cluster also , for and any q in \( 0 , 1 such an instance has w h p two satisfying assignment of overlap sim q thus , as expected from statistical physics predictions , the second step of the approach in mora et al fails for 2 sat"
cs.LG|stat.ML,"we address the problem of computing approximate marginals in gaussian probabilistic models by using mean field and fractional bethe approximations as an extension of welling and teh \( 2001 \) , we define the gaussian fractional bethe free energy in terms of the moment parameters of the approximate marginals and derive an upper and lower bound for it we give necessary conditions for the gaussian fractional bethe free energies to be bounded from below it turns out that the bounding condition is the same as the pairwise normalizability condition derived by malioutov et al \( 2006 \) as a sufficient condition for the convergence of the message passing algorithm by giving a counterexample , we disprove the conjecture in welling and teh \( 2001 \) even when the bethe free energy is not bounded from below , it can possess a local minimum to which the minimization algorithms can converge"
cs.DM|cs.DS,"many load balancing problems that arise in scientific computing applications ask to partition a graph with weights on the vertices and costs on the edges into a given number of almost equally weighted parts such that the maximum boundary cost over all parts is small here , this partitioning problem is considered for bounded degree graphs g \( v , e \) with edge costs c e r that have a p separator theorem for some p 1 , i e , any \( arbitrarily weighted \) subgraph of g can be separated into two parts of roughly the same weight by removing a vertex set s such that the edges incident to s in the subgraph have total cost at most proportional to \( sum e c p e \) \( 1 p \) , where the sum is over all edges e in the subgraph we show for all positive integers k and weights w that the vertices of g can be partitioned into k parts such that the weight of each part differs from the average weight by less than max w v v in v , and the boundary edges of each part have cost at most proportional to \( sum e c e p k \) \( 1 p \) max e c e the partition can be computed in time nearly proportional to the time for computing a separator s of g our upper bound on the boundary costs is shown to be tight up to a constant factor for infinitely many instances with a broad range of parameters previous results achieved this bound only if one has c 1 , w 1 , and one allows parts with weight exceeding the average by a constant fraction"
cs.LG|stat.ML,"approximate bayesian computation \( abc \) is a powerful and elegant framework for performing inference in simulation based models however , due to the difficulty in scaling likelihood estimates , abc remains useful for relatively low dimensional problems we introduce hamiltonian abc \( habc \) , a set of likelihood free algorithms that apply recent advances in scaling bayesian learning using hamiltonian monte carlo \( hmc \) and stochastic gradients we find that a small number forward simulations can effectively approximate the abc gradient , allowing hamiltonian dynamics to efficiently traverse parameter spaces we also describe a new simple yet general approach of incorporating random seeds into the state of the markov chain , further reducing the random walk behavior of habc we demonstrate habc on several typical abc problems , and show that habc samples comparably to regular bayesian inference using true gradients on a high dimensional problem from machine learning"
cs.LG|stat.ML,"in this paper , we propose autocompete , a highly automated machine learning framework for tackling machine learning competitions this framework has been learned by us , validated and improved over a period of more than two years by participating in online machine learning competitions it aims at minimizing human interference required to build a first useful predictive model and to assess the practical difficulty of a given machine learning challenge the proposed system helps in identifying data types , choosing a machine learn ing model , tuning hyper parameters , avoiding over fitting and optimization for a provided evaluation metric we also observe that the proposed system produces better \( or comparable \) results with less runtime as compared to other approaches"
cs.DM,"o'donnell , wright , wu and zhou soda 2014 introduced the notion of robustly asymmetric graphs roughly speaking , these are graphs in which for every 0 le rho le 1 , every permutation that permutes a rho fraction of the vertices maps a theta \( rho \) fraction of the edges to non edges we show that there are graphs for which the constant hidden in the theta notation is roughly 1"
cs.AI|cs.LG,"in this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers this measure is mostly applied in ecology , whereby species counts are used as a measure of diversity the measures used were shannon entropy , simpsons and the berger parker diversity indexes as the diversity indexes increased so did the accuracy of the ensemble an ensemble dominated by classifiers with the same structure produced poor accuracy uncertainty rule from information theory was also used to further define diversity genetic algorithms were used to find the optimal ensemble by using the diversity indices as the cost function the method of voting was used to aggregate the decisions"
cs.LG,"we propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots casting the problem as a structured prediction problem on sequential data , our main idea is to use long short term memory \( lstm \) , a special type of recurrent neural networks to model the variable range dependencies entailed in the task of video summarization our learning models attain the state of the art results on two benchmark video datasets detailed analysis justifies the design of the models in particular , we show that it is crucial to take into consideration the sequential structures in videos and model them besides advances in modeling techniques , we introduce techniques to address the need of a large number of annotated data for training complex learning models there , our main idea is to exploit the existence of auxiliary annotated video datasets , albeit heterogeneous in visual styles and contents specifically , we show domain adaptation techniques can improve summarization by reducing the discrepancies in statistical properties across those datasets"
cs.LG|stat.ML,"in this paper , we present two new communication efficient methods for distributed minimization of an average of functions the first algorithm is an inexact variant of the dane algorithm that allows any local algorithm to return an approximate solution to a local subproblem we show that such a strategy does not affect the theoretical guarantees of dane significantly in fact , our approach can be viewed as a robustification strategy since the method is substantially better behaved than dane on data partition arising in practice it is well known that dane algorithm does not match the communication complexity lower bounds to bridge this gap , we propose an accelerated variant of the first method , called aide , that not only matches the communication lower bounds but can also be implemented using a purely first order oracle our empirical results show that aide is superior to other communication efficient algorithms in settings that naturally arise in machine learning applications"
cs.IT|cs.SI,"suppose that a rumor originating from a single source among a set of suspects spreads in a network , how to root out this rumor source \? with the a priori knowledge of suspect nodes and an observation of infected nodes , we construct a maximum a posteriori \( map \) estimator to identify the rumor source using the susceptible infected \( si \) model the a priori suspect set and its associated connectivity bring about new ingredients to the problem , and thus we propose to use local rumor center , a generalized concept based on rumor centrality , to identify the source from suspects for regular tree type networks of node degree delta , we characterize pc \( n \) , the correct detection probability of the estimator upon observing n infected nodes , in both the finite and asymptotic regimes first , when every infected node is a suspect , pc \( n \) asymptotically grows from 0 25 to 0 307 with delta from 3 to infinity , a result first established in shah and zaman \( 2011 , 2012 \) via a different approach and it monotonically decreases with n and increases with delta second , when the suspects form a connected subgraph of the network , pc \( n \) asymptotically significantly exceeds the a priori probability if delta 2 , and reliable detection is achieved as delta becomes large furthermore , it monotonically decreases with n and increases with delta third , when there are only two suspects , pc \( n \) is asymptotically at least 0 75 if delta 2 and it increases with the distance between the two suspects fourth , when there are multiple suspects , among all possible connection patterns , that they form a connected subgraph of the network achieves the smallest detection probability our analysis leverages ideas from the polya 's urn model in probability theory and sheds insight into the behavior of the rumor spreading process not only in the asymptotic regime but also for the general finite n regime"
cs.LG,"recently , several classifiers that combine primary tumor data , like gene expression data , and secondary data sources , such as protein protein interaction networks , have been proposed for predicting outcome in breast cancer in these approaches , new composite features are typically constructed by aggregating the expression levels of several genes the secondary data sources are employed to guide this aggregation although many studies claim that these approaches improve classification performance over single gene classifiers , the gain in performance is difficult to assess this stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches contrary to previous claims , we find that composite feature classifiers do not outperform simple single gene classifiers we investigate the effect of \( 1 \) the number of selected features \( 2 \) the specific gene set from which features are selected \( 3 \) the size of the training set and \( 4 \) the heterogeneity of the data set on the performance of composite feature and single gene classifiers strikingly , we find that randomization of secondary data sources , which destroys all biological information in these sources , does not result in a deterioration in performance of composite feature classifiers finally , we show that when a proper correction for gene set size is performed , the stability of single gene sets is similar to the stability of composite feature sets based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer"
cs.AI|cs.LG|stat.ML,"we present a non parametric bayesian approach to structure learning with hidden causes previous bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump markov chain monte carlo to move between solutions in contrast , we assume that the number of hidden causes is unbounded , but only a finite number influence observable variables this makes it possible to use a gibbs sampler to approximate the distribution over causal structures we evaluate the performance of both approaches in discovering hidden causes in simulated data , and use our non parametric approach to discover hidden causes in a real medical dataset"
cs.DS|cs.LG,"we give a polynomial time algorithm for the lossy population recovery problem in this problem , the goal is to approximately learn an unknown distribution on binary strings of length n from lossy samples for some parameter mu each coordinate of the sample is preserved with probability mu and otherwise is replaced by a ` \? ' the running time and number of samples needed for our algorithm is polynomial in n and 1 varepsilon for each fixed mu 0 this improves on algorithm of wigderson and yehudayoff that runs in quasi polynomial time for any mu 0 and the polynomial time algorithm of dvir et al which was shown to work for mu gtrapprox 0 30 by batman et al in fact , our algorithm also works in the more general framework of batman et al in which there is no a priori bound on the size of the support of the distribution the algorithm we analyze is implicit in previous work our main contribution is to analyze the algorithm by showing \( via linear programming duality and connections to complex analysis \) that a certain matrix associated with the problem has a robust local inverse even though its condition number is exponentially small a corollary of our result is the first polynomial time algorithm for learning dnfs in the restriction access model of dvir et al"
cs.DS,"to protect sensitive information in a cross tabulated table , it is a common practice to suppress some of the cells in the table this paper investigates four levels of data security of a two dimensional table concerning the effectiveness of this practice these four levels of data security protect the information contained in , respectively , individual cells , individual rows and columns , several rows or columns as a whole , and a table as a whole the paper presents efficient algorithms and np completeness results for testing and achieving these four levels of data security all these complexity results are obtained by means of fundamental equivalences between the four levels of data security of a table and four types of connectivity of a graph constructed from that table"
cs.LG|stat.ML,"we propose an artificial neural network \( ann \) approach for discovering common hidden variables and for learning of invariant representations this approach uses synchronicity and concurrence to recognize desired structures in data and discard superfluous structures , in the absence of labels and a data generation model in the common variable discovery problem , the ann uses measurements from two distinct sensors to construct a representation of the common hidden variable that is manifested in both sensors , and discards sensor specific variables in the invariant representation learning problem , the network uses multiple observations of objects under transformations to construct a representation which is invariant to the transformations"
cs.LG|stat.ML,"despite the prevalence of collaborative filtering in recommendation systems , there has been little theoretical development on why and how well it works , especially in the online setting , where items are recommended to users over time we address this theoretical gap by introducing a model for online recommendation systems , cast item recommendation under the model as a learning problem , and analyze the performance of a cosine similarity collaborative filtering method in our model , each of n users either likes or dislikes each of m items we assume there to be k types of users , and all the users of a given type share a common string of probabilities determining the chance of liking each item at each time step , we recommend an item to each user , where a key distinction from related bandit literature is that once a user consumes an item \( e g , watches a movie \) , then that item cannot be recommended to the same user again the goal is to maximize the number of likable items recommended to users over time our main result establishes that after nearly log \( km \) initial learning time steps , a simple collaborative filtering algorithm achieves essentially optimal performance without knowing k the algorithm has an exploitation step that uses cosine similarity and two types of exploration steps , one to explore the space of items \( standard in the literature \) and the other to explore similarity between users \( novel to this work \)"
cs.LG,"many sciences have made significant breakthroughs by adopting online tools that help organize , structure and mine information that is too detailed to be printed in journals in this paper , we introduce openml , a place for machine learning researchers to share and organize data in fine detail , so that they can work more effectively , be more visible , and collaborate with others to tackle harder problems we discuss how openml relates to other examples of networked science and what benefits it brings for machine learning research , individual scientists , as well as students and practitioners"
cs.LG,"pedestrian detection is a problem of considerable practical interest adding to the list of successful applications of deep learning methods to vision , we report state of the art and competitive results on all major pedestrian datasets with a convolutional network model the model uses a few new twists , such as multi stage features , connections that skip layers to integrate global shape information with local distinctive motif information , and an unsupervised method based on convolutional sparse coding to pre train the filters at each stage"
cs.DM|cs.DS,"we investigate the parameterized complexity of the graph editing problem called editing to a graph with a given degree sequence , where the aim is to obtain a graph with a given degree sequence sigma by at most k vertex or edge deletions and edge additions we show that the problem is w 1 hard when parameterized by k for any combination of the allowed editing operations from the positive side , we show that the problem can be solved in time 2 o \( k \( delta k \) 2 \) n 2 log n for n vertex graphs , where delta max sigma , i e , the problem is fpt when parameterized by k delta we also show that editing to a graph with a given degree sequence has a polynomial kernel when parameterized by k delta if only edge additions are allowed , and there is no polynomial kernel unless np subseteq conp poly for all other combinations of allowed editing operations"
cs.DS,"a set d subseteq v is called a k tuple dominating set of a graph g \( v , e \) if left n g v cap d right geq k for all v in v , where n g v denotes the closed neighborhood of v a set d subseteq v is called a liar 's dominating set of a graph g \( v , e \) if \( i \) left n g v cap d right geq 2 for all v in v and \( ii \) for every pair of distinct vertices u , v in v , left \( n g u cup n g v \) cap d right geq 3 given a graph g , the decision versions of k tuple domination problem and the liar 's domination problem are to check whether there exists a k tuple dominating set and a liar 's dominating set of g of a given cardinality , respectively these two problems are known to be np complete cite liaochang2003 , slater2009 in this paper , we study the parameterized complexity of these problems we show that the k tuple domination problem and the liar 's domination problem are mathsf w 2 hard for general graphs but they admit linear kernels for graphs with bounded genus"
cs.DM|cs.DS,"we show that all perfect odd integer squares not divisible by 3 , can be usefully written as sqrt \( n \) a 18p , where the constant a is determined by the basic properties of n the equation can be solved deterministically by an efficient four step algorithm that is solely based on integer arithmetic there is no required multiplication or division by multiple digit integers , nor does the algorithm need a seed value it finds the integer p when n is a perfect square , and certifies n as a non square when the algorithm terminates without a solution the number of iterations scales approximately as log \( sqrt \( n \) 2 \) for square roots the paper also outlines how one of the methods discussed for squares can be extended to finding an arbitrary root of n finally , we present a rule that distinguishes products of twin primes from squares"
cs.LG,"in this article , a stochastic gradient based online learning algorithm for extreme learning machines \( elm \) is developed \( sg elm \) a stability criterion based on lyapunov approach is used to prove both asymptotic stability of estimation error and stability in the estimated parameters suitable for identification of nonlinear dynamic systems the developed algorithm not only guarantees stability , but also reduces the computational demand compared to the os elm approach based on recursive least squares in order to demonstrate the effectiveness of the algorithm on a real world scenario , an advanced combustion engine identification problem is considered the algorithm is applied to two case studies an online regression learning for system identification of a homogeneous charge compression ignition \( hcci \) engine and an online classification learning \( with class imbalance \) for identifying the dynamic operating envelope of the hcci engine the results indicate that the accuracy of the proposed sg elm is comparable to that of the state of the art but adds stability and a reduction in computational effort"
cs.DS,"an important application of distance geometry to biochemistry studies the embeddings of the vertices of a weighted graph in the three dimensional euclidean space such that the edge weights are equal to the euclidean distances between corresponding point pairs when the graph represents the backbone of a protein , one can exploit the natural vertex order to show that the search space for feasible embeddings is discrete the corresponding decision problem can be solved using a binary tree based search procedure which is exponential in the worst case we discuss assumptions that bound the search tree width to a polynomial size"
cs.DS,"a recent theorem of bissacot , et al proved using results about the cluster expansion in statistical mechanics extends the lov 'asz local lemma by weakening the conditions under which its conclusions holds in this note , we prove an algorithmic analog of this result , extending moser and tardos 's recent algorithmic local lemma , and providing an alternative proof of the theorem of bissacot , et al applicable in the moser tardos algorithmic framework"
cs.DS,"motivated by a computer science algorithm known as `linear probing with hashing' we study a new type of percolation model whose basic features include a sequential `dropping' of particles on a substrate followed by their transport via a `pushing' mechanism our exact solution in one dimension shows that , unlike the ordinary random percolation model , the drop push model has nontrivial spatial correlations generated by the dynamics itself the critical exponents in the drop push model are also different from that of the ordinary percolation the relevance of our results to computer science is pointed out"
cs.DM|cs.DS,"load balancing is a well studied problem , with balls in bins being the primary framework the greedy algorithm mathsf greedy d of azar et al places each ball by probing d 1 random bins and placing the ball in the least loaded of them it ensures a maximum load that is exponentially better than the strategy of placing each ball uniformly at random v ocking showed that a slightly asymmetric variant , mathsf left d , provides a further significant improvement however , this improvement comes at an additional computational cost of imposing structure on the bins here , we present a fully decentralized and easy to implement algorithm called mathsf firstdiff d that combines the simplicity of mathsf greedy d and the improved balance of mathsf left d the key idea in mathsf firstdiff d is to probe until a different bin size from the first observation is located , then place the ball although the number of probes could be quite large for some of the balls , we show that mathsf firstdiff d requires only d probes on average per ball \( in both the standard and the heavily loaded settings \) thus the number of probes is no greater than either that of mathsf greedy d or mathsf left d more importantly , we show that mathsf firstdiff d closely matches the improved maximum load ensured by mathsf left d in both the standard and heavily loaded settings we further provide a tight lower bound on the maximum load up to o \( log log log n \) terms we additionally give experimental data that mathsf firstdiff d is indeed as good as mathsf left d , if not better , in practice"
cs.LG,"judgements about personality based on facial appearance are strong effectors in social decision making and are known to impact on areas from presidential elections to jury decisions recent work has shown that it is possible to predict perception of memorability , trustworthiness , intelligence and other attributes in human face images the most successful of these approaches requires face images expertly annotated with key facial landmarks we demonstrate a convolutional neural network \( cnn \) model that is able perform the same task without the need for landmark features thereby greatly increasing efficiency the model has high accuracy , surpassing human level performance in some cases furthermore , we use a deconvolutional approach to visualize important features for perception of 22 attributes and show that these can be described as a composites of their positive and negative components by separately visualizing both"
cs.AI|cs.LG,"this paper investigates a problem of generating images from visual attributes given the prevalent research for image recognition , the conditional image generation problem is relatively under explored due to the challenges of learning a good generative model and handling rendering uncertainties in images to address this , we propose a variety of attribute conditioned deep variational auto encoders that enjoy both effective representation learning and bayesian modeling , from which images can be generated from specified attributes and sampled latent factors we experiment with natural face images and demonstrate that the proposed models are capable of generating realistic faces with diverse appearance we further evaluate the proposed models by performing attribute conditioned image progression , transfer and retrieval in particular , our generation method achieves superior performance in the retrieval experiment against traditional nearest neighbor based methods both qualitatively and quantitatively"
cs.AI|cs.LG,"ensemble learning aims to improve generalization ability by using multiple base learners it is well known that to construct a good ensemble , the base learners should be accurate as well as diverse in this paper , unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners specifically , a semi supervised ensemble method named udeed is proposed unlike existing semi supervised ensemble methods where error prone pseudo labels are estimated for unlabeled data to enlarge the labeled data to improve accuracy , udeed works by maximizing accuracies of base learners on labeled data while maximizing diversity among them on unlabeled data experiments show that udeed can effectively utilize unlabeled data for ensemble learning and is highly competitive to well established semi supervised ensemble methods"
cs.LG|stat.ML,"this paper presents an original approach for jointly fitting survival times and classifying samples into subgroups the coxlogit model is a generalized linear model with a common set of selected features for both tasks survival times and class labels are here assumed to be conditioned by a common risk score which depends on those features learning is then naturally expressed as maximizing the joint probability of subgroup labels and the ordering of survival events , conditioned to a common weight vector the model is estimated by minimizing a regularized log likelihood through a coordinate descent algorithm validation on synthetic and breast cancer data shows that the proposed approach outperforms a standard cox model or logistic regression when both predicting the survival times and classifying new samples into subgroups it is also better at selecting informative features for both tasks"
cs.DS,"submodular function maximization is a central problem in combinatorial optimization , generalizing many important problems including max cut in directed undirected graphs and in hypergraphs , certain constraint satisfaction problems , maximum entropy sampling , and maximum facility location problems unlike submodular minimization , submodular maximization is np hard for the problem of maximizing a non monotone submodular function , feige , mirrokni , and vondr 'ak recently developed a 2 over 5 approximation algorithm cite fmv07 , however , their algorithms do not handle side constraints in this paper , we give the first constant factor approximation algorithm for maximizing any non negative submodular function subject to multiple matroid or knapsack constraints we emphasize that our results are for em non monotone submodular functions in particular , for any constant k , we present a \( 1 over k 2 1 over k epsilon \) approximation for the submodular maximization problem under k matroid constraints , and a \( 1 over 5 epsilon \) approximation algorithm for this problem subject to k knapsack constraints \( epsilon 0 is any constant \) we improve the approximation guarantee of our algorithm to 1 over k 1 1 over k 1 epsilon for k ge 2 partition matroid constraints this idea also gives a \( 1 over k epsilon \) approximation for maximizing a em monotone submodular function subject to k ge 2 partition matroids , which improves over the previously best known guarantee of frac 1 k 1"
cs.DS|cs.SI,"we consider the minimum cut partitioning of a graph into more than two parts using spectral methods while there exist well established spectral algorithms for this problem that give good results , they have traditionally not been well motivated rather than being derived from first principles by minimizing graph cuts , they are typically presented without direct derivation and then proved after the fact to work in this paper , we take a contrasting approach in which we start with a matrix formulation of the minimum cut problem and then show , via a relaxed optimization , how it can be mapped onto a spectral embedding defined by the leading eigenvectors of the graph laplacian the end result is an algorithm that is similar in spirit to , but different in detail from , previous spectral partitioning approaches in tests of the algorithm we find that it outperforms previous approaches on certain particularly difficult partitioning problems"
cs.LG,"this paper adapts topic models to the psychometric testing of mooc students based on their online forum postings measurement theory from education and psychology provides statistical models for quantifying a person 's attainment of intangible attributes such as attitudes , abilities or intelligence such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions the set of items can be used to measure a latent skill if individuals' responses on them conform to a guttman scale such well scaled items differentiate between individuals and inferred levels span the entire range from most basic to the advanced in practice , education researchers manually devise items \( quiz questions \) while optimising well scaled conformance due to the costly nature and expert requirements of this process , psychometric testing has found limited use in everyday teaching we aim to develop usable measurement models for highly instrumented mooc delivery platforms , by using participation in automatically extracted online forum topics as items the challenge is to formalise the guttman scale educational constraint and incorporate it into topic models to favour topics that automatically conform to a guttman scale , we introduce a novel regularisation into non negative matrix factorisation based topic modelling we demonstrate the suitability of our approach with both quantitative experiments on three coursera moocs , and with a qualitative survey of topic interpretability on two moocs by domain expert interviews"
cs.DS,"we study the parameterized complexity of interdiction problems in graphs for an optimization problem on graphs , one can formulate an interdiction problem as a game consisting of two players , namely , an interdictor and an evader , who compete on an objective with opposing interests in edge interdiction problems , every edge of the input graph has an interdiction cost associated with it and the interdictor interdicts the graph by modifying the edges in the graph , and the number of such modifications is constrained by the interdictor 's budget the evader then solves the given optimization problem on the modified graph the action of the interdictor must impede the evader as much as possible we focus on edge interdiction problems related to minimum spanning tree , maximum matching and shortest paths these problems arise in different real world scenarios we derive several fixed parameter tractability and w 1 hardness results for these interdiction problems with respect to various parameters next , we show close relation between interdiction problems and partial cover problems on bipartite graphs where the goal is not to cover all elements but to minimize maximize the number of covered elements with specific number of sets hereby , we investigate the parameterized complexity of several partial cover problems on bipartite graphs"
cs.DS,"wireless network topologies change over time and maintaining routes requires frequent updates updates are costly in terms of consuming throughput available for data transmission , which is precious in wireless networks in this paper , we ask whether there exist low overhead schemes that produce low stretch routes this is studied by using the underlying geometric properties of the connectivity graph in wireless networks"
cs.DS,"we introduce in a general setting a dynamic programming method for solving reconfiguration problems our method is based on contracted solution graphs , which are obtained from solution graphs by performing an appropriate series of edge contractions that decrease the graph size without losing any critical information needed to solve the reconfiguration problem under consideration our general framework captures the approach behind known reconfiguration results of bonsma \( 2012 \) and hatanaka , ito and zhou \( 2014 \) as a third example , we apply the method to the following problem given two k colorings alpha and beta of a graph g , can alpha be modified into beta by recoloring one vertex of g at a time , while maintaining a k coloring throughout \? this problem is known to be pspace hard even for bipartite planar graphs and k 4 by applying our method in combination with a thorough exploitation of the graph structure we obtain a polynomial time algorithm for \( k 2 \) connected chordal graphs"
cs.DM,cigler simple derivation of usual and extended dobinski formula is recalled and it is noted that both may be interpreted as averages of powers of random variables with the corresponding usual or extended poisson distributions in parallel more general formulas of extended operator umbral calculi origin are revealed the formulas encompass both earlier cases as very specific ones
cs.LG|stat.ML,"the problem of developing binary classifiers from positive and unlabeled data is often encountered in machine learning a common requirement in this setting is to approximate posterior probabilities of positive and negative classes for a previously unseen data point this problem can be decomposed into two steps \( i \) the development of accurate predictors that discriminate between positive and unlabeled data , and \( ii \) the accurate estimation of the prior probabilities of positive and negative examples in this work we primarily focus on the latter subproblem we study nonparametric class prior estimation and formulate this problem as an estimation of mixing proportions in two component mixture models , given a sample from one of the components and another sample from the mixture itself we show that estimation of mixing proportions is generally ill defined and propose a canonical form to obtain identifiability while maintaining the flexibility to model any distribution we use insights from this theory to elucidate the optimization surface of the class priors and propose an algorithm for estimating them to address the problems of high dimensional density estimation , we provide practical transformations to low dimensional spaces that preserve class priors finally , we demonstrate the efficacy of our method on univariate and multivariate data"
cs.IT|stat.ML,"we describe the underlying probabilistic interpretation of alpha and beta divergences we first show that beta divergences are inherently tied to tweedie distributions , a particular type of exponential family , known as exponential dispersion models starting from the variance function of a tweedie model , we outline how to get alpha and beta divergences as special cases of csisz 'ar 's f and bregman divergences this result directly generalizes the well known relationship between the gaussian distribution and least squares estimation to tweedie models and beta divergence minimization"
cs.DM,"we consider a variation of cop vs robber on graph in which the robber is not restricted by the graph edges instead , he picks a time independent probability distribution on v \( g \) and moves according to this fixed distribution the cop moves from vertex to adjacent vertex with the goal of minimizing expected capture time players move simultaneously we show that when the gambler 's distribution is known , the expected capture time \( with best play \) on any connected n vertex graph is exactly n we also give bounds on the \( generally greater \) expected capture time when the gambler 's distribution is unknown to the cop"
cs.DS,"distributed data aggregation is an important task , allowing the decentralized determination of meaningful global properties , that can then be used to direct the execution of other applications the resulting values result from the distributed computation of functions like count , sum and average some application examples can found to determine the network size , total storage capacity , average load , majorities and many others in the last decade , many different approaches have been proposed , with different trade offs in terms of accuracy , reliability , message and time complexity due to the considerable amount and variety of aggregation algorithms , it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings , justifying the existence of a survey to aid in this task this work reviews the state of the art on distributed data aggregation algorithms , providing three main contributions first , it formally defines the concept of aggregation , characterizing the different types of aggregation functions second , it succinctly describes the main aggregation techniques , organizing them in a taxonomy finally , it provides some guidelines toward the selection and use of the most relevant techniques , summarizing their principal characteristics"
cs.DS,"randomized mechanisms , which map a set of bids to a probability distribution over outcomes rather than a single outcome , are an important but ill understood area of computational mechanism design we investigate the role of randomized outcomes \( henceforth , lotteries \) in the context of a fundamental and archetypical multi parameter mechanism design problem selling heterogeneous items to unit demand bidders to what extent can a seller improve her revenue by pricing lotteries rather than items , and does this modification of the problem affect its computational tractability \? our results show that the answers to these questions hinge on whether consumers can purchase only one lottery \( the buy one model \) or purchase any set of lotteries and receive an independent sample from each \( the buy many model \) in the buy one model , there is a polynomial time algorithm to compute the revenue maximizing envy free prices \( thus overcoming the inapproximability of the corresponding item pricing problem \) and the revenue of the optimal lottery system can exceed the revenue of the optimal item pricing by an unbounded factor as long as the number of item types exceeds 4 in the buy many model with n item types , the profit achieved by lottery pricing can exceed item pricing by a factor of o \( log n \) but not more , and optimal lottery pricing cannot be approximated within a factor of o \( n eps \) for some eps 0 , unless np has subexponential time randomized algorithms our lower bounds rely on a mixture of geometric and algebraic techniques , whereas the upper bounds use a novel rounding scheme to transform a mechanism with randomized outcomes into one with deterministic outcomes while losing only a bounded amount of revenue"
cs.DM|cs.DS,"when designing a preemptive online algorithm for the maximum matching problem , we wish to maintain a valid matching m while edges of the underlying graph are presented one after the other when presented with an edge e , the algorithm should decide whether to augment the matching m by adding e \( in which case e may be removed later on \) or to keep m in its current form without adding e \( in which case e is lost for good \) the objective is to eventually hold a matching m with maximum weight the main contribution of this paper is to establish new lower and upper bounds on the competitive ratio achievable by preemptive online algorithms 1 we provide a lower bound of 1 ln 2 1 693 on the competitive ratio of any randomized algorithm for the maximum cardinality matching problem , thus improving on the currently best known bound of e \( e 1 \) 1 581 due to karp , vazirani , and vazirani stoc'90 2 we devise a randomized algorithm that achieves an expected competitive ratio of 5 356 for maximum weight matching this finding demonstrates the power of randomization in this context , showing how to beat the tight bound of 3 2 sqrt 2 5 828 for deterministic algorithms , obtained by combining the 5 828 upper bound of mcgregor approx'05 and the recent 5 828 lower bound of varadaraja icalp'11"
cs.LG|stat.ML,"we introduce a framework for analyzing transductive combination of gaussian process \( gp \) experts , where independently trained gp experts are combined in a way that depends on test point location , in order to scale gps to big data the framework provides some theoretical justification for the generalized product of gp experts \( gpoe gp \) which was previously shown to work well in practice but lacks theoretical basis based on the proposed framework , an improvement over gpoe gp is introduced and empirically validated"
cs.LG|stat.ML,"reinforcement learning would enjoy better success on real world problems if domain knowledge could be imparted to the algorithm by the modelers most problems have both hidden state and unknown dynamics partially observable markov decision processes \( pomdps \) allow for the modeling of both unfortunately , they do not provide a natural framework in which to specify knowledge about the domain dynamics the designer must either admit to knowing nothing about the dynamics or completely specify the dynamics \( thereby turning it into a planning problem \) we propose a new framework called a partially known markov decision process \( pkmdp \) which allows the designer to specify known dynamics while still leaving portions of the environment s dynamics unknown the model represents not only the environment dynamics but also the agents knowledge of the dynamics we present a reinforcement learning algorithm for this model based on importance sampling the algorithm incorporates planning based on the known dynamics and learning about the unknown dynamics our results clearly demonstrate the ability to add domain knowledge and the resulting benefits for learning"
cs.LG|stat.ML,"given a real matrix a with n columns , the problem is to approximate the gram product aa t by c n weighted outer products of columns of a necessary and sufficient conditions for the exact computation of aa t \( in exact arithmetic \) from c rank \( a \) columns depend on the right singular vector matrix of a for a monte carlo matrix multiplication algorithm by drineas et al that samples outer products , we present probabilistic bounds for the 2 norm relative error due to randomization the bounds depend on the stable rank or the rank of a , but not on the matrix dimensions numerical experiments illustrate that the bounds are informative , even for stringent success probabilities and matrices of small dimension we also derive bounds for the smallest singular value and the condition number of matrices obtained by sampling rows from orthonormal matrices"
cs.AI|cs.SI,"people sometimes worry about the singularity vinge , 1993 kurzweil , 2005 , or about the world being taken over by artificially intelligent robots i believe the risks of these are very small however , few people recognize that we already share our world with artificial creatures that participate as intelligent agents in our society corporations our planet is inhabited by two distinct kinds of intelligent beings individual humans and corporate entities whose natures and interests are intimately linked to co exist well , we need to find ways to define the rights and responsibilities of both individual humans and corporate entities , and to find ways to ensure that corporate entities behave as responsible members of society"
cs.LG,"multiple instance learning \( mil \) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision we propose a novel mil formulation of multi class semantic segmentation learning by a fully convolutional network in this setting , we seek to learn a semantic segmentation model from just weak image level labels the model is trained end to end to jointly optimize the representation while disambiguating the pixel image label assignment fully convolutional training accepts inputs of any size , does not need object proposal pre processing , and offers a pixelwise loss map for selecting latent instances our multi class mil loss exploits the further supervision given by images with multiple labels we evaluate this approach through preliminary experiments on the pascal voc segmentation challenge"
cs.DS,"a workflow is a collection of steps that must be executed in some specific order to achieve an objective a computerised workflow management system may enforce authorisation policies and constraints , thereby restricting which users can perform particular steps in a workflow the existence of policies and constraints may mean that a workflow is unsatisfiable , in the sense that it is impossible to find an authorised user for each step in the workflow and satisfy all constraints in this paper , we consider the problem of finding the least bad assignment of users to workflow steps by assigning a weight to each policy and constraint violation to this end , we introduce a framework for associating costs with the violation of workflow policies and constraints and define the emph valued workflow satisfiability problem \( valued wsp \) , whose solution is an assignment of steps to users of minimum cost we establish the computational complexity of valued wsp with user independent constraints and show that it is fixed parameter tractable we then describe an algorithm for solving valued wsp with user independent constraints and evaluate its performance , comparing it to that of an off the shelf mixed integer programming package"
cs.DS,"in this manuscript , we analyze the sparse signal recovery \( compressive sensing \) problem from the perspective of convex optimization by stochastic proximal gradient descent this view allows us to significantly simplify the recovery analysis of compressive sensing more importantly , it leads to an efficient optimization algorithm for solving the regularized optimization problem related to the sparse recovery problem compared to the existing approaches , there are two advantages of the proposed algorithm first , it enjoys a geometric convergence rate and therefore is computationally efficient second , it guarantees that the support set of any intermediate solution generated by the proposed algorithm is concentrated on the support set of the optimal solution"
cs.AI|cs.DS,"previous work has shown the effectiveness of random walk hitting times as a measure of dissimilarity in a variety of graph based learning problems such as collaborative filtering , query suggestion or finding paraphrases however , application of hitting times has been limited to small datasets because of computational restrictions this paper develops a new approximation algorithm with which hitting times can be computed on very large , disk resident graphs , making their application possible to problems which were previously out of reach this will potentially benefit a range of large scale problems"
cs.LG|stat.ML,"what is a systematic way to efficiently apply a wide spectrum of advanced ml programs to industrial scale problems , using big models \( up to 100s of billions of parameters \) on big data \( up to terabytes or petabytes \) \? modern parallelization strategies employ fine grained operations and scheduling beyond the classic bulk synchronous processing paradigm popularized by mapreduce , or even specialized graph based execution that relies on graph representations of ml programs the variety of approaches tends to pull systems and algorithms design in different directions , and it remains difficult to find a universal platform applicable to a wide range of ml programs at scale we propose a general purpose framework that systematically addresses data and model parallel challenges in large scale ml , by observing that many ml programs are fundamentally optimization centric and admit error tolerant , iterative convergent algorithmic solutions this presents unique opportunities for an integrative system design , such as bounded error network synchronization and dynamic scheduling based on ml program structure we demonstrate the efficacy of these system designs versus well known implementations of modern ml algorithms , allowing ml programs to run in much less time and at considerably larger model sizes , even on modestly sized compute clusters"
cs.LG|stat.ML,"neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples in this regard , sparse models , e g the lasso , are applied to select the optimal features and achieve high diagnosis accuracy the lasso , however , usually results in independent unstable features stability , a manifest of reproducibility of statistical results subject to reasonable perturbations to data and the model , is an important focus in statistics , especially in the analysis of high dimensional data in this paper , we explore a nonnegative generalized fused lasso model for stable feature selection in the diagnosis of alzheimer 's disease in addition to sparsity , our model incorporates two important pathological priors the spatial cohesion of lesion voxels and the positive correlation between the features and the disease labels to optimize the model , we propose an efficient algorithm by proving a novel link between total variation and fast network flow algorithms via conic duality experiments show that the proposed nonnegative model performs much better in exploring the intrinsic structure of data via selecting stable features compared with other state of the arts"
cs.DM,"we prove that for any decision tree calculating a boolean function f 1 , 1 n to 1 , 1 , var f le sum i 1 n delta i inf i \( f \) , where delta i is the probability that the i th input variable is read and inf i \( f \) is the influence of the i th variable on f the variance , influence and probability are taken with respect to an arbitrary product measure on 1 , 1 n it follows that the minimum depth of a decision tree calculating a given balanced function is at least the reciprocal of the largest influence of any input variable likewise , any balanced boolean function with a decision tree of depth d has a variable with influence at least frac 1 d the only previous nontrivial lower bound known was omega \( d 2 d \) our inequality has many generalizations , allowing us to prove influence lower bounds for randomized decision trees , decision trees on arbitrary product probability spaces , and decision trees with non boolean outputs as an application of our results we give a very easy proof that the randomized query complexity of nontrivial monotone graph properties is at least omega \( v 4 3 p 1 3 \) , where v is the number of vertices and p leq half is the critical threshold probability this supersedes the milestone omega \( v 4 3 \) bound of hajnal and is sometimes superior to the best known lower bounds of chakrabarti khot and friedgut kahn wigderson"
cs.LG,"hashing has proven a valuable tool for large scale information retrieval despite much success , existing hashing methods optimize over simple objectives such as the reconstruction error or graph laplacian related loss functions , instead of the performance evaluation criteria of interest multivariate performance measures such as the auc and ndcg here we present a general framework \( termed structhash \) that allows one to directly optimize multivariate performance measures the resulting optimization problem can involve exponentially or infinitely many variables and constraints , which is more challenging than standard structured output learning to solve the structhash optimization problem , we use a combination of column generation and cutting plane techniques we demonstrate the generality of structhash by applying it to ranking prediction and image retrieval , and show that it outperforms a few state of the art hashing methods"
cs.DS|cs.SI,"motivated by applications to social network analysis \( sna \) , we study the problem of finding the maximum number of disjoint uni color paths in an edge colored graph we show the np hardness and the approximability of the problem , and both approximation and exact algorithms are proposed since short paths are much more significant in sna , we also study the length bounded version of the problem , in which the lengths of paths are required to be upper bounded by a fixed integer l it is shown that the problem can be solved in polynomial time for l 3 and is np hard for l geq 4 we also show that the problem can be approximated with ratio \( l 1 \) 2 epsilon in polynomial time for any epsilon 0 particularly , for l 4 , we develop an efficient 2 approximation algorithm"
cs.LG|cs.SI|stat.ML,"network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships oftentimes the object of interest is a relatively small subgraph in an enormous , potentially uninteresting background this aspect characterizes network detection as a big data problem graph partitioning and network discovery have been major research areas over the last ten years , driven by interest in internet search , cyber security , social networks , and criminal or terrorist activities the specific problem of network discovery is addressed as a special case of graph partitioning in which membership in a small subgraph of interest must be determined algebraic graph theory is used as the basis to analyze and compare different network detection methods a new bayesian network detection framework is introduced that partitions the graph based on prior information and direct observations the new approach , called space time threat propagation , is proved to maximize the probability of detection and is therefore optimum in the neyman pearson sense this optimality criterion is compared to spectral community detection approaches which divide the global graph into subsets or communities with optimal connectivity properties we also explore a new generative stochastic model for covert networks and analyze using receiver operating characteristics the detection performance of both classes of optimal detection techniques"
cs.DM,"an association scheme which is associated to a height t presuperscheme is said to be extensible to height t smith \( 1994 , 2007 \) showed that an association scheme x \( q , gamma \) of order d q is schurian iff x is extensible to height \( d 2 \) in this work , we formalize the maximal height t max \( x \) of an association scheme x as the largest positive integer such that x is extensible to height t \( we also include the possibility t max \( x \) infty , which is equivalent to t max \( x \) ge \( d 2 \) \) intuitively , the maximal height provides a natural measure of how close an association scheme is to being schurian for the purpose of computing the maximal height , we introduce the association scheme extension algorithm on input an association scheme x \( q , gamma \) of order d q and an integer t such that 1 le t le \( d 2 \) , the association scheme extension algorithm decides in time d \( o \( t \) \) if the scheme x is extensible to height t in particular , if t is a fixed constant , then the running time of the association scheme extension algorithm is polynomial in the order of x the association scheme extension algorithm is used to show that all non schurian association schemes up to order 26 are completely inextensible , i e they are not extensible to a positive height via the tensor product of association schemes , the latter result gives rise to a multitude of examples of infinite families of completely inextensible association schemes"
cs.DM|cs.IT,"it is known that the minimum broadcast rate of a linear index code over mathbb f q is equal to the minrank q of the underlying digraph in 3 it is proved that for mathbb f 2 and any positive integer k , minrank q \( g \) leq k iff there exists a homomorphism from the complement of the graph g to the complement of a particular undirected graph family called graph family g k as observed in 2 , by combining these two results one can relate the linear index coding problem of undirected graphs to the graph homomorphism problem in 4 , a direct connection between linear index coding problem and graph homomorphism problem is introduced in contrast to the former approach , the direct connection holds for digraphs as well and applies to any field size more precisely , in 4 , a graph family h k q is introduced and shown that whether or not the scalar linear index of a digraph g is less than or equal to k is equivalent to the existence of a graph homomorphism from the complement of g to the complement of h k q here , we first study the structure of the digraphs h k q analogous to the result of 2 about undirected graphs , we prove that h k q 's are vertex transitive digraphs using this , and by applying a lemma of hell and nesetril 5 , we derive a class of necessary conditions for digraphs g to satisfy lind q \( g \) leq k particularly , we obtain new lower bounds on lind q \( g \) our next result is about the computational complexity of scalar linear index of a digraph it is known that deciding whether the scalar linear index of an undirected graph is equal to k or not is np complete for k ge 3 and is polynomially decidable for k 1 , 2 3 for digraphs , it is shown in 6 that for the binary alphabet , the decision problem for k 2 is np complete we use graph homomorphism framework to extend this result to arbitrary alphabet"
cs.AI|cs.LG,"most machine learning models are static , but the world is dynamic , and increasing online deployment of learned models gives increasing urgency to the development of efficient and effective mechanisms to address learning in the context of non stationary distributions , or as it is commonly called concept drift however , the key issue of characterizing the different types of drift that can occur has not previously been subjected to rigorous definition and analysis in particular , while some qualitative drift categorizations have been proposed , few have been formally defined , and the quantitative descriptions required for detailed understanding of learner performance have not existed we present the first comprehensive framework for quantitative analysis of drift this supports the development of the first comprehensive set of formal definitions of types of concept drift the formal definitions clarify ambiguities and identify gaps in previous definitions , giving rise to a new comprehensive taxonomy of concept drift types and a solid foundation for research into mechanisms to detect and address concept drift"
cs.SI|stat.ML,"in this paper we present a review of the existing typologies of internet service users we zoom in on social networking services including blogs and crowdsourcing websites based on the results of the analysis of the considered typologies obtained by means of fca we developed a new user typology of a certain class of internet services , namely a collaboration innovation platform cluster analysis of data extracted from the collaboration platform witology was used to divide more than 500 participants into six groups based on three activity indicators idea generation , commenting , and evaluation \( assigning marks \) the obtained groups and their percentages appear to follow the 90 9 1 rule"
cs.LG,"the rise in popularity of microblogging services like twitter has led to increased use of content annotation strategies like the hashtag hashtags provide users with a tagging mechanism to help organize , group , and create visibility for their posts this is a simple idea but can be challenging for the user in practice which leads to infrequent usage in this paper , we will investigate various methods of recommending hashtags as new posts are created to encourage more widespread adoption and usage hashtag recommendation comes with numerous challenges including processing huge volumes of streaming data and content which is small and noisy we will investigate preprocessing methods to reduce noise in the data and determine an effective method of hashtag recommendation based on the popular classification algorithms"
cs.DS,"let g be a finite abelian group g with n elements in this paper we give a o \( n \) time algorithm for computing a basis of g furthermore , we obtain an algorithm for computing a basis from a generating system of g with m elements having time complexity o \( m sum p n e \( p \) lceil p 1 2 rceil mu \( p \) \) , where p runs over all the prime divisors of n , and p e \( p \) , mu \( p \) are the exponent and the number of cyclic groups which are direct factors of the p primary component of g , respectively in case where g is a cyclic group having a generating system with m elements , a o \( mn epsilon \) time algorithm for the computation of a basis of g is obtained"
cs.AI|cs.LG,"maier et al \( 2010 \) introduced the relational causal model \( rcm \) for representing and inferring causal relationships in relational data a lifted representation , called abstract ground graph \( agg \) , plays a central role in reasoning with and learning of rcm the correctness of the algorithm proposed by maier et al \( 2013a \) for learning rcm from data relies on the soundness and completeness of agg for relational d separation to reduce the learning of an rcm to learning of an agg we revisit the definition of agg and show that agg , as defined in maier et al \( 2013b \) , does not correctly abstract all ground graphs we revise the definition of agg to ensure that it correctly abstracts all ground graphs we further show that agg representation is not complete for relational d separation , that is , there can exist conditional independence relations in an rcm that are not entailed by agg a careful examination of the relationship between the lack of completeness of agg for relational d separation and faithfulness conditions suggests that weaker notions of completeness , namely adjacency faithfulness and orientation faithfulness between an rcm and its agg , can be used to learn an rcm from data"
cs.LG|stat.ML,"with few exceptions , the field of machine learning \( ml \) research has largely ignored the browser as a computational engine beyond an educational resource for ml , the browser has vast potential to not only improve the state of the art in ml research , but also , inexpensively and on a massive scale , to bring sophisticated ml learning and prediction to the public at large this paper introduces mlitb , a prototype ml framework written entirely in javascript , capable of performing large scale distributed computing with heterogeneous classes of devices the development of mlitb has been driven by several underlying objectives whose aim is to make ml learning and usage ubiquitous \( by using ubiquitous compute devices \) , cheap and effortlessly distributed , and collaborative this is achieved by allowing every internet capable device to run training algorithms and predictive models with no software installation and by saving models in universally readable formats our prototype library is capable of training deep neural networks \( dnns \) with synchronized , distributed stochastic gradient descent \( sgd \) mlitb offers several important opportunities for novel ml research , including development of distributed learning algorithms , advancement of web gpu algorithms , novel field and mobile applications , privacy preserving computing , and green grid computing mlitb is available as open source software"
cs.DS,"what is the value of input information in solving linear programming \? the celebrated ellipsoid algorithm tells us that the full information of input constraints is not necessary the algorithm works as long as there exists an oracle that , on a proposed candidate solution , returns a violation in the format of a separating hyperplane can linear programming still be efficiently solved if the returned violation is in other formats \? we study this question in a trial and error framework there is an oracle that , upon a proposed solution , returns the index of a violated constraint \( with the content of the constraint still hidden \) when more than one constraint is violated , two variants in the model are investigated \( 1 \) the oracle returns the index of a most violated constraint , measured by the euclidean distance of the proposed solution and the half spaces defined by the constraints in this case , the lp can be efficiently solved \( 2 \) the oracle returns the index of an arbitrary \( i e , worst case \) violated constraint in this case , we give an algorithm with running time exponential in the number of variables we then show that the exponential dependence on n is unfortunately necessary even for the query complexity these results put together shed light on the amount of information that one needs in order to solve a linear program efficiently the proofs of the results employ a variety of geometric techniques , including mcmullen 's upper bound theorem , the weighted spherical voronoi diagram , and the furthest voronoi diagram in addition , we give an alternative proof to a conjecture of l 'aszl 'o fejes t 'oth on bounding the number of disconnected components formed by the union of m convex bodies in r n our proof , inspired by the gauss bonnet theorem in global differential geometry , is independent of the known and reveals more clear insights into the problem and the bound"
cs.DM,"in cooperative games , the core is the most popular solution concept , and its properties are well known in the classical setting of cooperative games , it is generally assumed that all coalitions can form , i e , they are all feasible in many situations , this assumption is too strong and one has to deal with some unfeasible coalitions defining a game on a subcollection of the power set of the set of players has many implications on the mathematical structure of the core , depending on the precise structure of the subcollection of feasible coalitions many authors have contributed to this topic , and we give a unified view of these different results"
cs.LG|stat.ML,"the use of convex regularizers allow for easy optimization , though they often produce biased estimation and inferior prediction performance recently , nonconvex regularizers have attracted a lot of attention and outperformed convex ones however , the resultant optimization problem is much harder in this paper , for a large class of nonconvex regularizers , we propose to move the nonconvexity from the regularizer to the loss the nonconvex regularizer is then transformed to a familiar convex regularizer , while the resultant loss function can still be guaranteed to be smooth learning with the convexified regularizer can be performed by existing efficient algorithms originally designed for convex regularizers \( such as the standard proximal algorithm and frank wolfe algorithm \) moreover , it can be shown that critical points of the transformed problem are also critical points of the original problem extensive experiments on a number of nonconvex regularization problems show that the proposed procedure is much faster than the state of the art nonconvex solvers"
cs.AI|cs.LG,"a new algorithm named expected similarity estimation \( expose \) was recently proposed to solve the problem of large scale anomaly detection it is a non parametric and distribution free kernel method based on the hilbert space embedding of probability measures given a dataset of n samples , expose needs only mathcal o \( n \) \( linear time \) to build a model and mathcal o \( 1 \) \( constant time \) to make a prediction in this work we improve the linear computational complexity and show that an epsilon accurate model can be estimated in constant time , which has significant implications for large scale learning problems to achieve this goal , we cast the original expose formulation into a stochastic optimization problem it is crucial that this approach allows us to determine the number of iteration based on a desired accuracy epsilon , independent of the dataset size n we will show that the proposed stochastic gradient descent algorithm works in general \( possible infinite dimensional \) hilbert spaces , is easy to implement and requires no additional step size parameters"
cs.DS,"black box complexity theory provides lower bounds for the runtime of black box optimizers like evolutionary algorithms and serves as an inspiration for the design of new genetic algorithms several black box models covering different classes of algorithms exist , each highlighting a different aspect of the algorithms under considerations in this work we add to the existing black box notions a new emph elitist black box model , in which algorithms are required to base all decisions solely on \( a fixed number of \) the best search points sampled so far our model combines features of the ranking based and the memory restricted black box models with elitist selection we provide several examples for which the elitist black box complexity is exponentially larger than that the respective complexities in all previous black box models , thus showing that the elitist black box complexity can be much closer to the runtime of typical evolutionary algorithms we also introduce the concept of p monte carlo black box complexity , which measures the time it takes to optimize a problem with failure probability at most p even for small p , the p monte carlo black box complexity of a function class mathcal f can be smaller by an exponential factor than its typically regarded las vegas complexity \( which measures the emph expected time it takes to optimize mathcal f \)"
cs.DS,"we reexamine fundamental problems from computational geometry in the word ram model , where input coordinates are integers that fit in a machine word we develop a new algorithm for offline point location , a two dimensional analog of sorting where one needs to order points with respect to segments this result implies , for example , that the convex hull of n points in three dimensions can be constructed in \( randomized \) time n 2 o \( sqrt lglg n \) similar bounds hold for numerous other geometric problems , such as planar voronoi diagrams , planar off line nearest neighbor search , line segment intersection , and triangulation of non simple polygons in focs'06 , we developed a data structure for online point location , which implied a bound of o \( n lg n lglg n \) for three dimensional convex hulls and the other problems our current bounds are dramatically better , and a convincing improvement over the classic o \( nlg n \) algorithms as in the field of integer sorting , the main challenge is to find ways to manipulate information , while avoiding the online problem \( in that case , predecessor search \)"
cs.LG|stat.ML,"we study the problem of estimating the continuous response over time of interventions from observational time series a retrospective dataset where the policy by which the data are generated are unknown to the learner we are motivated by applications where response varies by individuals and therefore , estimating responses at the individual level are valuable for personalizing decision making we refer to this as the problem of estimating individualized treatment response \( itr \) curves in statistics , g computation formula has been commonly used for estimating treatment responses from observational data containing sequential treatment assignments however , past studies have focused predominantly on obtaining point in time estimates at the population level we leverage g computation formula and develop a novel method based on bayesian nonparametrics \( bnp \) that can flexibly model functional data and provide posterior inference over the treatment response curves both at the individual and population level on a challenging dataset containing time series from patients admitted to a hospital , we estimate treatment responses for treatments used in managing kidney function and show that the resulting fits are more accurate than alternative approaches accurate methods for obtaining itrs from observational data can dramatically accelerate the pace at which personalized treatment plans become possible"
cs.LG|stat.ML,"this study presents an energy economic approach for incremental decremental learning based on kernel ridge regression , a frequently used regressor on clouds to avoid reanalyzing the entire dataset when data change , the proposed mechanism supports incremental decremental processing for both single and multiple samples \( i e , batch processing \) moreover , incremental decremental analyses in empirical and intrinsic space are also introduced to handle with data matrices with a large number of samples or feature dimensions at the end of this study , we further the proposed mechanism to statistical kernelized bayesian regression , so that incremental decremental analyses become applicable"
cs.LG|stat.ML,"rich and complex time series data , such as those generated from engineering systems , financial markets , videos or neural recordings , are now a common feature of modern data analysis explaining the phenomena underlying these diverse data sets requires flexible and accurate models in this paper , we promote gaussian process dynamical systems \( gpds \) as a rich model class that is appropriate for such analysis in particular , we present a message passing algorithm for approximate inference in gpdss based on expectation propagation by posing inference as a general message passing problem , we iterate forward backward smoothing thus , we obtain more accurate posterior distributions over latent structures , resulting in improved predictive performance compared to state of the art gpds smoothers , which are special cases of our general message passing algorithm hence , we provide a unifying approach within which to contextualize message passing in gpdss"
cs.LG,"this paper presents an end to end convolutional neural network \( cnn \) for 2d 3d exemplar detection we demonstrate that the ability to adapt the features of natural images to better align with those of cad rendered views is critical to the success of our technique we show that the adaptation can be learned by compositing rendered views of textured object models on natural images our approach can be naturally incorporated into a cnn detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2d 3d exemplar detection we applied our method to two tasks instance detection , where we evaluated on the ikea dataset , and object category detection , where we out perform aubry et al for chair detection on a subset of the pascal voc dataset"
cs.DM,"unfolding a convex polyhedron into a simple planar polygon is a well studied problem in this paper , we study the limits of unfoldability by studying nonconvex polyhedra with the same combinatorial structure as convex polyhedra in particular , we give two examples of polyhedra , one with 24 convex faces and one with 36 triangular faces , that cannot be unfolded by cutting along edges we further show that such a polyhedron can indeed be unfolded if cuts are allowed to cross faces finally , we prove that ``open'' polyhedra with triangular faces may not be unfoldable no matter how they are cut"
cs.DM|cs.DS|cs.SI,"we design a space efficient algorithm that approximates the transitivity \( global clustering coefficient \) and total triangle count with only a single pass through a graph given as a stream of edges our procedure is based on the classic probabilistic result , the birthday paradox when the transitivity is constant and there are more edges than wedges \( common properties for social networks \) , we can prove that our algorithm requires o \( sqrt n \) space \( n is the number of vertices \) to provide accurate estimates we run a detailed set of experiments on a variety of real graphs and demonstrate that the memory requirement of the algorithm is a tiny fraction of the graph for example , even for a graph with 200 million edges , our algorithm stores just 60 , 000 edges to give accurate results being a single pass streaming algorithm , our procedure also maintains a real time estimate of the transitivity number of triangles of a graph , by storing a minuscule fraction of edges"
cs.LG,"deep neural networks currently demonstrate state of the art performance in several domains at the same time , models of this class are very demanding in terms of computational resources in particular , a large amount of memory is required by commonly used fully connected layers , making it hard to use the models on low end devices and stopping the further increase of the model size in this paper we convert the dense weight matrices of the fully connected layers to the tensor train format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved in particular , for the very deep vgg networks we report the compression factor of the dense weight matrix of a fully connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times"
cs.IT|stat.ML,"bob predicts a future observation based on a sample of size one alice can draw a sample of any size before issuing her prediction how much better can she do than bob \? perhaps surprisingly , under a large class of loss functions , which we refer to as the cover hart family , the best alice can do is to halve bob 's risk in this sense , half the information in an infinite sample is contained in a sample of size one the cover hart family is a convex cone that includes metrics and negative definite functions , subject to slight regularity conditions these results may help explain the small relative differences in empirical performance measures in applied classification and forecasting problems , as well as the success of reasoning and learning by analogy in general , and nearest neighbor techniques in particular"
cs.AI|stat.ML,"inference is an integral part of probabilistic topic models , but is often non trivial to derive an efficient algorithm for a specific model it is even much more challenging when we want to find a fast inference algorithm which always yields sparse latent representations of documents in this article , we introduce a simple framework for inference in probabilistic topic models , denoted by fw this framework is general and flexible enough to be easily adapted to mixture models it has a linear convergence rate , offers an easy way to incorporate prior knowledge , and provides us an easy way to directly trade off sparsity against quality and time we demonstrate the goodness and flexibility of fw over existing inference methods by a number of tasks finally , we show how inference in topic models with nonconjugate priors can be done efficiently"
cs.IT|cs.SI,"we study the utility of social learning in a distributed detection model with agents sharing the same goal a collective decision that optimizes an agreed upon criterion we show that social learning is helpful in some cases but is provably futile \( and thus essentially a distraction \) in other cases specifically , we consider bayesian binary hypothesis testing performed by a distributed detection and fusion system , where all decision making agents have binary votes that carry equal weight decision making agents in the team sequentially make local decisions based on their own private signals and all precedent local decisions it is shown that the optimal decision rule is not affected by precedent local decisions when all agents observe conditionally independent and identically distributed private signals perfect bayesian reasoning will cancel out all effects of social learning when the agents observe private signals with different signal to noise ratios , social learning is again futile if the team decision is only approved by unanimity otherwise , social learning can strictly improve the team performance furthermore , the order in which agents make their decisions affects the team decision"
cs.LG,"we study the segmental recurrent neural network for end to end acoustic modelling this model connects the segmental conditional random field \( crf \) with a recurrent neural network \( rnn \) used for feature extraction compared to most previous crf based acoustic models , it does not rely on an external system to provide features or segmentation boundaries instead , this model marginalises out all the possible segmentations , and features are extracted from the rnn trained together with the segmental crf in essence , this model is self contained and can be trained end to end in this paper , we discuss practical training and decoding issues as well as the method to speed up the training in the context of speech recognition we performed experiments on the timit dataset we achieved 17 3 phone error rate \( per \) from the first pass decoding the best reported result using crfs , despite the fact that we only used a zeroth order crf and without using any language model"
cs.DS|cs.LG|stat.ML,"in this paper , we provide a novel construction of the linear sized spectral sparsifiers of batson , spielman and srivastava bss14 while previous constructions required omega \( n 4 \) running time bss14 , zou12 , our sparsification routine can be implemented in almost quadratic running time o \( n 2 varepsilon \) the fundamental conceptual novelty of our work is the leveraging of a strong connection between sparsification and a regret minimization problem over density matrices this connection was known to provide an interpretation of the randomized sparsifiers of spielman and srivastava ss11 via the application of matrix multiplicative weight updates \( mwu \) chs11 , vis14 in this paper , we explain how matrix mwu naturally arises as an instance of the follow the regularized leader framework and generalize this approach to yield a larger class of updates this new class allows us to accelerate the construction of linear sized spectral sparsifiers , and give novel insights on the motivation behind batson , spielman and srivastava bss14"
cs.DS|cs.SI,"the hegselmann krause system \( hk system for short \) is one of the most popular models for the dynamics of opinion formation in multiagent systems agents are modeled as points in opinion space , and at every time step , each agent moves to the mass center of all the agents within unit distance the rate of convergence of hk systems has been the subject of several recent works in this work , we investigate two natural variations of the hk system and their effect on the dynamics in the first variation , we only allow pairs of agents who are friends in an underlying social network to communicate with each other in the second variation , agents may not move exactly to the mass center but somewhere close to it the dynamics of both variants are qualitatively very different from that of the classical hk system nevertheless , we prove that both these systems converge in polynomial number of non trivial steps , regardless of the social network in the first variant and noise patterns in the second variant"
cs.DM,"in this paper , some classes of discrete functions of k valued logic are considered , that depend on sets of their variables in a particular way obtained results allow to construct these functions and to present them in their tabular , analytical or matrix form , that is , as hypercubes , and in particular latin hypercubes results connected with identifying of variables of some classes of functions are obtained"
cs.DM|cs.DS|cs.LG,"we first consider the problem of learning k parities in the on line mistake bound model given a hidden vector x in 0 , 1 n with x k and a sequence of questions a 1 , a 2 , in 0 , 1 n , where the algorithm must reply to each question with a i , x pmod 2 , what is the best tradeoff between the number of mistakes made by the algorithm and its time complexity \? we improve the previous best result of buhrman et al by an exp \( k \) factor in the time complexity second , we consider the problem of learning k parities in the presence of classification noise of rate eta in \( 0 , 1 2 \) a polynomial time algorithm for this problem \( when eta 0 and k omega \( 1 \) \) is a longstanding challenge in learning theory grigorescu et al showed an algorithm running in time n choose k 2 1 4 eta 2 o \( 1 \) note that this algorithm inherently requires time n choose k 2 even when the noise rate eta is polynomially small we observe that for sufficiently small noise rate , it is possible to break the n choose k 2 barrier in particular , if for some function f \( n \) omega \( 1 \) and alpha in 1 2 , 1 \) , k n f \( n \) and eta o \( f \( n \) alpha log n \) , then there is an algorithm for the problem with running time poly \( n \) cdot n choose k 1 alpha cdot e k 4 01"
cs.DM,"the em atom bond connectivity \( abc \) index is one of the recently most investigated degree based molecular structure descriptors , that have applications in chemistry for a graph g , the abc index is defined as sum uv in e \( g \) sqrt frac \( d \( u \) d \( v \) 2 \) d \( u \) d \( v \) , where d \( u \) is the degree of vertex u in g and e \( g \) is the set of edges of g despite many attempts in the last few years , it is still an open problem to characterize trees with minimal abc index in this paper , we present an efficient approach of computing trees with minimal abc index , by considering the degree sequences of trees and some known properties of the graphs with minimal abc index the obtained results disprove some existing conjectures end suggest new ones to be set"
cs.LG|stat.ML,"satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty we propose two sets of satisficing objectives for the multi armed bandit problem , where the objective is to achieve reward based decision making performance above a given threshold we show that these new problems are equivalent to various standard multi armed bandit problems with maximizing objectives and use the equivalence to find bounds on performance the different objectives can result in qualitatively different behavior for example , agents explore their options continually in one case and only a finite number of times in another for the case of gaussian rewards we show an additional equivalence between the two sets of satisficing objectives that allows algorithms developed for one set to be applied to the other we then develop variants of the upper credible limit \( ucl \) algorithm that solve the problems with satisficing objectives and show that these modified ucl algorithms achieve efficient satisficing performance"
cs.AI|cs.IT,"an agent who lacks preferences and instead makes decisions using criteria that are costly to create should select efficient sets of criteria , where the cost of making a given number of choice distinctions is minimized under mild conditions , efficiency requires that binary criteria with only two categories per criterion are chosen when applied to the problem of determining the optimal number of digits in an information storage device , this result implies that binary digits \( bits \) are the efficient solution , even when the marginal cost of using additional digits declines rapidly to 0 this short paper pays particular attention to the symmetry conditions entailed when sets of criteria are efficient"
cs.LG,"learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real life data sets we ask what concept classes can be learned privately , namely , by an algorithm whose output does not depend too heavily on any one input or specific training example \? more precisely , we investigate learning algorithms that satisfy differential privacy , a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals we demonstrate that , ignoring computational constraints , it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class therefore , almost anything learnable is learnable privately specifically , if a concept class is learnable by a \( non private \) algorithm with polynomial sample complexity and output size , then it can be learned privately using a polynomial number of samples we also present a computationally efficient private pac learner for the class of parity functions local \( or randomized response \) algorithms are a practical class of private algorithms that have received extensive investigation we provide a precise characterization of local private learning algorithms we show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query \( sq \) model finally , we present a separation between the power of interactive and noninteractive local learning algorithms"
cs.LG|stat.ML,"this paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data we introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity our method improves the state of the art reconstruction method general transfer entropy \( gte \) we are able to better eliminate indirect links , improving therefore the quality of the network via a normalization and ensemble process of gte and three new informative features the approach is based on a simple combination of networks , which is remarkably fast the performance of our approach is benchmarked on simulated time series provided at the connectomics challenge and also submitted at the public competition"
cs.LG|stat.ML,"a major challenge in the training of recurrent neural networks is the so called vanishing or exploding gradient problem the use of a norm preserving transition operator can address this issue , but parametrization is challenging in this work we focus on unitary operators and describe a parametrization using the lie algebra u \( n \) associated with the lie group u \( n \) of n x n unitary matrices the exponential map provides a correspondence between these spaces , and allows us to define a unitary matrix using n 2 real coefficients relative to a basis of the lie algebra the parametrization is closed under additive updates of these coefficients , and thus provides a simple space in which to do gradient descent we demonstrate the effectiveness of this parametrization on the problem of learning arbitrary unitary operators , comparing to several baselines and outperforming a recently proposed lower dimensional parametrization"
cs.DM,"we provide a constructive characterisation of circuits in the simple \( 2 , 2 \) sparsity matroid a circuit is a simple graph g \( v , e \) with e 2 v 1 and the number of edges induced by any x subsetneq v is at most 2 x 2 insisting on simplicity results in the henneberg operation being enough only when the graph is sufficiently connected thus we introduce 3 different join operations to complete the characterisation extensions are discussed to when the sparsity matroid is connected and this is applied to the theory of frameworks on surfaces to provide a conjectured characterisation of when frameworks on an infinite circular cylinder are generically globally rigid"
cs.DM,"let d geq 1 be an integer from a set of d dimensional vectors , we obtain a d dot product graph by letting each vector bf a u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product bf a u cdot bf a v geq t , for some fixed , positive threshold t dot product graphs can be used to model social networks recognizing a d dot product graph is known to be np hard for all fixed d geq 2 to understand the position of d dot product graphs in the landscape of graph classes , we consider the case d 2 , and investigate how 2 dot product graphs relate to a number of other known graph classes"
stat.ML,"we give a simple proof that the frank wolfe algorithm obtains a stationary point at a rate of o \( 1 sqrt t \) on non convex objectives with a lipschitz continuous gradient our analysis is affine invariant and is the first , to the best of our knowledge , giving a similar rate to what was already proven for projected gradient methods \( though on slightly different measures of stationarity \)"
cs.LG|stat.ML,"we introduce a bayesian solution for the problem in forensic speaker recognition , where there may be very little background material for estimating score calibration parameters we work within the bayesian paradigm of evidence reporting and develop a principled probabilistic treatment of the problem , which results in a bayesian likelihood ratio as the vehicle for reporting weight of evidence we show in contrast , that reporting a likelihood ratio distribution does not solve this problem our solution is experimentally exercised on a simulated forensic scenario , using nist sre'12 scores , which demonstrates a clear advantage for the proposed method compared to the traditional plugin calibration recipe"
cs.LG|stat.ML,"compressed sensing is a powerful tool in applications such as magnetic resonance imaging \( mri \) it enables accurate recovery of images from highly undersampled measurements by exploiting the sparsity of the images or image patches in a transform domain or dictionary in this work , we focus on blind compressed sensing \( bcs \) , where the underlying sparse signal model is a priori unknown , and propose a framework to simultaneously reconstruct the underlying image as well as the unknown model from highly undersampled measurements specifically , our model is that the patches of the underlying image \( s \) are approximately sparse in a transform domain we also extend this model to a union of transforms model that better captures the diversity of features in natural images the proposed block coordinate descent type algorithms for blind compressed sensing are highly efficient , and are guaranteed to converge to at least the partial global and partial local minimizers of the highly non convex bcs problems our numerical experiments show that the proposed framework usually leads to better quality of image reconstructions in mri compared to several recent image reconstruction methods importantly , the learning of a union of sparsifying transforms leads to better image reconstructions than a single adaptive transform"
cs.LG|stat.ML,"when working with three dimensional data , choice of representation is key we explore voxel based models , and present evidence for the viability of voxellated representations in applications including shape modeling and object classification our key contributions are methods for training voxel based variational autoencoders , a user interface for exploring the latent space learned by the autoencoder , and a deep convolutional neural network architecture for object classification we address challenges unique to voxel based representations , and empirically evaluate our models on the modelnet benchmark , where we demonstrate a 51 5 relative improvement in the state of the art for object classification"
cs.LG,"human activity recognition has wide applications in medical research and human survey system in this project , we design a robust activity recognition system based on a smartphone the system uses a 3 dimentional smartphone accelerometer as the only sensor to collect time series signals , from which 31 features are generated in both time and frequency domain activities are classified using 4 different passive learning methods , i e , quadratic classifier , k nearest neighbor algorithm , support vector machine , and artificial neural networks dimensionality reduction is performed through both feature extraction and subset selection besides passive learning , we also apply active learning algorithms to reduce data labeling expense experiment results show that the classification rate of passive learning reaches 84 4 and it is robust to common positions and poses of cellphone the results of active learning on real data demonstrate a reduction of labeling labor to achieve comparable performance with passive learning"
cs.DM|cs.DS,"we consider the classic set cover problem in the data stream model for n elements and m sets we give a o \( 1 delta \) pass algorithm with a strongly sub linear tilde o \( mn delta \) space and logarithmic approximation factor this yields a significant improvement over the earlier algorithm of dimv14 which used exponentially larger number of passes we complement this result by showing that the tradeoff between the number of passes and space exhibited by our algorithm is in fact tight , at least when the approximation factor is equal to 1 specifically , we show that any algorithm that computes set cover exactly using \( 1 over 2 delta 1 \) passes must use tilde omega \( m n delta \) space finally , we show that any randomized one pass algorithm that distinguishes between covers of size 2 and 3 must use a linear \( i e , omega \( mn \) \) amount of space the only previous randomized lower bound for set cover , due to nisan02 , provided only a sub linear lower bound of omega \( m \) this indicates that using multiple passes might be necessary in order to achieve sub linear space bounds for this problem while guaranteeing small approximation factors"
cs.LG,"this article deals with stochastic processes endowed with the markov \( memoryless \) property and evolving over general \( uncountable \) state spaces the models further depend on a non deterministic quantity in the form of a control input , which can be selected to affect the probabilistic dynamics we address the synthesis of optimal controllers that maximize the probability associated to a rather general property of interest , known as the reach avoid specification the reach avoid specification deals with assessing the likelihood that any finite horizon trajectory of the model enters a given goal set , while avoiding a given set of undesired states \( both arbitrary subsets of the state space \) equivalently , the property can be expressed as entering a goal set , while dwelling within a set of allowed states the reach avoid property is a well known specification that lies at the core of a number of modal logics used in the field of formal verification , and which is relevant for safety critical applications ranging from robotics to air traffic management this article newly provides an approximate computational scheme for the reach avoid specification based on the fitted value iteration algorithm , which hinges on random sample extractions , and derives new formal probabilistic bounds on the error made by the approximation algorithm as such , the output of the numerical scheme is quantitatively assessed and thus meaningful for safety critical applications of the property of interest furthermore , this contribution provides tighter sample based probabilistic error bounds for the computation of the reach avoid problem based on the fitted value iteration the overall computational scheme is put in relationship with alternative approximation algorithms in the literature , and finally its performance is practically assessed over a benchmark case study"
cs.DM,"over the last 30 years , researchers have investigated connections between dimension for posets and planarity for graphs here we extend this line of research to the structural graph theory parameter tree width by proving that the dimension of a finite poset is bounded in terms of its height and the tree width of its cover graph"
cs.DM,one delivers here the extended bernoulli and taylor formula of a new sort with the rest term of the cauchy type recently derived by the author in the case of the so called psi difference calculus which constitutes the representative for the purpose case of extended umbral calculus the central importance of such a type formulas is beyond any doubt recent publications do confirm this historically established experience its links via umbrality to combinatorics are known at least since rota and mullin source papers then up to recently extended by many authors to be indicated in the sequel
cs.DS,"we present a flexible framework for the automated competitive analysis of on line scheduling algorithms for firm deadline real time tasks based on multi objective graphs given a taskset and an on line scheduling algorithm specified as a labeled transition system , along with some optional safety , liveness , and or limit average constraints for the adversary , we automatically compute the competitive ratio of the algorithm w r t a clairvoyant scheduler we demonstrate the flexibility and power of our approach by comparing the competitive ratio of several on line algorithms , including d over , that have been proposed in the past , for various tasksets our experimental results reveal that none of these algorithms is universally optimal , in the sense that there are tasksets where other schedulers provide better performance our framework is hence a very useful design tool for selecting optimal algorithms for a given application"
cs.DS|cs.IT,"one of the basic tasks in bioinformatics is localizing a short subsequence s , read while sequencing , in a long reference sequence r , like the human geneome a natural rapid approach would be finding a hash value for s and compare it with a prepared database of hash values for each of length s subsequences of r the problem with such approach is that it would only spot a perfect match , while in reality there are lots of small changes substitutions , deletions and insertions this issue could be repaired if having a hash function designed to tolerate some small distortion accordingly to an alignment metric \( like needleman wunch \) designed to make that two similar sequences should most likely give the same hash value this paper discusses construction of distortion resistant hashing \( drh \) to generate such fingerprints for rapid search of similar subsequences the proposed approach is based on the rate distortion theory in a nearly uniform subset of length s sequences , the hash value represents the closest sequence to s this gives some control of the distance of collisions sequences having the same hash value"
cs.DM|cs.DS,"we consider monotonicity problems for graph searching games variants of these games defined by the type of moves allowed for the players have been found to be closely connected to graph decompositions and associated width measures such as path or tree width of particular interest is the question whether these games are monotone , i e whether the cops can catch a robber without ever allowing the robber to reach positions that have been cleared before the monotonicity problem for graph searching games has intensely been studied in the literature , but for two types of games the problem was left unresolved these are the games on digraphs where the robber is invisible and lazy or visible and fast in this paper , we solve the problems by giving examples showing that both types of games are non monotone graph searching games on digraphs are closely related to recent proposals for digraph decompositions generalising tree width to directed graphs these proposals have partly been motivated by attempts to develop a structure theory for digraphs similar to the graph minor theory developed by robertson and seymour for undirected graphs , and partly by the immense number of algorithmic results using tree width of undirected graphs and the hope that part of this success might be reproducible on digraphs using a directed tree width unfortunately the number of applications for the digraphs measures introduced so far is still small we therefore explore the limits of the algorithmic applicability of digraph decompositions in particular , we show that various natural candidates for problems that might benefit from digraphs having small directed tree width remain np complete even on almost acyclic graphs"
cs.DS,"a central question in economics and computer science is when and how markets can arrive at an equilibrium many existing algorithms for computing equilibria in classes of arrow debreu markets rely on full knowledge of the utility functions of all agents , while in reality we often face unknown markets without this information a classic approach to unknown markets from economics is tatonnement dynamic price update processes that only require oracle access to query the aggregate demand of each good in this paper , we design a new class of tatonnement algorithms for the first time , we show how tatonnement can converge in polynomial time to market equilibria in linear markets and spending constraint markets , where the main obstacle is a non continuous demand function this also gives the first polynomial time algorithm for spending constraint markets and settles an open question raised in duan and mehlhorn , icalp'13 moreover , our algorithms can be applied to unknown markets with weak gross substitutes \( wgs \) property , in which they converge to \( 1 epsilon \) approximate market equilibria in time polynomial in market parameters and log \( 1 epsilon \) this exponentially improves the previous convergence rate of polynomial in market parameters and 1 epsilon our approach uses ideas developed for full information linear markets and applies them in unknown linear and non linear markets since our oracles return demands based on real utility functions , precision and representation of prices and demands become a major technical challenge here we develop new tools and insights , which may be of independent interest"
cs.DS,"we study the pattern matching automaton introduced in \( a unifying framework for seed sensitivity and its application to subset seeds \) for the purpose of seed based similarity search we show that our definition provides a compact automaton , much smaller than the one obtained by applying the aho corasick construction we study properties of this automaton and present an efficient implementation of the automaton construction we also present some experimental results and show that this automaton can be successfully applied to more general situations"
cs.LG,"the divide and conquer strategy , which breaks a massive data set into a series of manageable data blocks , and then combines the independent results of data blocks to obtain a final decision , has been recognized as a state of the art method to overcome challenges of massive data analysis in this paper , we merge the divide and conquer strategy with local average regression methods to infer the regressive relationship of input output pairs from a massive data set after theoretically analyzing the pros and cons , we find that although the divide and conquer local average regression can reach the optimal learning rate , the restriction to the number of data blocks is a bit strong , which makes it only feasible for small number of data blocks we then propose two variants to lessen \( or remove \) this restriction our results show that these variants can achieve the optimal learning rate with much milder restriction \( or without such restriction \) extensive experimental studies are carried out to verify our theoretical assertions"
cs.AI|cs.LG|stat.ML,"we consider the contextual version of a multi armed bandit problem with global convex constraints and concave objective function in each round , the outcome of pulling an arm is a context dependent vector , and the global constraints require the average of these vectors to lie in a certain convex set the objective is a concave function of this average vector the learning agent competes with an arbitrary set of context dependent policies this problem is a common generalization of problems considered by badanidiyuru et al \( 2014 \) and agrawal and devanur \( 2014 \) , with important applications we give computationally efficient algorithms with near optimal regret , generalizing the approach of agarwal et al \( 2014 \) for the non constrained version of the problem for the special case of budget constraints our regret bounds match those of badanidiyuru et al \( 2014 \) , answering their main open question of obtaining a computationally efficient algorithm"
cs.LG,"manifold learning methods are useful for high dimensional data analysis many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data typically , this process is computationally expensive and the produced embedding is limited to the training data in many real life scenarios , the ability to produce embedding of unseen samples is essential in this paper we propose a bayesian non parametric approach for out of sample extension the method is based on gaussian process regression and independent of the manifold learning algorithm additionally , the method naturally provides a measure for the degree of abnormality for a newly arrived data point that did not participate in the training process we derive the mathematical connection between the proposed method and the nystrom extension and show that the latter is a special case of the former we present extensive experimental results that demonstrate the performance of the proposed method and compare it to other existing out of sample extension methods"
cs.DM|cs.DS,"given natural limitations on the length dna sequences , designing phylogenetic reconstruction methods which are reliable under limited information is a crucial endeavor there have been two approaches to this problem reconstructing partial but reliable information about the tree \( cite mo07 , dmr08 , dhj06 , gms08 \) , and reaching deeper in the tree through reconstruction of ancestral sequences in the latter category , cite dmr06 settled an important conjecture of m steel , showing that , under the cfn model of evolution , all trees on n leaves with edge lengths bounded by the ising model phase transition can be recovered with high probability from genomes of length o \( log n \) with a polynomial time algorithm their methods had a running time of o \( n 10 \) here we enhance our methods from cite dhj06 with the learning of ancestral sequences and provide an algorithm for reconstructing a sub forest of the tree which is reliable given available data , without requiring a priori known bounds on the edge lengths of the tree our methods are based on an intuitive minimum spanning tree approach and run in o \( n 3 \) time for the case of full reconstruction of trees with edges under the phase transition , we maintain the same sequence length requirements as cite dmr06 , despite the considerably faster running time"
cs.DM,"we consider the problem of counting h colourings from an input graph g to a target graph h we show that if h is any fixed graph without trivial components , then the problem is as hard as the well known problem bis , which is the problem of \( approximately \) counting independent sets in a bipartite graph bis is a complete problem in an important complexity class for approximate counting , and is believed not to have an fpras if this is so , then our result shows that for every graph h without trivial components , the h colouring counting problem has no fpras this problem was studied a decade ago by goldberg , kelk and paterson they were able to show that approximately sampling h colourings is bis hard , but it was not known how to get the result for approximate counting our solution builds on non constructive ideas using the work of lovasz"
cs.DM|cs.DS,"we consider the following problem given a finite set of straight line segments in the plane , determine the positions of a minimal number of points on the segments , from which guards can see all segments this problem can be interpreted as looking for a minimal number of locations of policemen , guards , cameras or other sensors , that can observe a network of streets , corridors , tunnels , tubes , etc we show that the problem is strongly np complete even for a set of segments with a cubic graph structure , but in p for tree structures"
cs.DM,"several variants of the cr game have been studied in the past in this paper we examine the selfish cops and passive robber \( scpr \) variant , which is played between two cops , each one independently trying to catch a passive robber"
cs.DM,"we consider the hegselmann krause model for opinion dynamics and study the evolution of the system under various settings we first analyze the termination time of the synchronous hegselmann krause dynamics in arbitrary finite dimensions and show that the termination time in general only depends on the number of agents involved in the dynamics to the best of our knowledge , that is the sharpest bound for the termination time of such dynamics that removes dependency of the termination time from the dimension of the ambient space this answers an open question in 1 on how to obtain a tighter upper bound for the termination time furthermore , we study the asynchronous hegselmann krause model from a novel game theoretic approach and show that the evolution of an asynchronous hegselmann krause model is equivalent to a sequence of best response updates in a well designed potential game we then provide a polynomial upper bound for the expected time and expected number of switching topologies until the dynamic reaches an arbitrarily small neighborhood of its equilibrium points , provided that the agents update uniformly at random this is a step toward analysis of heterogeneous hegselmann krause dynamics finally , we consider the heterogeneous hegselmann krause dynamics and provide a necessary condition for the finite termination time of such dynamics in particular , we sketch some future directions toward more detailed analysis of the heterogeneous hegselmann krause model"
stat.ML,"technology offers new ways to measure the locations of the players and of the ball in sports this translates to the trajectories the ball takes on the field as a result of the tactics the team applies the challenge professionals in soccer are facing is to take the reverse path given the trajectories of the ball is it possible to infer the underlying strategy tactic of a team \? we propose a method based on dynamic time warping to reveal the tactics of a team through the analysis of repeating series of events based on the analysis of an entire season , we derive insights such as passing strategies for maintaining ball possession or counter attacks , and passing styles with a focus on the team or on the capabilities of the individual players"
cs.AI|cs.LG,"the uncertainty of classification outcomes is of crucial importance for many safety critical applications including , for example , medical diagnostics in such applications the uncertainty of classification can be reliably estimated within a bayesian model averaging technique that allows the use of prior information decision tree \( dt \) classification models used within such a technique gives experts additional information by making this classification scheme observable the use of the markov chain monte carlo \( mcmc \) methodology of stochastic sampling makes the bayesian dt technique feasible to perform however , in practice , the mcmc technique may become stuck in a particular dt which is far away from a region with a maximal posterior sampling such dts causes bias in the posterior estimates , and as a result the evaluation of classification uncertainty may be incorrect in a particular case , the negative effect of such sampling may be reduced by giving additional prior information on the shape of dts in this paper we describe a new approach based on sweeping the dts without additional priors on the favorite shape of dts the performances of bayesian dt techniques with the standard and sweeping strategies are compared on a synthetic data as well as on real datasets quantitatively evaluating the uncertainty in terms of entropy of class posterior probabilities , we found that the sweeping strategy is superior to the standard strategy"
cs.AI|cs.LG,"we represent the sequence of fmri \( functional magnetic resonance imaging \) brain volumes recorded during a cognitive stimulus by a graph which consists of a set of local meshes the corresponding cognitive process , encoded in the brain , is then represented by these meshes each of which is estimated assuming a linear relationship among the voxel time series in a predefined locality first , we define the concept of locality in two neighborhood systems , namely , the spatial and functional neighborhoods then , we construct spatially and functionally local meshes around each voxel , called seed voxel , by connecting it either to its spatial or functional p nearest neighbors the mesh formed around a voxel is a directed sub graph with a star topology , where the direction of the edges is taken towards the seed voxel at the center of the mesh we represent the time series recorded at each seed voxel in terms of linear combination of the time series of its p nearest neighbors in the mesh the relationships between a seed voxel and its neighbors are represented by the edge weights of each mesh , and are estimated by solving a linear regression equation the estimated mesh edge weights lead to a better representation of information in the brain for encoding and decoding of the cognitive tasks we test our model on a visual object recognition and emotional memory retrieval experiments using support vector machines that are trained using the mesh edge weights as features in the experimental analysis , we observe that the edge weights of the spatial and functional meshes perform better than the state of the art brain decoding models"
cs.DS|cs.LG|stat.ML,we introduce a new spatial data structure for high dimensional data called the emph approximate principal direction tree \( apd tree \) that adapts to the intrinsic dimension of the data our algorithm ensures vector quantization accuracy similar to that of computationally expensive pca trees with similar time complexity to that of lower accuracy rp trees apd trees use a small number of power method iterations to find splitting planes for recursively partitioning the data as such they provide a natural trade off between the running time and accuracy achieved by rp and pca trees our theoretical results establish a \) strong performance guarantees regardless of the convergence rate of the power method and b \) that o \( log d \) iterations suffice to establish the guarantee of pca trees when the intrinsic dimension is d we demonstrate this trade off and the efficacy of our data structure on both the cpu and gpu
cs.AI|cs.SI|stat.ML,"transactional network data can be thought of as a list of one to many communications \( e g , email \) between nodes in a social network most social network models convert this type of data into binary relations between pairs of nodes we develop a latent mixed membership model capable of modeling richer forms of transactional network data , including relations between more than two nodes the model can cluster nodes and predict transactions the block model nature of the model implies that groups can be characterized in very general ways this flexible notion of group structure enables discovery of rich structure in transactional networks estimation and inference are accomplished via a variational em algorithm simulations indicate that the learning algorithm can recover the correct generative model interesting structure is discovered in the enron email dataset and another dataset extracted from the reddit website analysis of the reddit data is facilitated by a novel performance measure for comparing two soft clusterings the new model is superior at discovering mixed membership in groups and in predicting transactions"
cs.DM,"we introduce a variant of the vertex distinguishing edge coloring problem , where each edge is assigned a subset of colors the label of a vertex is the union of the sets of colors on edges incident to it in this paper we investigate the problem of finding a coloring with the minimum number of colors where every pair of vertices receive distinct labels finding such a coloring generalizes several other well known problems of vertex distinguishing colorings in graphs we show that for any graph \( without connected component reduced to an edge or a single vertex \) , the minimum number of colors for which such a coloring exists can only take 3 possible values depending on the order of the graph moreover , we provide the exact value for paths , cycles and complete binary trees"
cs.DM,"a graph g is uniquely k r saturated if it contains no clique with r vertices and if for all edges e in the complement , g e has a unique clique with r vertices previously , few examples of uniquely k r saturated graphs were known , and little was known about their properties we search for these graphs by adapting orbital branching , a technique originally developed for symmetric integer linear programs we find several new uniquely k r saturated graphs with 4 leq r leq 7 , as well as two new infinite families based on cayley graphs for z n with a small number of generators"
cs.LG|stat.ML,"we address how to learn a binary classifier capable of abstaining from making a label prediction such a classifier hopes to abstain where it would be most inaccurate if forced to predict , so it has two goals in tension with each other minimizing errors , and avoiding abstaining unnecessarily often in this work , we exactly characterize the best achievable tradeoff between these two goals in a general semi supervised setting , given an ensemble of classifiers of varying competence as well as unlabeled data on which we wish to predict or abstain we give an algorithm for learning a classifier which trades off its errors with abstentions in a minimax optimal manner this algorithm is as efficient as linear learning and prediction , and comes with strong and robust theoretical guarantees our analysis extends to a large class of loss functions and other scenarios , including ensembles comprised of specialist classifiers that can themselves abstain"
cs.IT|stat.ML,"many modern tools in machine learning and signal processing , such as sparse dictionary learning , principal component analysis \( pca \) , non negative matrix factorization \( nmf \) , k means clustering , etc , rely on the factorization of a matrix obtained by concatenating high dimensional vectors from a training collection while the idealized task would be to optimize the expected quality of the factors over the underlying distribution of training vectors , it is achieved in practice by minimizing an empirical average over the considered collection the focus of this paper is to provide sample complexity estimates to uniformly control how much the empirical average deviates from the expected cost function standard arguments imply that the performance of the empirical predictor also exhibit such guarantees the level of genericity of the approach encompasses several possible constraints on the factors \( tensor product structure , shift invariance , sparsity ldots \) , thus providing a unified perspective on the sample complexity of several widely used matrix factorization schemes the derived generalization bounds behave proportional to sqrt log \( n \) n w r t the number of samples n for the considered matrix factorization techniques"
cs.LG,"recently , convolutional neural networks \( cnns \) have been used as a powerful tool to solve many problems of machine learning and computer vision in this paper , we aim to provide insight on the property of convolutional neural networks , as well as a generic method to improve the performance of many cnn architectures specifically , we first examine existing cnn models and observe an intriguing property that the filters in the lower layers form pairs \( i e , filters with opposite phase \) inspired by our observation , we propose a novel , simple yet effective activation scheme called concatenated relu \( crelu \) and theoretically analyze its reconstruction property in cnns we integrate crelu into several state of the art cnn architectures and demonstrate improvement in their recognition performance on cifar 10 100 and imagenet datasets with fewer trainable parameters our results suggest that better understanding of the properties of cnns can lead to significant performance improvement with a simple modification"
cs.DM,"the concern of this paper is a famous combinatorial formula known under the name exponential formula it occurs quite naturally in many contexts \( physics , mathematics , computer science \) roughly speaking , it expresses that the exponential generating function of a whole structure is equal to the exponential of those of connected substructures keeping this descriptive statement as a guideline , we develop a general framework to handle many different situations in which the exponential formula can be applied"
cs.DS,"we give a new framework for proving the existence of low degree , polynomial approximators for boolean functions with respect to broad classes of non product distributions our proofs use techniques related to the classical moment problem and deviate significantly from known fourier based methods , which require the underlying distribution to have some product structure our main application is the first polynomial time algorithm for agnostically learning any function of a constant number of halfspaces with respect to any log concave distribution \( for any constant accuracy parameter \) this result was not known even for the case of learning the intersection of two halfspaces without noise additionally , we show that in the smoothed analysis setting , the above results hold with respect to distributions that have sub exponential tails , a property satisfied by many natural and well studied distributions in machine learning given that our algorithms can be implemented using support vector machines \( svms \) with a polynomial kernel , these results give a rigorous theoretical explanation as to why many kernel methods work so well in practice"
cs.DS,"we present a new space efficient approach , \( sparsedtw \) , to compute the dynamic time warping \( dtw \) distance between two time series that always yields the optimal result this is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency the main idea behind our approach is to dynamically exploit the existence of similarity and or correlation between the time series the more the similarity between the time series the less space required to compute the dtw between them to the best of our knowledge , all other techniques to speedup dtw , impose apriori constraints and do not exploit similarity characteristics that may be present in the data we conduct experiments and demonstrate that sparsedtw outperforms previous approaches"
cs.LG|stat.ML,"learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores we make theoretical contributions to the learning to rank problem both in the online and batch settings first , we propose a perceptron like algorithm for learning a ranking function in an online setting our algorithm is an extension of the classic perceptron algorithm for the classification problem second , in the setting of batch learning , we introduce a sufficient condition for convex ranking surrogates to ensure a generalization bound that is independent of number of objects per query our bound holds when linear ranking functions are used a common practice in many learning to rank algorithms en route to developing the online algorithm and generalization bound , we propose a novel family of listwise large margin ranking surrogates our novel surrogate family is obtained by modifying a well known pairwise large margin ranking surrogate and is distinct from the listwise large margin surrogates developed using the structured prediction framework using the proposed family , we provide a guaranteed upper bound on the cumulative ndcg \( or map \) induced loss under the perceptron like algorithm we also show that the novel surrogates satisfy the generalization bound condition"
cs.DS,"this study provides new results about the probabilistic behaviour of a class of euclidean algorithms the asymptotic distribution of a whole class of cost parameters associated to these algorithms is normal for the cost corresponding to the number of steps hensley already has proved a local limit theorem we give a new proof , and extend his result to other euclidean algorithms and to a large class of digit costs , obtaining a faster , optimal , rate of convergence the paper is based on the dynamical systems methodology , and the main tool is the transfer operator in particular , we use recent results of dolgopyat"
cs.AI|cs.LG,"boosting algorithms have been widely used to tackle a plethora of problems in the last few years , a lot of approaches have been proposed to provide standard adaboost with cost sensitive capabilities , each with a different focus however , for the researcher , these algorithms shape a tangled set with diffuse differences and properties , lacking a unifying analysis to jointly compare , classify , evaluate and discuss those approaches on a common basis in this series of two papers we aim to revisit the various proposals , both from theoretical \( part i \) and practical \( part ii \) perspectives , in order to analyze their specific properties and behavior , with the final goal of identifying the algorithm providing the best and soundest results"
cs.DM|cs.DS,"the min rank of a graph was introduced by haemers \( 1978 \) to bound the shannon capacity of a graph this parameter of a graph has recently gained much more attention from the research community after the work of bar yossef et al \( 2006 \) in their paper , it was shown that the min rank of a graph g characterizes the optimal scalar linear solution of an instance of the index coding with side information \( icsi \) problem described by the graph g it was shown by peeters \( 1996 \) that computing the min rank of a general graph is an np hard problem there are very few known families of graphs whose min ranks can be found in polynomial time in this work , we introduce a new family of graphs with e \? fficiently computed min ranks specifically , we establish a polynomial time dynamic programming algorithm to compute the min ranks of graphs having simple tree structures intuitively , such graphs are obtained by gluing together , in a tree like structure , any set of graphs for which the min ranks can be determined in polynomial time a polynomial time algorithm to recognize such graphs is also proposed"
cs.IT|stat.ML,"networked sensing , where the goal is to perform complex inference using a large number of inexpensive and decentralized sensors , has become an increasingly attractive research topic due to its applications in wireless sensor networks and internet of things to reduce the communication , sensing and storage complexity , this paper proposes a simple sensing and estimation framework to faithfully recover the principal subspace of high dimensional data streams using a collection of binary measurements from distributed sensors , without transmitting the whole data the binary measurements are designed to indicate comparison outcomes of aggregated energy projections of the data samples over pairs of randomly selected directions when the covariance matrix is a low rank matrix , we propose a spectral estimator that recovers the principal subspace of the covariance matrix as the subspace spanned by the top eigenvectors of a properly designed surrogate matrix , which is provably accurate as soon as the number of binary measurements is sufficiently large an adaptive rank selection strategy based on soft thresholding is also presented furthermore , we propose a tailored spectral estimator when the covariance matrix is additionally toeplitz , and show reliable estimation can be obtained from a substantially smaller number of binary measurements our results hold even when a constant fraction of the binary measurements is randomly flipped finally , we develop a low complexity online algorithm to track the principal subspace when new measurements arrive sequentially numerical examples are provided to validate the proposed approach"
cs.DS,"for a polygonal domain with h holes and a total of n vertices , we present algorithms that compute the l 1 geodesic diameter in o \( n 2 h 4 \) time and the l 1 geodesic center in o \( \( n 4 n 2 h 4 \) alpha \( n \) \) time , where alpha \( cdot \) denotes the inverse ackermann function no algorithms were known for these problems before for the euclidean counterpart , the best algorithms compute the geodesic diameter in o \( n 7 73 \) or o \( n 7 \( h log n \) \) time , and compute the geodesic center in o \( n 12 epsilon \) time therefore , our algorithms are much faster than the algorithms for the euclidean problems our algorithms are based on several interesting observations on l 1 shortest paths in polygonal domains"
cs.AI|cs.LG,"induction is the process by which we obtain predictive laws or theories or models of the world we consider the structural aspect of induction we answer the question as to whether we can find a finite and minmalistic set of operations on structural elements in terms of which any theory can be expressed we identify abstraction \( grouping similar entities \) and super structuring \( combining topologically e g , spatio temporally close entities \) as the essential structural operations in the induction process we show that only two more structural operations , namely , reverse abstraction and reverse super structuring \( the duals of abstraction and super structuring respectively \) suffice in order to exploit the full power of turing equivalent generative grammars in induction we explore the implications of this theorem with respect to the nature of hidden variables , radical positivism and the 2 century old claim of david hume about the principles of connexion among ideas"
cs.DM,"the symmetric maximum , denoted by v , is an extension of the usual max operation so that 0 is the neutral element , and x is the symmetric \( or inverse \) of x , i e , x v \( x \) 0 however , such an extension does not preserve the associativity of max this fact asks for systematic ways of parenthesing \( or bracketing \) terms of a sequence \( with more than two arguments \) when using such an extended maximum we refer to such systematic \( predefined \) ways of parenthesing as computation rules as it turns out there are infinitely many computation rules each of which corresponding to a systematic way of bracketing arguments of sequences essentially , computation rules reduce to deleting terms of sequences based on the condition x v \( x \) 0 this observation gives raise to a quasi order on the set of such computation rules say that rule 1 is below rule 2 if for all sequences of numbers , rule 1 deletes more terms in the sequence than rule 2 in this paper we present a study of this quasi ordering of computation rules in particular , we show that the induced poset of all equivalence classes of computation rules is uncountably infinite , has infinitely many maximal elements , has infinitely many atoms , and it embeds the powerset of natural numbers ordered by inclusion"
cs.DM|cs.DS,"the set cover problem \( scp \) and set packing problem \( spp \) are standard np hard combinatorial optimization problems their decision problem versions are shown to be np complete in karp 's 1972 paper we specify a rough guide to constructing approximation heuristics that may have widespread applications and apply it to devise greedy approximation algorithms for scp and spp , where the selection heuristic is a variation of that in the standard greedy approximation algorithm our technique involves assigning to each input set a valuation and then selecting , in each round , the set whose valuation is highest we prove that the technique we use for determining a valuation of the input sets yields a unique value for all set cover instances for both scp and spp we give experimental evidence that the valuations we specify are unique and can be computed to high precision quickly by an iterative algorithm others have experimented with testing the observed approximation ratio of various algorithms over a variety of randomly generated instances , and we have extensive experimental evidence to show the quality of the new algorithm relative to greedy heuristics in common use our algorithms are somewhat more computationally intensive than the standard heuristics , though they are still practical for large instances we discuss some ways to speed up our algorithms that do not significantly distort their effectiveness in practice on random instances"
cs.IT|stat.ML,"we consider a distributed parameter estimation problem , in which multiple terminals send messages related to their local observations using limited rates to a fusion center who will obtain an estimate of a parameter related to observations of all terminals it is well known that if the transmission rates are in the slepian wolf region , the fusion center can fully recover all observations and hence can construct an estimator having the same performance as that of the centralized case one natural question is whether slepian wolf rates are necessary to achieve the same estimation performance as that of the centralized case in this paper , we show that the answer to this question is negative we establish our result by explicitly constructing a minimum variance unbiased estimator \( mvue \) that has the same performance as that of the optimal estimator in the centralized case while requiring information rates less than the conditions required in the slepian wolf rate region"
cs.AI|cs.LG,"multi view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process however , many applications provide only a partial mapping between the views , creating a challenge for current methods to address this problem , we propose a multi view algorithm based on constrained clustering that can operate with an incomplete mapping given a set of pairwise constraints in each view , our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views , allowing the propagated constraints to be transferred across views via the partial mapping it uses co em to iteratively estimate the propagation within each view based on the current clustering model , transfer the constraints across views , and then update the clustering model by alternating the learning process between views , this approach produces a unified clustering model that is consistent with all views we show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi view clustering to be reliably applied when given a limited mapping between the views our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data , explaining their benefit to clustering performance in both single and multi view learning scenarios"
cs.DS,"in this survey , we describe two recent developments in quantum algorithms the first new development is a quantum algorithm for evaluating a boolean formula consisting of and and or gates of size n in time o \( sqrt n \) this provides quantum speedups for any problem that can be expressed via boolean formulas this result can be also extended to span problems , a generalization of boolean formulas this provides an optimal quantum algorithm for any boolean function in the black box query model the second new development is a quantum algorithm for solving systems of linear equations in contrast with traditional algorithms that run in time o \( n 2 37 \) where n is the size of the system , the quantum algorithm runs in time o \( log c n \) it outputs a quantum state describing the solution of the system"
cs.LG,"machine learning applications are limited by computational power in this paper , we gain novel insights into the application of quantum annealing \( qa \) to machine learning \( ml \) through experiments in natural language processing \( nlp \) , seizure prediction , and linear separability testing these experiments are performed on qa simulators and early stage commercial qa hardware and compared to an unprecedented number of traditional ml techniques we extend qboost , an early implementation of a binary classifier that utilizes a quantum annealer , via resampling and ensembling of predicted probabilities to produce a more robust class estimator to determine the strengths and weaknesses of this approach , resampled qboost \( rqboost \) is tested across several datasets and compared to qboost and traditional ml we show and explain how qboost in combination with a commercial qa device are unable to perfectly separate binary class data which is linearly separable via logistic regression with shrinkage we further explore the performance of rqboost in the space of nlp and seizure prediction and find qa enabled ml using qboost and rqboost is outperformed by traditional techniques additionally , we provide a detailed discussion of algorithmic constraints and trade offs imposed by the use of this qa hardware through these experiments , we provide unique insights into the state of quantum ml via boosting and the use of quantum annealing hardware that are valuable to institutions interested in applying qa to problems in ml and beyond"
cs.DS,"the truncated fourier transform \( tft \) was introduced by van der hoeven in 2004 as a means of smoothing the jumps in running time of the ordinary fft algorithm that occur at power of two input sizes however , the tft still introduces these jumps in memory usage we describe in place variants of the forward and inverse tft algorithms , achieving time complexity o \( n log n \) with only o \( 1 \) auxiliary space as an application , we extend the second author 's results on space restricted fft based polynomial multiplication to polynomials of arbitrary degree"
cs.DS,"in a recent paper , brusco , k ohn and steinley ann oper res 206 611 626 \( 2013 \) conjecture that the 2 bins special case of the one dimensional minimax bin packing problem with bin size constraints might be solvable in polynomial time in this note , we show that this problem is np hard for the special case and that it is strongly np hard for the general problem we also propose a pseudo polynomial algorithm for the special case and a constructive heuristic for the general problem"
cs.LG|stat.ML,we develop an off policy actor critic algorithm for learning an optimal policy from a training set composed of data from multiple individuals this algorithm is developed with a view towards its use in mobile health
cs.LG|stat.ML,"next generation deep neural networks for classification hosted on embedded platforms will rely on fast , efficient , and accurate learning algorithms initialization of weights in learning networks has a great impact on the classification accuracy in this paper we focus on deriving good initial weights by modeling the error function of a deep neural network as a high dimensional landscape we observe that due to the inherent complexity in its algebraic structure , such an error function may conform to general results of the statistics of large systems to this end we apply some results from random matrix theory to analyse these functions we model the error function in terms of a hamiltonian in n dimensions and derive some theoretical results about its general behavior these results are further used to make better initial guesses of weights for the learning algorithm"
cs.AI|cs.LG|stat.ML,"recently several researchers have investigated techniques for using data to learn bayesian networks containing compact representations for the conditional probability distributions \( cpds \) stored at each node the majority of this work has concentrated on using decision tree representations for the cpds in addition , researchers typically apply non bayesian \( or asymptotically bayesian \) scoring functions such as mdl to evaluate the goodness of fit of networks to the data in this paper we investigate a bayesian approach to learning bayesian networks that contain the more general decision graph representations of the cpds first , we describe how to evaluate the posterior probability that is , the bayesian score of such a network , given a database of observed cases second , we describe various search spaces that can be used , in conjunction with a scoring function and a search procedure , to identify one or more high scoring networks finally , we present an experimental evaluation of the search spaces , using a greedy algorithm and a bayesian scoring function"
cs.LG,"clustering analysis is a method to organize raw data into categories based on a measure of similarity it has been successfully applied to diverse fields from science to business and engineering by endowing data points with physical meaning like particles in the physical world and then leaning their evolving tendency of moving from higher to lower potentials , data points in the proposed clustering algorithm sequentially hop to the locations of their transfer points and gather , after a few steps , at the locations of cluster centers with the locally lowest potentials , where cluster members can be easily identified the whole clustering process is simple and efficient , and can be performed either automatically or interactively , with reliable performances on test data of diverse shapes , attributes , and dimensionalities"
cs.DS,"we present a variant of the hyper quadtree that divides a multidimensional space according to the hyperplanes associated to the principal components of the data in each hyperquadrant each of the 2 lambda hyper quadrants is a data partition in a lambda dimension subspace , whose intrinsic dimensionality lambda leq d is reduced from the root dimensionality d by the principal components analysis , which discards the irrelevant eigenvalues of the local covariance matrix in the present method a component is irrelevant if its length is smaller than , or comparable to , the local inter data spacing thus , the covariance hyper quadtree is fully adaptive to the local dimensionality the proposed data structure is used to compute the anisotropic k nearest neighbors \( knn \) , supported by the mahalanobis metric as an application , we used the present k nearest neighbors method to perform density estimation over a noisy data distribution such estimation method can be further incorporated to the smoothed particle hydrodynamics , allowing computer simulations of anisotropic fluid flows"
stat.ML,"the goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local \( possibly nonsmooth \) convex functions using only local computation and communication it arises in various application domains , including distributed tracking and localization , multi agent co ordination , estimation in sensor networks , and large scale optimization in machine learning we develop and analyze distributed algorithms based on dual averaging of subgradients , and we provide sharp bounds on their convergence rates as a function of the network size and topology our method of analysis allows for a clear separation between the convergence of the optimization algorithm itself and the effects of communication constraints arising from the network structure in particular , we show that the number of iterations required by our algorithm scales inversely in the spectral gap of the network the sharpness of this prediction is confirmed both by theoretical lower bounds and simulations for various networks our approach includes both the cases of deterministic optimization and communication , as well as problems with stochastic optimization and or communication"
cs.LG|stat.ML,"boosting methods are highly popular and effective supervised learning methods which combine weak learners into a single accurate model with good statistical performance in this paper , we analyze two well known boosting methods , adaboost and incremental forward stagewise regression \( fs varepsilon \) , by establishing their precise connections to the mirror descent algorithm , which is a first order method in convex optimization as a consequence of these connections we obtain novel computational guarantees for these boosting methods in particular , we characterize convergence bounds of adaboost , related to both the margin and log exponential loss function , for any step size sequence furthermore , this paper presents , for the first time , precise computational complexity results for fs varepsilon"
cs.LG|stat.ML,"we present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions we give a mathematical construction for optimal predictors of such processes , in the form of hidden markov models we then describe an algorithm , cssr \( causal state splitting reconstruction \) , which approximates the ideal predictor from data we discuss the reliability of cssr , its data requirements , and its performance in simulations finally , we compare our approach to existing methods using variablelength markov models and cross validated hidden markov models , and show theoretically and experimentally that our method delivers results superior to the former and at least comparable to the latter"
cs.DM,"we study the interaction between network effects and external incentives on file sharing behavior in peer to peer \( p2p \) networks many current or envisioned p2p networks reward individuals for sharing files , via financial incentives or social recognition peers weigh this reward against the cost of sharing incurred when others download the shared file as a result , if other nearby nodes share files as well , the cost to an individual node decreases such positive network sharing effects can be expected to increase the rate of peers who share files in this paper , we formulate a natural model for the network effects of sharing behavior , which we term the demand model we prove that the model has desirable diminishing returns properties , meaning that the network benefit of increasing payments decreases when the payments are already high this result holds quite generally , for submodular objective functions on the part of the network operator in fact , we show a stronger result the demand model leads to a coverage process , meaning that there is a distribution over graphs such that reachability under this distribution exactly captures the joint distribution of nodes which end up sharing the existence of such distributions has advantages in simulating and estimating the performance of the system we establish this result via a general theorem characterizing which types of models lead to coverage processes , and also show that all coverage processes possess the desirable submodular properties we complement our theoretical results with experiments on several real world p2p topologies we compare our model quantitatively against more na ive models ignoring network effects a main outcome of the experiments is that a good incentive scheme should make the reward dependent on a node 's degree in the network"
cs.LG,"marketplaces specializing in malicious hacking products including malware and exploits have recently become more prominent on the darkweb and deepweb we scrape 17 such sites and collect information about such products in a unified database schema using a combination of manual labeling and unsupervised clustering , we examine a corpus of products in order to understand their various categories and how they become specialized with respect to vendor and marketplace this initial study presents how we effectively employed unsupervised techniques to this data as well as the types of insights we gained on various categories of malicious hacking products"
cs.LG|stat.ML,"we consider training a deep neural network to generate samples from an unknown distribution given i i d data we frame learning as an optimization minimizing a two sample test statistic informally speaking , a good generator network produces samples that cause a two sample test to fail to reject the null hypothesis as our two sample test statistic , we use an unbiased estimate of the maximum mean discrepancy , which is the centerpiece of the nonparametric kernel two sample test proposed by gretton et al \( 2012 \) we compare to the adversarial nets framework introduced by goodfellow et al \( 2014 \) , in which learning is a two player game between a generator network and an adversarial discriminator network , both trained to outwit the other from this perspective , the mmd statistic plays the role of the discriminator in addition to empirical comparisons , we prove bounds on the generalization error incurred by optimizing the empirical mmd"
cs.AI|cs.LG,"in clinical data sets we often find static information \( e g gender of the patients , blood type , etc \) combined with sequences of data that are recorded during multiple hospital visits \( e g medications prescribed , tests performed , etc \) recurrent neural networks \( rnns \) have proven to be very successful for modelling sequences of data in many areas of machine learning in this work we present an approach based on rnns that is specifically designed for the clinical domain and that combines static and dynamic information in order to predict future events we work with a database collected in the charit ' e hospital in berlin that contains all the information concerning patients that underwent a kidney transplantation after the transplantation three main endpoints can occur rejection of the kidney , loss of the kidney and death of the patient our goal is to predict , given the electronic health record of each patient , whether any of those endpoints will occur within the next six or twelve months after each visit to the clinic we compared different types of rnns that we developed for this work , a model based on a feedforward neural network and a logistic regression model we found that the rnn that we developed based on gated recurrent units provides the best performance for this task we also performed an additional experiment using these models to predict next actions and found that for such use case the model based on a feedforward neural network outperformed the other models our hypothesis is that long term dependencies are not as relevant in this task"
cs.DM|cs.SI,"graphs are used to model interactions in a variety of contexts , and there is a growing need to quickly assess the structure of a graph some of the most useful graph metrics , especially those measuring social cohesion , are based on triangles despite the importance of these triadic measures , associated algorithms can be extremely expensive we propose a new method based on wedge sampling this versatile technique allows for the fast and accurate approximation of all current variants of clustering coefficients and enables rapid uniform sampling of the triangles of a graph our methods come with provable and practical time approximation tradeoffs for all computations we provide extensive results that show our methods are orders of magnitude faster than the state of the art , while providing nearly the accuracy of full enumeration our results will enable more wide scale adoption of triadic measures for analysis of extremely large graphs , as demonstrated on several real world examples"
cs.DM,"we propose a novel method for the construction of orthogonal arrays the algorithm makes use of the kronecker product operator in association with unit column vectors to generate new orthogonal arrays from existing orthogonal arrays the effectiveness of the proposed algorithm lies in the fact that it works well with any linear seed orthogonal array without imposing any constraints on the strength or the number of levels the resulting orthogonal array has the same strength as the seed orthogonal array we also discuss the proof of correctness of the algorithm in the results section we provide a list of new orthogonal arrays generated using this algorithm , that are currently not present in the libraries of orthogonal arrays"
cs.AI|cs.LG,"we present a novel approach for learning nonlinear dynamic models , which leads to a new set of tools capable of solving problems that are otherwise difficult we provide theory showing this new approach is consistent for models with long range structure , and apply the approach to motion capture and high dimensional video data , yielding results superior to standard alternatives"
cs.DM,"we present a new , novel approach to obtaining a network 's connectivity more specifically , we show that there exists a relationship between a network 's graph isoperimetric properties and its conditional connectivity a network 's connectivity is the minimum number of nodes , whose removal will cause the network disconnected it is a basic and important measure for the network 's reliability , hence its overall robustness several conditional connectivities have been proposed in the past for the purpose of accurately reflecting various realistic network situations , with extra connectivity being one such conditional connectivity in this paper , we will use isoperimetric properties of the hypercube network to obtain its extra connectivity the result of the paper for the first time establishes a relationship between the age old isoperimetric problem and network connectivity"
cs.DM,"we prove that every internally 4 connected non planar bipartite graph has an odd k 3 , 3 subdivision that is , a subgraph obtained from k 3 , 3 by replacing its edges by internally disjoint odd paths with the same ends the proof gives rise to a polynomial time algorithm to find such a subdivision \( a bipartite graph g is internally 4 connected if it is 3 connected , has at least five vertices , and there is no partition \( a , b , c \) of v \( g \) such that a , b 1 , c 3 and g has no edge with one end in a and the other in b \)"
cs.LG|stat.ML,training examples are not all equally informative active learning strategies leverage this observation in order to massively reduce the number of examples that need to be labeled we leverage the same observation to build a generic strategy for parallelizing learning algorithms this strategy is effective because the search for informative examples is highly parallelizable and because we show that its performance does not deteriorate when the sifting process relies on a slightly outdated model parallel active learning is particularly attractive to train nonlinear models with non linear representations because there are few practical parallel learning algorithms for such models we report preliminary experiments using both kernel svms and sgd trained neural networks
cs.DM,"let g be an r outerplanar graph with n vertices we provide a sequence of log \( n \) r \( 32r 2 \) subgraphs of g , each separated from the rest by at most 2r vertices , and a subset of integer labeled vertices for each subgraph , such that gluing any two graphs in the sequence will yield an r outerplanar graph gluing means to take the disjoint union and then to identify vertices with the same labels we apply the above sequence to the problem of finding an r outerplanar hypergraph support that is , for a given hypergraph we are asked to find an r outerplanar graph on the same vertex set , such that each hyperedge induces a connected graph we \( re \) prove that this problem is \( strongly uniformly \) fixed parameter tractable with respect to r combined with the number of hyperedges in the hypergraph"
cs.DS,"integer linear programs \( ilps \) are a widely applied framework for dealing with combinatorial problems that arise in practice it is known , e g , by the success of cplex , that preprocessing and simplification can greatly speed up the process of optimizing an ilp the present work seeks to further the theoretical understanding of preprocessing for ilps by initiating a rigorous study within the framework of parameterized complexity and kernelization a famous result of lenstra \( mathematics of operations research , 1983 \) shows that feasibility of any ilp with n variables and m constraints can be decided in time o \( c n 3 m c' \) thus , by a folklore argument , any such ilp admits a kernelization to an equivalent instance of size o \( c n 3 \) it is known , that unless np subseteq conp poly and the polynomial hierarchy collapses , no kernelization with size bound polynomial in n is possible however , this lower bound only applies for the case when constraints may include an arbitrary number of variables since it follows from lower bounds for satisfiability and hitting set , whose bounded arity variants admit polynomial kernelizations we consider the feasibility problem for ilps ax b where a is an r row sparse matrix parameterized by the number of variables we show that the kernelizability of this problem depends strongly on the range of the variables if the range is unbounded then this problem does not admit a polynomial kernelization unless np subseteq conp poly if , on the other hand , the range of each variable is polynomially bounded in n then we do get a polynomial kernelization additionally , this holds also for the more general case when the maximum range d is an additional parameter , i e , the size obtained is polynomial in n d"
cs.AI|stat.ML,"most of markov chain monte carlo \( mcmc \) and sequential monte carlo \( smc \) algorithms in existing probabilistic programming systems suboptimally use only model priors as proposal distributions in this work , we describe an approach for training a discriminative model , namely a neural network , in order to approximate the optimal proposal by using posterior estimates from previous runs of inference we show an example that incorporates a data driven proposal for use in a non parametric model in the anglican probabilistic programming system our results show that data driven proposals can significantly improve inference performance so that considerably fewer particles are necessary to perform a good posterior estimation"
cs.AI|cs.LG|stat.ML,"there exist a number of reinforcement learning algorithms which learnby climbing the gradient of expected reward their long runconvergence has been proved , even in partially observableenvironments with non deterministic actions , and without the need fora system model however , the variance of the gradient estimator hasbeen found to be a significant practical problem recent approacheshave discounted future rewards , introducing a bias variance trade offinto the gradient estimate we incorporate a reward baseline into thelearning system , and show that it affects variance without introducingfurther bias in particular , as we approach the zero bias , high variance parameterization , the optimal \( or variance minimizing \) constant reward baseline is equal to the long term average expectedreward modified policy gradient algorithms are presented , and anumber of experiments demonstrate their improvement over previous work"
cs.LG,"optimal transport \( ot \) defines a powerful framework to compare probability distributions in a geometrically faithful way however , the practical impact of ot is still limited because of its computational burden we propose a new class of stochastic optimization algorithms to cope with large scale problems routinely encountered in machine learning applications these methods are able to manipulate arbitrary distributions \( either discrete or continuous \) by simply requiring to be able to draw samples from them , which is the typical setup in high dimensional learning problems this alleviates the need to discretize these densities , while giving access to provably convergent methods that output the correct distance without discretization error these algorithms rely on two main ideas \( a \) the dual ot problem can be re cast as the maximization of an expectation \( b \) entropic regularization of the primal ot problem results in a smooth dual optimization optimization which can be addressed with algorithms that have a provably faster convergence we instantiate these ideas in three different setups \( i \) when comparing a discrete distribution to another , we show that incremental stochastic optimization schemes can beat sinkhorn 's algorithm , the current state of the art finite dimensional ot solver \( ii \) when comparing a discrete distribution to a continuous density , a semi discrete reformulation of the dual program is amenable to averaged stochastic gradient descent , leading to better performance than approximately solving the problem by discretization \( iii \) when dealing with two continuous densities , we propose a stochastic gradient descent over a reproducing kernel hilbert space \( rkhs \) this is currently the only known method to solve this problem , apart from computing ot on finite samples we backup these claims on a set of discrete , semi discrete and continuous benchmark problems"
cs.LG|stat.ML,"advice efficient prediction with expert advice \( in analogy to label efficient prediction \) is a variant of prediction with expert advice game , where on each round of the game we are allowed to ask for advice of a limited number m out of n experts this setting is especially interesting when asking for advice of every expert on every round is expensive we present an algorithm for advice efficient prediction with expert advice that achieves o \( sqrt frac n m t ln n \) regret on t rounds of the game"
stat.ML,"we present a systematic analysis on the performance of a phonetic recogniser when the window of input features is not symmetric with respect to the current frame the recogniser is based on context dependent deep neural networks \( cd dnns \) and hidden markov models \( hmms \) the objective is to reduce the latency of the system by reducing the number of future feature frames required to estimate the current output our tests performed on the timit database show that the performance does not degrade when the input window is shifted up to 5 frames in the past compared to common practice \( no future frame \) this corresponds to improving the latency by 50 ms in our settings our tests also show that the best results are not obtained with the symmetric window commonly employed , but with an asymmetric window with eight past and two future context frames , although this observation should be confirmed on other data sets the reduction in latency suggested by our results is critical for specific applications such as real time lip synchronisation for tele presence , but may also be beneficial in general applications to improve the lag in human machine spoken interaction"
cs.LG|stat.ML,mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of vovk 's aggregating algorithm we provide a new interpretation of mixability via convex analysis that highlights the role of the kullback leibler divergence in its definition this naturally generalises to what we call phi mixability where the bregman divergence d phi replaces the kl divergence we prove that losses that are phi mixable also enjoy constant regret bounds via a generalised aggregating algorithm that is similar to mirror descent
cs.DS,"all to all broadcast is a collective communication in a network with the constraint that every node must send to each other certain piece of its data this paper addresses the problem of optimal all port all to all broadcast in multidimensional tori the optimality criteria considered are the minimum exchange steps , no duplicated data in the sense that only new data are conveyed to receivers and the balance of the communication links load it is proved that under these constraints , an optimal broadcast is not feasible in any multidimensional torus then , the tori which are capable of optimal broadcasts are characterized"
cs.SI|stat.ML,"the problem to accurately and parsimoniously characterize random series of events \( rses \) present in the web , such as e mail conversations or twitter hashtags , is not trivial reports found in the literature reveal two apparent conflicting visions of how rses should be modeled from one side , the poissonian processes , of which consecutive events follow each other at a relatively regular time and should not be correlated on the other side , the self exciting processes , which are able to generate bursts of correlated events and periods of inactivities the existence of many and sometimes conflicting approaches to model rses is a consequence of the unpredictability of the aggregated dynamics of our individual and routine activities , which sometimes show simple patterns , but sometimes results in irregular rising and falling trends in this paper we propose a highly parsimonious way to characterize general rses , namely the burstiness scale \( busca \) model busca views each rse as a mix of two independent process a poissonian and a self exciting one here we describe a fast method to extract the two parameters of busca that , together , gives the burstyness scale , which represents how much of the rse is due to bursty and viral effects we validated our method in eight diverse and large datasets containing real random series of events seen in twitter , yelp , e mail conversations , digg , and online forums results showed that , even using only two parameters , busca is able to accurately describe rses seen in these diverse systems , what can leverage many applications"
cs.DM,"we give a family of counter examples showing that the two sequences of polytopes phi n , n and psi n , n are different these polytopes were defined recently by s friedland in an attempt at a polynomial time algorithm for graph isomorphism"
cs.AI|cs.LG|stat.ML,"this paper proposes and evaluates the k greedy equivalence search algorithm \( kes \) for learning bayesian networks \( bns \) from complete data the main characteristic of kes is that it allows a trade off between greediness and randomness , thus exploring different good local optima when greediness is set at maximum , kes corresponds to the greedy equivalence search algorithm \( ges \) when greediness is kept at minimum , we prove that under mild assumptions kes asymptotically returns any inclusion optimal bn with nonzero probability experimental results for both synthetic and real data are reported showing that kes often finds a better local optima than ges moreover , we use kes to experimentally confirm that the number of different local optima is often huge"
cs.DM,"we analyze in this paper the longest increasing contiguous sequence or maximal ascending run of random variables with common uniform distribution but not independent their dependence is characterized by the fact that two successive random variables cannot take the same value using a markov chain approach , we study the distribution of the maximal ascending run and we develop an algorithm to compute it this problem comes from the analysis of several self organizing protocols designed for large scale wireless sensor networks , and we show how our results apply to this domain"
cs.AI|cs.LG,"this paper proposed a model to predict the stock price based on combining self organizing map \( som \) and fuzzy support vector machines \( f svm \) extraction of fuzzy rules from raw data based on the combining of statistical machine learning models is base of this proposed approach in the proposed model , som is used as a clustering algorithm to partition the whole input space into the several disjoint regions for each partition , a set of fuzzy rules is extracted based on a f svm combining model then fuzzy rules sets are used to predict the test data using fuzzy inference algorithms the performance of the proposed approach is compared with other models using four data sets"
cs.LG|stat.ML,"we propose an extensive analysis of the behavior of majority votes in binary classification in particular , we introduce a risk bound for majority votes , called the c bound , that takes into account the average quality of the voters and their average disagreement we also propose an extensive pac bayesian analysis that shows how the c bound can be estimated from various observations contained in the training data the analysis intends to be self contained and can be used as introductory material to pac bayesian statistical learning theory it starts from a general pac bayesian perspective and ends with uncommon pac bayesian bounds some of these bounds contain no kullback leibler divergence and others allow kernel functions to be used as voters \( via the sample compression setting \) finally , out of the analysis , we propose the mincq learning algorithm that basically minimizes the c bound mincq reduces to a simple quadratic program aside from being theoretically grounded , mincq achieves state of the art performance , as shown in our extensive empirical comparison with both adaboost and the support vector machine"
cs.DM|cs.DS,"we study the problem of finding large cuts in d regular triangle free graphs in prior work , shearer \( 1992 \) gives a randomised algorithm that finds a cut of expected size \( 1 2 0 177 sqrt d \) m , where m is the number of edges we give a simpler algorithm that does much better it finds a cut of expected size \( 1 2 0 28125 sqrt d \) m as a corollary , this shows that in any d regular triangle free graph there exists a cut of at least this size our algorithm can be interpreted as a very efficient randomised distributed algorithm each node needs to produce only one random bit , and the algorithm runs in one synchronous communication round this work is also a case study of applying computational techniques in the design of distributed algorithms our algorithm was designed by a computer program that searched for optimal algorithms for small values of d"
cs.IT|cs.LG|stat.ML,"change detection \( cd \) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series despite having received significant attention , one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and the understanding of changepoints we propose hierarchical quickest change detection \( hqcd \) , a framework that formalizes the process of incorporating additional correlated sources for early changepoint detection the core ideas behind hqcd are rooted in the theory of quickest detection and hqcd can be regarded as its novel generalization to a hierarchical setting the sources are classified into targets and surrogates , and hqcd leverages this structure to systematically assimilate observed data to update changepoint statistics across layers the decision on actual changepoints are provided by minimizing the delay while still maintaining reliability bounds in addition , hqcd also uncovers interesting relations between changes at targets from changes across surrogates we validate hqcd for reliability and performance against several state of the art methods for both synthetic dataset \( known changepoints \) and several real life examples \( unknown changepoints \) our experiments indicate that we gain significant robustness without loss of detection delay through hqcd our real life experiments also showcase the usefulness of the hierarchical setting by connecting the surrogate sources \( such as twitter chatter \) to target sources \( such as employment related protests that ultimately lead to major uprisings \)"
cs.LG|stat.ML,"we study consistency of learning algorithms for a multi class performance metric that is a non decomposable function of the confusion matrix of a classifier and cannot be expressed as a sum of losses on individual data points examples of such performance metrics include the macro f measure popular in information retrieval and the g mean metric used in class imbalanced problems while there has been much work in recent years in understanding the consistency properties of learning algorithms for `binary' non decomposable metrics , little is known either about the form of the optimal classifier for a general multi class non decomposable metric , or about how these learning algorithms generalize to the multi class case in this paper , we provide a unified framework for analysing a multi class non decomposable performance metric , where the problem of finding the optimal classifier for the performance metric is viewed as an optimization problem over the space of all confusion matrices achievable under the given distribution using this framework , we show that \( under a continuous distribution \) the optimal classifier for a multi class performance metric can be obtained as the solution of a cost sensitive classification problem , thus generalizing several previous results on specific binary non decomposable metrics we then design a consistent learning algorithm for concave multi class performance metrics that proceeds via a sequence of cost sensitive classification problems , and can be seen as applying the conditional gradient \( cg \) optimization method over the space of feasible confusion matrices to our knowledge , this is the first efficient learning algorithm \( whose running time is polynomial in the number of classes \) that is consistent for a large family of multi class non decomposable metrics our consistency proof uses a novel technique based on the convergence analysis of the cg method"
cs.DS,"in this paper we examine a number of term rewriting system for integer number representations , building further upon the datatype defining systems described in 2 in particular , we look at automated methods for proving confluence and termination in binary and decimal term rewriting systems for both append and tree constructor functions we find that some of these term rewriting systems are not strongly terminating , which we resolve with minor changes to these systems moreover , most of the term rewriting systems discussed do not exhibit the confluence property , which seems more difficult to resolve"
cs.LG|stat.ML,"we define recurrent gaussian processes \( rgp \) models , a general family of bayesian nonparametric models with recurrent gp priors which are able to learn dynamical patterns from sequential data similar to recurrent neural networks \( rnns \) , rgps can have different formulations for their internal states , distinct inference methods and be extended with deep structures in such context , we propose a novel deep rgp model whose autoregressive states are latent , thereby performing representation and dynamical learning simultaneously to fully exploit the bayesian nature of the rgp model we develop the recurrent variational bayes \( revarb \) framework , which enables efficient inference and strong regularization through coherent propagation of uncertainty across the rgp layers and states we also introduce a rgp extension where variational parameters are greatly reduced by being reparametrized through rnn based sequential recognition models we apply our model to the tasks of nonlinear system identification and human motion modeling the promising obtained results indicate that our rgp model maintains its highly flexibility while being able to avoid overfitting and being applicable even when larger datasets are not available"
cs.LG,"we propose a new simple and natural algorithm for learning the optimal q value function of a discounted cost markov decision process \( mdp \) when the transition kernels are unknown unlike the classical learning algorithms for mdps , such as q learning and `actor critic' algorithms , this algorithm does n't depend on a stochastic approximation based method we show that our algorithm , which we call the empirical q value iteration \( eqvi \) algorithm , converges almost surely to the optimal q value function to the best of our knowledge , this is the first algorithm for learning in mdps that guarantees an almost sure convergence without using stochastic approximations we also give a rate of convergence or a non aymptotic sample complexity bound , and also show that an asynchronous \( or online \) version of the algorithm will also work preliminary experimental results suggest a faster rate of convergence to a ball park estimate for our algorithm compared to stochastic approximation based algorithms in fact , the asynchronous setting eqvi vastly outperforms the popular and widely used q learning algorithm"
cs.LG,"walrasian equilibrium prices can be said to coordinate markets they support a welfare optimal allocation in which each buyer is buying bundle of goods that is individually most preferred however , this clean story has two caveats first , the prices alone are not sufficient to coordinate the market , and buyers may need to select among their most preferred bundles in a coordinated way to find a feasible allocation second , we do n't in practice expect to encounter exact equilibrium prices tailored to the market , but instead only approximate prices , somehow encoding ``distributional'' information about the market how well do prices work to coordinate markets when tie breaking is not coordinated , and they encode only distributional information \? we answer this question first , we provide a genericity condition such that for buyers with matroid based valuations , overdemand with respect to equilibrium prices is at most 1 , independent of the supply of goods , even when tie breaking is done in an uncoordinated fashion second , we provide learning theoretic results that show that such prices are robust to changing the buyers in the market , so long as all buyers are sampled from the same \( unknown \) distribution"
cs.LG|stat.ML,"transfer learning has received a lot of attention in the machine learning community over the last years , and several effective algorithms have been developed however , relatively little is known about their theoretical properties , especially in the setting of lifelong learning , where the goal is to transfer information to tasks for which no data have been observed so far in this work we study lifelong learning from a theoretical perspective our main result is a pac bayesian generalization bound that offers a unified view on existing paradigms for transfer learning , such as the transfer of parameters or the transfer of low dimensional representations we also use the bound to derive two principled lifelong learning algorithms , and we show that these yield results comparable with existing methods"
cs.LG,"biometric systems suffer from some drawbacks a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture one solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together \( multiple captures of the same biometric modality , multiple feature extraction algorithms , multiple biometric modalities \) in this paper , we are interested in score level fusion functions application \( i e , we use a multibiometric authentication scheme which accept or deny the claimant for using an application \) in the state of the art , the weighted sum of scores \( which is a linear classifier \) and the use of an svm \( which is a non linear classifier \) provided by different biometric systems provide one of the best performances we present a new method based on the use of genetic programming giving similar or better performances \( depending on the complexity of the database \) we derive a score fusion function by assembling some classical primitives functions \( , , , \) we have validated the proposed method on three significant biometric benchmark datasets from the state of the art"
cs.LG,"many sequential processing tasks require complex nonlinear transition functions from one step to the next however , recurrent neural networks with such 'deep' transition functions remain difficult to train , even when using long short term memory networks we introduce a novel theoretical analysis of recurrent networks based on ger v s gorin 's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the lstm cell based on this analysis we propose recurrent highway networks \( rhn \) , which are long not only in time but also in space , generalizing lstms to larger step to step depths experiments indicate that the proposed architecture results in complex but efficient models , beating previous models for character prediction on the hutter prize dataset with less than half of the parameters"
cs.DM,we show that f vectors of matroid complexes of realisable matroids are log concave this was conjectured by mason in 1972 our proof uses the recent result by huh and katz who showed that the coefficients of the characteristic polynomial of a realisable matroid form a log concave sequence we also discuss the relationship between log concavity of f vectors and h vectors of matroids in the last section we explain the connection between zonotopal algebra and f vectors and characteristic polynomials of matroids
cs.DM,"our work becomes integrated into the general problem of the stability of the network ad hoc some , works attacked \( affected \) this problem among these works , we find the modelling of the network ad hoc in the form of a graph we can resume the problem of coherence of the network ad hoc of a problem of allocation of frequency we study a new class of graphs , the fat extended p4 graphs , and we give a polynomial time algorithm to calculate the grundy number of the graphs in this class this result implies that the grundy number can be found in polynomial time for many graphs"
cs.DS,"in this paper we present the self stabilizing implementation of a class of token based algorithms in the current work we only consider interactions between weak nodes they are uniform , they do not have unique identifiers , are static and their interactions are restricted to a subset of nodes called neighbours while interacting , a pair of neighbouring nodes may create mobile agents \( that materialize in the current work the token abstraction \) that perform traversals of the network and accelerate the system stabilization in this work we only explore the power of oblivious stateless agents our work shows that the agent paradigm is an elegant distributed tool for achieving self stabilization in tiny interaction protocols \( tip \) nevertheless , in order to reach the full power of classical self stabilizing algorithms more complex classes of agents have to be considered \( e g agents with memory , identifiers or communication skills \) interestingly , our work proposes for the first time a model that unifies the recent studies in mobile robots \( agents \) that evolve in a discrete space and the already established population protocols paradigm"
cs.DM,"a drawing of a graph g in the plane is weakly simple if it can be turned into an embedding by an arbitrarily small perturbation we show that testing , whether a straight line drawing of a planar graph g in the plane is weakly simple , can be carried out in polynomial time , if a desired embedding of g belongs to a fixed isotopy class in other words , we show that c planarity with pipes is tractable for graphs with fixed embeddings to the best of our knowledge an analogous result was previously known essentially only when g is a cycle"
cs.LG|stat.ML,"in pu learning , a binary classifier is trained only from positive \( p \) and unlabeled \( u \) data without negative \( n \) data although n data is missing , it sometimes outperforms pn learning \( i e , supervised learning \) in experiments in this paper , we theoretically compare pu \( and the opposite nu \) learning against pn learning , and prove that , one of pu and nu learning given infinite u data will almost always improve on pn learning our theoretical finding is also validated experimentally"
cs.DM,"we revisit fermat 's factorization method for a positive integer n that is a product of two primes p and q such an integer is used as the modulus for both encryption and decryption operations of an rsa cryptosystem the security of rsa relies on the hardness of factoring this modulus as a consequence of our analysis , two variants of fermat 's approach emerge we also present a comparison between the two methods' effective regions though our study does not yield a new state of the art algorithm for integer factorization , we believe that it reveals some interesting observations that are open for further analysis"
cs.AI|cs.DS,"in many real life optimisation problems , there are multiple interacting components in a solution for example , different components might specify assignments to different kinds of resource often , each component is associated with different sets of soft constraints , and so with different measures of soft constraint violation the goal is then to minimise a linear combination of such measures this paper studies an approach to such problems , which can be thought of as multiphase exploitation of multiple objective value restricted submodels in this approach , only one computationally difficult component of a problem and the associated subset of objectives is considered at first this produces partial solutions , which define interesting neighbourhoods in the search space of the complete problem often , it is possible to pick the initial component so that variable aggregation can be performed at the first stage , and the neighbourhoods to be explored next are guaranteed to contain feasible solutions using integer programming , it is then easy to implement heuristics producing solutions with bounds on their quality our study is performed on a university course timetabling problem used in the 2007 international timetabling competition , also known as the udine course timetabling problem in the proposed heuristic , an objective restricted neighbourhood generator produces assignments of periods to events , with decreasing numbers of violations of two period related soft constraints those are relaxed into assignments of events to days , which define neighbourhoods that are easier to search with respect to all four soft constraints integer programming formulations for all subproblems are given and evaluated using ilog cplex 11 the wider applicability of this approach is analysed and discussed"
cs.DM|cs.DS,"we consider the following question given an \( x , y \) bigraph g and a set s subset x , does g contain two disjoint matchings m 1 and m 2 such that m 1 saturates x and m 2 saturates s \? when s geq x 1 , this question is solvable by finding an appropriate factor of the graph in contrast , we show that when s is allowed to be an arbitrary subset of x , the problem is np hard"
cs.LG|stat.ML,"support vector machines \( svms \) are powerful learners that have led to state of the art results in various computer vision problems svms suffer from various drawbacks in terms of selecting the right kernel , which depends on the image descriptors , as well as computational and memory efficiency this paper introduces a novel kernel , which serves such issues well the kernel is learned by exploiting a large amount of low complex , randomized binary mappings of the input feature this leads to an efficient svm , while also alleviating the task of kernel selection we demonstrate the capabilities of our kernel on 6 standard vision benchmarks , in which we combine several common image descriptors , namely histograms \( flowers17 and daimler \) , attribute like descriptors \( uci , osr , and a voc08 \) , and sparse quantization \( imagenet \) results show that our kernel learning adapts well to the different descriptors types , achieving the performance of the kernels specifically tuned for each image descriptor , and with similar evaluation cost as efficient svm methods"
cs.DM|cs.DS,"a normal helly circular arc graph is the intersection graph of arcs on a circle of which no three or less arcs cover the whole circle lin , soulignac , and szwarcfiter discrete appl math 2013 characterized circular arc graphs that are not normal helly circular arc graphs , and used it to develop the first recognition algorithm for this graph class as open problems , they ask for the forbidden induced subgraph characterization and a direct recognition algorithm for normal helly circular arc graphs , both of which are resolved by the current paper moreover , when the input is not a normal helly circular arc graph , our recognition algorithm finds in linear time a minimal forbidden induced subgraph as certificate"
cs.DM|cs.DS,"we introduce and study finite d volumes the high dimensional generalization of finite metric spaces having developed a suitable combinatorial machinery , we define ell 1 volumes and show that they contain euclidean volumes and hypertree volumes we show that they can approximate any d volume with o \( n d \) multiplicative distortion on the other hand , contrary to bourgain 's theorem for d 1 , there exists a 2 volume that on n vertices that cannot be approximated by any ell 1 volume with distortion smaller than tilde omega \( n 1 5 \) we further address the problem of ell 1 dimension reduction in the context of ell 1 volumes , and show that this phenomenon does occur , although not to the same striking degree as it does for euclidean metrics and volumes in particular , we show that any ell 1 metric on n points can be \( 1 epsilon \) approximated by a sum of o \( n epsilon 2 \) cut metrics , improving over the best previously known bound of o \( n log n \) due to schechtman in order to deal with dimension reduction , we extend the techniques and ideas introduced by karger and bencz 'u r , and spielman et al in the context of graph sparsification , and develop general methods with a wide range of applications"
cs.DM|cs.SI,"we introduce a new class of \( dynamical \) systems that inherently capture cascading effects \( viewed as consequential effects \) and are naturally amenable to combinations we develop an axiomatic general theory around those systems , and guide the endeavor towards an understanding of cascading failure the theory evolves as an interplay of lattices and fixed points , and its results may be instantiated to commonly studied models of cascade effects we characterize the systems through their fixed points , and equip them with two operators we uncover properties of the operators , and express global systems through combinations of local systems we enhance the theory with a notion of failure , and understand the class of shocks inducing a system to failure we develop a notion of mu rank to capture the energy of a system , and understand the minimal amount of effort required to fail a system , termed resilience we deduce a dual notion of fragility and show that the combination of systems sets a limit on the amount of fragility inherited"
cs.DM|cs.DS|cs.SI,"in this paper we consider a fundamental problem in the area of viral marketing , called t scriptsize arget s scriptsize et s scriptsize election problem in a a viral marketing setting , social networks are modeled by graphs with potential customers of a new product as vertices and friend relationships as edges , where each vertex v is assigned a threshold value theta \( v \) the thresholds represent the different latent tendencies of customers \( vertices \) to buy the new product when their friend \( neighbors \) do consider a repetitive process on social network \( g , theta \) where each vertex v is associated with two states , active and inactive , which indicate whether v is persuaded into buying the new product suppose we are given a target set s subseteq v \( g \) initially , all vertices in g are inactive at time step 0 , we choose all vertices in s to become active then , at every time step t 0 , all vertices that were active in time step t 1 remain active , and we activate any vertex v if at least theta \( v \) of its neighbors were active at time step t 1 the activation process terminates when no more vertices can get activated we are interested in the following optimization problem , called t scriptsize arget s scriptsize et s scriptsize election finding a target set s of smallest possible size that activates all vertices of g there is an important and well studied threshold called strict majority threshold , where for every vertex v in g we have theta \( v \) lceil \( d \( v \) 1 \) 2 rceil and d \( v \) is the degree of v in g in this paper , we consider the t scriptsize arget s scriptsize et s scriptsize election problem under strict majority thresholds and focus on three popular regular network structures cycle permutation graphs , generalized petersen graphs and torus cordalis"
cs.IT|stat.ML,"in this paper , the `approximate message passing' \( amp \) algorithm , initially developed for compressed sensing of signals under i i d gaussian measurement matrices , has been extended to a multi terminal setting \( mamp algorithm \) it has been shown that similar to its single terminal counterpart , the behavior of mamp algorithm is fully characterized by a `state evolution' \( se \) equation for large block lengths this equation has been used to obtain the rate distortion curve of a multi terminal memoryless source it is observed that by spatially coupling the measurement matrices , the rate distortion curve of mamp algorithm undergoes a phase transition , where the measurement rate region corresponding to a low distortion \( approximately zero distortion \) regime is fully characterized by the joint and conditional renyi information dimension \( rid \) of the multi terminal source this measurement rate region is very similar to the rate region of the slepian wolf distributed source coding problem where the rid plays a role similar to the discrete entropy simulations have been done to investigate the empirical behavior of mamp algorithm it is observed that simulation results match very well with predictions of se equation for reasonably large block lengths"
cs.IT|stat.ML,"approximate message passing \( amp \) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem the amp framework provides modularity in the choice of signal prior here we propose a hierarchical form of the gauss bernouilli prior which utilizes a restricted boltzmann machine \( rbm \) trained on the signal support to push reconstruction performance beyond that of simple iid priors for signals whose support can be well represented by a trained binary rbm we present and analyze two methods of rbm factorization and demonstrate how these affect signal reconstruction performance within our proposed algorithm finally , using the mnist handwritten digit dataset , we show experimentally that using an rbm allows amp to approach oracle support performance"
cs.DM,"let m \( n \) be the minimum number of edges in an n uniform simple hypergraph that is not two colorable we prove that m \( n \) omega \( 4 n ln 2 \( n \) \) our result generalizes to r coloring of b simple uniform hypergraphs for fixed r and b we prove that a maximum vertex degree in b simple n uniform hypergraph that is not r colorable must be omega \( r n ln \( n \) \) by trimming arguments it implies that every such graph has omega \( \( r n ln \( n \) \) b 1 b \) edges for any fixed r geq 2 our techniques yield also a lower bound omega \( r n ln \( n \) \) for van der waerden numbers w \( n , r \)"
cs.LG|stat.ML,"neural language models \( lms \) based on recurrent neural networks \( rnn \) are some of the most successful word and character level lms why do they work so well , in particular better than linear neural lms \? possible explanations are that rnns have an implicitly better regularization or that rnns have a higher capacity for storing patterns due to their nonlinearities or both here we argue for the first explanation in the limit of little training data and the second explanation for large amounts of text data we show state of the art performance on the popular and small penn dataset when rnn lms are regularized with random dropout nonetheless , we show even better performance from a simplified , much less expressive linear rnn model without off diagonal entries in the recurrent matrix we call this model an impulse response lm \( irlm \) using random dropout , column normalization and annealed learning rates , irlms develop neurons that keep a memory of up to 50 words in the past and achieve a perplexity of 102 5 on the penn dataset on two large datasets however , the same regularization methods are unsuccessful for both models and the rnn 's expressivity allows it to overtake the irlm by 10 and 20 percent perplexity , respectively despite the perplexity gap , irlms still outperform rnns on the microsoft research sentence completion \( mrsc \) task we develop a slightly modified irlm that separates long context units \( lcus \) from short context units and show that the lcus alone achieve a state of the art performance on the mrsc task of 60 8 our analysis indicates that a fruitful direction of research for neural lms lies in developing more accessible internal representations , and suggests an optimization regime of very high momentum terms for effectively training such models"
cs.LG|stat.ML,"we present and study a partial information model of online learning , where a decision maker repeatedly chooses from a finite set of actions , and observes some subset of the associated losses this naturally models several situations where the losses of different actions are related , and knowing the loss of one action provides information on the loss of other actions moreover , it generalizes and interpolates between the well studied full information setting \( where all losses are revealed \) and the bandit setting \( where only the loss of the action chosen by the player is revealed \) we provide several algorithms addressing different variants of our setting , and provide tight regret bounds depending on combinatorial properties of the information feedback structure"
cs.DS|cs.LG|stat.ML,"we formulate weighted graph clustering as a prediction problem given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights this formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph we adapt the pac bayesian analysis of co clustering \( seldin and tishby , 2008 seldin , 2009 \) to derive a pac bayesian generalization bound for graph clustering the bound shows that graph clustering should optimize a trade off between empirical data fit and the mutual information that clusters preserve on the graph nodes a similar trade off derived from information theoretic considerations was already shown to produce state of the art results in practice \( slonim et al , 2005 yom tov and slonim , 2009 \) this paper supports the empirical evidence by providing a better theoretical foundation , suggesting formal generalization guarantees , and offering a more accurate way to deal with finite sample issues we derive a bound minimization algorithm and show that it provides good results in real life problems and that the derived pac bayesian bound is reasonably tight"
cs.DM|cs.DS,"the main goal of this paper is to formalize and explore a connection between chromatic properties of graphs with geometric representations and competitive analysis of on line algorithms , which became apparent after the recent construction of triangle free geometric intersection graphs with arbitrarily large chromatic number due to pawlik et al we show that on line graph coloring problems give rise to classes of game graphs with a natural geometric interpretation we use this concept to estimate the chromatic number of graphs with geometric representations by finding , for appropriate simpler graphs , on line coloring algorithms using few colors or proving that no such algorithms exist we derive upper and lower bounds on the maximum chromatic number that rectangle overlap graphs , subtree overlap graphs , and interval filament graphs \( all of which generalize interval overlap graphs \) can have when their clique number is bounded the bounds are absolute for interval filament graphs and asymptotic of the form \( log log n \) f \( omega \) for rectangle and subtree overlap graphs in particular , we provide the first construction of geometric intersection graphs with bounded clique number and with chromatic number asymptotically greater than log log n we also introduce a concept of k k free colorings and show that for some geometric representations , the k 3 free chromatic number can be bounded in terms of the clique number although the ordinary \( k 2 free \) chromatic number cannot such a result for segment intersection graphs would imply a well known conjecture that k quasi planar geometric graphs have linearly many edges"
stat.ML,"in this paper is proposed a new heuristic approach belonging to the field of evolutionary estimation of distribution algorithms \( edas \) edas builds a probability model and a set of solutions is sampled from the model which characterizes the distribution of such solutions the main framework of the proposed method is an estimation of distribution algorithm , in which an adaptive gibbs sampling is used to generate new promising solutions and , in combination with a local search strategy , it improves the individual solutions produced in each iteration the estimation of distribution algorithm with adaptive gibbs sampling we are proposing in this paper is called ageda we experimentally evaluate and compare this algorithm against two deterministic procedures and several stochastic methods in three well known test problems for unconstrained global optimization it is empirically shown that our heuristic is robust in problems that involve three central aspects that mainly determine the difficulty of global optimization problems , namely high dimensionality , multi modality and non smoothness"
cs.AI|stat.ML,"protein protein interaction \( ppi \) prediction is an important problem in machine learning and computational biology however , there is no data set for training or evaluation purposes , where all the instances are accurately labeled instead , what is available are instances of positive class \( with possibly noisy labels \) and no instances of negative class the non availability of negative class data is typically handled with the observation that randomly chosen protein pairs have a nearly 100 chance of being negative class , as only 1 in 1 , 500 protein pairs expected is expected to be an interacting pair in this paper , we focused on the problem that non availability of accurately labeled testing data sets in the domain of protein protein interaction \( ppi \) prediction may lead to biased evaluation results we first showed that not acknowledging the inherent skew in the interactome \( i e rare occurrence of positive instances \) leads to an over estimated accuracy of the predictor then we show that , with the belief that positive interactions are a rare category , sampling random pairs of proteins excluding known interacting proteins set as the negative testing data set could lead to an under estimated evaluation result we formalized those two problems to validate the above claim , and based on the formalization , we proposed a balancing method to cancel out the over estimation with under estimation finally , our experiments validated the theoretical aspects and showed that this balancing evaluation could evaluate the exact performance without availability of golden standard data sets"
cs.DM,"the topic of diversity is an interesting subject , both as a purely mathematical concept and also for its applications to important real life situations unfortunately , although the meaning of diversity seems intuitively clear , no precise mathematical definition exists in this paper , we adopt an axiomatic approach to the problem , and attempt to produce a satisfactory measure"
cs.DM,"we consider qualitative and quantitative verification problems for infinite state markov chains we call a markov chain decisive w r t a given set of target states f if it almost certainly eventually reaches either f or a state from which f can no longer be reached while all finite markov chains are trivially decisive \( for every set f \) , this also holds for many classes of infinite markov chains infinite markov chains which contain a finite attractor are decisive w r t every set f in particular , this holds for probabilistic lossy channel systems \( plcs \) furthermore , all globally coarse markov chains are decisive this class includes probabilistic vector addition systems \( pvass \) and probabilistic noisy turing machines \( pntm \) we consider both safety and liveness problems for decisive markov chains , i e , the probabilities that a given set of states f is eventually reached or reached infinitely often , respectively 1 we express the qualitative problems in abstract terms for decisive markov chains , and show an almost complete picture of its decidability for plcs , pvass and pntm 2 we also show that the path enumeration algorithm of iyer and narasimha terminates for decisive markov chains and can thus be used to solve the approximate quantitative safety problem a modified variant of this algorithm solves the approximate quantitative liveness problem 3 finally , we show that the exact probability of \( repeatedly \) reaching f cannot be effectively expressed \( in a uniform way \) in tarski algebra for either plcs , pvass or \( p \) ntm"
cs.AI|cs.LG|stat.ML,"probabilistic models analyze data by relying on a set of assumptions when a model performs poorly , we challenge its assumptions this approach has led to myriad hand crafted robust models they offer protection against small deviations from their assumptions we propose a simple way to systematically mitigate mismatch of a large class of probabilistic models the idea is to raise the likelihood of each observation to a weight inferring these weights allows a model to identify observations that match its assumptions down weighting others enables robust inference and improved predictive accuracy we study four different forms of model mismatch , ranging from missing latent groups to structure misspecification a poisson factorization analysis of the movielens dataset shows the benefits of reweighting in a real data scenario"
cs.DS|cs.IT,"let p p \( i \) be a measure of strictly positive probabilities on the set of nonnegative integers although the countable number of inputs prevents usage of the huffman algorithm , there are nontrivial p for which known methods find a source code that is optimal in the sense of minimizing expected codeword length for some applications , however , a source code should instead minimize one of a family of nonlinear objective functions , beta exponential means , those of the form log a sum i p \( i \) a n \( i \) , where n \( i \) is the length of the i th codeword and a is a positive constant applications of such minimizations include a problem of maximizing the chance of message receipt in single shot communications \( a 1 \) and a problem of minimizing the chance of buffer overflow in a queueing system \( a 1 \) this paper introduces methods for finding codes optimal for such exponential means one method applies to geometric distributions , while another applies to distributions with lighter tails the latter algorithm is applied to poisson distributions both are extended to minimizing maximum pointwise redundancy"
cs.DM,"the manickam miklos singhi conjecture states that when n is at least 4k , every multiset of n real numbers with nonnegative total sum has at least \( n 1 choose k 1 \) k subsets with nonnegative sum we develop a branch and cut strategy using a linear programming formulation to show that verifying the conjecture for fixed values of k is a finite problem to improve our search , we develop a zero error randomized propagation algorithm using implementations of these algorithms , we verify a stronger form of the conjecture for all k at most seven"
cs.IT|cs.LG,"model free reinforcement learning algorithms such as q learning perform poorly in the early stages of learning in noisy environments , because much effort is spent on unlearning biased estimates of the state action function the bias comes from selecting , among several noisy estimates , the apparent optimum , which may actually be suboptimal we propose g learning , a new off policy learning algorithm that regularizes the noise in the space of optimal actions by penalizing deterministic policies at the beginning of the learning moreover , it enables naturally incorporating prior distributions over optimal actions when available the stochastic nature of g learning also makes it more cost effective than q learning in noiseless but exploration risky domains we illustrate these ideas in several examples where g learning results in significant improvements of the learning rate and the learning cost"
cs.DM|cs.DS,"we first devise a branching algorithm that computes a minimum independent dominating set on any graph with running time o \( 2 0 424n \) and polynomial space this improves the o \( 2 0 441n \) result by \( s gaspers and m liedloff , a branch and reduce algorithm for finding a minimum independent dominating set in graphs , proc wg'06 \) we then show that , for every r 3 , it is possible to compute an r \( \( r 1 \) r \) log 2 \( r \) approximate solution for min independent dominating set within time o \( 2 \( nlog 2 \( r \) r \) \)"
cs.DM,"we present several enumeration results holding in sets of words called neutral and which satisfy restrictive conditions on the set of possible extensions of nonempty words these formulae concern return words and bifix codes they generalize formulae previously known for sturmian sets or more generally for tree sets we also give a geometric example of this class of sets , namely the natural coding of some interval exchange transformations"
cs.LG|stat.ML,"early diagnosis , playing an important role in preventing progress and treating the alzheimer 's disease \( ad \) , is based on classification of features extracted from brain images the features have to accurately capture main ad related variations of anatomical brain structures , such as , e g , ventricles size , hippocampus shape , cortical thickness , and brain volume this paper proposes to predict the ad with a deep 3d convolutional neural network \( 3d cnn \) , which can learn generic features capturing ad biomarkers and adapt to different domain datasets the 3d cnn is built upon a 3d convolutional autoencoder , which is pre trained to capture anatomical shape variations in structural brain mri scans fully connected upper layers of the 3d cnn are then fine tuned for each task specific ad classification experiments on the emph adni mri dataset with no skull stripping preprocessing have shown our 3d cnn outperforms several conventional classifiers by accuracy and robustness abilities of the 3d cnn to generalize the features learnt and adapt to other domains have been validated on the emph caddementia dataset"
cs.LG|stat.ML,"there is a broad range of bionlp tasks for which active learning \( al \) can significantly reduce annotation costs and a specific al algorithm we have developed is particularly effective in reducing annotation costs for these tasks we have previously developed an al algorithm called closestinitpa that works best with tasks that have the following characteristics redundancy in training material , burdensome annotation costs , support vector machines \( svms \) work well for the task , and imbalanced datasets \( i e when set up as a binary classification problem , one class is substantially rarer than the other \) many bionlp tasks have these characteristics and thus our al algorithm is a natural approach to apply to bionlp tasks"
cs.IT|stat.ML,"we propose a novel receiver for orthogonal frequency division multiplexing \( ofdm \) transmissions in impulsive noise environments impulsive noise arises in many modern wireless and wireline communication systems , such as wi fi and powerline communications , due to uncoordinated interference that is much stronger than thermal noise we first show that the bit error rate optimal receiver jointly estimates the propagation channel coefficients , the noise impulses , the finite alphabet symbols , and the unknown bits we then propose a near optimal yet computationally tractable approach to this joint estimation problem using loopy belief propagation in particular , we merge the recently proposed generalized approximate message passing \( gamp \) algorithm with the forward backward algorithm and soft input soft output decoding using a turbo approach numerical results indicate that the proposed receiver drastically outperforms existing receivers under impulsive noise and comes within 1 db of the matched filter bound meanwhile , with n tones , the proposed factor graph based receiver has only o \( n log n \) complexity , and it can be parallelized"
stat.ML,"motivated by vision tasks such as robust face and object recognition , we consider the following general problem given a collection of low dimensional linear subspaces in a high dimensional ambient \( image \) space and a query point \( image \) , efficiently determine the nearest subspace to the query in ell 1 distance we show in theory that cauchy random embedding of the objects into significantly lower dimensional spaces helps preserve the identity of the nearest subspace with constant probability this offers the possibility of efficiently selecting several candidates for accurate search we sketch preliminary experiments on robust face and digit recognition to corroborate our theory"
cs.DM|cs.DS,"a cycle cover of a graph is a set of cycles such that every vertex is part of exactly one cycle an l cycle cover is a cycle cover in which the length of every cycle is in the set l the weight of a cycle cover of an edge weighted graph is the sum of the weights of its edges we come close to settling the complexity and approximability of computing l cycle covers on the one hand , we show that for almost all l , computing l cycle covers of maximum weight in directed and undirected graphs is apx hard and np hard most of our hardness results hold even if the edge weights are restricted to zero and one on the other hand , we show that the problem of computing l cycle covers of maximum weight can be approximated within a factor of 2 for undirected graphs and within a factor of 8 3 in the case of directed graphs this holds for arbitrary sets l"
cs.DM,"the subgraph homeomorphism problem , shp \( h \) , has been shown to be polynomial time solvable for any fixed pattern graph h , but practical algorithms have been developed only for a few specific pattern graphs among these are the wheels with four , five , and six spokes this paper examines the subgraph homeomorphism problem where the pattern graph is a wheel with seven spokes , and gives a result that describes graphs with no w 7 subdivision , showing how they can be built up , using certain operations , from smaller `pieces' that meet certain conditions we also discuss algorithmic aspects of the problem"
cs.DM|cs.IT,the discrete logarithm problem is one of the backbones in public key cryptography in this paper we study the discrete logarithm problem in the group of circulant matrices over a finite field this gives rise to secure and fast public key cryptosystems
cs.AI|cs.LG,"knowledge tracing where a machine models the knowledge of a student as they interact with coursework is a well established problem in computer supported education though effectively modeling student knowledge would have high educational impact , the task has many inherent challenges in this paper we explore the utility of using recurrent neural networks \( rnns \) to model student learning the rnn family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge , and can capture more complex representations of student knowledge using neural networks results in substantial improvements in prediction performance on a range of knowledge tracing datasets moreover the learned model can be used for intelligent curriculum design and allows straightforward interpretation and discovery of structure in student tasks these results suggest a promising new line of research for knowledge tracing and an exemplary application task for rnns"
cs.DM|cs.SI,"random s intersection graphs have recently received much interest 1 9 in a random s intersection graph , each vertex is assigned to a set of items in some manner , and two vertices have an edge in between if and only if they share at least s items in particular , in a uniform random s intersection graph , each vertex independently selects the same number of items uniformly at random from a common item pool , while in a binomial random s intersection graph , each item in some item pool is independently attached to each vertex with the same probability these two graph models have numerous applications e g , using uniform random s intersection graph for cryptanalysis 14 , 15 , and to model secure wireless sensor networks 8 10 and online social networks 11 , 12 , and using a binomial random s intersection graph for clustering analysis 17 , classification 18 and the design of integrated circuits 34 for binomial uniform random s intersection graphs , we present results related to k connectivity and minimum vertex degree specifically , we derive the asymptotically exact probabilities and asymptotic zero one laws for the following three properties \( i \) k vertex connectivity , \( ii \) k edge connectivity and \( iii \) the property of minimum vertex degree being at least k recently , similar results have been obtained by bloznelis and rybarczyk 5 , but their findings are only for uniform random s intersection graphs , not for binomial random s intersection graphs , and require parameter conditions which are disjoint from ours our parameter conditions are useful in practical sensor network applications of the graphs while theirs are not in terms of binomial s intersection graphs , for the three properties above , our paper reports the first result on the exact probabilities as well as the first result on the zero one laws"
cs.LG|stat.ML,"modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification , object and part detection , boundary extraction , etc however , a major advantage that natural intelligences still have is that they work well for emph all perceptual problems together , solving them efficiently and coherently in an emph integrated manner in order to capture some of these advantages in machine perception , we ask two questions whether deep neural networks can learn universal image representations , useful not only for a single task but for all of them , and how the solutions to the different tasks can be integrated in this framework we answer by proposing a new architecture , which we call emph multinet , in which not only deep image features are shared between tasks , but where tasks can interact in a recurrent manner by encoding the results of their analysis in a common shared representation of the data in this manner , we show that the performance of individual tasks in standard benchmarks can be improved first by sharing features between them and then , more significantly , by integrating their solutions in the common representation"
stat.ML,"statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden markov trees \( hmts \) however , in linear inverse problems such as deconvolution , tomography , and compressed sensing , the presence of a sensing or observation matrix produces a linear mixing of the simple markovian dependency structure this leads to reconstruction problems that are non convex optimizations past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods in this paper , we propose new modeling approaches based on group sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently we show that the methods we develop perform significantly better in deconvolution and compressed sensing applications , while being as computationally efficient as standard coefficient wise approaches such as lasso"
cs.DM,"for many of the unsolved problems concerning cycles and matchings in graphs it is known that it is sufficient to prove them for emph snarks , the class of nontrivial 3 regular graphs which cannot be 3 edge coloured in the first part of this paper we present a new algorithm for generating all non isomorphic snarks of a given order our implementation of the new algorithm is 14 times faster than previous programs for generating snarks , and 29 times faster for generating weak snarks using this program we have generated all non isomorphic snarks on n leq 36 vertices previously lists up to n 28 vertices have been published in the second part of the paper we analyze the sets of generated snarks with respect to a number of properties and conjectures we find that some of the strongest versions of the cycle double cover conjecture hold for all snarks of these orders , as does jaeger 's petersen colouring conjecture , which in turn implies that fulkerson 's conjecture has no small counterexamples in contrast to these positive results we also find counterexamples to eight previously published conjectures concerning cycle coverings and the general cycle structure of cubic graphs"
cs.LG,"we propose a new majorization minimization \( mm \) method for non smooth and non convex programs , which is general enough to include the existing mm methods besides the local majorization condition , we only require that the difference between the directional derivatives of the objective function and its surrogate function vanishes when the number of iterations approaches infinity , which is a very weak condition so our method can use a surrogate function that directly approximates the non smooth objective function in comparison , all the existing mm methods construct the surrogate function by approximating the smooth component of the objective function we apply our relaxed mm methods to the robust matrix factorization \( rmf \) problem with different regularizations , where our locally majorant algorithm shows advantages over the state of the art approaches for rmf this is the first algorithm for rmf ensuring , without extra assumptions , that any limit point of the iterates is a stationary point"
cs.DM|cs.SI,"we develop a framework to track the structure of temporal networks with a signal processing approach the method is based on the duality between networks and signals using a multidimensional scaling technique this enables a study of the network structure using frequency patterns of the corresponding signals an extension is proposed for temporal networks , thereby enabling a tracking of the network structure over time a method to automatically extract the most significant frequency patterns and their activation coefficients over time is then introduced , using nonnegative matrix factorization of the temporal spectra the framework , inspired by audio decomposition , allows transforming back these frequency patterns into networks , to highlight the evolution of the underlying structure of the network over time the effectiveness of the method is first evidenced on a toy example , prior being used to study a temporal network of face to face contacts the extraction of sub networks highlights significant structures decomposed on time intervals"
cs.LG,"despite the common usage of a canonical , data independent , hemodynamic response function \( hrf \) , it is known that the shape of the hrf varies across brain regions and subjects this suggests that a data driven estimation of this function could lead to more statistical power when modeling bold fmri data however , unconstrained estimation of the hrf can yield highly unstable results when the number of free parameters is large we develop a method for the joint estimation of activation and hrf using a rank constraint causing the estimated hrf to be equal across events conditions , yet permitting it to be different across voxels model estimation leads to an optimization problem that we propose to solve with an efficient quasi newton method exploiting fast gradient computations this model , called glm with rank 1 constraint \( r1 glm \) , can be extended to the setting of glm with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments we compare 10 different hrf modeling methods in terms of encoding and decoding score in two different datasets our results show that the r1 glm model significantly outperforms competing methods in both encoding and decoding settings , positioning it as an attractive method both from the points of view of accuracy and computational efficiency"
cs.IT|cs.LG|cs.SI,"we study a social network consisting of agents organized as a hierarchical m ary rooted tree , common in enterprise and military organizational structures the goal is to aggregate information to solve a binary hypothesis testing problem each agent at a leaf of the tree , and only such an agent , makes a direct measurement of the underlying true hypothesis the leaf agent then makes a decision and sends it to its supervising agent , at the next level of the tree each supervising agent aggregates the decisions from the m members of its group , produces a summary message , and sends it to its supervisor at the next level , and so on ultimately , the agent at the root of the tree makes an overall decision we derive upper and lower bounds for the type i and ii error probabilities associated with this decision with respect to the number of leaf agents , which in turn characterize the converge rates of the type i , type ii , and total error probabilities we also provide a message passing scheme involving non binary message alphabets and characterize the exponent of the error probability with respect to the message alphabet size"
cs.LG|stat.ML,"stochastic gradient descent \( sgd \) is one of the most widely used techniques for online optimization in machine learning in this work , we accelerate sgd by adaptively learning how to sample the most useful training examples at each time step first , we show that sgd can be used to learn the best possible sampling distribution of an importance sampling estimator second , we show that the sampling distribution of a sgd algorithm can be estimated online by incrementally minimizing the variance of the gradient the resulting algorithm called adaptive weighted sgd \( aw sgd \) maintains a set of parameters to optimize , as well as a set of parameters to sample learning examples we show that aw sgd yields faster convergence in three different applications \( i \) image classification with deep features , where the sampling of images depends on their labels , \( ii \) matrix factorization , where rows and columns are not sampled uniformly , and \( iii \) reinforcement learning , where the optimized and explore policies are estimated at the same time"
cs.IT|stat.ML,"the performance of a modulation classifier is highly sensitive to channel signal to noise ratio \( snr \) in this paper , we focus on amplitude phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood \( ml \) approach in order to alleviate the computational complexity associated with ml estimation , we adopt the expectation maximization \( em \) algorithm due to snr diversity , the proposed multi radio framework provides robustness to channel snr numerical results show the superiority of the proposed approach with respect to single radio approaches as well as to modulation classifiers using moments based estimators"
cs.DM,"in this paper we introduce , and characterize , a class of graph parameters obtained from tensor invariants of the symplectic group these parameters are similar to partition functions of vertex models , as introduced by de la harpe and jones , p de la harpe , v f r jones , graph invariants related to statistical mechanical models examples and problems , sl journal of combinatorial theory , series b bf 57 \( 1993 \) 207 227 yet they give a completely different class of graph invariants we moreover show that certain evaluations of the cycle partition polynomial give examples of graph parameters that can be obtained this way"
cs.LG|stat.ML,"this paper introduces the hypervolume maximization with a single solution as an alternative to the mean loss minimization the relationship between the two problems is proved through bounds on the cost function when an optimal solution to one of the problems is evaluated on the other , with a hyperparameter to control the similarity between the two problems this same hyperparameter allows higher weight to be placed on samples with higher loss when computing the hypervolume 's gradient , whose normalized version can range from the mean loss to the max loss an experiment on mnist with a neural network is used to validate the theory developed , showing that the hypervolume maximization can behave similarly to the mean loss minimization and can also provide better performance , resulting on a 20 reduction of the classification error on the test set"
cs.LG|stat.ML,"in this paper , we first demonstrate that b bit minwise hashing , whose estimators are positive definite kernels , can be naturally integrated with learning algorithms such as svm and logistic regression we adopt a simple scheme to transform the nonlinear \( resemblance \) kernel into linear \( inner product \) kernel and hence large scale problems can be solved extremely efficiently our method provides a simple effective solution to large scale learning in massive and extremely high dimensional datasets , especially when data do not fit in memory we then compare b bit minwise hashing with the vowpal wabbit \( vw \) algorithm \( which is related the count min \( cm \) sketch \) interestingly , vw has the same variances as random projections our theoretical and empirical comparisons illustrate that usually b bit minwise hashing is significantly more accurate \( at the same storage \) than vw \( and random projections \) in binary data furthermore , b bit minwise hashing can be combined with vw to achieve further improvements in terms of training speed , especially when b is large"
cs.LG,"in context of document classification , where in a corpus of documents their label tags are readily known , an opportunity lies in utilizing label information to learn document representation spaces with better discriminative properties to this end , in this paper application of a variational bayesian supervised nonnegative matrix factorization \( supervised vbnmf \) with label driven sparsity structure of coefficients is proposed for learning of discriminative nonsubtractive latent semantic components occuring in tf idf document representations constraints are such that the components pursued are made to be frequently occuring in a small set of labels only , making it possible to yield document representations with distinctive label specific sparse activation patterns a simple measure of quality of this kind of sparsity structure , dubbed inter label sparsity , is introduced and experimentally brought into tight connection with classification performance representing a great practical convenience , inter label sparsity is shown to be easily controlled in supervised vbnmf by a single parameter"
cs.DM,"we study the rigidity of body and cad frameworks which capture the majority of the geometric constraints used in 3d mechanical engineering cad software we present a combinatorial characterization of the generic minimal rigidity of a subset of body and cad frameworks in which we treat 20 of the 21 body and cad constraints , omitting only point point coincidences while the handful of classical combinatorial characterizations of rigidity focus on distance constraints between points , this is the first result simultaneously addressing coincidence , angular , and distance constraints our result is stated in terms of the partitioning of a graph into edge disjoint spanning trees this combinatorial approach provides the theoretical basis for the development of deterministic algorithms \( that will not depend on numerical methods \) for analyzing the rigidity of body and cad frameworks"
cs.LG|stat.ML,"we propose an optimization method for minimizing the finite sums of smooth convex functions our method incorporates an accelerated gradient descent \( agd \) and a stochastic variance reduction gradient \( svrg \) in a mini batch setting unlike svrg , our method can be directly applied to non strongly and strongly convex problems we show that our method achieves a lower overall complexity than the recently proposed methods that supports non strongly convex problems moreover , this method has a fast rate of convergence for strongly convex problems our experiments show the effectiveness of our method"
cs.IT|cs.LG,"this paper introduces a novel message passing \( mp \) framework for the collaborative filtering \( cf \) problem associated with recommender systems we model the movie rating prediction problem popularized by the netflix prize , using a probabilistic factor graph model and study the model by deriving generalization error bounds in terms of the training error based on the model , we develop a new mp algorithm , termed imp , for learning the model to show superiority of the imp algorithm , we compare it with the closely related expectation maximization \( em \) based algorithm and a number of other matrix completion algorithms our simulation results on netflix data show that , while the methods perform similarly with large amounts of data , the imp algorithm is superior for small amounts of data this improves the cold start problem of the cf systems in practice another advantage of the imp algorithm is that it can be analyzed using the technique of density evolution \( de \) that was originally developed for mp decoding of error correcting codes"
cs.LG,"boosting has attracted much research attention in the past decade the success of boosting algorithms may be interpreted in terms of the margin theory recently it has been shown that generalization error of classifiers can be obtained by explicitly taking the margin distribution of the training data into account most of the current boosting algorithms in practice usually optimizes a convex loss function and do not make use of the margin distribution in this work we design a new boosting algorithm , termed margin distribution boosting \( mdboost \) , which directly maximizes the average margin and minimizes the margin variance simultaneously this way the margin distribution is optimized a totally corrective optimization algorithm based on column generation is proposed to implement mdboost experiments on uci datasets show that mdboost outperforms adaboost and lpboost in most cases"
cs.IT|stat.ML,"multi cell cooperative processing with limited backhaul traffic is studied for cellular uplinks aiming at reduced backhaul overhead , a sparsity regularized multi cell receive filter design problem is formulated both unstructured distributed cooperation as well as clustered cooperation , in which base station groups are formed for tight cooperation , are considered dynamic clustered cooperation , where the sparse equalizer and the cooperation clusters are jointly determined , is solved via alternating minimization based on spectral clustering and group sparse regression furthermore , decentralized implementations of both unstructured and clustered cooperation schemes are developed for scalability , robustness and computational efficiency extensive numerical tests verify the efficacy of the proposed methods"
cs.DM,"we prove that there are 24 4 critical p 6 free graphs , and give the complete list we remark that , if h is connected and not a subgraph of p 6 , there are infinitely many 4 critical h free graphs our result answers questions of golovach et al and seymour"
cs.LG,"this paper presents a kind of effective synchronization clustering method based on a linearized version of vicsek model this method can be represented by an effective synchronization clustering algorithm \( esync \) , an improved version of esync algorithm \( iesync \) , a shrinking synchronization clustering algorithm based on another linear vicsek model \( ssync \) , and an effective multi level synchronization clustering algorithm \( msync \) after some analysis and comparisions , we find that esync algorithm based on the linearized version of the vicsek model has better synchronization effect than sync algorithm based on an extensive kuramoto model and a similar synchronization clustering algorithm based on the original vicsek model by simulated experiments of some artificial data sets , we observe that esync algorithm , iesync algorithm , and ssync algorithm can get better synchronization effect although it needs less iterative times and less time than sync algorithm in some simulations , we also observe that iesync algorithm and ssync algorithm can get some improvements in time cost than esync algorithm at last , it gives some research expectations to popularize this algorithm"
cs.LG,"we consider the problem of detecting robotic grasps in an rgb d view of a scene containing objects in this work , we apply a deep learning approach to solve this problem , which avoids time consuming hand design of features this presents two main challenges first , we need to evaluate a huge number of candidate grasps in order to make detection fast , as well as robust , we present a two step cascaded structure with two deep networks , where the top detections from the first are re evaluated by the second the first network has fewer features , is faster to run , and can effectively prune out unlikely candidate grasps the second , with more features , is slower but has to run only on the top few detections second , we need to handle multimodal inputs well , for which we present a method to apply structured regularization on the weights based on multimodal group regularization we demonstrate that our method outperforms the previous state of the art methods in robotic grasp detection , and can be used to successfully execute grasps on two different robotic platforms"
cs.LG,"support vector data description \( svdd \) is a machine learning technique used for single class classification and outlier detection svdd based k chart was first introduced by sun and tsung for monitoring multivariate processes when underlying distribution of process parameters or quality characteristics depart from normality the method first trains a svdd model on data obtained from stable or in control operations of the process to obtain a threshold r \( 2 \) and kernel center a for each new observation , its kernel distance from the kernel center a is calculated the kernel distance is compared against the threshold r \( 2 \) to determine if the observation is within the control limits the non parametric k chart provides an attractive alternative to the traditional control charts such as the hotelling 's t \( 2 \) charts when distribution of the underlying multivariate data is either non normal or is unknown but there are challenges when k chart is deployed in practice the k chart requires calculating kernel distance of each new observation but there are no guidelines on how to interpret the kernel distance plot and infer about shifts in process mean or changes in process variation this limits the application of k charts in big data applications such as equipment health monitoring , where observations are generated at a very high frequency in this scenario , the analyst using the k chart is inundated with kernel distance results at a very high frequency , generally without any recourse for detecting presence of any assignable causes of variation we propose a new svdd based control chart , called as k \( t \) chart , which addresses challenges encountered when using k chart for big data applications the k \( t \) charts can be used to simultaneously track process variation and central tendency we illustrate the successful use of k \( t \) chart using the tennessee eastman process data"
cs.LG,"many factors can affect the predictability of public bus services such as traffic , weather and local events other aspects , such as day of week or hour of day , may influence bus travel times as well , either directly or in conjunction with other variables however , the exact nature of such relationships between travel times and predictor variables is , in most situations , not known in this paper we develop a framework that allows for flexible modeling of bus travel times through the use of additive models in particular , we model travel times as a sum of linear as well as nonlinear terms that are modeled as smooth functions of predictor variables the proposed class of models provides a principled statistical framework that is highly flexible in terms of model building the experimental results demonstrate uniformly superior performance of our best model as compared to previous prediction methods when applied to a very large gps data set obtained from buses operating in the city of rio de janeiro"
cs.LG|stat.ML,"complex valued signals are used in the modeling of many systems in engineering and science , hence being of fundamental interest often , random complex valued signals are considered to be proper a proper complex random variable or process is uncorrelated with its complex conjugate this assumption is a good model of the underlying physics in many problems , and simplifies the computations while linear processing and neural networks have been widely studied for these signals , the development of complex valued nonlinear kernel approaches remains an open problem in this paper we propose gaussian processes for regression as a framework to develop 1 \) a solution for proper complex valued kernel regression and 2 \) the design of the reproducing kernel for complex valued inputs , using the convolutional approach for cross covariances in this design we pay attention to preserve , in the complex domain , the measure of similarity between near inputs the hyperparameters of the kernel are learned maximizing the marginal likelihood using wirtinger derivatives besides , the approach is connected to the multiple output learning scenario in the experiments included , we first solve a proper complex gaussian process where the cross covariance does not cancel , a challenging scenario when dealing with proper complex signals then we successfully use these novel results to solve some problems previously proposed in the literature as benchmarks , reporting a remarkable improvement in the estimation error"
cs.LG|stat.ML,"frank wolfe algorithms have recently regained the attention of the machine learning community their solid theoretical properties and sparsity guarantees make them a suitable choice for a wide range of problems in this field in addition , several variants of the basic procedure exist that improve its theoretical properties and practical performance in this paper , we investigate the application of some of these techniques to machine learning , focusing in particular on a parallel tangent \( partan \) variant of the fw algorithm that has not been previously suggested or studied for this type of problems we provide experiments both in a standard setting and using a stochastic speed up technique , showing that the considered algorithms obtain promising results on several medium and large scale benchmark datasets for svm classification"
cs.LG|stat.ML,"we study the theoretical advantages of active learning over passive learning specifically , we prove that , in noise free classifier learning for vc classes , any passive learning algorithm can be transformed into an active learning algorithm with asymptotically strictly superior label complexity for all nontrivial target functions and distributions we further provide a general characterization of the magnitudes of these improvements in terms of a novel generalization of the disagreement coefficient we also extend these results to active learning in the presence of label noise , and find that even under broad classes of noise distributions , we can typically guarantee strict improvements over the known results for passive learning"
cs.LG|stat.ML,"in this work , we introduce a deep structured conditional random field \( ds crf \) model for the purpose of state based object silhouette tracking the proposed ds crf model consists of a series of state layers , where each state layer spatially characterizes the object silhouette at a particular point in time the interactions between adjacent state layers are established by inter layer connectivity dynamically determined based on inter frame optical flow by incorporate both spatial and temporal context in a dynamic fashion within such a deep structured probabilistic graphical model , the proposed ds crf model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time , as well as under different situations such as occlusion and multiple targets within the scene experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed ds crf approach provides strong object silhouette tracking performance when compared to baseline methods such as mean shift tracking , as well as state of the art methods such as context tracking and boosted particle filtering"
cs.LG|stat.ML,"given a convex optimization problem and its dual , there are many possible first order algorithms in this paper , we show the equivalence between mirror descent algorithms and algorithms generalizing the conditional gradient method this is done through convex duality , and implies notably that for certain problems , such as for supervised machine learning problems with non smooth losses or problems regularized by non smooth regularizers , the primal subgradient method and the dual conditional gradient method are formally equivalent the dual interpretation leads to a form of line search for mirror descent , as well as guarantees of convergence for primal dual certificates"
cs.LG,"many recent advances in computer vision have demonstrated the impressive power of dense and nonsubmodular energy functions in solving visual labeling problems however , minimizing such energies is challenging none of existing techniques \( such as s t graph cut , qpbo , bp and trw s \) can individually do this well in this paper , we present an efficient method , namely essp , to optimize binary mrfs with arbitrary pairwise potentials , which could be nonsubmodular and with dense connectivity we also provide a comparative study of our approach and several recent promising methods from our study , we make some reasonable recommendations of combining existing methods that perform the best in different situations for this challenging problem experimental results validate that for dense and nonsubmodular energy functions , the proposed approach can usually obtain lower energies than the best combination of other techniques using comparably reasonable time"
cs.LG|stat.ML,"bayesian optimization techniques have been successfully applied to robotics , planning , sensor placement , recommendation , advertising , intelligent user interfaces and automatic algorithm configuration despite these successes , the approach is restricted to problems of moderate dimension , and several workshops on bayesian optimization have identified its scaling to high dimensions as one of the holy grails of the field in this paper , we introduce a novel random embedding idea to attack this problem the resulting random embedding bayesian optimization \( rembo \) algorithm is very simple , has important invariance properties , and applies to domains with both categorical and continuous variables we present a thorough theoretical analysis of rembo empirical results confirm that rembo can effectively solve problems with billions of dimensions , provided the intrinsic dimensionality is low they also show that rembo achieves state of the art performance in optimizing the 47 discrete parameters of a popular mixed integer linear programming solver"
cs.AI|cs.LG|stat.ML,"recently , there has been significant progress in understanding reinforcement learning in discounted infinite horizon markov decision processes \( mdps \) by deriving tight sample complexity bounds however , in many real world applications , an interactive learning agent operates for a fixed or bounded period of time , for example tutoring students for exams or handling customer service requests such scenarios can often be better treated as episodic fixed horizon mdps , for which only looser bounds on the sample complexity exist a natural notion of sample complexity in this setting is the number of episodes required to guarantee a certain performance with high probability \( pac guarantee \) in this paper , we derive an upper pac bound tilde o \( frac mathcal s 2 mathcal a h 2 epsilon 2 ln frac 1 delta \) and a lower pac bound tilde omega \( frac mathcal s mathcal a h 2 epsilon 2 ln frac 1 delta c \) that match up to log terms and an additional linear dependency on the number of states mathcal s the lower bound is the first of its kind for this setting our upper bound leverages bernstein 's inequality to improve on previous bounds for episodic finite horizon mdps which have a time horizon dependency of at least h 3"
cs.IT|cs.LG,"minimum description length \( mdl \) is an important principle for induction and prediction , with strong relations to optimal bayesian learning this paper deals with learning non i i d processes by means of two part mdl , where the underlying model class is countable we consider the online learning framework , i e observations come in one by one , and the predictor is allowed to update his state of mind after each time step we identify two ways of predicting by mdl for this setup , namely a static and a dynamic one \( a third variant , hybrid mdl , will turn out inferior \) we will prove that under the only assumption that the data is generated by a distribution contained in the model class , the mdl predictions converge to the true values almost surely this is accomplished by proving finite bounds on the quadratic , the hellinger , and the kullback leibler loss of the mdl learner , which are however exponentially worse than for bayesian prediction we demonstrate that these bounds are sharp , even for model classes containing only bernoulli distributions we show how these bounds imply regret bounds for arbitrary loss functions our results apply to a wide range of setups , namely sequence prediction , pattern classification , regression , and universal induction in the sense of algorithmic information theory among others"
cs.IT|cs.SI,"this paper studies the effect of boundary value conditions on consensus networks consider a network where some nodes keep their estimates constant while other nodes average their estimates with that of their neighbors we analyze such networks and show that in contrast to standard consensus networks , the network estimate converges to a general harmonic function on the graph furthermore , the final value depends only on the value at the boundary nodes this has important implications in consensus networks for example , we show that consensus networks are extremely sensitive to the existence of a single malicious node or consistent errors in a single node we also discuss applications of this result in social and sensor networks we investigate the existence of boundary nodes in human social networks via an experimental study involving human subjects finally , the paper is concluded with the numerical studies of the boundary value problems in consensus networks"
cs.LG|stat.ML,we propose a relaxation based approximate inference algorithm that samples near map configurations of a binary pairwise markov random field we experiment on map inference tasks in several restricted boltzmann machines we also use our underlying sampler to estimate the log partition function of restricted boltzmann machines and compare against other sampling based methods
cs.DS|cs.LG|stat.ML,"this work provides improved guarantees for streaming principle component analysis \( pca \) given a 1 , ldots , a n in mathbb r d times d sampled independently from distributions satisfying mathbb e a i sigma for sigma succeq mathbf 0 , this work provides an o \( d \) space linear time single pass streaming algorithm for estimating the top eigenvector of sigma the algorithm nearly matches \( and in certain cases improves upon \) the accuracy obtained by the standard batch method that computes top eigenvector of the empirical covariance frac 1 n sum i in n a i as analyzed by the matrix bernstein inequality moreover , to achieve constant accuracy , our algorithm improves upon the best previous known sample complexities of streaming algorithms by either a multiplicative factor of o \( d \) or 1 mathrm gap where mathrm gap is the relative distance between the top two eigenvalues of sigma these results are achieved through a novel analysis of the classic oja 's algorithm , one of the oldest and most popular algorithms for streaming pca in particular , this work shows that simply picking a random initial point w 0 and applying the update rule w i 1 w i eta i a i w i suffices to accurately estimate the top eigenvector , with a suitable choice of eta i we believe our result sheds light on how to efficiently perform streaming pca both in theory and in practice and we hope that our analysis may serve as the basis for analyzing many variants and extensions of streaming pca"
cs.DS|cs.IT,"we show that the popular water filling algorithm for maximizing the mutual information in parallel gaussian channels is sub modular the sub modularity of water filling algorithm is then used to derive online basestation allocation algorithms , where mobile users are assigned to one of many possible basestations immediately and irrevocably upon arrival without knowing the future user information the goal of the allocation is to maximize the sum rate of the system under power allocation at each basestation we present online algorithms with competitive ratio of at most 2 when compared to offline algorithms that have knowledge of all future user arrivals"
cs.LG,"we develop a new method for visualizing and refining the invariances of learned representations given two reference images \( typically , differing by some transformation \) , we synthesize a sequence of images lying on a path between them that is of minimal length in the space of a representation \( a representational geodesic \) if the transformation relating the two reference images is an invariant of the representation , this sequence should follow the gradual evolution of this transformation we use this method to assess the invariances of state of the art image classification networks and find that surprisingly , they do not exhibit invariance to basic parametric transformations of translation , rotation , and dilation our method also suggests a remedy for these failures , and following this prescription , we show that the modified representation exhibits a high degree of invariance for a range of geometric image transformations"
cs.AI|cs.LG|stat.ML,"determinantal point processes \( dpps \) , which arise in random matrix theory and quantum physics , are natural models for subset selection problems where diversity is preferred among many remarkable properties , dpps offer tractable algorithms for exact inference , including computing marginal probabilities and sampling however , an important open question has been how to learn a dpp from labeled training data in this paper we propose a natural feature based parameterization of conditional dpps , and show how it leads to a convex and efficient learning formulation we analyze the relationship between our model and binary markov random fields with repulsive potentials , which are qualitatively similar but computationally intractable finally , we apply our approach to the task of extractive summarization , where the goal is to choose a small subset of sentences conveying the most important information from a set of documents in this task there is a fundamental tradeoff between sentences that are highly relevant to the collection as a whole , and sentences that are diverse and not repetitive our parameterization allows us to naturally balance these two characteristics we evaluate our system on data from the duc 2003 04 multi document summarization task , achieving state of the art results"
cs.IT|cs.LG,"multi class classification is mandatory for real world problems and one of promising techniques for multi class classification is error correcting output code we propose a method for constructing the error correcting output code to obtain the suitable combination of positive and negative classes encoded to represent binary classifiers the minimum weight perfect matching algorithm is applied to find the optimal pairs of subset of classes by using the generalization performance as a weighting criterion based on our method , each subset of classes with positive and negative labels is appropriately combined for learning the binary classifiers experimental results show that our technique gives significantly higher performance compared to traditional methods including the dense random code and the sparse random code both in terms of accuracy and classification times moreover , our method requires significantly smaller number of binary classifiers while maintaining accuracy compared to the one versus one"
cs.LG|stat.ML,"clever sampling methods can be used to improve the handling of big data and increase its usefulness the subject of this study is remote sensing , specifically airborne laser scanning point clouds representing different classes of ground cover the aim is to derive a supervised learning model for the classification using carts in order to measure the effect of different sampling methods on the classification accuracy , various experiments with varying types of sampling methods , sample sizes , and accuracy metrics have been designed numerical results for a subset of a large surveying project covering the lower rhine area in germany are shown general conclusions regarding sampling design are drawn and presented"
stat.ML,"brain computer interfaces \( bcis \) have been gaining momentum in making human computer interaction more natural , especially for people with neuro muscular disabilities among the existing solutions the systems relying on electroencephalograms \( eeg \) occupy the most prominent place due to their non invasiveness however , the process of translating eeg signals into computer commands is far from trivial , since it requires the optimization of many different parameters that need to be tuned jointly in this report , we focus on the category of eeg based bcis that rely on steady state visual evoked potentials \( ssveps \) and perform a comparative evaluation of the most promising algorithms existing in the literature more specifically , we define a set of algorithms for each of the various different parameters composing a bci system \( i e filtering , artifact removal , feature extraction , feature selection and classification \) and study each parameter independently by keeping all other parameters fixed the results obtained from this evaluation process are provided together with a dataset consisting of the 256 channel , eeg signals of 11 subjects , as well as a processing toolbox for reproducing the results and supporting further experimentation in this way , we manage to make available for the community a state of the art baseline for ssvep based bcis that can be used as a basis for introducing novel methods and approaches"
cs.IT|cs.LG|stat.ML,"statistical inference and information processing of high dimensional data often require efficient and accurate estimation of their second order statistics with rapidly changing data , limited processing power and storage at the acquisition devices , it is desirable to extract the covariance structure from a single pass over the data and a small number of stored measurements in this paper , we explore a quadratic \( or rank one \) measurement model which imposes minimal memory requirements and low computational complexity during the sampling process , and is shown to be optimal in preserving various low dimensional covariance structures specifically , four popular structural assumptions of covariance matrices , namely low rank , toeplitz low rank , sparsity , jointly rank one and sparse structure , are investigated , while recovery is achieved via convex relaxation paradigms for the respective structure the proposed quadratic sampling framework has a variety of potential applications including streaming data processing , high frequency wireless communication , phase space tomography and phase retrieval in optics , and non coherent subspace detection our method admits universally accurate covariance estimation in the absence of noise , as soon as the number of measurements exceeds the information theoretic limits we also demonstrate the robustness of this approach against noise and imperfect structural assumptions our analysis is established upon a novel notion called the mixed norm restricted isometry property \( rip ell 2 ell 1 \) , as well as the conventional rip ell 2 ell 2 for near isotropic and bounded measurements in addition , our results improve upon the best known phase retrieval \( including both dense and sparse signals \) guarantees using phaselift with a significantly simpler approach"
cs.LG|stat.ML,"exponential models of distributions are widely used in machine learning for classiffication and modelling it is well known that they can be interpreted as maximum entropy models under empirical expectation constraints in this work , we argue that for classiffication tasks , mutual information is a more suitable information theoretic measure to be optimized we show how the principle of minimum mutual information generalizes that of maximum entropy , and provides a comprehensive framework for building discriminative classiffiers a game theoretic interpretation of our approach is then given , and several generalization bounds provided we present iterative algorithms for solving the minimum information problem and its convex dual , and demonstrate their performance on various classiffication tasks the results show that minimum information classiffiers outperform the corresponding maximum entropy models"
cs.IT|stat.ML,"the theory of compressed sensing asserts that an unknown signal x in mathbb r p can be accurately recovered from an underdetermined set of n linear measurements with n ll p , provided that x is sufficiently sparse however , in applications , the degree of sparsity x 0 is typically unknown , and the problem of directly estimating x 0 has been a longstanding gap between theory and practice a closely related issue is that x 0 is a highly idealized measure of sparsity , and for real signals with entries not exactly equal to 0 , the value x 0 p is not a useful description of compressibility in our previous conference paper that examined these problems , lopes 2013 , we considered an alternative measure of soft sparsity , x 1 2 x 2 2 , and designed a procedure to estimate x 1 2 x 2 2 that does not rely on sparsity assumptions the present work offers a new deconvolution based method for estimating unknown sparsity , which has wider applicability and sharper theoretical guarantees whereas our earlier work was limited to estimating the quantity x 1 2 x 2 2 , the current paper introduces a family of entropy based sparsity measures s q \( x \) big \( frac x q x 1 big \) frac q 1 q parameterized by q in 0 , infty two other main advantages of the new approach are that it handles measurement noise with infinite variance , and that it yields confidence intervals for s q \( x \) with asymptotically exact coverage probability \( whereas our previous intervals were conservative \) in addition to confidence intervals , we also analyze several other aspects of our proposed estimator hat s q \( x \) and show that randomized measurements are an essential aspect of our procedure"
cs.AI|cs.LG,"feature based format is the main data representation format used by machine learning algorithms when the features do not properly describe the initial data , performance starts to degrade some algorithms address this problem by internally changing the representation space , but the newly constructed features are rarely comprehensible we seek to construct , in an unsupervised way , new features that are more appropriate for describing a given dataset and , at the same time , comprehensible for a human user we propose two algorithms that construct the new features as conjunctions of the initial primitive features or their negations the generated feature sets have reduced correlations between features and succeed in catching some of the hidden relations between individuals in a dataset for example , a feature like sky wedge neg building wedge panorama would be true for non urban images and is more informative than simple features expressing the presence or the absence of an object the notion of pareto optimality is used to evaluate feature sets and to obtain a balance between total correlation and the complexity of the resulted feature set statistical hypothesis testing is used in order to automatically determine the values of the parameters used for constructing a data dependent feature set we experimentally show that our approaches achieve the construction of informative feature sets for multiple datasets"
cs.IT|cs.LG,"kolmogorov smirnov \( k s \) test a non parametric method to measure the goodness of fit , is applied for automatic modulation classification \( amc \) in this paper the basic procedure involves computing the empirical cumulative distribution function \( ecdf \) of some decision statistic derived from the received signal , and comparing it with the cdfs of the signal under each candidate modulation format the k s based modulation classifier is first developed for awgn channel , then it is applied to ofdm sdma systems to cancel multiuser interference regarding the complexity issue of k s modulation classification , we propose a low complexity method based on the robustness of the k s classifier extensive simulation results demonstrate that compared with the traditional cumulant based classifiers , the proposed k s classifier offers superior classification performance and requires less number of signal samples \( thus is fast \)"
cs.LG,"an important class of problems involves training deep neural networks with sparse prediction targets of very high dimension d these occur naturally in e g neural language models or the learning of word embeddings , often posed as predicting the probability of next words among a vocabulary of size d \( e g 200 000 \) computing the equally large , but typically non sparse d dimensional output vector from a last hidden layer of reasonable dimension d \( e g 500 \) incurs a prohibitive o \( dd \) computational cost for each example , as does updating the d x d output weight matrix and computing the gradient needed for backpropagation to previous layers while efficient handling of large sparse network inputs is trivial , this case of large sparse targets is not , and has thus so far been sidestepped with approximate alternatives such as hierarchical softmax or sampling based approximations during training in this work we develop an original algorithmic approach that , for a family of loss functions that includes squared error and spherical softmax , can compute the exact loss , gradient update for the output weights , and gradient for backpropagation , all in o \( d 2 \) per example instead of o \( dd \) , remarkably without ever computing the d dimensional output the proposed algorithm is expected to yield an actual speedup of at least d 4d , i e two orders of magnitude for typical sizes , for that critical part of the computations that often dominates the training time in this kind of network architecture"
cs.DM|cs.DS,"in this paper we show that the problem of identifying an edge \( i , j \) in a graph g such that there exists an optimal vertex cover s of g containing exactly one of the nodes i and j is np hard such an edge is called a weak edge we then develop a polynomial time approximation algorithm for the vertex cover problem with performance guarantee 2 frac 1 1 sigma , where sigma is an upper bound on a measure related to a weak edge of a graph further , we discuss a new relaxation of the vertex cover problem which is used in our approximation algorithm to obtain smaller values of sigma we also obtain linear programming representations of the vertex cover problem for special graphs our results provide new insights into the approximability of the vertex cover problem a long standing open problem"
cs.IT|cs.SI,"new phase transition phenomena have recently been discovered for the stochastic block model , for the special case of two non overlapping symmetric communities this paper investigates whether a general phenomenon takes place for multiple communities without imposing symmetry in the general stochastic block model text sbm \( n , p , q \) , n vertices are split into k communities of relative size p i , and vertices in community i and j connect independently with probability q i , j this paper investigates the partial and exact recovery of communities in the general sbm \( in the constant and logarithmic degree regimes \) , and uses the generality of the results to tackle overlapping communities it is shown that exact recovery in text sbm \( n , p , ln \( n \) q n \) is solvable if and only if min i j dd \( theta i , theta j \) geq 1 , where theta i is the i th column of diag \( p \) q and dd is a generalization of the chernoff and hellinger divergence defined by dd \( x , y \) max t in 0 , 1 sum i in k \( tx i \( 1 t \) y i x i t y i 1 t \) this gives an operational meaning to dd , related to the operational meaning of the kl divergence in the channel coding theorem moreover , an algorithm is developed that runs in quasi linear time and recovers the communities in the general sbm all the way down to the optimal threshold , showing that exact recovery is efficiently solvable whenever it is information theoretically solvable \( the entries of q are assumed to be nonzero \) this is the first algorithm with such performance guarantees for multiple communities to obtain this algorithm , a first stage algorithm is developed that recovers communities in the constant degree regime with an accuracy guarantee that can be made arbitrarily close to 1 when a prescribed signal to noise ratio \( defined in term of the spectrum of diag \( p \) q \) tends to infinity"
cs.LG,"a lot of time is spent searching for the most performing data mining algorithms applied in clinical diagnosis the study set out to identify the most performing predictive data mining algorithms applied in the diagnosis of erythemato squamous diseases the study used naive bayes , multilayer perceptron and j48 decision tree induction to build predictive data mining models on 366 instances of erythemato squamous diseases datasets also , 10 fold cross validation and sets of performance metrics were used to evaluate the baseline predictive performance of the classifiers the comparative analysis shows that the naive bayes performed best with accuracy of 97 4 , multilayer perceptron came out second with accuracy of 96 6 , and j48 came out the worst with accuracy of 93 5 the evaluation of these classifiers on clinical datasets , gave an insight into the predictive ability of different data mining algorithms applicable in clinical diagnosis especially in the diagnosis of erythemato squamous diseases"
cs.LG|stat.ML,"we propose a novel approach for pixel classification in hyperspectral images , leveraging on both the spatial and spectral information in the data the introduced method relies on a recently proposed framework for learning on distributions by representing them with mean elements in reproducing kernel hilbert spaces \( rkhs \) and formulating a classification algorithm therein in particular , we associate each pixel to an empirical distribution of its neighbouring pixels , a judicious representation of which in an rkhs , in conjunction with the spectral information contained in the pixel itself , give a new explicit set of features that can be fed into a suite of standard classification techniques we opt for a well established framework of support vector machines \( svm \) furthermore , the computational complexity is reduced via random fourier features formalism we study the consistency and the convergence rates of the proposed method and the experiments demonstrate strong performance on hyperspectral data with gains in comparison to the state of the art results"
cs.LG|stat.ML,"we face network data from various sources , such as protein interactions and online social networks a critical problem is to model network interactions and identify latent groups of network nodes this problem is challenging due to many reasons for example , the network nodes are interdependent instead of independent of each other , and the data are known to be very noisy \( e g , missing edges \) to address these challenges , we propose a new relational model for network data , sparse matrix variate gaussian process blockmodel \( smgb \) our model generalizes popular bilinear generative models and captures nonlinear network interactions using a matrix variate gaussian process with latent membership variables we also assign sparse prior distributions on the latent membership variables to learn sparse group assignments for individual network nodes to estimate the latent variables efficiently from data , we develop an efficient variational expectation maximization method we compared our approaches with several state of the art network models on both synthetic and real world network datasets experimental results demonstrate smgbs outperform the alternative approaches in terms of discovering latent classes or predicting unknown interactions"
cs.IT|cs.LG,"a sensing policy for the restless multi armed bandit problem with stationary but unknown reward distributions is proposed the work is presented in the context of cognitive radios in which the bandit problem arises when deciding which parts of the spectrum to sense and exploit it is shown that the proposed policy attains asymptotically logarithmic weak regret rate when the rewards are bounded independent and identically distributed or finite state markovian simulation results verifying uniformly logarithmic weak regret are also presented the proposed policy is a centrally coordinated index policy , in which the index of a frequency band is comprised of a sample mean term and a confidence term the sample mean term promotes spectrum exploitation whereas the confidence term encourages exploration the confidence term is designed such that the time interval between consecutive sensing instances of any suboptimal band grows exponentially this exponential growth between suboptimal sensing time instances leads to logarithmically growing weak regret simulation results demonstrate that the proposed policy performs better than other similar methods in the literature"
cs.DM|cs.IT,"an interleaver is a critical component for the channel coding performance of turbo codes algebraic constructions are important because they admit analytical designs and simple , practical hardware implementation the spread factor of an interleaver is a common measure for turbo coding applications maximum spread interleavers are interleavers whose spread factors achieve the upper bound an infinite sequence of quadratic permutation polynomials over integer rings that generate maximum spread interleavers is presented new properties of permutation polynomial interleavers are investigated from an algebraic geometric perspective resulting in a new non linearity metric for interleavers a new interleaver metric that is a function of both the non linearity metric and the spread factor is proposed it is numerically demonstrated that the spread factor has a diminishing importance with the block length a table of good interleavers for a variety of interleaver lengths according to the new metric is listed extensive computer simulation results with impressive frame error rates confirm the efficacy of the new metric further , when tail biting constituent codes are used , the resulting turbo codes are quasi cyclic"
cs.LG,"i present the first algorithm for stochastic finite armed bandits that simultaneously enjoys order optimal problem dependent regret and worst case regret the algorithm is based on ucb , but with a carefully chosen confidence parameter that optimally balances the risk of failing confidence intervals against the cost of excessive optimism a brief empirical evaluation suggests the new algorithm is at least competitive with thompson sampling"
cs.AI|cs.DS,"one way of getting a better view of data is using frequent patterns in this paper frequent patterns are subsets that occur a minimal number of times in a stream of itemsets however , the discovery of frequent patterns in streams has always been problematic because streams are potentially endless it is in principle impossible to say if a pattern is often occurring or not furthermore the number of patterns can be huge and a good overview of the structure of the stream is lost quickly the proposed approach will use clustering to facilitate the analysis of the structure of the stream a clustering on the co occurrence of patterns will give the user an improved view on the structure of the stream some patterns might occur so much together that they should form a combined pattern in this way the patterns in the clustering will be the largest frequent patterns maximal frequent patterns our approach to decide if patterns occur often together will be based on a method of clustering when only the distance between pairs is known the number of maximal frequent patterns is much smaller and combined with clustering methods these patterns provide a good view on the structure of the stream"
cs.DM|cs.IT,we present a novel upper bound for the optimal index coding rate our bound uses a graph theoretic quantity called the local chromatic number we show how a good local coloring can be used to create a good index code the local coloring is used as an alignment guide to assign index coding vectors from a general position mds code we further show that a natural lp relaxation yields an even stronger index code our bounds provably outperform the state of the art on index coding but at most by a constant factor
cs.DS|cs.LG,"we investigate the direct sum problem in the context of differentially private pac learning what is the sample complexity of solving k learning tasks simultaneously under differential privacy , and how does this cost compare to that of solving k learning tasks without privacy \? in our setting , an individual example consists of a domain element x labeled by k unknown concepts \( c 1 , ldots , c k \) the goal of a multi learner is to output k hypotheses \( h 1 , ldots , h k \) that generalize the input examples without concern for privacy , the sample complexity needed to simultaneously learn k concepts is essentially the same as needed for learning a single concept under differential privacy , the basic strategy of learning each hypothesis independently yields sample complexity that grows polynomially with k for some concept classes , we give multi learners that require fewer samples than the basic strategy unfortunately , however , we also give lower bounds showing that even for very simple concept classes , the sample cost of private multi learning must grow polynomially in k"
cs.DM|cs.DS,"in this paper , we introduce maximum composition ordering problems the input is n real functions f 1 , dots , f n mathbb r to mathbb r and a constant c in mathbb r we consider two settings total and partial compositions the maximum total composition ordering problem is to compute a permutation sigma n to n which maximizes f sigma \( n \) circ f sigma \( n 1 \) circ dots circ f sigma \( 1 \) \( c \) , where n 1 , dots , n the maximum partial composition ordering problem is to compute a permutation sigma n to n and a nonnegative integer k \( 0 le k le n \) which maximize f sigma \( k \) circ f sigma \( k 1 \) circ dots circ f sigma \( 1 \) \( c \) we propose o \( n log n \) time algorithms for the maximum total and partial composition ordering problems for monotone linear functions f i , which generalize linear deterioration and shortening models for the time dependent scheduling problem we also show that the maximum partial composition ordering problem can be solved in polynomial time if f i is of form max a ix b i , c i for some constants a i , \( ge 0 \) , b i and c i we finally prove that there exists no constant factor approximation algorithm for the problems , even if f i 's are monotone , piecewise linear functions with at most two pieces , unless p np"
cs.LG|stat.ML,"the k nearest neighbor classification method \( k nnc \) is one of the simplest nonparametric classification methods the mutual k nn classification method \( m k nnc \) is a variant of k nnc based on mutual neighborship we propose another variant of k nnc , the symmetric k nn classification method \( s k nnc \) based on both mutual neighborship and one sided neighborship the performance of m k nnc and s k nnc depends on the parameter k as the one of k nnc does we propose the ways how m k nn and s k nn classification can be performed based on bayesian mutual and symmetric k nn regression methods with the selection schemes for the parameter k bayesian mutual and symmetric k nn regression methods are based on gaussian process models , and it turns out that they can do m k nn and s k nn classification with new encodings of target values \( class labels \) the simulation results show that the proposed methods are better than or comparable to k nnc , m k nnc and s k nnc with the parameter k selected by the leave one out cross validation method not only for an artificial data set but also for real world data sets"
cs.LG,"current digital currency schemes provide instantaneous exchange on precise commodity , in which precise means a buyer can possibly verify the function of the commodity without error however , imprecise commodities , e g statistical data , with error existing are abundant in digital world existing digital currency schemes do not offer a mechanism to help the buyer for payment decision on precision of commodity , which may lead the buyer to a dilemma between having to buy and being unconfident in this paper , we design a currency schemes idcs for imprecise digital commodity idcs completes a trade in three stages of handshake between a buyer and providers we present an idcs prototype implementation that assigns weights on the trustworthy of the providers , and calculates a confidence level for the buyer to decide the quality of a imprecise commodity in experiment , we characterize the performance of idcs prototype under varying impact factors"
cs.SI|stat.ML,"information spreads across social and technological networks , but often the network structures are hidden from us and we only observe the traces left by the diffusion processes , called cascades can we recover the hidden network structures from these observed cascades \? what kind of cascades and how many cascades do we need \? are there some network structures which are more difficult than others to recover \? can we design efficient inference algorithms with provable guarantees \? despite the increasing availability of cascade data and methods for inferring networks from these data , a thorough theoretical understanding of the above questions remains largely unexplored in the literature in this paper , we investigate the network structure inference problem for a general family of continuous time diffusion models using an l 1 regularized likelihood maximization framework we show that , as long as the cascade sampling process satisfies a natural incoherence condition , our framework can recover the correct network structure with high probability if we observe o \( d 3 log n \) cascades , where d is the maximum number of parents of a node and n is the total number of nodes moreover , we develop a simple and efficient soft thresholding inference algorithm , which we use to illustrate the consequences of our theoretical results , and show that our framework outperforms other alternatives in practice"
cs.LG,"in this document we propose a new improvement for boosting techniques as proposed in friedman '99 by the use of non convex cost functional the idea is to introduce a correlation term to better deal with forecasting of additive time series the problem is discussed in a theoretical way to prove the existence of minimizing sequence , and in a numerical way to propose a new argmin algorithm the model has been used to perform the touristic presence forecast for the winter season 1999 2000 in trentino \( italian alps \)"
cs.LG|stat.ML,"portfolio methods provide an effective , principled way of combining a collection of acquisition functions in the context of bayesian optimization we introduce a novel approach to this problem motivated by an information theoretic consideration our construction additionally provides an extension of thompson sampling to continuous domains with gp priors we show that our method outperforms a range of other portfolio methods on several synthetic problems , automated machine learning tasks , and a simulated control task finally , the effectiveness of even the random portfolio strategy suggests that portfolios in general should play a more pivotal role in bayesian optimization"
cs.AI|stat.ML,"expectation maximization \( em \) has recently been shown to be an efficient algorithm for learning finite state controllers \( fscs \) in large decentralized pomdps \( dec pomdps \) however , current methods use fixed size fscs and often converge to maxima that are far from optimal this paper considers a variable size fsc to represent the local policy of each agent these variable size fscs are constructed using a stick breaking prior , leading to a new framework called emph decentralized stick breaking policy representation \( dec sbpr \) this approach learns the controller parameters with a variational bayesian algorithm without having to assume that the dec pomdp model is available the performance of dec sbpr is demonstrated on several benchmark problems , showing that the algorithm scales to large problems while outperforming other state of the art methods"
cs.LG|stat.ML,"we present two alternative ways to apply pac bayesian analysis to sequences of dependent random variables the first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent bernoulli random variables this lemma provides an alternative tool to hoeffding azuma inequality to bound concentration of martingale values our second approach is based on integration of hoeffding azuma inequality with pac bayesian analysis we also introduce a way to apply pac bayesian analysis in situation of limited feedback we combine the new tools to derive pac bayesian generalization and regret bounds for the multiarmed bandit problem although our regret bound is not yet as tight as state of the art regret bounds based on other well established techniques , our results significantly expand the range of potential applications of pac bayesian analysis and introduce a new analysis tool to reinforcement learning and many other fields , where martingales and limited feedback are encountered"
cs.LG|stat.ML,"incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy however , as it is provided by humans , domain knowledge can only be specified with some degree of uncertainty we propose to explicitly model such uncertainty through probabilistic constraints over the parameter space in contrast to hard parameter constraints , our approach is effective also when the domain knowledge is inaccurate and generally results in superior modeling accuracy we focus on generative and conditional modeling where the parameters are assigned a dirichlet or gaussian prior and demonstrate the framework with experiments on both synthetic and real world data"
cs.LG|cs.SI,"this work is an attempt to discover hidden structural configurations in learning activity sequences of students in massive open online courses \( moocs \) leveraging combined representations of video clickstream interactions and forum activities , we seek to fundamentally understand traits that are predictive of decreasing engagement over time grounded in the interdisciplinary field of network science , we follow a graph based approach to successfully extract indicators of active and passive mooc participation that reflect persistence and regularity in the overall interaction footprint using these rich educational semantics , we focus on the problem of predicting student attrition , one of the major highlights of mooc literature in the recent years our results indicate an improvement over a baseline ngram based approach in capturing attrition intensifying features from the learning activities that mooc learners engage in implications for some compelling future research are discussed"
cs.LG|stat.ML,"microbial identification is a central issue in microbiology , in particular in the fields of infectious diseases diagnosis and industrial quality control the concept of species is tightly linked to the concept of biological and clinical classification where the proximity between species is generally measured in terms of evolutionary distances and or clinical phenotypes surprisingly , the information provided by this well known hierarchical structure is rarely used by machine learning based automatic microbial identification systems structured machine learning methods were recently proposed for taking into account the structure embedded in a hierarchy and using it as additional a priori information , and could therefore allow to improve microbial identification systems we test and compare several state of the art machine learning methods for microbial identification on a new matrix assisted laser desorption ionization time of flight mass spectrometry \( maldi tof ms \) dataset we include in the benchmark standard and structured methods , that leverage the knowledge of the underlying hierarchical structure in the learning process our results show that although some methods perform better than others , structured methods do not consistently perform better than their flat counterparts we postulate that this is partly due to the fact that standard methods already reach a high level of accuracy in this context , and that they mainly confuse species close to each other in the tree , a case where using the known hierarchy is not helpful"
cs.LG|stat.ML,"as machine learning is applied to an increasing variety of complex problems , which are defined by high dimensional and complex data sets , the necessity for task oriented feature learning grows in importance with the advancement of deep learning algorithms , various successful feature learning techniques have evolved in this paper , we present a novel way of learning discriminative features by training deep neural nets which have encoder or decoder type architecture similar to an autoencoder we demonstrate that our approach can learn discriminative features which can perform better at pattern classification tasks when the number of training samples is relatively small in size"
cs.DS|cs.IT,"learning a hidden hypergraph is a natural generalization of the classical group testing problem that consists in detecting unknown hypergraph h un h \( v , e \) by carrying out edge detecting tests in the given paper we focus our attention only on a specific family mathcal f \( t , s , ell \) of localized hypergraphs for which the total number of vertices v t , the number of edges e le s , s ll t , and the cardinality of any edge e le ell , ell ll t our goal is to identify all edges of h un in mathcal f \( t , s , ell \) by using the minimal number of tests we provide an adaptive algorithm that matches the information theory bound , i e , the total number of tests of the algorithm in the worst case is at most s ell log 2 t \( 1 o \( 1 \) \)"
cs.AI|cs.LG|stat.ML,"explicit high order feature interactions efficiently capture essential structural knowledge about the data of interest and have been used for constructing generative models we present a supervised discriminative high order parametric embedding \( hope \) approach to data visualization and compression compared to deep embedding models with complicated deep architectures , hope generates more effective high order feature mapping through an embarrassingly simple shallow model furthermore , two approaches to generating a small number of exemplars conveying high order interactions to represent large scale data sets are proposed these exemplars in combination with the feature mapping learned by hope effectively capture essential data variations moreover , through hope , these exemplars are employed to increase the computational efficiency of knn classification for fast information retrieval by thousands of times for classification in two dimensional embedding space on mnist and usps datasets , our shallow method hope with simple sigmoid transformations significantly outperforms state of the art supervised deep embedding models based on deep neural networks , and even achieved historically low test error rate of 0 65 in two dimensional space on mnist , which demonstrates the representational efficiency and power of supervised shallow models with high order feature interactions"
cs.DS|cs.IT,"we consider large scale sensor networks with n nodes , out of which k are in possession , \( e g , have sensed or collected in some other way \) k information packets in the scenarios in which network nodes are vulnerable because of , for example , limited energy or a hostile environment , it is desirable to disseminate the acquired information throughout the network so that each of the n nodes stores one \( possibly coded \) packet and the original k source packets can be recovered later in a computationally simple way from any \( 1 epsilon \) k nodes for some small epsilon 0 we developed two distributed algorithms for solving this problem based on simple random walks and fountain codes unlike all previously developed schemes , our solution is truly distributed , that is , nodes do not know n , k or connectivity in the network , except in their own neighborhoods , and they do not maintain any routing tables in the first algorithm , all the sensors have the knowledge of n and k in the second algorithm , each sensor estimates these parameters through the random walk dissemination we present analysis of the communication transmission and encoding decoding complexity of these two algorithms , and provide extensive simulation results as well"
cs.DS|cs.SI,"there are many indexes \( measures or metrics \) in social network analysis \( sna \) , like density , cohesion , etc in this paper , we define a new sna index called comfortability one among the lack of many factors , which affect the effectiveness of a group , is comfortability so , comfortability is one of the important attributes \( characteristics \) for a successful team work it is important to find a comfortable and successful team in any given social network in this paper , comfortable team , better comfortable team and highly comfortable team of a social network are defined based on textbf graph theoretic concepts and some of their structural properties are analyzed it is proved that forming better comfortable team or highly comfortable team in any connected network are np complete using the concepts of domination in graph theory next , we give a polynomial time approximation algorithm for finding such a highly comfortable team in any given network with performance ratio o \( ln delta \) , where delta is the maximum degree of a given network \( graph \) the time complexity of the algorithm is proved to be o \( n 3 \) , where n is the number of persons \( vertices \) in the network \( graph \) it is also proved that our algorithm has reasonably reduced the dispersion rate"
cs.LG|stat.ML,"we propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings we show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches , making it possible to perform hierarchical sparse coding on a corpus of billions of word tokens experiments on various benchmark tasks word similarity ranking , analogies , sentence completion , and sentiment analysis demonstrate that the method outperforms or is competitive with state of the art methods our word representations are available at url http www ark cs cmu edu dyogatam wordvecs"
cs.DM|cs.DS,"we present an efficient algorithm to compute permanents , mixed discriminants and hyperdeterminants of structured matrices and multidimensional arrays \( tensors \) we describe the sparsity structure of an array in terms of a graph , and we assume that its treewidth , denoted as omega , is small our algorithm requires o \( n 2 omega \) arithmetic operations to compute permanents , and o \( n 2 n 3 omega \) for mixed discriminants and hyperdeterminants we finally show that mixed volume computation continues to be hard under bounded treewidth assumptions"
cs.DS|cs.IT,"it is proved in this work that exhaustively determining bad patterns in arbitrary , finite low density parity check \( ldpc \) codes , including stopping sets for binary erasure channels \( becs \) and trapping sets \( also known as near codewords \) for general memoryless symmetric channels , is an np complete problem , and efficient algorithms are provided for codes of practical short lengths n 500 by exploiting the sparse connectivity of ldpc codes , the stopping sets of size 13 and the trapping sets of size 11 can be efficiently exhaustively determined for the first time , and the resulting exhaustive list is of great importance for code analysis and finite code optimization the featured tree based narrowing search distinguishes this algorithm from existing ones for which inexhaustive methods are employed one important byproduct is a pair of upper bounds on the bit error rate \( ber \) frame error rate \( fer \) iterative decoding performance of arbitrary codes over becs that can be evaluated for any value of the erasure probability , including both the waterfall and the error floor regions the tightness of these upper bounds and the exhaustion capability of the proposed algorithm are proved when combining an optimal leaf finding module with the tree based search these upper bounds also provide a worst case performance guarantee which is crucial to optimizing ldpc codes for extremely low error rate applications , e g , optical satellite communications extensive numerical experiments are conducted that include both randomly and algebraically constructed ldpc codes , the results of which demonstrate the superior efficiency of the exhaustion algorithm and its significant value for finite length code optimization"
cs.DS|cs.IT,"we present a theoretical framework for the compression of automata , which are widely used in speech processing and other natural language processing tasks the framework extends to graph compression similar to stationary ergodic processes , we formulate a probabilistic process of graph and automata generation that captures real world phenomena and provide a universal compression scheme lza for this probabilistic model further , we show that lza significantly outperforms other compression techniques such as gzip and the unix compress command for several synthetic and real data sets"
cs.LG|stat.ML,"bayesian reinforcement learning \( brl \) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over them this paper presents monte carlo brl \( mc brl \) , a simple and general approach to brl mc brl samples a priori a finite set of hypotheses for the model parameter values and forms a discrete partially observable markov decision process \( pomdp \) whose state space is a cross product of the state space for the reinforcement learning task and the sampled model parameter space the pomdp does not require conjugate distributions for belief representation , as earlier works do , and can be solved relatively easily with point based approximation algorithms mc brl naturally handles both fully and partially observable worlds theoretical and experimental results show that the discrete pomdp approximates the underlying brl task well with guaranteed performance"
cs.DM|cs.DS,"the steiner multicut problem asks , given an undirected graph g , terminals sets t1 , , tt subseteq v \( g \) of size at most p , and an integer k , whether there is a set s of at most k edges or nodes s t of each set ti at least one pair of terminals is in different connected components of g s this problem generalizes several graph cut problems , in particular the multicut problem \( the case p 2 \) , which is fixed parameter tractable for the parameter k marx and razgon , bousquet et al , stoc 2011 we provide a dichotomy of the parameterized complexity of steiner multicut that is , for any combination of k , t , p , and the treewidth tw \( g \) as constant , parameter , or unbounded , and for all versions of the problem \( edge deletion and node deletion with and without deletable terminals \) , we prove either that the problem is fixed parameter tractable or that the problem is hard \( w 1 hard or even \( para \) np complete \) we highlight that the edge deletion version of steiner multicut is fixed parameter tractable for the parameter k t on general graphs \( but has no polynomial kernel , even on trees \) we present two proofs one using the randomized contractions technique of chitnis et al , and one relying on new structural lemmas that decompose the steiner cut into important separators and minimal s t cuts in contrast , both node deletion versions of steiner multicut are w 1 hard for the parameter k t on general graphs all versions of steiner multicut are w 1 hard for the parameter k , even when p 3 and the graph is a tree plus one node hence , the results of marx and razgon , and bousquet et al do not generalize to steiner multicut since we allow k , t , p , and tw \( g \) to be any constants , our characterization includes a dichotomy for steiner multicut on trees \( for tw \( g \) 1 \) , and a polynomial time versus np hardness dichotomy \( by restricting k , t , p , tw \( g \) to constant or unbounded \)"
cs.LG|stat.ML,"with this positional paper we present a representation learning view on predicate invention the intention of this proposal is to bridge the relational and deep learning communities on the problem of predicate invention we propose a theory reconstruction approach , a formalism that extends autoencoder approach to representation learning to the relational settings our intention is to start a discussion to define a unifying framework for predicate invention and theory revision"
cs.DM|cs.DS,"interval routing is a space efficient method to realize a distributed routing function in this paper we show that every circular arc graph allows a shortest path strict 2 interval routing scheme , i e , by introducing a global order on the vertices and assigning at most two \( strict \) intervals in this order to the ends of every edge allows to depict a routing function that implies exclusively shortest paths since circular arc graphs do not allow shortest path 1 interval routing schemes in general , the result implies that the class of circular arc graphs has strict compactness 2 , which was a hitherto open question additionally , we show that the constructed 2 interval routing scheme is a 1 interval routing scheme with at most one additional interval assigned at each vertex and we an outline algorithm to calculate the routing scheme for circular arc graphs in o \( c n 2 \) time , where n is the number of vertices and c is the number of maximal cliques"
cs.AI|cs.DS,"the monotone duality problem is defined as follows given two monotone formulas f and g in iredundant dnf , decide whether f and g are dual this problem is the same as duality testing for hypergraphs , that is , checking whether a hypergraph h consists of precisely all minimal transversals of a simple hypergraph g by exploiting a recent problem decomposition method by boros and makino \( icalp 2009 \) , we show that duality testing for hypergraphs , and thus for monotone dnfs , is feasible in dspace log 2 n , i e , in quadratic logspace as the monotone duality problem is equivalent to a number of problems in the areas of databases , data mining , and knowledge discovery , the results presented here yield new complexity results for those problems , too for example , it follows from our results that whenever for a boolean valued relation \( whose attributes represent items \) , a number of maximal frequent itemsets and a number of minimal infrequent itemsets are known , then it can be decided in quadratic logspace whether there exist additional frequent or infrequent itemsets"
cs.LG|stat.ML,"the question of how to incorporate curvature information in stochastic approximation methods is challenging the direct application of classical quasi newton updating techniques for deterministic optimization leads to noisy curvature estimates that have harmful effects on the robustness of the iteration in this paper , we propose a stochastic quasi newton method that is efficient , robust and scalable it employs the classical bfgs update formula in its limited memory form , and is based on the observation that it is beneficial to collect curvature information pointwise , and at regular intervals , through \( sub sampled \) hessian vector products this technique differs from the classical approach that would compute differences of gradients , and where controlling the quality of the curvature estimates can be difficult we present numerical results on problems arising in machine learning that suggest that the proposed method shows much promise"
cs.DM|cs.SI,"in this work we analyze basic properties of random apollonian networks cite zhang , zhou , a popular stochastic model which generates planar graphs with power law properties specifically , let k be a constant and delta 1 geq delta 2 geq geq delta k be the degrees of the k highest degree vertices we prove that at time t , for any function f with f \( t \) rightarrow infty as t rightarrow infty , frac t 1 2 f \( t \) leq delta 1 leq f \( t \) t 1 2 and for i 2 , , k o \( 1 \) , frac t 1 2 f \( t \) leq delta i leq delta i 1 frac t 1 2 f \( t \) with high probability \( whp \) then , we show that the k largest eigenvalues of the adjacency matrix of this graph satisfy lambda k \( 1 pm o \( 1 \) \) delta k 1 2 whp furthermore , we prove a refined upper bound on the asymptotic growth of the diameter , i e , that whp the diameter d \( g t \) at time t satisfies d \( g t \) leq rho log t where frac 1 rho eta is the unique solution greater than 1 of the equation eta 1 log eta log 3 finally , we investigate other properties of the model"
cs.DM|stat.ML,"graphical models are popular statistical tools which are used to represent dependent or causal complex systems statistically equivalent causal or directed graphical models are said to belong to a markov equivalent class it is of great interest to describe and understand the space of such classes however , with currently known algorithms , sampling over such classes is only feasible for graphs with fewer than approximately 20 vertices in this paper , we design reversible irreducible markov chains on the space of markov equivalent classes by proposing a perfect set of operators that determine the transitions of the markov chain the stationary distribution of a proposed markov chain has a closed form and can be computed easily specifically , we construct a concrete perfect set of operators on sparse markov equivalence classes by introducing appropriate conditions on each possible operator algorithms and their accelerated versions are provided to efficiently generate markov chains and to explore properties of markov equivalence classes of sparse directed acyclic graphs \( dags \) with thousands of vertices we find experimentally that in most markov equivalence classes of sparse dags , \( 1 \) most edges are directed , \( 2 \) most undirected subgraphs are small and \( 3 \) the number of these undirected subgraphs grows approximately linearly with the number of vertices the article contains supplement arxiv 1303 0632 , http dx doi org 10 1214 13 aos1125supp"
cs.DM|cs.DS,"finding a vertex subset in a graph that satisfies a certain property is one of the most studied topics in algorithmic graph theory the focus herein is often on minimizing or maximizing the size of the solution , that is , the size of the desired vertex set in several applications , however , we also want to limit the exposure of the solution to the rest of the graph this is the case , for example , when the solution represents persons that ought to deal with sensitive information or a segregated community in this work , we thus explore the \( parameterized \) complexity of finding such secluded vertex subsets for a wide variety of properties that they shall fulfill more precisely , we study the constraint that the \( open or closed \) neighborhood of the solution shall be bounded by a parameter and the influence of this constraint on the complexity of minimizing separators , feedback vertex sets , mathcal f free vertex deletion sets , dominating sets , and the maximization of independent sets"
cs.LG|stat.ML,"variable selection and dimension reduction are two commonly adopted approaches for high dimensional data analysis , but have traditionally been treated separately here we propose an integrated approach , called sparse gradient learning \( sgl \) , for variable selection and dimension reduction via learning the gradients of the prediction function directly from samples by imposing a sparsity constraint on the gradients , variable selection is achieved by selecting variables corresponding to non zero partial derivatives , and effective dimensions are extracted based on the eigenvectors of the derived sparse empirical gradient covariance matrix an error analysis is given for the convergence of the estimated gradients to the true ones in both the euclidean and the manifold setting we also develop an efficient forward backward splitting algorithm to solve the sgl problem , making the framework practically scalable for medium or large datasets the utility of sgl for variable selection and feature extraction is explicitly given and illustrated on artificial data as well as real world examples the main advantages of our method include variable selection for both linear and nonlinear predictions , effective dimension reduction with sparse loadings , and an efficient algorithm for large p , small n problems"
cs.DM|cs.IT,"fractional repetition \( fr \) codes is a family of codes for distributed storage systems that allow for uncoded exact repairs having the minimum repair bandwidth however , in contrast to minimum bandwidth regenerating \( mbr \) codes , where a random set of a certain size of available nodes is used for a node repair , the repairs with fr codes are table based this usually allows to store more data compared to mbr codes in this work , we consider bounds on the fractional repetition capacity , which is the maximum amount of data that can be stored using an fr code optimal fr codes which attain these bounds are presented the constructions of these fr codes are based on combinatorial designs and on families of regular and biregular graphs these constructions of fr codes for given parameters raise some interesting questions in graph theory these questions and some of their solutions are discussed in this paper in addition , based on a connection between fr codes and batch codes , we propose a new family of codes for dss , namely fractional repetition batch codes , which have the properties of batch codes and fr codes simultaneously these are the first codes for dss which allow for uncoded efficient exact repairs and load balancing which can be performed by several users in parallel other concepts related to fr codes are also discussed"
cs.LG|stat.ML,"we introduce thurstonian boltzmann machines \( tbm \) , a unified architecture that can naturally incorporate a wide range of data inputs at the same time our motivation rests in the thurstonian view that many discrete data types can be considered as being generated from a subset of underlying latent continuous variables , and in the observation that each realisation of a discrete type imposes certain inequalities on those variables thus learning and inference in tbm reduce to making sense of a set of inequalities our proposed tbm naturally supports the following types gaussian , intervals , censored , binary , categorical , muticategorical , ordinal , \( in \) complete rank with and without ties we demonstrate the versatility and capacity of the proposed model on three applications of very different natures namely handwritten digit recognition , collaborative filtering and complex social survey analysis"
cs.AI|cs.IT,"in this study , under general frame of many connected intelligent particles systems \( macips \) , we reproduce two new simple subsets of such intelligent complex network , namely hybrid intelligent systems , involved a few prominent intelligent computing and approximate reasoning methods self organizing feature map \( som \) , neuro fuzzy inference system and rough set theory \( rst \) over this , we show how our algorithms can be construed as a linkage of government society interaction , where government catches various fashions of behavior solid \( absolute \) or flexible so , transition of such society , by changing of connectivity parameters \( noise \) from order to disorder is inferred add to this , one may find an indirect mapping among financial systems and eventual market fluctuations with macips"
cs.DM|cs.DS,"the nemhauser trotter local optimization theorem applies to the np hard vertex cover problem and has applications in approximation as well as parameterized algorithmics we present a framework that generalizes nemhauser and trotter 's result to vertex deletion and graph packing problems , introducing novel algorithmic strategies based on purely combinatorial arguments \( not referring to linear programming as the nemhauser trotter result originally did \) we exhibit our framework using a generalization of vertex cover , called bounded degree deletion , that has promise to become an important tool in the analysis of gene and other biological networks for some fixed d geq 0 , bounded degree deletion asks to delete as few vertices as possible from a graph in order to transform it into a graph with maximum vertex degree at most d vertex cover is the special case of d 0 our generalization of the nemhauser trotter theorem implies that bounded degree deletion has a problem kernel with a linear number of vertices for every constant d we also outline an application of our extremal combinatorial approach to the problem of packing stars with a bounded number of leaves finally , charting the border between \( parameterized \) tractability and intractability for bounded degree deletion , we provide a w 2 hardness result for bounded degree deletion in case of unbounded d values"
cs.DM|cs.DS,we show that finding lexicographically minimal n bases in a matroid can be done in polynomial time in the oracle model this follows from a more general result that the shifted problem over a matroid can be solved in polynomial time as well
stat.ML,"this paper describes convex , a convex optimization modeling framework in julia convex translates problems from a user friendly functional language into an abstract syntax tree describing the problem this concise representation of the global structure of the problem allows convex to infer whether the problem complies with the rules of disciplined convex programming \( dcp \) , and to pass the problem to a suitable solver these operations are carried out in julia using multiple dispatch , which dramatically reduces the time required to verify dcp compliance and to parse a problem into conic form convex then automatically chooses an appropriate backend solver to solve the conic form problem"
cs.IT|stat.ML,"in blind hyperspectral unmixing \( hu \) , the pure pixel assumption is well known to be powerful in enabling simple and effective blind hu solutions however , the pure pixel assumption is not always satisfied in an exact sense , especially for scenarios where pixels are heavily mixed in the no pure pixel case , a good blind hu approach to consider is the minimum volume enclosing simplex \( mves \) empirical experience has suggested that mves algorithms can perform well without pure pixels , although it was not totally clear why this is true from a theoretical viewpoint this paper aims to address the latter issue we develop an analysis framework wherein the perfect endmember identifiability of mves is studied under the noiseless case we prove that mves is indeed robust against lack of pure pixels , as long as the pixels do not get too heavily mixed and too asymmetrically spread the theoretical results are verified by numerical simulations"
cs.LG|stat.ML,"we present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood , thus providing an elegant and principled framework for creating novel component analysis models as well as constructing probabilistic equivalents of deterministic component analysis methods under our framework , we unify many very popular and well studied component analysis algorithms , such as principal component analysis \( pca \) , linear discriminant analysis \( lda \) , locality preserving projections \( lpp \) and slow feature analysis \( sfa \) , some of which have no probabilistic equivalents in literature thus far we firstly define the markov random fields \( mrfs \) which encapsulate the latent connectivity of the aforementioned component analysis techniques subsequently , we show that the projection directions produced by all pca , lda , lpp and sfa are also produced by the maximum likelihood \( ml \) solution of a single joint probability density function , composed by selecting one of the defined mrf priors while utilising a simple observation model furthermore , we propose novel expectation maximization \( em \) algorithms , exploiting the proposed joint pdf , while we generalize the proposed methodologies to arbitrary connectivities via parameterizable mrf products theoretical analysis and experiments on both simulated and real world data show the usefulness of the proposed framework , by deriving methods which well outperform state of the art equivalents"
cs.AI|stat.ML,"we aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time critical domains , such as military field operations and disaster response deployment plans for these operations are frequently negotiated on the fly by teams of human planners a human operator then translates the agreed upon plan into machine instructions for the robots we present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team 's planning conversation our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans this hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session we validate the algorithm through human subject experimentation and show we are able to infer a human team 's final plan with 83 accuracy on average we also describe a robot demonstration in which two people plan and execute a first response collaborative task with a pr2 robot to the best of our knowledge , this is the first work that integrates a logical planning technique within a generative model to perform plan inference"
cs.LG|stat.ML,"we propose a new framework for estimating generative models via an adversarial process , in which we simultaneously train two models a generative model g that captures the data distribution , and a discriminative model d that estimates the probability that a sample came from the training data rather than g the training procedure for g is to maximize the probability of d making a mistake this framework corresponds to a minimax two player game in the space of arbitrary functions g and d , a unique solution exists , with g recovering the training data distribution and d equal to 1 2 everywhere in the case where g and d are defined by multilayer perceptrons , the entire system can be trained with backpropagation there is no need for any markov chains or unrolled approximate inference networks during either training or generation of samples experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples"
cs.IT|stat.ML,"this paper studies the sample complexity of searching over multiple populations we consider a large number of populations , each corresponding to either distribution p0 or p1 the goal of the search problem studied here is to find one population corresponding to distribution p1 with as few samples as possible the main contribution is to quantify the number of samples needed to correctly find one such population we consider two general approaches non adaptive sampling methods , which sample each population a predetermined number of times until a population following p1 is found , and adaptive sampling methods , which employ sequential sampling schemes for each population we first derive a lower bound on the number of samples required by any sampling scheme we then consider an adaptive procedure consisting of a series of sequential probability ratio tests , and show it comes within a constant factor of the lower bound we give explicit expressions for this constant when samples of the populations follow gaussian and bernoulli distributions an alternative adaptive scheme is discussed which does not require full knowledge of p1 , and comes within a constant factor of the optimal scheme for comparison , a lower bound on the sampling requirements of any non adaptive scheme is presented"
cs.DS|cs.SI,"recently there has been much interest in graph based learning , with applications in collaborative filtering for recommender networks , link prediction for social networks and fraud detection these networks can consist of millions of entities , and so it is very important to develop highly efficient techniques we are especially interested in accelerating random walk approaches to compute some very interesting proximity measures of these kinds of graphs these measures have been shown to do well empirically \( liben nowell kleinberg , 2003 brand , 2005 \) we introduce a truncated variation on a well known measure , namely commute times arising from random walks on graphs we present a very novel algorithm to compute all interesting pairs of approximate nearest neighbors in truncated commute times , without computing it between all pairs we show results on both simulated and real graphs of size up to 100 000 entities , which indicate near linear scaling in computation time"
cs.LG|stat.ML,"in rdbms data analysis has received considerable attention in the past decade and has been widely used in sensitive domains to extract patterns in data using machine learning for these domains , there has also been growing concern about privacy , and differential privacy has emerged as the gold standard for private data analysis however , while differentially private machine learning and in rdbms data analytics have been studied separately , little previous work has explored private learning in an in rdbms system this work considers a specific algorithm stochastic gradient descent \( sgd \) for differentially private machine learning and explores how to integrate it into an rdbms system we find that previous solutions on differentially private sgd have characteristics that render them unattractive for an rdbms implementation to address this , we propose an algorithm that runs sgd for a constant number of passes and then adds noise to the resulting output we provide a novel analysis of the privacy properties of this algorithm we then integrate it , along with two existing versions of differentially private sgd , in the postgres rdbms experimental results demonstrate that our proposed approach can be easily integrated into an rdbms , incurs virtually no overhead , and yields substantially better test accuracy than the other private sgd algorithm implementations"
cs.LG|stat.ML,"we extend generative adversarial networks \( gans \) to the semi supervised context by forcing the discriminator network to output class labels we train a generative model g and a discriminator d on a dataset with inputs belonging to one of n classes at training time , d is made to predict which of n 1 classes the input belongs to , where an extra class is added to correspond to the outputs of g we show that this method can be used to create a more data efficient classifier and that it allows for generating higher quality samples than a regular gan"
cs.IT|stat.ML,"receiver algorithms which combine belief propagation \( bp \) with the mean field \( mf \) approximation are well suited for inference of both continuous and discrete random variables in wireless scenarios involving detection of multiple signals , the standard construction of the combined bp mf framework includes the equalization or multi user detection functions within the mf subgraph in this paper , we show that the mf approximation is not particularly effective for multi signal detection we develop a new factor graph construction for application of the bp mf framework to problems involving the detection of multiple signals we then develop a low complexity variant to the proposed construction in which gaussian bp is applied to the equalization factors in this case , the factor graph of the joint probability distribution is divided into three subgraphs \( i \) a mf subgraph comprised of the observation factors and channel estimation , \( ii \) a gaussian bp subgraph which is applied to multi signal detection , and \( iii \) a discrete bp subgraph which is applied to demodulation and decoding expectation propagation is used to approximate discrete distributions with a gaussian distribution and links the discrete bp and gaussian bp subgraphs the result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi signal detection"
cs.DM|cs.IT,"this article presents an analysis of the secure key broadcasting scheme proposed by wu , ruan , lai and tseng the study of the parameters of the system is based on a connection with a special type of symmetric equations over finite fields we present two different attacks against the system , whose efficiency depends on the choice of the parameters in particular , a time memory tradeoff attack is described , effective when a parameter of the scheme is chosen without care in such a situation , more than one third of the cases can be broken with a time and space complexity in the range of the square root of the complexity of the best attack suggested by wu et al against their system this leads to a feasible attack in a realistic scenario"
cs.DS|cs.LG|stat.ML,"the maximum a posteriori \( map \) configuration of binary variable models with submodular graph structured energy functions can be found efficiently and exactly by graph cuts max product belief propagation \( mp \) has been shown to be suboptimal on this class of energy functions by a canonical counterexample where mp converges to a suboptimal fixed point \( kulesza pereira , 2008 \) in this work , we show that under a particular scheduling and damping scheme , mp is equivalent to graph cuts , and thus optimal we explain the apparent contradiction by showing that with proper scheduling and damping , mp always converges to an optimal fixed point thus , the canonical counterexample only shows the suboptimality of mp with a particular suboptimal choice of schedule and damping with proper choices , mp is optimal"
cs.AI|cs.DS,"the notion of arc consistency plays a central role in constraint satisfaction it is known that the notion of local consistency can be extended to constraint optimisation problems defined by soft constraint frameworks based on an idempotent cost combination operator this excludes non idempotent operators such as which define problems which are very important in practical applications such as max csp , where the aim is to minimize the number of violated constraints in this paper , we show that using a weak additional axiom satisfied by most existing soft constraints proposals , it is possible to define a notion of soft arc consistency that extends the classical notion of arc consistency and this even in the case of non idempotent cost combination operators a polynomial time algorithm for enforcing this soft arc consistency exists and its space and time complexities are identical to that of enforcing arc consistency in csps when the cost combination operator is strictly monotonic \( for example max csp \) a directional version of arc consistency is potentially even stronger than the non directional version , since it allows non local propagation of penalties we demonstrate the utility of directional arc consistency by showing that it not only solves soft constraint problems on trees , but that it also implies a form of local optimality , which we call arc irreducibility"
cs.DS|cs.SI,"social networks initially had been places for people to contact each other , find friends or new acquaintances as such they ever proved interesting for machine aided analysis recent developments , however , pivoted social networks to being among the main fields of information exchange , opinion expression and debate as a result there is growing interest in both analyzing and integrating social network services in this environment efficient information retrieval is hindered by the vast amount and varying quality of the user generated content guiding users to relevant information is a valuable service and also a difficult task , where a crucial part of the process is accurately resolving duplicate entities to real world ones in this paper we propose a novel approach that utilizes the principles of link mining to successfully extend the methodology of entity resolution to multitype problems the proposed method is presented using an illustrative social network based real world example and validated by comprehensive evaluation of the results"
cs.LG|stat.ML,"we propose a simple and efficient method for ranking features in multi label classification the method produces a ranking of features showing their relevance in predicting labels , which in turn allows to choose a final subset of features the procedure is based on markov networks and allows to model the dependencies between labels and features in a direct way in the first step we build a simple network using only labels and then we test how much adding a single feature affects the initial network more specifically , in the first step we use the ising model whereas the second step is based on the score statistic , which allows to test a significance of added features very quickly the proposed approach does not require transformation of label space , gives interpretable results and allows for attractive visualization of dependency structure we give a theoretical justification of the procedure by discussing some theoretical properties of the ising model and the score statistic we also discuss feature ranking procedure based on fitting ising model using l 1 regularized logistic regressions numerical experiments show that the proposed methods outperform the conventional approaches on the considered artificial and real datasets"
cs.AI|cs.DS,"we present an alternative methodology for the analysis of algorithms , based on the concept of expected discounted reward this methodology naturally handles algorithms that do not always terminate , so it can \( theoretically \) be used with partial algorithms for undecidable problems , such as those found in artificial general intelligence \( agi \) and automated theorem proving we mention new approaches to self improving agi and logical uncertainty enabled by this methodology"
cs.DM|cs.DS,"tutte proved that every 3 connected graph on more than 4 nodes has a contractible edge barnette and gruenbaum proved the existence of a removable edge in the same setting we show that the sequence of contractions and the sequence of removals from g to the k 4 can be computed in o \( v 2 \) time by extending barnette and gruenbaum 's theorem as an application , we derive a certificate for the 3 connectedness of graphs that can be easily computed and verified"
cs.DM|cs.DS|cs.SI,"we study the power of textit local information algorithms for optimization problems on social networks we focus on sequential algorithms for which the network topology is initially unknown and is revealed only within a local neighborhood of vertices that have been irrevocably added to the output set the distinguishing feature of this setting is that locality is necessitated by constraints on the network information visible to the algorithm , rather than being desirable for reasons of efficiency or parallelizability in this sense , changes to the level of network visibility can have a significant impact on algorithm design we study a range of problems under this model of algorithms with local information we first consider the case in which the underlying graph is a preferential attachment network we show that one can find the node of maximum degree in the network in a polylogarithmic number of steps , using an opportunistic algorithm that repeatedly queries the visible node of maximum degree this addresses an open question of bollob 'a s and riordan in contrast , local information algorithms require a linear number of queries to solve the problem on arbitrary networks motivated by problems faced by recruiters in online networks , we also consider network coverage problems such as finding a minimum dominating set for this optimization problem we show that , if each node added to the output set reveals sufficient information about the set 's neighborhood , then it is possible to design randomized algorithms for general networks that nearly match the best approximations possible even with full access to the graph structure we show that this level of visibility is necessary we conclude that a network provider 's decision of how much structure to make visible to its users can have a significant effect on a user 's ability to interact strategically with the network"
cs.LG|stat.ML,"spectral clustering is a fast and popular algorithm for finding clusters in networks recently , chaudhuri et al \( 2012 \) and amini et al \( 2012 \) proposed inspired variations on the algorithm that artificially inflate the node degrees for improved statistical performance the current paper extends the previous statistical estimation results to the more canonical spectral clustering algorithm in a way that removes any assumption on the minimum degree and provides guidance on the choice of the tuning parameter moreover , our results show how the star shape in the eigenvectors a common feature of empirical networks can be explained by the degree corrected stochastic blockmodel and the extended planted partition model , two statistical models that allow for highly heterogeneous degrees throughout , the paper characterizes and justifies several of the variations of the spectral clustering algorithm in terms of these models"
cs.IT|stat.ML,"we consider the problem of quantifying the quality of a model selection problem for a graphical model we discuss this by formulating the problem as a detection problem model selection problems usually minimize the distance with the model distribution for the special case of gaussian distributions , this problem simplifies to the covariance selection problem which is widely discussed in literature by dempster 1 where the kullback leibler \( kl \) divergence is minimized or equivalently the likelihood criterion maximized to compute the model covariance matrix while this solution is optimal for gaussian distributions in the sense of the kl divergence , it is not optimal when compared with other information divergences and criteria such as area under the curve \( auc \) in this paper , we discuss the quality of model approximation using the auc and its bounds as an average measure of accuracy in detection problem we compute upper and lower bounds for the auc we define the correlation approximation matrix \( cam \) and show that the kl divergence and auc and its upper and lower bounds depend on the eigenvalues of the cam we also show the relationship between the auc , the kl divergence and the roc curve by optimizing with respect to the roc curve in the examples provided , we pick tree structures as the simplest graphical models we perform simulations on fully connected graphs and compute the tree structured models by applying the widely used chow liu algorithm examples show that the quality of tree approximation models are not good in general based on information divergences , auc and its bounds when the number of nodes in the graphical model is large specially for 1 auc , it is shown both in theory and using simulations that the 1 auc for the tree approximation model decays exponentially as the dimension of the graphical model increases"
cs.DM|cs.IT,"we consider the problem of constructing optimal authentication codes with splitting new infinite families of such codes are obtained in particular , we establish the first known infinite family of optimal authentication codes with splitting that are secure against spoofing attacks of order two"
cs.LG|stat.ML,"in this paper we develop a method for the nonlinear filtering of dynamic population codes with unknown latent dynamics this method is based on a parameterized function which updates beliefs while keeping the dimension of the belief space constant in order to optimize this function , we construct a graphical model representation of the belief latent state dynamics , and show that a certain marginalization of the graphical model has the form of a conditional exponential family harmonium this marginalization allows us to maximize the likelihood of observations , and thereby optimize the parameterized function , by the method of contrastive divergence minimization by defining the parameterized function as a multilayer perceptron , we show how the stochastic contrastive divergence gradient may be computed with backpropagation we conclude by demonstrating the effectiveness of this approach on a filtering problem based on a stochastic pendulum"
cs.IT|stat.ML,"we suggest using a pair of metrics which quantify the extent to which the prior and likelihood functions influence inferences of parameters within a parametric bayesian model , one of which is closely related to the reference prior of berger and bernardo our hope is that the utilization of these metrics will allow for the precise quantification of prior and likelihood information and mitigate the use of potentially nebulous terminology such as informative , objectivity , and subjectivity we develop a monte carlo algorithm to estimate these metrics and demonstrate that they possess desirable properties via a combination of theoretical results , simulations , and applications on public medical data sets while we do not suggest a default prior or likelihood , we suggest a way to quantify the information of the prior and likelihood functions utilized in a parametric bayesian model hence these metrics may be useful diagnostic tools when performing a bayesian analysis"
cs.LG|stat.ML,"we study online prediction of bounded stationary ergodic processes to do so , we consider the setting of prediction of individual sequences and build a deterministic regression tree that performs asymptotically as well as the best l lipschitz constant predictors then , we show why the obtained regret bound entails the asymptotical optimality with respect to the class of bounded stationary ergodic processes"
cs.IT|stat.ML,"this letter presents an improved version of diffusion least mean ppower \( lmp \) algorithm for distributed estimation instead of sum of mean square errors , a weighted sum of mean square error is defined as the cost function for global and local cost functions of a network of sensors the weight coefficients are updated by a simple steepest descent recursion to minimize the error signal of the global and local adaptive algorithm simulation results show the advantages of the proposed weighted diffusion lmp over the diffusion lmp algorithm specially in the non uniform noise conditions in a sensor network"
cs.AI|cs.DS,"when genetic algorithms are used to evolve decision trees , key tree quality parameters can be recursively computed and re used across generations of partially similar decision trees simply storing instance indices at leaves is enough for fitness to be piecewise computed in a lossless fashion we show the derivation of the \( substantial \) expected speed up on two bounding case problems and trace the attractive property of lossless fitness inheritance to the divide and conquer nature of decision trees the theoretical results are supported by experimental evidence"
cs.LG|stat.ML,"we propose a general model explanation system \( mes \) for explaining the output of black box classifiers this paper describes extensions to turner \( 2015 \) , which is referred to frequently in the text we use the motivating example of a classifier trained to detect fraud in a credit card transaction history the key aspect is that we provide explanations applicable to a single prediction , rather than provide an interpretable set of parameters we focus on explaining positive predictions \( alerts \) however , the presented methodology is symmetrically applicable to negative predictions"
cs.DM|cs.SI,"we study the influence of the seed graph in the preferential attachment model , focusing on the case of trees we first show that the seed has no effect from a weak local limit point of view on the other hand , we conjecture that different seeds lead to different distributions of limiting trees from a total variation point of view we take a first step in proving this conjecture by showing that seeds with different degree profiles lead to different limiting distributions for the \( appropriately normalized \) maximum degree , implying that such seeds lead to different \( in total variation \) limiting trees"
cs.AI|cs.IT,"spatial search problems abound in the real world , from locating hidden nuclear or chemical sources to finding skiers after an avalanche we exemplify the formalism and solution for spatial searches involving two agents that may or may not choose to share information during a search for certain classes of tasks , sharing information between multiple searchers makes cooperative searching advantageous in some examples , agents are able to realize synergy by aggregating information and moving based on local judgments about maximal information gathering expectations we also explore one and two dimensional simplified situations analytically and numerically to provide a framework for analyzing more complex problems these general considerations provide a guide for designing optimal algorithms for real world search problems"
cs.SI|stat.ML,"matrix factorization is a key component of collaborative filtering based recommendation systems because it allows us to complete sparse user by item ratings matrices under a low rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings this paradigm has had immeasurable practical success , but it is not the complete story for understanding and inferring the preferences of people first , peoples' preferences and their observable manifestations as ratings evolve over time along general patterns of trajectories second , an individual person 's preferences evolve over time through influence of their social connections in this paper , we develop a unified process model for both types of dynamics within a state space approach , together with an efficient optimization scheme for estimation within that model the model combines elements from recent developments in dynamic matrix factorization , opinion dynamics and social learning , and trust based recommendation the estimation builds upon recent advances in numerical nonlinear optimization empirical results on a large scale data set from the epinions website demonstrate consistent reduction in root mean squared error by consideration of the two types of dynamics"
cs.DS|cs.IT,"a framework with two scalar parameters is introduced for various problems of finding a prefix code minimizing a coding penalty function the framework encompasses problems previously proposed by huffman , campbell , nath , and drmota and szpankowski , shedding light on the relationships among these problems in particular , nath 's range of problems can be seen as bridging the minimum average redundancy problem of huffman with the minimum maximum pointwise redundancy problem of drmota and szpankowski using this framework , two linear time huffman like algorithms are devised for the minimum maximum pointwise redundancy problem , the only one in the framework not previously solved with a huffman like algorithm both algorithms provide solutions common to this problem and a subrange of nath 's problems , the second algorithm being distinguished by its ability to find the minimum variance solution among all solutions common to the minimum maximum pointwise redundancy and nath problems simple redundancy bounds are also presented"
cs.AI|cs.LG|stat.ML,"metrics specifying distances between data points can be learned in a discriminative manner or from generative models in this paper , we show how to unify generative and discriminative learning of metrics via a kernel learning framework specifically , we learn local metrics optimized from parametric generative models these are then used as base kernels to construct a global kernel that minimizes a discriminative training criterion we consider both linear and nonlinear combinations of local metric kernels our empirical results show that these combinations significantly improve performance on classification tasks the proposed learning algorithm is also very efficient , achieving order of magnitude speedup in training time compared to previous discriminative baseline methods"
cs.DM|cs.DS,"the declustering problem is to allocate given data on parallel working storage devices in such a manner that typical requests find their data evenly distributed on the devices using deep results from discrepancy theory , we improve previous work of several authors concerning range queries to higher dimensional data we give a declustering scheme with an additive error of o d \( log d 1 m \) independent of the data size , where d is the dimension , m the number of storage devices and d 1 does not exceed the smallest prime power in the canonical decomposition of m into prime powers in particular , our schemes work for arbitrary m in dimensions two and three for general d , they work for all m geq d 1 that are powers of two concerning lower bounds , we show that a recent proof of a omega d \( log frac d 1 2 m \) bound contains an error we close the gap in the proof and thus establish the bound"
stat.ML,"this new research explores the effects of various training methods on a polish to english statistical machine translation system for medical texts various elements of the emea parallel text corpora from the opus project were used as the basis for training of phrase tables and language models and for development , tuning and testing of the translation system the bleu , nist , meteor , ribes and ter metrics have been used to evaluate the effects of various system and data preparations on translation results our experiments included systems that used pos tagging , factored phrase models , hierarchical models , syntactic taggers , and many different alignment methods we also conducted a deep analysis of polish data as preparatory work for automatic data correction such as true casing and punctuation normalization phase"
cs.DS|cs.IT,"we consider the problem of energy efficient transmission in delay constrained cooperative multihop wireless networks the combinatorial nature of cooperative multihop schemes makes it difficult to design efficient polynomial time algorithms for deciding which nodes should take part in cooperation , and when and with what power they should transmit in this work , we tackle this problem in memoryless networks with or without delay constraints , i e , quality of service guarantee we analyze a wide class of setups , including unicast , multicast , and broadcast , and two main cooperative approaches , namely energy accumulation \( ea \) and mutual information accumulation \( mia \) we provide a generalized algorithmic formulation of the problem that encompasses all those cases we investigate the similarities and differences of ea and mia in our generalized formulation we prove that the broadcast and multicast problems are , in general , not only np hard but also o \( log \( n \) \) inapproximable we break these problems into three parts ordering , scheduling and power control , and propose a novel algorithm that , given an ordering , can optimally solve the joint power allocation and scheduling problems simultaneously in polynomial time we further show empirically that this algorithm used in conjunction with an ordering derived heuristically using the dijkstra 's shortest path algorithm yields near optimal performance in typical settings for the unicast case , we prove that although the problem remains np hard with mia , it can be solved optimally and in polynomial time when ea is used we further use our algorithm to study numerically the trade off between delay and power efficiency in cooperative broadcast and compare the performance of ea vs mia as well as the performance of our cooperative algorithm with a smart noncooperative algorithm in a broadcast setting"
cs.LG|cs.SI|stat.ML,"we analyze a model of learning and belief formation in networks in which agents follow bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed they do so by making rational inferences about their observations which include a sequence of independent and identically distributed private signals as well as the beliefs of their neighboring agents at each time fully rational agents would successively apply bayes rule to the entire history of observations this leads to forebodingly complex inferences due to lack of knowledge about the global network structure that causes those observations to address these complexities , we consider a learning without recall model , which in addition to providing a tractable framework for analyzing the behavior of rational agents in social networks , can also provide a behavioral foundation for the variety of non bayesian update rules in the literature we present the implications of various choices for time varying priors of such agents and how this choice affects learning and its rate"
cs.LG|stat.ML,"this paper considers the problem of clustering a partially observed unweighted graph i e , one where for some node pairs we know there is an edge between them , for some others we know there is no edge , and for the remaining we do not know whether or not there is an edge we want to organize the nodes into disjoint clusters so that there is relatively dense \( observed \) connectivity within clusters , and sparse across clusters we take a novel yet natural approach to this problem , by focusing on finding the clustering that minimizes the number of disagreements i e , the sum of the number of \( observed \) missing edges within clusters , and \( observed \) present edges across clusters our algorithm uses convex optimization its basis is a reduction of disagreement minimization to the problem of recovering an \( unknown \) low rank matrix and an \( unknown \) sparse matrix from their partially observed sum we evaluate the performance of our algorithm on the classical planted partition stochastic block model our main theorem provides sufficient conditions for the success of our algorithm as a function of the minimum cluster size , edge density and observation probability in particular , the results characterize the tradeoff between the observation probability and the edge density gap when there are a constant number of clusters of equal size , our results are optimal up to logarithmic factors"
cs.AI|cs.DS,"recent work introduced the cube and conquer technique to solve hard sat instances it partitions the search space into cubes using a lookahead solver each cube is tackled by a conflict driven clause learning \( cdcl \) solver crucial for strong performance is the cutoff heuristic that decides when to switch from lookahead to cdcl yet , this offline heuristic is far from ideal in this paper , we present a novel hybrid solver that applies the cube and conquer steps simultaneously a lookahead and a cdcl solver work together on each cube , while communication is restricted to synchronization our concurrent cube and conquer solver can solve many instances faster than pure lookahead , pure cdcl and offline cube and conquer , and can abort early in favor of a pure cdcl search if an instance is not suitable for cube and conquer techniques"
cs.DS|cs.IT,"an approximate sparse recovery system in ell 1 norm consists of parameters k , epsilon , n , an m by n measurement phi , and a recovery algorithm , mathcal r given a vector , mathbf x , the system approximates x by widehat mathbf x mathcal r \( phi mathbf x \) , which must satisfy widehat mathbf x mathbf x 1 leq \( 1 epsilon \) mathbf x mathbf x k 1 we consider the 'for all' model , in which a single matrix phi , possibly 'constructed' non explicitly using the probabilistic method , is used for all signals mathbf x the best existing sublinear algorithm by porat and strauss \( soda'12 \) uses o \( epsilon 3 k log \( n k \) \) measurements and runs in time o \( k 1 alpha n alpha \) for any constant alpha 0 in this paper , we improve the number of measurements to o \( epsilon 2 k log \( n k \) \) , matching the best existing upper bound \( attained by super linear algorithms \) , and the runtime to o \( k 1 beta textrm poly \( log n , 1 epsilon \) \) , with a modest restriction that epsilon leq \( log k log n \) gamma , for any constants beta , gamma 0 when k leq log c n for some c 0 , the runtime is reduced to o \( k textrm poly \( n , 1 epsilon \) \) with no restrictions on epsilon , we have an approximation recovery system with m o \( k epsilon log \( n k \) \( \( log n log k \) gamma 1 epsilon \) \) measurements"
cs.LG|stat.ML,"kernel methods are an extremely popular set of techniques used for many important machine learning and data analysis applications in addition to having good practical performances , these methods are supported by a well developed theory kernel methods use an implicit mapping of the input data into a high dimensional feature space defined by a kernel function , i e , a function returning the inner product between the images of two data points in the feature space central to any kernel method is the kernel matrix , which is built by evaluating the kernel function on a given sample dataset in this paper , we initiate the study of non asymptotic spectral theory of random kernel matrices these are n x n random matrices whose \( i , j \) th entry is obtained by evaluating the kernel function on x i and x j , where x 1 , , x n are a set of n independent random high dimensional vectors our main contribution is to obtain tight upper bounds on the spectral norm \( largest eigenvalue \) of random kernel matrices constructed by commonly used kernel functions based on polynomials and gaussian radial basis as an application of these results , we provide lower bounds on the distortion needed for releasing the coefficients of kernel ridge regression under attribute privacy , a general privacy notion which captures a large class of privacy definitions kernel ridge regression is standard method for performing non parametric regression that regularly outperforms traditional regression approaches in various domains our privacy distortion lower bounds are the first for any kernel technique , and our analysis assumes realistic scenarios for the input , unlike all previous lower bounds for other release problems which only hold under very restrictive input settings"
cs.AI|cs.SI|stat.ML,"public debates are a common platform for presenting and juxtaposing diverging views on important issues in this work we propose a methodology for tracking how ideas flow between participants throughout a debate we use this approach in a case study of oxford style debates a competitive format where the winner is determined by audience votes and show how the outcome of a debate depends on aspects of conversational flow in particular , we find that winners tend to make better use of a debate 's interactive component than losers , by actively pursuing their opponents' points rather than promoting their own ideas over the course of the conversation"
stat.ML,"constrained optimization of high dimensional numerical problems plays an important role in many scientific and industrial applications function evaluations in many industrial applications are severely limited and no analytical information about objective function and constraint functions is available for such expensive black box optimization tasks , the constraint optimization algorithm cobra was proposed , making use of rbf surrogate modeling for both the objective and the constraint functions cobra has shown remarkable success in solving reliably complex benchmark problems in less than 500 function evaluations unfortunately , cobra requires careful adjustment of parameters in order to do so in this work we present a new self adjusting algorithm sacobra , which is based on cobra and capable to achieve high quality results with very few function evaluations and no parameter tuning it is shown with the help of performance profiles on a set of benchmark problems \( g problems , mopta08 \) that sacobra consistently outperforms any cobra algorithm with fixed parameter setting we analyze the importance of the several new elements in sacobra and find that each element of sacobra plays a role to boost up the overall optimization performance we discuss the reasons behind and get in this way a better understanding of high quality rbf surrogate modeling"
cs.DM|cs.DS,"we prove that finding a rooted subtree with at least k leaves in a digraph is a fixed parameter tractable problem a similar result holds for finding rooted spanning trees with many leaves in digraphs from a wide family cal l that includes all strong and acyclic digraphs this settles completely an open question of fellows and solves another one for digraphs in cal l our algorithms are based on the following combinatorial result which can be viewed as a generalization of many results for a `spanning tree with many leaves' in the undirected case , and which is interesting on its own if a digraph d in cal l of order n with minimum in degree at least 3 contains a rooted spanning tree , then d contains one with at least \( n 2 \) 1 5 1 leaves"
cs.LG|stat.ML,"recovering a large matrix from limited measurements is a challenging task arising in many real applications , such as image inpainting , compressive sensing and medical imaging , and this kind of problems are mostly formulated as low rank matrix approximation problems due to the rank operator being non convex and discontinuous , most of the recent theoretical studies use the nuclear norm as a convex relaxation and the low rank matrix recovery problem is solved through minimization of the nuclear norm regularized problem however , a major limitation of nuclear norm minimization is that all the singular values are simultaneously minimized and the rank may not be well approximated cite hu2012fast correspondingly , in this paper , we propose a new multi stage algorithm , which makes use of the concept of truncated nuclear norm regularization \( tnnr \) proposed in citep hu2012fast and iterative support detection \( isd \) proposed in citep wang2010sparse to overcome the above limitation besides matrix completion problems considered in citep hu2012fast , the proposed method can be also extended to the general low rank matrix recovery problems extensive experiments well validate the superiority of our new algorithms over other state of the art methods"
cs.DM|cs.IT,"recent results from statistical physics show that large classes of complex networks , both man made and of natural origin , are characterized by high clustering properties yet strikingly short path lengths between pairs of nodes this class of networks are said to have a small world topology in the context of communication networks , navigable small world topologies , i e those which admit efficient distributed routing algorithms , are deemed particularly effective , for example in resource discovery tasks and peer to peer applications breaking with the traditional approach to small world topologies that privileges graph parameters pertaining to connectivity , and intrigued by the fundamental limits of communication in networks that exploit this type of topology , we investigate the capacity of these networks from the perspective of network information flow our contribution includes upper and lower bounds for the capacity of standard and navigable small world models , and the somewhat surprising result that , with high probability , random rewiring does not alter the capacity of a small world network"
cs.LG|stat.ML,"determinantal point processes \( dpps \) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset we study the problem of learning the parameters \( the kernel matrix \) of a dpp from labeled training data we make two contributions first , we show how to reparameterize a dpp 's kernel matrix with multiple kernel functions , thus enhancing modeling flexibility second , we propose a novel parameter estimation technique based on the principle of large margin separation in contrast to the state of the art method of maximum likelihood estimation , our large margin loss function explicitly models errors in selecting the target subsets , and it can be customized to trade off different types of errors \( precision vs recall \) extensive empirical studies validate our contributions , including applications on challenging document and video summarization , where flexibility in modeling the kernel matrix and balancing different errors is indispensable"
cs.DM|cs.DS,"given an integer weighted bipartite graph g \( u sqcup v , e \) , w e rightarrow mathbb z we consider the problems of finding all the edges that occur in some minimum weight matching of maximum cardinality and enumerating all the minimum weight perfect matchings moreover , we construct a subgraph g cs of g which depends on an epsilon optimal solution of the dual linear program associated to the assignment problem on g , w that allows us to reduced this problems to their unweighed variants on g cs for instance , when g has a perfect matching and we have an epsilon optimal solution of the dual linear program associated to the assignment problem on g , w , we solve the problem of finding all the edges that occur in some minimum weight perfect matching in linear time on the number of edges therefore , starting from scratch we get an algorithm that solves this problem in time o \( sqrt n m log \( nw \) \) , where n u geq v , m e , and w rm max w \( e \) , , e in e"
cs.AI|cs.DS,"data represented as strings abounds in biology , linguistics , document mining , web search and many other fields such data often have a hierarchical structure , either because they were artificially designed and composed in a hierarchical manner or because there is an underlying evolutionary process that creates repeatedly more complex strings from simpler substrings we propose a framework , referred to as lexis , that produces an optimized hierarchical representation of a given set of target strings the resulting hierarchy , lexis dag , shows how to construct each target through the concatenation of intermediate substrings , minimizing the total number of such concatenations or dag edges the lexis optimization problem is related to the smallest grammar problem after we prove its np hardness for two cost formulations , we propose an efficient greedy algorithm for the construction of lexis dags we also consider the problem of identifying the set of intermediate nodes \( substrings \) that collectively form the core of a lexis dag , which is important in the analysis of lexis dags we show that the lexis framework can be applied in diverse applications such as optimized synthesis of dna fragments in genomic libraries , hierarchical structure discovery in protein sequences , dictionary based text compression , and feature extraction from a set of documents"
cs.LG|stat.ML,"in this paper , we explore different ways to extend a recurrent neural network \( rnn \) to a textit deep rnn we start by arguing that the concept of depth in an rnn is not as clear as it is in feedforward neural networks by carefully analyzing and understanding the architecture of an rnn , however , we find three points of an rnn which may be made deeper \( 1 \) input to hidden function , \( 2 \) hidden to hidden transition and \( 3 \) hidden to output function based on this observation , we propose two novel architectures of a deep rnn which are orthogonal to an earlier attempt of stacking multiple recurrent layers to build a deep rnn \( schmidhuber , 1992 el hihi and bengio , 1996 \) we provide an alternative interpretation of these deep rnns using a novel framework based on neural operators the proposed deep rnns are empirically evaluated on the tasks of polyphonic music prediction and language modeling the experimental result supports our claim that the proposed deep rnns benefit from the depth and outperform the conventional , shallow rnns"
cs.DM|cs.DS,"the partition of graphs into nice subgraphs is a central algorithmic problem with strong ties to matching theory we study the partitioning of undirected graphs into same size stars , a problem known to be np complete even for the case of stars on three vertices we perform a thorough computational complexity study of the problem on subclasses of perfect graphs and identify several polynomial time solvable cases , for example , on interval graphs and bipartite permutation graphs , and also np complete cases , for example , on grid graphs and chordal graphs"
cs.AI|cs.LG,"entity resolution \( er \) , an important and common data cleaning problem , is about detecting data duplicate representations for the same external entities , and merging them into single representations relatively recently , declarative rules called matching dependencies \( mds \) have been proposed for specifying similarity conditions under which attribute values in database records are merged in this work we show the process and the benefits of integrating three components of er \( a \) classifiers for duplicate non duplicate record pairs built using machine learning \( ml \) techniques , \( b \) mds for supporting both the blocking phase of ml and the merge itself and \( c \) the use of the declarative language logiql an extended form of datalog supported by the logicblox platform for data processing , and the specification and enforcement of mds"
cs.DM|cs.DS,"bodlaender et al 's sidma 2014 cross composition technique is a popular method for excluding polynomial size problem kernels for np hard parameterized problems we present a new technique exploiting triangle based fractal structures for extending the range of applicability of cross compositions our technique makes it possible to prove new no polynomial kernel results for a number of problems dealing with length bounded cuts in particular , answering an open question of golovach and thilikos discrete optim 2011 , we show that , unless a collapse in the polynomial hierarchy occurs , the np hard length bounded edge cut problem \( delete at most k edges such that the resulting graph has no s t path of length shorter than ell \) parameterized by the combined k and ell has no polynomial size problem kernel our framework applies to planar as well as directed variants of the basic problems and also applies to both edge and vertex deletion problems key words fixed parameter tractability polynomial kernels kernelization kernel lower bounds cross compositions graph modification problems fractals"
cs.DM|cs.DS,"we prove several results about the complexity of the role colouring problem a role colouring of a graph g is an assignment of colours to the vertices of g such that two vertices of the same colour have identical sets of colours in their neighbourhoods we show that the problem of finding a role colouring with 1 k n colours is np hard for planar graphs we show that restricting the problem to trees yields a polynomially solvable case , as long as k is either constant or has a constant difference with n , the number of vertices in the tree finally , we prove that cographs are always k role colourable for 1 k leq n and construct such a colouring in polynomial time"
cs.LG|stat.ML,"the graphical lasso citep fht2007a is an algorithm for learning the structure in an undirected gaussian graphical model , using ell 1 regularization to control the number of zeros in the precision matrix b theta b sigma 1 citep bga2008 , yuan lin 07 the texttt r package gl citep fht2007a is popular , fast , and allows one to efficiently build a path of models for different values of the tuning parameter convergence of gl can be tricky the converged precision matrix might not be the inverse of the estimated covariance , and occasionally it fails to converge with warm starts in this paper we explain this behavior , and propose new algorithms that appear to outperform gl by studying the normal equations we see that , gl is solving the em dual of the graphical lasso penalized likelihood , by block coordinate ascent a result which can also be found in cite bga2008 in this dual , the target of estimation is b sigma , the covariance matrix , rather than the precision matrix b theta we propose similar primal algorithms pgl and dpgl , that also operate by block coordinate descent , where b theta is the optimization target we study all of these algorithms , and in particular different approaches to solving their coordinate sub problems we conclude that dpgl is superior from several points of view"
cs.AI|stat.ML,"a nonparametric bayesian extension of factor analysis \( fa \) is proposed where observed data mathbf y is modeled as a linear superposition , mathbf g , of a potentially infinite number of hidden factors , mathbf x the indian buffet process \( ibp \) is used as a prior on mathbf g to incorporate sparsity and to allow the number of latent features to be inferred the model 's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for e coli , and on three biological data sets of increasing complexity"
cs.DM|cs.IT,"in 1997 , z zhang and r w yeung found the first example of a conditional information inequality in four variables that is not shannon type this linear inequality for entropies is called conditional \( or constraint \) since it holds only under condition that some linear equations are satisfied for the involved entropies later , the same authors and other researchers discovered several unconditional information inequalities that do not follow from shannon 's inequalities for entropy in this paper we show that some non shannon type conditional inequalities are essentially conditional , i e , they cannot be extended to any unconditional inequality we prove one new essentially conditional information inequality for shannon 's entropy and discuss conditional information inequalities for kolmogorov complexity"
cs.LG|stat.ML,"dictionary learning problems are concerned with finding a collection of vectors usually referred to as the dictionary , such that the representation of random vectors with a given distribution using this dictionary is optimal most of the recent research in dictionary learning is focused on developing dictionaries which offer sparse representation , i e , optimal representation in the ell 0 sense we consider the problem of finding an optimal dictionary with which representation of samples of a random vector on an average is optimal optimality of representation is defined in the sense of attaining minimal average ell 2 norm of the coefficient vector used to represent the random vector with the help of recent results related to rank one decompositions of real symmetric positive semi definite matrices , an explicit solution for an ell 2 optimal dictionary is obtained"
cs.AI|stat.ML,"this paper presents an improvement to model learning when using multi class logitboost for classification motivated by the statistical view , logitboost can be seen as additive tree regression two important factors in this setting are 1 \) coupled classifier output due to a sum to zero constraint , and 2 \) the dense hessian matrices that arise when computing tree node split gain and node value fittings in general , this setting is too complicated for a tractable model learning algorithm however , too aggressive simplification of the setting may lead to degraded performance for example , the original logitboost is outperformed by abc logitboost due to the latter 's more careful treatment of the above two factors in this paper we propose techniques to address the two main difficulties of the logitboost setting 1 \) we adopt a vector tree \( i e each node value is vector \) that enforces a sum to zero constraint , and 2 \) we use an adaptive block coordinate descent that exploits the dense hessian when computing tree split gain and node values higher classification accuracy and faster convergence rates are observed for a range of public data sets when compared to both the original and the abc logitboost implementations"
cs.DM|cs.IT,"one approach to designing structured low density parity check \( ldpc \) codes with large girth is to shorten codes with small girth in such a manner that the deleted columns of the parity check matrix contain all the variables involved in short cycles this approach is especially effective if the parity check matrix of a code is a matrix composed of blocks of circulant permutation matrices , as is the case for the class of codes known as array codes we show how to shorten array codes by deleting certain columns of their parity check matrices so as to increase their girth the shortening approach is based on the observation that for array codes , and in fact for a slightly more general class of ldpc codes , the cycles in the corresponding tanner graph are governed by certain homogeneous linear equations with integer coefficients consequently , we can selectively eliminate cycles from an array code by only retaining those columns from the parity check matrix of the original code that are indexed by integer sequences that do not contain solutions to the equations governing those cycles we provide ramsey theoretic estimates for the maximum number of columns that can be retained from the original parity check matrix with the property that the sequence of their indices avoid solutions to various types of cycle governing equations this translates to estimates of the rate penalty incurred in shortening a code to eliminate cycles simulation results show that for the codes considered , shortening them to increase the girth can lead to significant gains in signal to noise ratio in the case of communication over an additive white gaussian noise channel"
cs.DM|cs.SI,"with the arrival of modern internet era , large public networks of various types have come to existence to benefit the society as a whole and several research areas such as sociology , economics and geography in particular however , the societal and research benefits of these networks have also given rise to potentially significant privacy issues in the sense that malicious entities may violate the privacy of the users of such a network by analyzing the network and deliberately using such privacy violations for deleterious purposes such considerations have given rise to a new active research area that deals with the quantification of privacy of users in large networks and the corresponding investigation of computational complexity issues of computing such quantified privacy measures in this paper , we formalize three such privacy measures for large networks and provide non trivial theoretical computational complexity results for computing these measures our results show the first two measures can be computed efficiently , whereas the third measure is provably hard to compute within a logarithmic approximation factor furthermore , we also provide computational complexity results for the case when the privacy requirement of the network is severely restricted , including an efficient logarithmic approximation"
cs.DM|cs.IT,"an external description for aperiodically sampled mimo linear systems has been developed emphasis is on the sampling period sequence , included among the variables to be handled the computational procedure is simple and no use of polynomial matrix theory is required this input output description is believed to be a basic formulation for its later application to the problem of optimal control and or identification of linear dynamical systems"
cs.DM|cs.LG|stat.ML,"we propose an algebraic combinatorial method for solving large sparse linear systems of equations locally that is , a method which can compute single evaluations of the signal without computing the whole signal the method scales only in the sparsity of the system and not in its size , and allows to provide error estimates for any solution method at the heart of our approach is the so called regression matroid , a combinatorial object associated to sparsity patterns , which allows to replace inversion of the large matrix with the inversion of a kernel matrix that is constant size we show that our method provides the best linear unbiased estimator \( blue \) for this setting and the minimum variance unbiased estimator \( mvue \) under gaussian noise assumptions , and furthermore we show that the size of the kernel matrix which is to be inverted can be traded off with accuracy"
cs.LG|stat.ML,"we propose sparfa trace , a new machine learning based framework for time varying learning and content analytics for education applications we develop a novel message passing based , blind , approximate kalman filter for sparse factor analysis \( sparfa \) , that jointly \( i \) traces learner concept knowledge over time , \( ii \) analyzes learner concept knowledge state transitions \( induced by interacting with learning resources , such as textbook sections , lecture videos , etc , or the forgetting effect \) , and \( iii \) estimates the content organization and intrinsic difficulty of the assessment questions these quantities are estimated solely from binary valued \( correct incorrect \) graded learner response data and a summary of the specific actions each learner performs \( e g , answering a question or studying a learning resource \) at each time instance experimental results on two online course datasets demonstrate that sparfa trace is capable of tracing each learner 's concept knowledge evolution over time , as well as analyzing the quality and content organization of learning resources , the question concept associations , and the question intrinsic difficulties moreover , we show that sparfa trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education"
cs.DS|cs.IT,"an \( n , m \) vector code mathcal c subseteq mathbb f n is a collection of m codewords where n elements \( from the field mathbb f \) in each of the codewords are referred to as code blocks assuming that mathbb f cong mathbb b ell , the code blocks are treated as ell length vectors over the base field mathbb b equivalently , the code is said to have the sub packetization level ell this paper addresses the problem of constructing mds vector codes which enable exact reconstruction of each code block by downloading small amount of information from the remaining code blocks the repair bandwidth of a code measures the information flow from the remaining code blocks during the reconstruction of a single code block this problem naturally arises in the context of distributed storage systems as the node repair problem 4 assuming that m mathbb b k ell , the repair bandwidth of an mds vector code is lower bounded by big \( frac n 1 n k big \) cdot ell symbols \( over the base field mathbb b \) which is also referred to as the cut set bound 4 for all values of n and k , the mds vector codes that attain the cut set bound with the sub packetization level ell \( n k \) lceil n \( n k \) rceil are known in the literature 23 , 35 this paper presents a construction for mds vector codes which simultaneously ensures both small repair bandwidth and small sub packetization level the obtained codes have the smallest possible sub packetization level ell o \( n k \) for an mds vector code and the repair bandwidth which is at most twice the cut set bound the paper then generalizes this code construction so that the repair bandwidth of the obtained codes approach the cut set bound at the cost of increased sub packetization level the constructions presented in this paper give mds vector codes which are linear over the base field mathbb b"
cs.AI|cs.DS,"we present a suite of algorithms for dimension independent similarity computation \( disco \) to compute all pairwise similarities between very high dimensional sparse vectors all of our results are provably independent of dimension , meaning apart from the initial cost of trivially reading in the data , all subsequent operations are independent of the dimension , thus the dimension can be very large we study cosine , dice , overlap , and the jaccard similarity measures for jaccard similiarity we include an improved version of minhash our results are geared toward the mapreduce framework we empirically validate our theorems at large scale using data from the social networking site twitter at time of writing , our algorithms are live in production at twitter com"
cs.LG|stat.ML,"driven by a large number of potential applications in areas like bioinformatics , information retrieval and social network analysis , the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community to this end , current approaches typically consider datasets containing crisp relations , so that standard classification methods can be adopted however , relations between objects like similarities and preferences are often expressed in a graded manner in real world applications a general kernel based framework for learning relations from data is introduced here it extends existing approaches because both crisp and graded relations are considered , and it unifies existing approaches because different types of graded relations can be modeled , including symmetric and reciprocal relations this framework establishes important links between recent developments in fuzzy set theory and machine learning its usefulness is demonstrated through various experiments on synthetic and real world data"
cs.IT|stat.ML,"the support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations , and arises in a diverse range of settings such as group testing , compressive sensing , and subset selection in regression in this paper , we take a unified approach to support recovery problems , considering general probabilistic observation models relating a sparse data vector to an observation vector we study the information theoretic limits of both exact and partial support recovery , taking a novel approach based on information spectrum techniques we provide general achievability and converse bounds characterizing the trade off between the error probability and number of measurements , and we specialize these bounds to variants of models from group testing , linear regression , and 1 bit compressive sensing in several cases , our bounds not only provide matching scaling laws in the necessary and sufficient number of measurements , but also sharp thresholds with matching constant factors our approach has several advantages over previous approaches for the achievability part , we obtain sharp thresholds under broader scalings of the sparsity level and other parameters \( e g signal to noise ratio \) compared to several previous works , and for the converse part , we not only provide conditions under which the error probability fails to vanish , but also conditions under which it tends to one"
cs.DM|cs.DS,"in recent years , binary quadratic programming \( bqp \) has been successively applied to solve several combinatorial optimization problems we consider in this paper a study of using the bqp model to solve the minimum sum coloring problem \( mscp \) for this purpose , we recast the mscp with a quadratic model which is then solved via a recently proposed path relinking \( pr \) algorithm designed for the general bqp based on a set of mscp benchmark instances , we investigate the performance of this solution approach compared with existing methods"
cs.IT,"we extend the results of adcock , carlsson , and carlsson by constructing numeric invariants from the computation of a multidimensional persistence module as given by carlsson , singh , and zomorodian"
cs.IT,"in this paper , the sum capacity of the gaussian multiple input multiple output \( mimo \) cognitive radio channel \( mcc \) is expressed as a convex problem with finite number of linear constraints , allowing for polynomial time interior point techniques to find the solution in addition , a specialized class of sum power iterative waterfilling algorithms is determined that exploits the inherent structure of the sum capacity problem these algorithms not only determine the maximizing sum capacity value , but also the transmit policies that achieve this optimum the paper concludes by providing numerical results which demonstrate that the algorithm takes very few iterations to converge to the optimum"
cs.LG,"automatic text summarization is widely regarded as the highly difficult problem , partially because of the lack of large text summarization data set due to the great challenge of constructing the large scale summaries for full text , in this paper , we introduce a large corpus of chinese short text summarization dataset constructed from the chinese microblogging website sina weibo , which will be released to public soon this corpus consists of over 2 million real chinese short texts with short summaries given by the writer of each text we also manually tagged the relevance of 10 , 666 short summaries with their corresponding short texts based on the corpus , we introduce recurrent neural network for the summary generation and achieve promising results , which not only shows the usefulness of the proposed corpus for short text summarization research , but also provides a baseline for further research on this topic"
cs.DM|cs.IT,"our main technical result is that , in the coset leader graph of a linear binary code of block length n , the metric balls spanned by constant weight vectors grow exponentially slower than those in 0 , 1 n following the approach of friedman and tillich \( 2006 \) , we use this fact to improve on the first linear programming bound on the rate of ldpc codes , as the function of their minimal distance this improvement , combined with the techniques of ben haim and lytsin \( 2006 \) , improves the rate vs distance bounds for ldpc codes in a significant sub range of relative distances"
cs.IT,"in the problem of matrix compressed sensing we aim to recover a low rank matrix from few of its element wise linear projections in this contribution we analyze the asymptotic performance of a bayes optimal inference procedure for a model where the matrix to be recovered is a product of random matrices the results that we obtain using the replica method describe the state evolution of the recently introduced p big amp algorithm we show the existence of different types of phase transitions , their implications for the solvability of the problem , and we compare the results of the theoretical analysis to the performance reached by p big amp remarkably the asymptotic replica equations for matrix compressed sensing are the same as those for a related but formally different problem of matrix factorization"
cs.IT,"this paper proposes a new perspective on the problem of multidimensional spectral factorization , through helical mapping d dimensional \( d d \) data arrays are vectorized , processed by 1 d cepstral analysis and then remapped onto the original space partial differential equations \( pdes \) are the basic framework to describe the evolution of physical phenomena we observe that the minimum phase helical solution asymptotically converges to the d d semi causal solution , and allows to decouple the two solutions arising from pdes describing physical systems we prove this equivalence in the theoretical framework of cepstral analysis , and we also illustrate the validity of helical factorization through a 2 d wave propagation example and a 3 d application to helioseismology"
cs.IT,"to ensure reliable communication in randomly varying and error prone channels , wireless systems use adaptive modulation and coding \( amc \) as well as hybrid arq \( harq \) in order to elucidate their compatibility and interaction , we compare the throughput provided by amc , harq , and their combination \( amc harq \) under two operational conditions in slow and fast block fading channels considering both , incremental redundancy harq \( harq ir \) and repetition redundancy harq \( harq rr \) we optimize the rate decision regions for amc harq and compare them in terms of attainable throughput under a fairly general model of the channel variation and the decoding functions , we conclude that i \) adding harq on top of amc may be counterproductive in the high average signal to noise ratio regime for fast fading channels , and ii \) harq is useful for slow fading channels , but it provides moderate throughput gains we provide explanations for these results which allow us to propose paths to improve amc harq systems"
cs.LG,"deep neural networks are commonly trained using stochastic non convex optimization procedures , which are driven by gradient information estimated on fractions \( batches \) of the dataset while it is commonly accepted that batch size is an important parameter for offline tuning , the benefits of online selection of batches remain poorly understood we investigate online batch selection strategies for two state of the art methods of stochastic gradient based optimization , adadelta and adam as the loss function to be minimized for the whole dataset is an aggregation of loss functions of individual datapoints , intuitively , datapoints with the greatest loss should be considered \( selected in a batch \) more frequently however , the limitations of this intuition and the proper control of the selection pressure over time are open questions we propose a simple strategy where all datapoints are ranked w r t their latest known loss value and the probability to be selected decays exponentially as a function of rank our experimental results on the mnist dataset suggest that selecting batches speeds up both adadelta and adam by a factor of about 5"
cs.DM,"in order to approximate transandental functions , several algorithms were proposed historically , polynomial interpolation , infinite series , cdots and other , times , and based algorithms were studied for this purpose the cordic \( coordinate rotation digital computer \) introduced by jack e volder in 1959 , and generalized by j s walther a few years later , is a hardware based algorithmfor the approximation of trigonometric , hyperbolic andlogarithmic functions as a consequence , cordic is used for applications indiverse areas such as signal and image processing for these reasons , several modified versions were proposed in this article , we present anoverview of the cordic algorithm for the computation of the circular functions , essentially the scaling free version , and we will give a substential improvement to the commonly used one"
cs.SI,"the prosperity of location based social networking services enables geo social group queries for group based activity planning and marketing this paper proposes a new family of geo social group queries with minimum acquaintance constraint \( gsgqs \) , which are more appealing than existing geo social group queries in terms of producing a cohesive group that guarantees the worst case acquaintance level gsgqs , also specified with various spatial constraints , are more complex than conventional spatial queries particularly , those with a strict k nn spatial constraint are proved to be np hard for efficient processing of general gsgq queries on large location based social networks , we devise two social aware index structures , namely sar tree and sar tree the latter features a novel clustering technique that considers both spatial and social factors based on sar tree and sar tree , efficient algorithms are developed to process various gsgqs extensive experiments on real world gowalla and dianping datasets show that our proposed methods substantially outperform the baseline algorithms based on r tree"
cs.IT,"low density parity check \( ldpc \) codes are capable of achieving excellent performance and provide a useful alternative for high performance applications however , at medium to high signal to noise ratios \( snr \) , an observable error floor arises from the loss of independence of messages passed under iterative graph based decoding in this paper , the error floor performance of short block length codes is improved by use of a novel candidate selection metric in code graph construction the proposed multipath emd approach avoids harmful structures in the graph by evaluating certain properties of the cycles which may be introduced in each edge placement we present multipath emd based designs for several structured ldpc codes including quasi cyclic and irregular repeat accumulate codes in addition , an extended class of diversity achieving codes on the challenging block fading channel is proposed and considered with the multipath emd design this combined approach is demonstrated to provide gains in decoder convergence and error rate performance a simulation study evaluates the performance of the proposed and existing state of the art methods"
cs.LG|stat.ML,"in recent work on both generative and discriminative score to log likelihood ratio calibration , it was shown that linear transforms give good accuracy only for a limited range of operating points moreover , these methods required tailoring of the calibration training objective functions in order to target the desired region of best accuracy here , we generalize the linear recipes to non linear ones we experiment with a non linear , non parametric , discriminative pav solution , as well as parametric , generative , maximum likelihood solutions that use gaussian , student 's t and normal inverse gaussian score distributions experiments on nist sre'12 scores suggest that the non linear methods provide wider ranges of optimal accuracy and can be trained without having to resort to objective function tailoring"
cs.IT,"in this paper we present a class of linear whitening filters termed linear extended whitening filters \( ewfs \) which are whitening filters that have desirable secondary properties and can be used for simplifying algorithms , or achieving desired side effects on given secondary matrices , random vectors or random processes further , we present an application of ewfs for simplification of qr decomposition based ml detection algorithm in wireless communication"
cs.AI,"this paper considers global optimization with a black box unknown objective function that can be non convex and non differentiable such a difficult optimization problem arises in many real world applications , such as parameter tuning in machine learning , engineering design problem , and planning with a complex physics simulator this paper proposes a new global optimization algorithm , called locally oriented global optimization \( logo \) , to aim for both fast convergence in practice and finite time error bound in theory the advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions further , we modify the logo algorithm to specifically solve a planning problem via policy search with continuous state action space and long time horizon while maintaining its finite time error bound we apply the proposed planning method to accident management of a nuclear power plant the result of the application study demonstrates the practical utility of our method"
cs.SI,"in this work we study the critical behavior of a three state \( 1 , 1 , 0 \) opinion model with independence each agent has a probability q to act as independent , i e , he she can choose his her opinion independently of the opinions of the other agents on the other hand , with the complementary probability 1 q the agent interacts with a randomly chosen individual through a kinetic exchange our analytical and numerical results show that the independence mechanism acts as a noise that induces an order disorder transition at critical points q c that depend on the individuals' flexibility for a special value of this flexibility the system undergoes a transition to an absorbing state with all opinions 0"
cs.DS,"vpsolver is a vector packing solver based on an arc flow formulation with graph compression in this paper , we present the algorithm introduced in vpsolver 3 0 0 for building compressed arc flow models for the multiple choice vector packing problem"
cs.IT,"an information theoretic analysis of a multi keyhole channel , which includes a number of statistically independent keyholes with possibly different correlation matrices , is given when the number of keyholes or and the number of tx rx antennas is large , there is an equivalent rayleigh fading channel such that the outage capacities of both channels are asymptotically equal in the case of a large number of antennas and for a broad class of fading distributions , the instantaneous capacity is shown to be asymptotically gaussian in distribution , and compact , closed form expressions for the mean and variance are given motivated by the asymptotic analysis , a simple , full ordering scalar measure of spatial correlation and power imbalance in mimo channels is introduced , which quantifies the negative impact of these two factors on the outage capacity in a simple and well tractable way it does not require the eigenvalue decomposition , and has the full ordering property the size asymptotic results are used to prove telatar 's conjecture for semi correlated multi keyhole and rayleigh channels since the keyhole channel model approximates well the relay channel in the amplify and forward mode in certain scenarios , these results also apply to the latter"
cs.LG,"the damped gauss newton \( dgn \) algorithm for candecomp parafac \( cp \) decomposition can handle the challenges of collinearity of factors and different magnitudes of factors nevertheless , for factorization of an n d tensor of size i 1 times i n with rank r , the algorithm is computationally demanding due to construction of large approximate hessian of size \( rt times rt \) and its inversion where t sum n i n in this paper , we propose a fast implementation of the dgn algorithm which is based on novel expressions of the inverse approximate hessian in block form the new implementation has lower computational complexity , besides computation of the gradient \( this part is common to both methods \) , requiring the inversion of a matrix of size nr 2 times nr 2 , which is much smaller than the whole approximate hessian , if t gg nr in addition , the implementation has lower memory requirements , because neither the hessian nor its inverse never need to be stored in their entirety a variant of the algorithm working with complex valued data is proposed as well complexity and performance of the proposed algorithm is compared with those of dgn and als with line search on examples of difficult benchmark tensors"
cs.IT,"we consider the problem of reconstructing wideband frequency spectra from distributed , compressive measurements the measurements are made by a network of nodes , each independently mixing the ambient spectra with low frequency , random signals the reconstruction takes place via local transmissions between nodes , each performing simple statistical operations such as ridge regression and shrinkage"
cs.LG,"in this work , we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture , which incorporates both color and motion features we propose a new human body pose dataset , flic motion , that extends the flic dataset with additional motion features we apply our architecture to this dataset and report significantly better performance than current state of the art pose detection systems"
cs.DS,"let integers r ge 2 and d ge 3 be fixed let cal g d be the set of graphs with no induced path on d vertices we study the problem of packing k vertex disjoint copies of k 1 , r \( k ge 2 \) into a graph g from parameterized preprocessing , i e , kernelization , point of view we show that every graph g in cal g d can be reduced , in polynomial time , to a graph g' in cal g d with o \( k \) vertices such that g has at least k vertex disjoint copies of k 1 , r if and only if g' has such a result is known for arbitrary graphs g when r 2 and we conjecture that it holds for every r ge 2"
cs.DM,"we investigate a natural heyting algebra structure on the set of dyck paths of the same length we provide a geometrical description of the operations of pseudocomplement and relative pseudocomplement , as well as of regular elements we also find a logic theoretic interpretation of such heyting algebras , which we call dyck algebras , by showing that they are the algebraic counterpart of a certain fragment of a classical interval temporal logic \( also known as halpern shoham logic \) finally , we propose a generalization of our approach , suggesting a similar study of the heyting algebra arising from the poset of intervals of a finite poset using birkh off duality in order to illustrate this , we show how several combinatorial parameters of dyck paths can be expressed in terms of the heyting algebra structure of dyck algebras together with a certain total order on the set of atoms of each dyck algebra"
cs.LG|stat.ML,"we introduce a simple and effective method for regularizing large convolutional neural networks we replace the conventional deterministic pooling operations with a stochastic procedure , randomly picking the activation within each pooling region according to a multinomial distribution , given by the activities within the pooling region the approach is hyper parameter free and can be combined with other regularization approaches , such as dropout and data augmentation we achieve state of the art performance on four image datasets , relative to other approaches that do not utilize data augmentation"
cs.IT,"subspace codes were introduced by k otter and kschischang for error control in random linear network coding in this paper , a layered type of subspace codes is considered , which can be viewed as a superposition of multiple component subspace codes exploiting the layered structure , we develop two decoding algorithms for these codes the first algorithm operates by separately decoding each component code the second algorithm is similar to the successive interference cancellation \( sic \) algorithm for conventional superposition coding , and further permits an iterative version we show that both algorithms decode not only deterministically up to but also probabilistically beyond the error correction capability of the overall code finally we present possible applications of layered subspace codes in several network coding scenarios"
cs.IT,"the purpose of this paper is twofold first , we provide a novel analysis of a baseband time reversal \( tr \) beamforming system using two propagation models commonly used in indoor wireless communications this analysis applies to pico and femtocells in conventional wideband systems such as wifi networks we derive a new closed form approximation for the inter symbol interference \( isi \) power in such scenarios without using rate back off , which leads to a more accurate estimation of the probability of bit error compared to previous works we define performance parameters for the spatial focusing and time compression properties of tr beamforming and find closed form approximations for them second , we propose an equalized tr \( etr \) technique that mitigates the isi of conventional tr etr uses a zf pre equalizer at the transmitter in cascade configuration with the tr pre filter we derive theoretical performance bounds for etr and show that it greatly enhances the performance of conventional tr with minimal impact to its beamforming capability by means of numerical simulations , we verify our closed form approximations and show that the proposed etr technique outperforms conventional tr with respect to the ber under any snr , even though the total received power is greater for conventional tr"
cs.LG,"an epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain this unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place the automatic detection of seizures is necessary since the visual screening of eeg recordings is a time consuming task and requires experts to improve the diagnosis four linear least squares based preprocessing models are proposed to extract key features of an eeg signal in order to detect seizures the first two models are newly developed the original signal \( eeg \) is approximated by a sinusoidal curve its amplitude is formed by a polynomial function and compared with the pre developed spline function different statistical measures namely classification accuracy , true positive and negative rates , false positive and negative rates and precision are utilized to assess the performance of the proposed models these metrics are derived from confusion matrices obtained from classifiers different classifiers are used over the original dataset and the set of extracted features the proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases the first and third models are promising feature extraction methods logistic , lazyib1 , lazyib5 and j48 are the best classifiers their true positive and negative rates are 1 while false positive and negative rates are zero and the corresponding precision values are 1 numerical results suggest that these models are robust and efficient for detecting epileptic seizure"
cs.IT,"due to the short and bursty incoming messages , channel access activities in a wireless random access system are often fractional the lack of frequent data support consequently makes it difficult for the receiver to estimate and track the time varying channel states with high precision this paper investigates random multiple access communication over a compound wireless channel where channel realization is known neither at the transmitters nor at the receiver an achievable rate and error probability tradeoff bound is derived under the non asymptotic assumption of a finite codeword length the results are then extended to the random multiple access system where the receiver is only interested in decoding messages from a user subset"
cs.IT,"the joint base station \( bs \) association and beamforming problem has been studied extensively in recent years , yet the computational complexity for even the simplest siso case has not been fully characterized in this paper , we consider the problems for an uplink siso simo cellular network under the max min fairness criterion we first prove that the problems for both the siso and simo scenarios are polynomial time solvable secondly , we present a fixed point based binary search \( bs fp \) algorithm for both siso and simo scenarios whereby a qos \( quality of service \) constrained subproblem is solved at each step by a fixed point method thirdly , we propose a normalized fixed point \( nfp \) iterative algorithm to directly solve the original problem and prove its geometric convergence to global optima although it is not known whether the nfp algorithm is a polynomial time algorithm , empirically it converges to the global optima orders of magnitude faster than the polynomial time algorithms , making it suitable for applications in huge scale networks"
cs.AI,"combinatory categorial grammar \( ccg \) is a grammar formalism used for natural language parsing ccg assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories to parse a sentence in this work we propose and implement a new approach to ccg parsing that relies on a prominent knowledge representation formalism , answer set programming \( asp \) a declarative programming paradigm we formulate the task of ccg parsing as a planning problem and use an asp computational tool to compute solutions that correspond to valid parses compared to other approaches , there is no need to implement a specific parsing algorithm using such a declarative method our approach aims at producing all semantically distinct parse trees for a given sentence from this goal , normalization and efficiency issues arise , and we deal with them by combining and extending existing strategies we have implemented a ccg parsing tool kit aspccgtk that uses asp as its main computational means the c c supertagger can be used as a preprocessor within aspccgtk , which allows us to achieve wide coverage natural language parsing"
cs.IT,"let n 2m , m odd , e m , and p odd prime with p equiv1 mathrm mod 4 let d frac \( p m 1 \) 2 2 \( p e 1 \) in this paper , we study the cross correlation between a p ary m sequence s t of period p 2m 1 and its decimation s dt our result shows that the cross correlation function is six valued and that it takes the values in 1 , pm p m 1 , frac 1 pm p frac e 2 2 p m 1 , frac \( 1 p e \) 2 p m 1 also , the distribution of the cross correlation is completely determined"
cs.AI,"plausibility models are kripke models that agents use to reason about knowledge and belief , both of themselves and of each other such models are used to interpret the notions of conditional belief , degrees of belief , and safe belief the logic of conditional belief contains that modality and also the knowledge modality , and similarly for the logic of degrees of belief and the logic of safe belief with respect to these logics , plausibility models may contain too much information a proper notion of bisimulation is required that characterises them we define that notion of bisimulation and prove the required characterisations on the class of image finite and preimage finite models \( with respect to the plausibility relation \) , two pointed kripke models are modally equivalent in either of the three logics , if and only if they are bisimilar as a result , the information content of such a model can be similarly expressed in the logic of conditional belief , or the logic of degrees of belief , or that of safe belief this , we found a surprising result still , that does not mean that the logics are equally expressive the logics of conditional and degrees of belief are incomparable , the logics of degrees of belief and safe belief are incomparable , while the logic of safe belief is more expressive than the logic of conditional belief in view of the result on bisimulation characterisation , this is an equally surprising result we hope our insights may contribute to the growing community of formal epistemology and on the relation between qualitative and quantitative modelling"
cs.IT,"this letter derives some new exponential bounds for discrete time , real valued , conditionally symmetric martingales with bounded jumps the new bounds are extended to conditionally symmetric sub supermartingales , and they are compared to some existing bounds"
cs.IT,an accurate channel estimation is crucial for the novel time domain synchronous orthogonal frequency division multiplexing \( tds ofdm \) scheme in which pseudo noise \( pn \) sequences serve as both guard intervals \( gi \) for ofdm data symbols and training sequences for synchronization channel estimation this paper studies the channel estimation method based on the cross correlation of pn sequences a theoretical analysis of this estimator is conducted and several improved estimators are then proposed to reduce the estimation error floor encountered by the pn correlation based estimator it is shown through mathematical derivations and simulations that the new estimators approach or even achieve the cramer rao bound
cs.DM,a perfect matching in a 3 uniform hypergraph on n 3k vertices is a subset of frac n 3 disjoint edges we prove that if h is a 3 uniform hypergraph on n 3k vertices such that every vertex belongs to at least n 1 choose 2 2n 3 choose 2 1 edges then h contains a perfect matching we give a construction to show that this result is best possible
cs.DS|cs.SI,"we explain how massive instances of scale free graphs following the barabasi albert model can be generated very quickly in an embarrassingly parallel way this makes this popular model available for studying big data graph problems as a demonstration , we generated a petaedge graph in less than an hour"
cs.SI,"there are both benefits and drawbacks to cultural diversity it can lead to friction and exacerbate differences however , as with biological diversity , cultural diversity is valuable in times of upheaval if a previously effective solution no longer works , it is good to have alternatives available what factors give rise to cultural diversity \? this paper describes a preliminary investigation of this question using a computational model of cultural evolution the model is composed of neural network based agents that evolve fitter ideas for actions by \( 1 \) inventing new ideas through modification of existing ones , and \( 2 \) imitating neighbors' ideas numerical simulations indicate that the diversity of ideas in a population is positively correlated with both the proportion of creators to imitators in the population , and the rate at which creators create this is the case for both minimum and peak diversity of actions over the duration of a run"
cs.SI,the main objectives and instruments to develop belarusian educational and research web portal of nuclear knowledge are discussed draft structure of portal is presented
cs.DM,"it is known that theta \( log n \) chords must be added to an n cycle to produce a pancyclic graph for vertex pancyclicity , where every vertex belongs to a cycle of every length , theta \( n \) chords are required a possibly `intermediate' variation is the following given k , 1 leq k leq n , how many chords must be added to ensure that there exist cycles of every length each of which passes exactly k chords \? for fixed k , we establish a lower bound of omega big \( n 1 k big \) on the growth rate"
cs.IT,"formal concept analysis fca is a data analysis method which enables to discover hidden knowledge existing in data a kind of hidden knowledge extracted from data is association rules different quality measures were reported in the literature to extract only relevant association rules given a dataset , the choice of a good quality measure remains a challenging task for a user given a quality measures evaluation matrix according to semantic properties , this paper describes how fca can highlight quality measures with similar behavior in order to help the user during his choice the aim of this article is the discovery of interestingness measures im clusters , able to validate those found due to the hierarchical and partitioning clustering methods ahc and k means then , based on the theoretical study of sixty one interestingness measures according to nineteen properties , proposed in a recent study , fca describes several groups of measures"
cs.AI|cs.LG,this preprint has been withdrawn by the author for revision
cs.LG,"deep neural networks \( dnn \) have achieved state of the art results in a wide range of tasks , with the best results obtained with large training sets and large models in the past , gpus enabled these breakthroughs because of their greater computational speed in the future , faster computation at both training and test time is likely to be crucial for further progress and for consumer applications on low power devices as a result , there is much interest in research and development of dedicated hardware for deep learning \( dl \) binary weights , i e , weights which are constrained to only two possible values \( e g 1 or 1 \) , would bring great benefits to specialized dl hardware by replacing many multiply accumulate operations by simple accumulations , as multipliers are the most space and power hungry components of the digital implementation of neural networks we introduce binaryconnect , a method which consists in training a dnn with binary weights during the forward and backward propagations , while retaining precision of the stored weights in which gradients are accumulated like other dropout schemes , we show that binaryconnect acts as regularizer and we obtain near state of the art results with binaryconnect on the permutation invariant mnist , cifar 10 and svhn"
cs.IT,"given the output of a data source taking values in a finite alphabet , we wish to detect change points , that is times when the statistical properties of the source change motivated by ideas of match lengths in information theory , we introduce a novel non parametric estimator which we call creche \( crossings enumeration change estimator \) we present simulation evidence that this estimator performs well , both for simulated sources and for real data formed by concatenating text sources for example , we show that we can accurately detect the point at which a source changes from a markov chain to an iid source with the same stationary distribution our estimator requires no assumptions about the form of the source distribution , and avoids the need to estimate its probabilities further , we establish consistency of the creche estimator under a related toy model , by establishing a fluid limit and using martingale arguments"
cs.LG,"using the recently developed framework of daniely et al , 2014 , we show that under a natural assumption on the complexity of refuting random k sat formulas , learning dnf formulas is hard furthermore , the same assumption implies the hardness of learning intersections of omega \( log \( n \) \) halfspaces , agnostically learning conjunctions , as well as virtually all \( distribution free \) learning problems that were previously shown hard \( under complexity assumptions \)"
cs.IT,"we give upper and lower bounds on the amount of quantum communication required to perform the task of quantum state redistribution in a one shot setting in quantum state redistribution as considered in luo and devetak \( 2009 \) and devetak and yard \( 2008 \) , there are 4 systems of interest the a system held by alice , the b system held by bob , the c system that is to be transmitted from alice to bob , and the r system that holds a purification of the state in the abc registers our bounds are in terms of smooth conditional min and max entropies , and the smooth max information the protocol for the upper bound has a clear structure , building on the work oppenheim \( 2008 \) it decomposes the quantum state redistribution task into two simpler quantum state merging tasks by introducing a coherent relay there remains a gap between our upper and lower bounds this gap vanishes in the independent and identical \( iid \) asymptotic limit and the remaining terms can then be rewritten as a quantum conditional mutual information , thus yielding an alternative proof of optimality of this communication rate for iid asymptotic quantum state redistribution"
cs.IT,"a reply to the commentaries of yana \( 2013 \) , and some jots on information theory"
cs.IT,"in this paper , we propose a cross layer scheduling algorithm that achieves a throughput epsilon close to the optimal throughput in multi hop wireless networks with a tradeoff of o \( 1 epsilon \) in delay guarantees the algorithm aims to solve a joint congestion control , routing , and scheduling problem in a multi hop wireless network while satisfying per flow average end to end delay guarantees and minimum data rate requirements this problem has been solved for both backlogged as well as arbitrary arrival rate systems moreover , we discuss the design of a class of low complexity suboptimal algorithms , the effects of delayed feedback on the optimal algorithm , and the extensions of the proposed algorithm to different interference models with arbitrary link capacities"
cs.IT,"this paper considers the problem of secret key agreement with public discussion subject to a peak power constraint a on the channel input the optimal input distribution is proved to be discrete with finite support the result is obtained by first transforming the secret key channel model into an equivalent gaussian wiretap channel with better noise statistics at the legitimate receiver and then using the fact that the optimal distribution of the gaussian wiretap channel is discrete to overcome the computationally heavy search for the optimal discrete distribution , several suboptimal schemes are proposed and shown numerically to perform close to the capacity moreover , lower and upper bounds for the secret key capacity are provided and used to prove that the secret key capacity converges for asymptotic high values of a , to the secret key capacity with an average power constraint a 2 finally , when the amplitude constraint a is small \( a to 0 \) , the secret key capacity is proved to be asymptotically equal to the capacity of the legitimate user with an amplitude constraint a and no secrecy constraint"
cs.DS,"lookahead search is perhaps the most natural and widely used game playing strategy given the practical importance of the method , the aim of this paper is to provide a theoretical performance examination of lookahead search in a wide variety of applications to determine a strategy play using lookahead search , each agent predicts multiple levels of possible re actions to her move \( via the use of a search tree \) , and then chooses the play that optimizes her future payoff accounting for these re actions there are several choices of optimization function the agents can choose , where the most appropriate choice of function will depend on the specifics of the actual game we illustrate this in our examples furthermore , the type of search tree chosen by computationally constrained agent can vary we focus on the case where agents can evaluate only a bounded number , k , of moves into the future that is , we use depth k search trees and call this approach em k lookahead search we apply our method in five well known settings adword auctions industrial organization \( cournot 's model \) congestion games valid utility games and basic utility games cost sharing network design games we consider two questions first , what is the expected social quality of outcome when agents apply lookahead search \? second , what interactive behaviours can be exhibited when players use lookahead search \?"
cs.AI,"ontologies are built on systems that conceptually evolve over time in addition , techniques and languages for building ontologies evolve too this has led to numerous studies in the field of ontology versioning and ontology evolution this paper presents a new way to manage the lifecycle of an ontology incorporating both versioning tools and evolution process this solution , called versiongraph , is integrated in the source ontology since its creation in order to make it possible to evolve and to be versioned change management is strongly related to the model in which the ontology is represented therefore , we focus on the owl language in order to take into account the impact of the changes on the logical consistency of the ontology like specified in owl dl"
cs.DM,"the grundy number of a graph g is the maximum number k of colors used to color the vertices of g such that the coloring is proper and every vertex x colored with color i , is adjacent to \( i 1 \) vertices colored with each color j , in this paper we give bounds for the grundy number of some graphs and cartesian products of graphs in particular , we determine an exact value of this parameter for n dimensional meshes and some n dimensional toroidal meshes finally , we present an algorithm to generate all graphs for a given grundy number"
cs.DM|cs.DS,"let g be a unit disk graph in the plane defined by n disks whose positions are known for the case when g is unweighted , we give a simple algorithm to compute a shortest path tree from a given source in o \( n log n \) time for the case when g is weighted , we show that a shortest path tree from a given source can be computed in o \( n 1 varepsilon \) time , improving the previous best time bound of o \( n 4 3 varepsilon \)"
cs.DM|cs.DS,"let \( g , t \) be an instance of the \( vertex \) multiway cut problem where g is a graph and t is a set of terminals for t in t , a set of nonterminal vertices separating t from t setminus t is called an emph isolating cut of t the largest among all the smallest isolating cuts is a natural lower bound for a multiway cut of \( g , t \) denote this lower bound by m and let k be an integer in this paper we propose an o \( kn k 3 \) algorithm that computes a multiway cut of \( g , t \) of size at most m k or reports that there is no such multiway cut the core of the proposed algorithm is the following combinatorial result let g be a graph and let x , y be two disjoint subsets of vertices of g let m be the smallest size of a vertex x y separator then , for the given integer k , the number of emph important x y separators cite marxtcs of size at most m k is at most sum i 0 k n choose i"
cs.IT,"in this work , two types of codes such that they both dominate and locate the vertices of a graph are studied those codes might be sets of detectors in a network or processors controlling a system whose set of responses should determine a malfunctioning processor or an intruder here , we present our more significant contributions on lambda codes and eta codes concerning concerning bounds , extremal values and realization theorems"
cs.AI|cs.LG,"we consider the task of generative dialogue modeling for movie scripts to this end , we extend the recently proposed hierarchical recurrent encoder decoder neural network and demonstrate that this model is competitive with state of the art neural language models and backoff n gram models we show that its performance can be improved considerably by bootstrapping the learning from a larger question answer pair corpus and from pretrained word embeddings"
cs.IT,"this work considers load balance control among the relays under the secure transmission protocol via relay cooperation in two hop wireless networks without the information of both eavesdropper channels and locations the available two hop secure transmission protocols in physical layer secrecy framework cannot provide a flexible load balance control , which may significantly limit their application scopes this paper proposes a secure transmission protocol in case that the path loss is identical between all pairs of nodes , in which the relay is randomly selected from the first k preferable assistant relays this protocol enables load balance among relays to be flexibly controlled by a proper setting of the parameter k , and covers the available works as special cases , like ones with the optimal relay selection \( k 1 \) and ones with the random relay selection \( k n , i e the number of system nodes \) the theoretic analysis is further provided to determine the maximum number of eavesdroppers one network can tolerate by applying the proposed protocol to ensure a desired performance in terms of the secrecy outage probability and transmission outage probability"
cs.DS|cs.IT,"this paper presents an information theoretic approach to address the phasor measurement unit \( pmu \) placement problem in electric power systems different from the conventional 'topological observability' based approaches , this paper advocates a much more refined , information theoretic criterion , namely the mutual information \( mi \) between the pmu measurements and the power system states the proposed mi criterion can not only include the full system observability as a special case , but also can rigorously model the remaining uncertainties in the power system states with pmu measurements , so as to generate highly informative pmu configurations further , the mi criterion can facilitate robust pmu placement by explicitly modeling probabilistic pmu outages we propose a greedy pmu placement algorithm , and show that it achieves an approximation ratio of \( 1 1 e \) for any pmu placement budget we further show that the performance is the best that one can achieve in practice , in the sense that it is np hard to achieve any approximation ratio beyond \( 1 1 e \) such performance guarantee makes the greedy algorithm very attractive in the practical scenario of multi stage installations for utilities with limited budgets finally , simulation results demonstrate near optimal performance of the proposed pmu placement algorithm"
cs.IT,"while the efficiency of mimo transmissions in a rich scattering environment has been demonstrated , less is known about the situation where the fading matrix coefficients come from a line of sight model in this paper , we study in detail how this line of sight assumption affects the performance of distributed mimo transmissions between far away clusters of nodes in a wireless network our analysis pertains to the study of a new class of random matrices"
cs.IT,"the problem of quickest detection of an anomalous process among m processes is considered at each time , a subset of the processes can be observed , and the observations from each chosen process follow two different distributions , depending on whether the process is normal or abnormal the objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint this problem can be considered as a special case of active hypothesis testing first considered by chernoff in 1959 where a randomized strategy , referred to as the chernoff test , was proposed and shown to be asymptotically \( as the error probability approaches zero \) optimal for the special case considered in this paper , we show that a simple deterministic test achieves asymptotic optimality and offers better performance in the finite regime we further extend the problem to the case where multiple anomalous processes are present in particular , we examine the case where only an upper bound on the number of anomalous processes is known"
cs.IT,"in this paper we develop a statistical mechanical interpretation of the noiseless source coding scheme based on an absolutely optimal instantaneous code the notions in statistical mechanics such as statistical mechanical entropy , temperature , and thermal equilibrium are translated into the context of noiseless source coding especially , it is discovered that the temperature 1 corresponds to the average codeword length of an instantaneous code in this statistical mechanical interpretation of noiseless source coding scheme this correspondence is also verified by the investigation using box counting dimension using the notion of temperature and statistical mechanical arguments , some information theoretic relations can be derived in the manner which appeals to intuition"
cs.AI,"this paper briefly characterizes the field of cognitive computing as an exemplification , the field of natural language question answering is introduced together with its specific challenges a possibility to master these challenges is illustrated by a detailed presentation of the loganswer system , which is a successful representative of the field of natural language question answering"
cs.IT,"a new lower bound on the minimum hamming distance of linear quasi cyclic codes over finite fields is proposed it is based on spectral analysis and generalizes the semenov trifonov bound in a similar way as the hartmann tzeng bound extends the bch approach for cyclic codes furthermore , a syndrome based algebraic decoding algorithm is given"
cs.SI,"most of the complex social , technological and biological networks have a significant community structure therefore the community structure of complex networks has to be considered as a universal property , together with the much explored small world and scale free properties of these networks despite the large interest in characterizing the community structures of real networks , not enough attention has been devoted to the detection of universal mechanisms able to spontaneously generate networks with communities triadic closure is a natural mechanism to make new connections , especially in social networks here we show that models of network growth based on simple triadic closure naturally lead to the emergence of community structure , together with fat tailed distributions of node degree , high clustering coefficients communities emerge from the initial stochastic heterogeneity in the concentration of links , followed by a cycle of growth and fragmentation communities are the more pronounced , the sparser the graph , and disappear for high values of link density and randomness in the attachment procedure by introducing a fitness based link attractivity for the nodes , we find a novel phase transition , where communities disappear for high heterogeneity of the fitness distribution , but a new mesoscopic organization of the nodes emerges , with groups of nodes being shared between just a few superhubs , which attract most of the links of the system"
cs.AI|cs.LG,"we address an important problem in sequence to sequence \( seq2seq \) learning referred to as copying , in which certain segments in the input sequence are selectively replicated in the output sequence a similar phenomenon is observable in human language communication for example , humans tend to repeat entity names or even long phrases in conversation the challenge with regard to copying in seq2seq is that new machinery is needed to decide when to perform the operation in this paper , we incorporate copying into neural network based seq2seq learning and propose a new model called copynet with encoder decoder structure copynet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub sequences in the input sequence and put them at proper places in the output sequence our empirical study on both synthetic data sets and real world data sets demonstrates the efficacy of copynet for example , copynet can outperform regular rnn based model with remarkable margins on text summarization tasks"
cs.IT,"the exact average complexity analysis of the basic sphere decoder for general space time codes applied to multiple input multiple output \( mimo \) wireless channel is known to be difficult in this work , we shed the light on the computational complexity of sphere decoding for the quasi static , lattice space time \( last \) coded mimo channel specifically , we drive an upper bound of the tail distribution of the decoder 's computational complexity we show that , when the computational complexity exceeds a certain limit , this upper bound becomes dominated by the outage probability achieved by last coding and sphere decoding schemes we then calculate the minimum average computational complexity that is required by the decoder to achieve near optimal performance in terms of the system parameters our results indicate that there exists a cut off rate \( multiplexing gain \) for which the average complexity remains bounded"
cs.DS,"let a and b be two finite sets of points with total cardinality n , the many to many point matching with demands and capacities matches each point ai in a to at least alpha'i and at most alphai points in b , and each point bj in b to at least betaj and at most beta'j points in a for all 1 i s and 1 j t in this paper , we present an upper bound for this problem using our new polynomial time algorithm"
cs.SI,"previous studies on the invulnerability of scale free networks under edge attacks supported the conclusion that scale free networks would be fragile under selective attacks however , these studies are based on qualitative methods with obscure definitions on the robustness this paper therefore employs a quantitative method to analyze the invulnerability of the scale free networks , and uses four scale free networks as the experimental group and four random networks as the control group the experimental results show that some scale free networks are robust under selective edge attacks , different to previous studies thus , this paper analyzes the difference between the experimental results and previous studies , and suggests reasonable explanations"
cs.IT,"this work considers the multiple access multicast error correction scenario over a packetized network with z malicious edge adversaries the network has min cut m and packets of length ell , and each sink demands all information from the set of sources sources the capacity region is characterized for both a side channel model \( where sources and sinks share some random bits that are secret from the adversary \) and an omniscient adversarial model \( where no limitations on the adversary 's knowledge are assumed \) in the side channel adversarial model , the use of a secret channel allows higher rates to be achieved compared to the omniscient adversarial model , and a polynomial complexity capacity achieving code is provided for the omniscient adversarial model , two capacity achieving constructions are given the first is based on random subspace code design and has complexity exponential in ell m , while the second uses a novel multiple field extension technique and has o \( ell m sources \) complexity , which is polynomial in the network size our code constructions are end to end in that all nodes except the sources and sinks are oblivious to the adversaries and may simply implement predesigned linear network codes \( random or otherwise \) also , the sources act independently without knowledge of the data from other sources"
cs.IT,"orthogonal frequency division multiplexing \( ofdm \) is an attractive modulation and multiple access techniques for channels with a nonflat frequency response , as it saves the need for complex equalizers it can offer high quality performance in terms of bandwidth efficiency , robustness against multipath fading and cost effective implementation however , its main disadvantage is the high peak to average power ratio \( papr \) of the output signal as a result , a linear behavior of the system over a large dynamic range is needed and therefore the efficiency of the output amplifier is reduced in this paper , we investigate the effect of some of these sets of time waveforms on the ofdm system performance in terms of bit error rate \( ber \) we evaluate the system performance in awgn channels the obtained results indicate that the reduction in papr of the investigated methods is associated with considerable improvement in ber performance of the system , in multipath channels , as compared to conventional ofdm these promising results indicate that pulse shaping with reduced papr is an attractive solution for an ofdm system"
cs.LG|stat.ML,"one of the most tedious tasks in the application of machine learning is model selection , i e hyperparameter selection fortunately , recent progress has been made in the automation of this process , through the use of sequential model based optimization \( smbo \) methods this can be used to optimize a cross validation performance of a learning algorithm over the value of its hyperparameters however , it is well known that ensembles of learned models almost consistently outperform a single model , even if properly selected in this paper , we thus propose an extension of smbo methods that automatically constructs such ensembles this method builds on a recently proposed ensemble construction paradigm known as agnostic bayesian learning in experiments on 22 regression and 39 classification data sets , we confirm the success of this proposed approach , which is able to outperform model selection with smbo"
cs.LG|stat.ML,"deep gaussian processes \( dgps \) are multi layer hierarchical generalisations of gaussian processes \( gps \) and are formally equivalent to neural networks with multiple , infinitely wide hidden layers dgps are nonparametric probabilistic models and as such are arguably more flexible , have a greater capacity to generalise , and provide better calibrated uncertainty estimates than alternative deep models this paper develops a new approximate bayesian learning scheme that enables dgps to be applied to a range of medium to large scale regression problems for the first time the new method uses an approximate expectation propagation procedure and a novel and efficient extension of the probabilistic backpropagation algorithm for learning we evaluate the new method for non linear regression on eleven real world datasets , showing that it always outperforms gp regression and is almost always better than state of the art deterministic and sampling based approximate inference methods for bayesian neural networks as a by product , this work provides a comprehensive analysis of six approximate bayesian methods for training neural networks"
cs.IT|stat.ML,"we investigate connections between information theoretic and estimation theoretic quantities in vector poisson channel models in particular , we generalize the gradient of mutual information with respect to key system parameters from the scalar to the vector poisson channel model we also propose , as another contribution , a generalization of the classical bregman divergence that offers a means to encapsulate under a unifying framework the gradient of mutual information results for scalar and vector poisson and gaussian channel models the so called generalized bregman divergence is also shown to exhibit various properties akin to the properties of the classical version the vector poisson channel model is drawing considerable attention in view of its application in various domains as an example , the availability of the gradient of mutual information can be used in conjunction with gradient descent methods to effect compressive sensing projection designs in emerging x ray and document classification applications"
cs.SI,"reliability is one of the important measures of how well the system meets its design objective , and mathematically is the probability that a system will perform satisfactorily for at least a given period of time when the system is described by a connected network of n components \( nodes \) and their l connection \( links \) , the reliability of the system becomes a difficult network design problem which solutions are of great practical interest in science and engineering this paper discusses the numerical method of finding the most reliable network for a given n and l using genetic algorithm for a given topology of the network , the reliability is numerically computed using adjacency matrix for a search in the space of all possible topologies of the connected network with n nodes and l links , genetic operators such as mutation and crossover are applied to the adjacency matrix through a string representation in the context of graphs , the mutation of strings in genetic algorithm corresponds to the rewiring of graphs , while crossover corresponds to the interchange of the sub graphs for small networks where the most reliable network can be found by exhaustive search , genetic algorithm is very efficient for larger networks , our results not only demonstrate the efficiency of our algorithm , but also suggest that the most reliable network will have high symmetry"
cs.IT,"the similarity of the mathematical description of random field spin systems to orthogonal frequency division multiplexing \( ofdm \) scheme for wireless communication is exploited in an intercarrier interference \( ici \) canceller used in the demodulation of ofdm the translational symmetry in the fourier domain generically concentrates the major contribution of ici from each subcarrier in the subcarrier 's neighborhood this observation in conjunction with mean field approach leads to a development of an ici canceller whose necessary cost of computation scales linearly with respect to the number of subcarriers it is also shown that the dynamics of the mean field canceller are well captured by a discrete map of a single macroscopic variable , without taking the spatial and time correlations of estimated variables into account"
cs.DS,"we consider graph properties that can be checked from labels , i e , bit sequences , of logarithmic length attached to vertices we prove that there exists such a labeling for checking a first order formula with free set variables in the graphs of every class that is emph nicely locally cwd decomposable this notion generalizes that of a emph nicely locally tree decomposable class the graphs of such classes can be covered by graphs of bounded emph clique width with limited overlaps we also consider such labelings for emph bounded first order formulas on graph classes of emph bounded expansion some of these results are extended to counting queries"
cs.SI,"drawing on a large database of publicly announced r d alliances , we empirically investigate the evolution of r d networks and the process of alliance formation in several manufacturing sectors over a 24 year period \( 1986 2009 \) our goal is to empirically evaluate the temporal and sectoral robustness of a large set of network indicators , thus providing a more complete description of r d networks with respect to the existing literature we find that most network properties are not only invariant across sectors , but also independent of the scale of aggregation at which they are observed , and we highlight the presence of core periphery architectures in explaining some properties emphasized in previous empirical studies \( e g asymmetric degree distributions and small worlds \) in addition , we show that many properties of r d networks are characterized by a rise and fall dynamics with a peak in the mid nineties we find that such dynamics is driven by mechanisms of accumulative advantage , structural homophily and multiconnectivity in particular , the change from the rise to the fall phase is associated to a structural break in the importance of multiconnectivity"
cs.DM,"multidimensional combinatorial substitutions are rules that replace symbols by finite patterns of symbols in mathbb z d we focus on the case where the patterns are not necessarily rectangular , which requires a specific description of the way they are glued together in the image by a substitution two problems can arise when defining a substitution in such a way it can fail to be consistent , and the patterns in an image by the substitution might overlap we prove that it is undecidable whether a two dimensional substitution is consistent or overlapping , and we provide practical algorithms to decide these properties in some particular cases"
cs.IT,"we consider a novel group testing procedure , termed semi quantitative group testing , motivated by a class of problems arising in genome sequence processing semi quantitative group testing \( sqgt \) is a non binary pooling scheme that may be viewed as a combination of an adder model followed by a quantizer for the new testing scheme we define the capacity and evaluate the capacity for some special choices of parameters using information theoretic methods we also define a new class of disjunct codes suitable for sqgt , termed sq disjunct codes we also provide both explicit and probabilistic code construction methods for sqgt with simple decoding algorithms"
cs.LG,"compared to machines , humans are extremely good at classifying images into categories , especially when they possess prior knowledge of the categories at hand if this prior information is not available , supervision in the form of teaching images is required to learn categories more quickly , people should see important and representative images first , followed by less important images later or not at all however , image importance is individual specific , i e a teaching image is important to a student if it changes their overall ability to discriminate between classes further , students keep learning , so while image importance depends on their current knowledge , it also varies with time in this work we propose an interactive machine teaching algorithm that enables a computer to teach challenging visual concepts to a human our adaptive algorithm chooses , online , which labeled images from a teaching set should be shown to the student as they learn we show that a teaching strategy that probabilistically models the student 's ability and progress , based on their correct and incorrect answers , produces better 'experts' we present results using real human participants across several varied and challenging real world datasets"
cs.AI,"simdialog is a visual editor for dialog in computer games this paper presents the design of simdialog , illustrating how script writers and non programmers can easily create dialog for video games with complex branching structures and dynamic response characteristics the system creates dialog as a directed graph this allows for play using the dialog with a state based cause and effect system that controls selection of non player character responses and can provide a basic scoring mechanism for games"
cs.IT,"multidimensional lattice constellations which present signal space diversity \( ssd \) have been extensively studied for single antenna transmission over fading channels , with focus on their optimal design for achieving high diversity gain in this two part series of papers we present a novel combinatorial geometrical approach based on parallelotope geometry , for the performance evaluation of multidimensional finite lattice constellations with arbitrary structure , dimension and rank in part i , we present an analytical expression for the exact symbol error probability \( sep \) of multidimensional signal sets , and two novel closed form bounds , named multiple sphere lower bound \( mlsb \) and multiple sphere upper bound \( msub \) part ii extends the analysis to the transmission over fading channels , where multidimensional signal sets are commonly used to combat fading degradation numerical and simulation results show that the proposed geometrical approach leads to accurate and tight expressions , which can be efficiently used for the performance evaluation and the design of multidimensional lattice constellations , both in additive white gaussian noise \( awgn \) and fading channels"
cs.SI,"we address the problem of identifying a graph structure from the observation of signals defined on its nodes fundamentally , the unknown graph encodes direct relationships between signal elements , which we aim to recover from observable indirect relationships generated by a diffusion process on the graph the fresh look advocated here permeates benefits from convex optimization and stationarity of graph signals , in order to identify the graph shift operator \( a matrix representation of the graph \) given only its eigenvectors these spectral templates can be obtained , e g , from the sample covariance of independent graph signals diffused on the sought network the novel idea is to find a graph shift that , while being consistent with the provided spectral information , endows the network with certain desired properties such as sparsity to that end we develop efficient inference algorithms stemming from provably tight convex relaxations of natural nonconvex criteria , particularizing the results for two shifts the adjacency matrix and the normalized laplacian algorithms and theoretical recovery conditions are developed not only when the templates are perfectly known , but also when the eigenvectors are noisy or when only a subset of them are given numerical tests showcase the effectiveness of the proposed algorithms in recovering social , brain , and amino acid networks"
cs.LG,"this paper addresses the problem of non bayesian learning over multi agent networks , where agents repeatedly collect partially informative observations about an unknown state of the world , and try to collaboratively learn the true state we focus on the impact of the adversarial agents on the performance of consensus based non bayesian learning , where non faulty agents combine local learning updates with consensus primitives in particular , we consider the scenario where an unknown subset of agents suffer byzantine faults agents suffering byzantine faults behave arbitrarily two different learning rules are proposed"
cs.DM,"in this paper , we show how to use stochastic approximation to compute hitting time of a stochastic process , based on the study of the time for a fluid approximation of this process to be at distance 1 n of its fixed point this approach is developed to study a generalized version of the coupon collector problem the system is composed by n independent identical markov chains at each time step , one markov chain is picked at random and performs one transition we show that the time at which all chains have hit the same state is bounded by a n log n b n log log n o \( n \) where a and b are two constants depending on eigenvalues of the markov chain"
cs.IT,"this paper addresses the assignment of transmission and sleep time slots between interfering transmitters with the objective of minimal power consumption in particular , we address the constructive alignment of discontinuous transmission \( dtx \) time slots under link rate constraints due to the complexity of the combinatorial optimization problem at hand , we resort to heuristic assignment strategies we derive four time slot alignment solutions \( sequential alignment , random alignment , p persistent ranking and dtx alignment with memory \) and identify trade offs one solution , dtx alignment with memory , addresses trade offs of the other three by maintaining memory of past alignment and channel quality to buffer short term changes in channel quality all strategies are found to exhibit similar convergence behavior , but different power consumption and retransmission probabilities dtx alignment with memory is shown to achieve up to 40 savings in power consumption and more than 20 lower retransmission probability than the state of the art"
cs.AI,"business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate , in a negative or positive way , with respect to its expected or desirable outcomes deviant executions of a business process include those that violate compliance rules , or executions that undershoot or exceed performance targets deviance mining is concerned with uncovering the reasons for deviant executions by analyzing business process event logs this article provides a systematic review and comparative evaluation of deviance mining approaches based on a family of data mining techniques known as sequence classification using real life logs from multiple domains , we evaluate a range of feature types and classification methods in terms of their ability to accurately discriminate between normal and deviant executions of a process we also analyze the interestingness of the rule sets extracted using different methods we observe that feature sets extracted using pattern mining techniques only slightly outperform simpler feature sets based on counts of individual activity occurrences in a trace"
cs.LG|stat.ML,"with inspiration from random forests \( rf \) in the context of classification , a new clustering ensemble method cluster forests \( cf \) is proposed geometrically , cf randomly probes a high dimensional data cloud to obtain good local clusterings and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset the search for good local clusterings is guided by a cluster quality measure kappa cf progressively improves each local clustering in a fashion that resembles the tree growth in rf empirical studies on several real world datasets under two different performance metrics show that cf compares favorably to its competitors theoretical analysis reveals that the kappa measure makes it possible to grow the local clustering in a desirable way it is noise resistant a closed form expression is obtained for the mis clustering rate of spectral clustering under a perturbation model , which yields new insights into some aspects of spectral clustering"
cs.SI,"we introduce two models of inclusion hierarchies random graph hierarchy \( rgh \) and limited random graph hierarchy \( lrgh \) in both models a set of nodes at a given hierarchy level is connected randomly , as in the erd h o s r ' e nyi random graph , with a fixed average degree equal to a system parameter c clusters of the resulting network are treated as nodes at the next hierarchy level and they are connected again at this level and so on , until the process cannot continue in the rgh model we use all clusters , including those of size 1 , when building the next hierarchy level , while in the lrgh model clusters of size 1 stop participating in further steps we find that in both models the number of nodes at a given hierarchy level h decreases approximately exponentially with h the height of the hierarchy h , i e the number of all hierarchy levels , increases logarithmically with the system size n , i e with the number of nodes at the first level the height h decreases monotonically with the connectivity parameter c in the rgh model and it reaches a maximum for a certain c max in the lrgh model the distribution of separate cluster sizes in the lrgh model is a power law with an exponent about 1 25 the above results follow from approximate analytical calculations and have been confirmed by numerical simulations"
cs.AI|cs.SI|stat.ML,"the wisdom of crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people it is claimed that , if these criteria are satisfied , then the aggregate decisions made by a group will often be better than those of its individual members inspired by this concept , we present a novel feedback framework for the cluster ensemble problem , which we call wisdom of crowds cluster ensemble \( wocce \) although many conventional cluster ensemble methods focusing on diversity have recently been proposed , wocce analyzes the conditions necessary for a crowd to exhibit this collective wisdom these include decentralization criteria for generating primary results , independence criteria for the base algorithms , and diversity criteria for the ensemble members we suggest appropriate procedures for evaluating these measures , and propose a new measure to assess the diversity we evaluate the performance of wocce against some other traditional base algorithms as well as state of the art ensemble methods the results demonstrate the efficiency of wocce 's aggregate decision making compared to other algorithms"
cs.AI,"the paper introduces a new technique for compressing binary decision diagrams in those cases where random access is not required using this technique , compression and decompression can be done in linear time in the size of the bdd and compression will in many cases reduce the size of the bdd to 1 2 bits per node empirical results for our compression technique are presented , including comparisons with previously introduced techniques , showing that the new technique dominate on all tested instances"
cs.AI,"logics for knowledge representation suffer from over specialization while each logic may provide an ideal representation formalism for some problems , it is less than optimal for others a solution to this problem is to choose from several logics and , when necessary , combine the representations in general , such an approach results in a very difficult problem of combination however , if we can choose the logics from a uniform framework then the problem of combining them is greatly simplified in this paper , we develop such a framework for defeasible logics it supports all defeasible logics that satisfy a strong negation principle we use logic meta programs as the basis for the framework"
cs.IT,"we analyze the dispersions of distributed lossless source coding \( the slepian wolf problem \) , the multiple access channel and the asymmetric broadcast channel for the two encoder slepian wolf problem , we introduce a quantity known as the entropy dispersion matrix , which is analogous to the scalar dispersions that have gained interest recently we prove a global dispersion result that can be expressed in terms of this entropy dispersion matrix and provides intuition on the approximate rate losses at a given blocklength and error probability to gain better intuition about the rate at which the non asymptotic rate region converges to the slepian wolf boundary , we define and characterize two operational dispersions the local dispersion and the weighted sum rate dispersion the former represents the rate of convergence to a point on the slepian wolf boundary while the latter represents the fastest rate for which a weighted sum of the two rates converges to its asymptotic fundamental limit interestingly , when we approach either of the two corner points , the local dispersion is characterized not by a univariate gaussian but a bivariate one as well as a subset of off diagonal elements of the aforementioned entropy dispersion matrix finally , we demonstrate the versatility of our achievability proof technique by providing inner bounds for the multiple access channel and the asymmetric broadcast channel in terms of dispersion matrices all our proofs are unified a so called vector rate redundancy theorem which is proved using the multidimensional berry esseen theorem"
cs.IT,"the classical sampling nyquist shannon kotelnikov theorem states that a band limited continuous time function is uniquely defined by infinite two sided sampling series taken with a sufficient frequency the paper shows that these band limited functions allows an arbitrarily close uniform approximation by functions that are uniquely defined by their extremely sparse subsamples representing arbitrarily small fractions of one sided equidistant sample series with fixed oversampling parameter in particular , an arbitrarily small adjustment of a band limited underlying function makes redundant every \( m 1 \) members of any set of m samples for an arbitrarily large m this allows to bypass , in a certain sense , the restriction on the sampling rate defined by the critical nyquist rate"
cs.SI,"we present an unsupervised model for inducing signed social networks from the content exchanged across network edges inference in this model solves three problems simultaneously \( 1 \) identifying the sign of each edge \( 2 \) characterizing the distribution over content for each edge type \( 3 \) estimating weights for triadic features that map directly to theoretical models such as structural balance to avoid the intractable sum over possible labelings of each network , we employ noise contrastive estimation , thus obtaining the structural parameters efficiently we apply this model to a dataset of movie scripts , with the goal of learning the social function of address terms such as 'madame' , 'comrade' , and 'dude' working without any labeled data , our system offers a coherent clustering of address terms , while at the same time making intuitively plausible judgments of the formality of social relations in each film"
cs.IT,"in this pair of papers \( part i and part ii in this issue \) , we investigate the issue of power control and subcarrier assignment in a sectorized two cell downlink ofdma system impaired by multicell interference as recommended for wimax , we assume that the first part of the available bandwidth is likely to be reused by different base stations \( and is thus subject to multicell interference \) and that the second part of the bandwidth is shared in an orthogonal way between the different base stations \( and is thus protected from multicell interference \) although the problem of multicell resource allocation is nonconvex in this scenario , we provide in part i the general form of the global solution in particular , the optimal resource allocation turns out to be binary in the sense that , except for at most one pivot user in each cell , any user receives data either in the reused bandwidth or in the protected bandwidth , but not in both the determination of the optimal resource allocation essentially reduces to the determination of the latter pivot position"
cs.AI|cs.LG,"we train a number of neural networks to play games bowling , breakout and seaquest using information stored in the memory of a video game console atari 2600 we consider four models of neural networks which differ in size and architecture two networks which use only information contained in the ram and two mixed networks which use both information in the ram and information from the screen as the benchmark we used the convolutional model proposed in nips and received comparable results in all considered games quite surprisingly , in the case of seaquest we were able to train ram only agents which behave better than the benchmark screen only agent mixing screen and ram did not lead to an improved performance comparing to screen only and ram only agents"
cs.IT,"we derive the density evolution equations for non binary low density parity check \( ldpc \) ensembles when transmission takes place over the binary erasure channel we introduce ensembles defined with respect to the general linear group over the binary field for these ensembles the density evolution equations can be written compactly the density evolution for the general linear group helps us in understanding the density evolution for codes defined with respect to finite fields we compute thresholds for different alphabet sizes for various ldpc ensembles surprisingly , the threshold is not a monotonic function of the alphabet size we state the stability condition for non binary ldpc ensembles over any binary memoryless symmetric channel we also give upper bounds on the map thresholds for various non binary ensembles based on exit curves and the area theorem"
cs.IT,"we consider the classical two encoder multiterminal source coding problem where distortion is measured under logarithmic loss we provide a single letter characterization of the achievable rate distortion region for arbitrarily correlated sources with finite alphabets in doing so , we also give the rate distortion region for the m encoder ceo problem \( also under logarithmic loss \) several applications and examples are given"
cs.IT,"we study the performance of estimators of a sparse nonrandom vector based on an observation which is linearly transformed and corrupted by additive white gaussian noise using the reproducing kernel hilbert space framework , we derive a new lower bound on the estimator variance for a given differentiable bias function \( including the unbiased case \) and an almost arbitrary transformation matrix \( including the underdetermined case considered in compressed sensing theory \) for the special case of a sparse vector corrupted by white gaussian noise i e , without a linear transformation and unbiased estimation , our lower bound improves on previously proposed bounds"
cs.DM,"we give the definition of lazard and hall sets in the context of transitive factorizations of free monoids the equivalence of the two properties is proved this allows to build new effective bases of free partially commutative lie algebras the commutation graphs for which such sets exist are completely characterized and we explicit , in this context , the classical pbw rewriting process"
cs.DM,"if a graph has no induced subgraph isomorphic to h 1 or h 2 then it is said to be \( h 1 , h 2 \) free dabrowski and paulusma found 13 open cases for the question whether the clique width of \( h 1 , h 2 \) free graphs is bounded one of them is the class of \( s 1 , 2 , 2 , triangle \) free graphs in this paper we show that these graphs have bounded clique width thus , also \( p 1 2p 2 , triangle \) free graphs have bounded clique width which solves another open problem of dabrowski and paulusma"
cs.IT,"a fundamental problem in any communication system is given a communication channel between a transmitter and a receiver , how many independent signals can be exchanged between them \? arbitrary communication channels that can be described by linear compact channel operators mapping between normed spaces are examined in this paper the \( well known \) notions of degrees of freedom at level epsilon and essential dimension of such channels are developed in this general setting we argue that the degrees of freedom at level epsilon and the essential dimension fundamentally limit the number of independent signals that can be exchanged between the transmitter and the receiver we also generalise the concept of singular values of compact operators to be applicable to compact operators defined on arbitrary normed spaces which do not necessarily carry a hilbert space structure we show how these generalised singular values can be used to calculate the degrees of freedom at level epsilon and the essential dimension of compact operators that describe communication channels we describe physically realistic channels that require such general channel models"
cs.DS|cs.LG,"overfitting is the bane of data analysts , even when data are plentiful formal approaches to understanding this problem focus on statistical inference and generalization of individual analysis procedures yet the practice of data analysis is an inherently interactive and adaptive process new analyses and hypotheses are proposed after seeing the results of previous ones , parameters are tuned on the basis of obtained results , and datasets are shared and reused an investigation of this gap has recently been initiated by the authors in \( dwork et al , 2014 \) , where we focused on the problem of estimating expectations of adaptively chosen functions in this paper , we give a simple and practical method for reusing a holdout \( or testing \) set to validate the accuracy of hypotheses produced by a learning algorithm operating on a training set reusing a holdout set adaptively multiple times can easily lead to overfitting to the holdout set itself we give an algorithm that enables the validation of a large number of adaptively chosen hypotheses , while provably avoiding overfitting we illustrate the advantages of our algorithm over the standard use of the holdout set via a simple synthetic experiment we also formalize and address the general problem of data reuse in adaptive data analysis we show how the differential privacy based approach given in \( dwork et al , 2014 \) is applicable much more broadly to adaptive data analysis we then show that a simple approach based on description length can also be used to give guarantees of statistical validity in adaptive settings finally , we demonstrate that these incomparable approaches can be unified via the notion of approximate max information that we introduce"
cs.AI,"a sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions these devices \( nodes \) are expected to operate autonomously , be battery powered and have very limited computational capabilities this makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem in this document we discuss performance of artificial immune systems \( ais \) when used as the mechanism for detecting misbehavior we show that \( i \) mechanism of the ais have to be carefully applied in order to avoid security weaknesses , \( ii \) the choice of genes and their interaction have a profound influence on the performance of the ais , \( iii \) randomly created detectors do not comply with limitations imposed by communications protocols and \( iv \) the data traffic pattern seems not to impact significantly the overall performance we identified a specific mac layer based gene that showed to be especially useful for detection genes measure a network 's performance from a node 's viewpoint furthermore , we identified an interesting complementarity property of genes this property exploits the local nature of sensor networks and moves the burden of excessive communication from normally behaving nodes to misbehaving nodes these results have a direct impact on the design of ais for sensor networks and on engineering of sensor networks"
cs.DM,"we consider the problem of optimizing a nonlinear objective function over a weighted independence system presented by a linear optimization oracle we provide a polynomial time algorithm that determines an r best solution for nonlinear functions of the total weight of an independent set , where r is a constant that depends on certain frobenius numbers of the individual weights and is independent of the size of the ground set in contrast , we show that finding an optimal \( 0 best \) solution requires exponential time even in a very special case of the problem"
cs.IT,"we analyze fading interference relay networks where m single antenna source destination terminal pairs communicate concurrently and in the same frequency band through a set of k single antenna relays using half duplex two hop relaying assuming that the relays have channel state information \( csi \) , it is shown that in the large m limit , provided k grows fast enough as a function of m , the network decouples in the sense that the individual source destination terminal pair capacities are strictly positive the corresponding required rate of growth of k as a function of m is found to be sufficient to also make the individual source destination fading links converge to nonfading links we say that the network crystallizes as it breaks up into a set of effectively isolated wires in the air a large deviations analysis is performed to characterize the crystallization rate , i e , the rate \( as a function of m , k \) at which the decoupled links converge to nonfading links in the course of this analysis , we develop a new technique for characterizing the large deviations behavior of certain sums of dependent random variables for the case of no csi at the relay level , assuming amplify and forward relaying , we compute the per source destination terminal pair capacity for m , k converging to infinity , with k m staying fixed , using tools from large random matrix theory"
cs.IT,"in 1975 , carleial presented a special case of an interference channel in which the interference does not reduce the capacity of the constituent point to point gaussian channels in this work , we show that if the inequalities in the conditions that carleial stated are strict , the dispersions are similarly unaffected more precisely , in this work , we characterize the second order coding rates of the gaussian interference channel in the strictly very strong interference regime in other words , we characterize the speed of convergence of rates of optimal block codes towards a boundary point of the \( rectangular \) capacity region these second order rates are expressed in terms of the average probability of error and variances of some modified information densities which coincide with the dispersion of the \( single user \) gaussian channel we thus conclude that the dispersions are unaffected by interference in this channel model"
cs.SI,"in this paper , we study factors that influence tag reuse behavior in social tagging systems our work is guided by the activation equation of the cognitive model act r , which states that the usefulness of information in human memory depends on the three factors usage frequency , recency and semantic context it is our aim to shed light on the influence of these factors on tag reuse in our experiments , we utilize six datasets from the social tagging systems flickr , citeulike , bibsonomy , delicious , lastfm and movielens , covering a range of various tagging settings our results confirm that frequency , recency and semantic context positively influence the reuse probability of tags however , the extent to which each factor individually influences tag reuse strongly depends on the type of folksonomy present in a social tagging system our work can serve as guideline for researchers and developers of tag based recommender systems when designing algorithms for social tagging environments"
cs.IT,"we show that the best nonnegative rank r approximation of a nonnegative tensor is almost always unique and that nonnegative tensors with nonunique best nonnegative rank r approximation form a semialgebraic set contained in an algebraic hypersurface we then establish a singular vector variant of the perron frobenius theorem for positive tensors that may be used for answering various questions involving nonnegative tensor approximations in particular , we apply it to show that the best nonnegative rank 1 approximation of a positive tensor is always unique and that a best nonnegative rank r approximation of a positive tensor can almost never be obtained by deflation"
cs.IT,"in a em locally recoverable or em repairable code , any symbol of a codeword can be recovered by reading only a small \( constant \) number of other symbols the notion of local recoverability is important in the area of distributed storage where a most frequent error event is a single storage node failure \( erasure \) a common objective is to repair the node by downloading data from as few other storage node as possible in this paper , we bound the minimum distance of a code in terms of its length , size and locality unlike previous bounds , our bound follows from a significantly simple analysis and depends on the size of the alphabet being used it turns out that the binary simplex codes satisfy our bound with equality hence the simplex codes are the first example of a optimal binary locally repairable code family we also provide achievability results based on random coding and concatenated codes that are numerically verified to be close to our bounds"
cs.IT,"the performance of second order statistics \( sos \) based semi blind channel estimation in long code ds cdma systems is analyzed the covariance matrix of sos estimates is obtained in the large system limit , and is used to analyze the large sample performance of two sos based semi blind channel estimation algorithms a notion of blind estimation efficiency is also defined and is examined via simulation results"
cs.LG|cs.SI|stat.ML,"in this paper , we explore salient questions about user interests , conversations and friendships in the facebook social network , using a novel latent space model that integrates several data types a key challenge of studying facebook 's data is the wide range of data modalities such as text , network links , and categorical labels our latent space model seamlessly combines all three data modalities over millions of users , allowing us to study the interplay between user friendships , interests , and higher order network wide social trends on facebook the recovered insights not only answer our initial questions , but also reveal surprising facts about user interests in the context of facebook 's ecosystem we also confirm that our results are significant with respect to evidential information from the study subjects"
cs.IT,"an important enabler towards the successful deployment of any new element feature to the cellular network is the investigation and characterization of the operational conditions where its introduction will enhance performance even though there has been significant research activity on the potential of device to device \( d2d \) communications , there are currently no clear indications of whether d2d communications are actually able to provide benefits for a wide range of operational conditions , thus justifying their introduction to the system this paper attempts to fill this gap by taking a stochastic geometry approach on characterizing the set \( region \) of operational conditions for which d2d communications enhance performance in terms of average user rate for the practically interesting case of a heavy loaded network , the operational region is provided in closed form as a function of a variety of parameters such as maximum d2d link distances and user densities , reflecting a wide range of operational conditions \( points \) it is shown that under the appropriate deployment scheme , d2d communications can indeed be beneficial not only for the usually considered regime of proximal communications but to a wide range of operational conditions that include d2d link distances comparable to the distance to the cellular access point and considerably large user densities"
cs.IT,"consider r sensors , each one intends to send a function x i \( e g a signal or image \) to a receiver common to all r sensors before transmission , each x i is multiplied by an encoding matrix a i during transmission each a ix i gets convolved with a function h i the receiver records the function y , given by the sum of all these convolved signals assume that the receiver knowns all the a i , but does neither know the x i nor the h i when and under which conditions is it possible to recover the individual signals x i and the channels h i from just one received signal y \? this challenging problem , which intertwines blind deconvolution with blind demixing , appears in a variety of applications , such as audio processing , image processing , neuroscience , spectroscopy , and astronomy it is also expected to play a central role in connection with the future internet of things we will prove that under reasonable and practical assumptions , it is possible to solve this otherwise highly ill posed problem and recover the r transmitted functions x i and the impulse responses h i in a robust , reliable , and efficient manner from just one single received function y by solving a semidefinite program we derive explicit bounds on the number of measurements needed for successful recovery and prove that our method is robust in presence of noise our theory is actually a bit pessimistic , since numerical experiments demonstrate that , quite remarkably , recovery is still possible if the number of measurements is close to the number of degrees of freedom"
cs.IT,"we consider the problem of differentially private query release through a synthetic database approach departing from the existing approaches that require the query set to be specified in advance , we advocate to devise query set independent mechanisms , with an ambitious goal of providing accurate answers , while meeting the privacy constraints , for all queries in a general query class specifically , a differentially private mechanism is constructed to encode rich stochastic structure into the synthetic database , and customized companion estimators are then derived to provide accurate answers by making use of all available information , including the mechanism \( which is public information \) and the query functions accordingly , the distortion under the best of this kind of mechanisms at the worst case query in a general query class , so called the minimax distortion , provides a fundamental characterization of differentially private query release for the general class of statistical queries , we prove that with the squared error distortion measure , the minimax distortion is o \( 1 n \) by deriving asymptotically tight upper and lower bounds in the regime that the database size n goes to infinity the upper bound is achievable by a mechanism mathcal e and its corresponding companion estimators , which points directly to the feasibility of the proposed approach in large databases we further evaluate the mechanism mathcal e and the companion estimators through experiments on real datasets from netflix and facebook experimental results show improvement over the state of art mwem algorithm and verify the scaling behavior o \( 1 n \) of the minimax distortion"
cs.DM,"we show some new examples how limit theory can help understanding other combinatorial structures first , we generalize the manickam mikl 'os singhi conjecture , using limit theory then we introduce two limit problems of alpern 's caching game , which are good approximations of the finite game when some parameters tend to infinity with the use of these limit problems , we show a surprising result which disproves some conjectures about the finite problem"
cs.AI,"artificial immune systems have been successfully applied to a number of problem domains including fault tolerance and data mining , but have been shown to scale poorly when applied to computer intrusion detec tion despite the fact that the biological immune system is a very effective anomaly detector this may be because ais algorithms have previously been based on the adaptive immune system and biologically naive mod els this paper focuses on describing and testing a more complex and biologically authentic ais model , inspired by the interactions between the innate and adaptive immune systems its performance on a realistic process anomaly detection problem is shown to be better than standard ais methods \( negative selection \) , policy based anomaly detection methods \( systrace \) , and an alternative innate ais approach \( the dca \) in addition , it is shown that runtime information can be used in combination with system call information to enhance detection capability"
cs.IT,"the capacity of caching networks has received considerable attention in the past few years a particularly studied setting is the case of a single server \( e g , a base station \) and multiple users , each of which caches segments of files in a finite library each user requests one \( whole \) file in the library and the server sends a common coded multicast message to satisfy all users at once the problem consists of finding the smallest possible codeword length to satisfy such requests in this paper we consider the generalization to the case where each user places l geq 1 requests the obvious naive scheme consists of applying l times the order optimal scheme for a single request , obtaining a linear in l scaling of the multicast codeword length we propose a new achievable scheme based on multiple groupcast index coding that achieves a significant gain over the naive scheme furthermore , through an information theoretic converse we find that the proposed scheme is approximately optimal within a constant factor of \( at most \) 18"
cs.AI|cs.LG,"this paper presents a model for end to end learning of task oriented dialog systems the main component of the model is a recurrent neural network \( an lstm \) , which maps from raw dialog history directly to a distribution over system actions the lstm automatically infers a representation of dialog history , which relieves the system developer of much of the manual feature engineering of dialog state in addition , the developer can provide software that expresses business rules and provides access to programmatic apis , enabling the lstm to take actions in the real world on behalf of the user the lstm can be optimized using supervised learning \( sl \) , where a domain expert provides example dialogs which the lstm should imitate or using reinforcement learning \( rl \) , where the system improves by interacting directly with end users experiments show that sl and rl are complementary sl alone can derive a reasonable initial policy from a small number of training dialogs and starting rl optimization with a policy trained with sl substantially accelerates the learning rate of rl"
cs.IT,"this article examines wireless communications from a source to a destination in the presence of an eavesdropper from a security reliability tradeoff \( srt \) perspective explicitly , the security is quantified in terms of the intercept probability experienced at the eavesdropper , while the outage probability encountered at the destination is used to measure the transmission reliability we characterize the srt of conventional direct transmission from the source to the destination and show that if the outage probability is increased , the intercept probability decreases , and vice versa we first demonstrate that the employment of relay nodes for assisting the source destination transmissions is capable of defending against eavesdropping , followed by quantifying the benefits of single relay selection \( srs \) as well as of multi relay selection \( mrs \) schemes more specifically , in the srs scheme , only the single best relay is selected for forwarding the source signal to the destination , whereas the mrs scheme allows multiple relays to participate in this process it is illustrated that both the srs and mrs methods achieve a better srt than the conventional direct transmission , especially upon increasing the number of relays numerical results also show that as expected , the mrs outperforms the srs in terms of its srt additionally , we present some open challenges and future directions for wireless relay aided physical layer security"
cs.LG,"the research question this report addresses is how , and to what extent , those directly involved with the design , development and employment of a specific black box algorithm can be certain that it is not unlawfully discriminating \( directly and or indirectly \) against particular persons with protected characteristics \( e g gender , race and ethnicity \) \?"
cs.IT,"we consider communication over the binary erasure channel \( bec \) using low density parity check \( ldpc \) codes and belief propagation \( bp \) decoding for fixed numbers of bp iterations , the bit error probability approaches a limit as blocklength tends to infinity , and the limit is obtained via density evolution on the other hand , the difference between the bit error probability of codes with blocklength n and that in the large blocklength limit is asymptotically alpha \( epsilon , t \) n theta \( n 2 \) where alpha \( epsilon , t \) denotes a specific constant determined by the code ensemble considered , the number t of iterations , and the erasure probability epsilon of the bec in this paper , we derive a set of recursive formulas which allows evaluation of the constant alpha \( epsilon , t \) for standard irregular ensembles the dominant difference alpha \( epsilon , t \) n can be considered as effects of cycle free and single cycle structures of local graphs furthermore , it is confirmed via numerical simulations that estimation of the bit error probability using alpha \( epsilon , t \) is accurate even for small blocklengths"
cs.AI,"electronic geometry textbook is a knowledge management system that manages geometric textbook knowledge to enable users to construct and share dynamic geometry textbooks interactively and efficiently based on a knowledge base organizing and storing the knowledge represented in specific languages , the system implements interfaces for maintaining the data representing that knowledge as well as relations among those data , for automatically generating readable documents for viewing or printing , and for automatically discovering the relations among knowledge data an interface has been developed for users to create geometry textbooks with automatic checking , in real time , of the consistency of the structure of each resulting textbook by integrating an external geometric theorem prover and an external dynamic geometry software package , the system offers the facilities for automatically proving theorems and generating dynamic figures in the created textbooks this paper provides a comprehensive account of the current version of electronic geometry textbook"
cs.IT,"next generation cellular networks will consist of multiple tiers of cells and users associated with different network tiers may have different priorities \( e g , macrocell picocell femtocell networks with macro tier prioritized over pico tier , which is again prioritized over femto tier \) designing efficient joint power and admission control \( jpac \) algorithms for such networks under a co channel deployment \( i e , underlay \) scenario is of significant importance feasibility checking of a given target signal to noise plus interference ratio \( sinr \) vector is generally the most significant contributor to the complexity of jpac algorithms in single multi tier underlay cellular networks this is generally accomplished through iterative strategies whose complexity is either unpredictable or of o \( m 3 \) , when the well known relationship between the sinr vector and the power vector is used , where m is the number of users links in this paper , we derive a novel relationship between a given sinr vector and its corresponding uplink downlink power vector based on which the feasibility checking can be performed with a complexity of o \( b 3 m b \) , where b is the number of base stations this is significantly less compared to o \( m 3 \) in many cellular wireless networks since the number of base stations is generally much lower than the number of users links in such networks the developed novel relationship between the sinr and power vector not only substantially reduces the complexity of designing jpac algorithms , but also provides insights into developing efficient but low complexity power update strategies for prioritized multi tier cellular networks we propose two such algorithms and through simulations , we show that our proposed algorithms outperform the existing ones in prioritized cellular networks"
cs.DM|cs.DS,"we propose to classify the power of algorithms by the complexity of the problems that they can be used to solve instead of restricting to the problem a particular algorithm was designed to solve explicitly , however , we include problems that , with polynomial overhead , can be solved 'implicitly' during the algorithm 's execution for example , we allow to solve a decision problem by suitably transforming the input , executing the algorithm , and observing whether a specific bit in its internal configuration ever switches during the execution we show that the simplex method , the network simplex method \( both with dantzig 's original pivot rule \) , and the successive shortest path algorithm are np mighty , that is , each of these algorithms can be used to solve any problem in np this result casts a more favorable light on these algorithms' exponential worst case running times furthermore , as a consequence of our approach , we obtain several novel hardness results for example , for a given input to the simplex algorithm , deciding whether a given variable ever enters the basis during the algorithm 's execution and determining the number of iterations needed are both np hard problems finally , we close a long standing open problem in the area of network flows over time by showing that earliest arrival flows are np hard to obtain"
cs.DS,"we study the stochastic versions of a broad class of combinatorial problems where the weights of the elements in the input dataset are uncertain the class of problems that we study includes shortest paths , minimum weight spanning trees , and minimum weight matchings , and other combinatorial problems like knapsack we observe that the expected value is inadequate in capturing different types of em risk averse or em risk prone behaviors , and instead we consider a more general objective which is to maximize the em expected utility of the solution for some given utility function , rather than the expected weight \( expected weight becomes a special case \) under the assumption that there is a pseudopolynomial time algorithm for the em exact version of the problem \( this is true for the problems mentioned above \) , we can obtain the following approximation results for several important classes of utility functions \( 1 \) if the utility function uti is continuous , upper bounded by a constant and lim x rightarrow infty uti \( x \) 0 , we show that we can obtain a polynomial time approximation algorithm with an em additive error epsilon for any constant epsilon 0 \( 2 \) if the utility function uti is a concave increasing function , we can obtain a polynomial time approximation scheme \( ptas \) \( 3 \) if the utility function uti is increasing and has a bounded derivative , we can obtain a polynomial time approximation scheme our results recover or generalize several prior results on stochastic shortest path , stochastic spanning tree , and stochastic knapsack our algorithm for utility maximization makes use of the separability of exponential utility and a technique to decompose a general utility function into exponential utility functions , which may be useful in other stochastic optimization problems"
cs.AI|cs.LG|stat.ML,"this paper proposes gprop , a deep reinforcement learning algorithm for continuous policies with compatible function approximation the algorithm is based on two innovations firstly , we present a temporal difference based method for learning the gradient of the value function secondly , we present the deviator actor critic \( dac \) model , which comprises three neural networks that estimate the value function , its gradient , and determine the actor 's policy respectively we evaluate gprop on two challenging tasks a contextual bandit problem constructed from nonparametric regression datasets that is designed to probe the ability of reinforcement learning algorithms to accurately estimate gradients and the octopus arm , a challenging reinforcement learning benchmark gprop is competitive with fully supervised methods on the bandit task and achieves the best performance to date on the octopus arm"
cs.AI|stat.ML,"in this paper we propose two new algorithms based on biclustering analysis , which can be used at the basis of a recommender system for educational orientation of russian school graduates the first algorithm was designed to help students make a choice between different university faculties when some of their preferences are known the second algorithm was developed for the special situation when nothing is known about their preferences the final version of this recommender system will be used by higher school of economics"
cs.IT,"we study a simple question when are dynamic relaying strategies essential in optimizing the diversity multiplexing tradeoff \( dmt \) in half duplex wireless relay networks \? this is motivated by apparently two contrasting results even for a simple 3 node network , with a single half duplex relay when all channels are assumed to be i i d fading , a static schedule where the relay listens half the time and transmits half the time combined with quantize map forward \( qmf \) relaying is known to achieve the full duplex performance however , when there is no direct link between source and destination , a dynamic decode forward \( ddf \) strategy is needed to achieve the optimal tradeoff in this case , a static schedule is strictly suboptimal and the optimal tradeoff is significantly worse than the full duplex performance in this paper we study the general case when the direct link is neither as strong as the other links nor fully non existent , and identify regimes where dynamic schedules are necessary and those where static schedules are enough we identify 4 qualitatively different regimes for the single relay channel where the tradeoff between diversity and multiplexing is significantly different we show that in all these regimes one of the above two strategies is sufficient to achieve the optimal tradeoff by developing a new upper bound on the best achievable tradeoff under channel state information available only at the receivers a natural next question is whether these two strategies are sufficient to achieve the dmt of more general half duplex wireless networks we propose a generalization of the two existing schemes through a dynamic qmf \( dqmf \) strategy , where the relay listens for a fraction of time depending on received csi but not long enough to be able to decode we show that such a dqmf strategy is needed to achieve the optimal dmt in a parallel channel with two relays"
cs.IT,"the large majority of commercially available multiple input multiple output \( mimo \) radio channel measurement devices \( sounders \) is based on time division multiplexed switching \( tdms \) of a single transmit receive radio frequency chain into the elements of a transmit receive antenna array while being cost effective , such a solution can cause significant measurement errors due to phase noise and frequency offset in the local oscillators in this paper , we systematically analyze the resulting errors and show that , in practice , overestimation of channel capacity by several hundred percent can occur overestimation is caused by phase noise \( and to a lesser extent frequency offset \) leading to an increase of the mimo channel rank our analysis furthermore reveals that the impact of phase errors is , in general , most pronounced if the physical channel has low rank \( typical for line of sight or poor scattering scenarios \) the extreme case of a rank 1 physical channel is analyzed in detail finally , we present measurement results obtained from a commercially employed tdms based mimo channel sounder in the light of the findings of this paper , the results obtained through mimo channel measurement campaigns using tdms based channel sounders should be interpreted with great care"
cs.DM,"we tackle the problem of constructing increasing chord graphs spanning point sets we prove that , for every point set p with n points , there exists an increasing chord planar graph with o \( n \) steiner points spanning p further , we prove that , for every convex point set p with n points , there exists an increasing chord graph with o \( n log n \) edges \( and with no steiner points \) spanning p"
cs.IT,"in this paper , we study band allocation of mathcal m s buffered secondary users \( sus \) to mathcal m p orthogonal primary licensed bands , where each primary band is assigned to one primary user \( pu \) each su is assigned to one of the available primary bands with a certain probability designed to satisfy some specified quality of service \( qos \) requirements for the sus in the proposed system , only one su is assigned to a particular band the optimization problem used to obtain the stability region 's envelope \( closure \) is shown to be a linear program we compare the stability region of the proposed system with that of a system where each su chooses a band randomly with some assignment probability we also compare with a fixed \( deterministic \) assignment system , where only one su is assigned to one of the primary bands all the time we prove the advantage of the proposed system over the other systems"
cs.LG|stat.ML,"invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks nevertheless , it is unclear to which extent state of the art classifiers are invariant to basic transformations such as rotations and translations this is mainly due to the lack of general methods that properly measure such an invariance in this paper , we propose a rigorous and systematic approach for quantifying the invariance to geometric transformations of any classifier our key idea is to cast the problem of assessing a classifier 's invariance as the computation of geodesics along the manifold of transformed images we propose the manitest method , built on the efficient fast marching algorithm to compute the invariance of classifiers our new method quantifies in particular the importance of data augmentation for learning invariance from data , and the increased invariance of convolutional neural networks with depth we foresee that the proposed generic tool for measuring invariance to a large class of geometric transformations and arbitrary classifiers will have many applications for evaluating and comparing classifiers based on their invariance , and help improving the invariance of existing classifiers"
cs.IT|stat.ML,"this paper examines a general class of noisy matrix completion tasks where the goal is to estimate a matrix from observations obtained at a subset of its entries , each of which is subject to random noise or corruption our specific focus is on settings where the matrix to be estimated is well approximated by a product of two \( a priori unknown \) matrices , one of which is sparse such structural models referred to here as sparse factor models have been widely used , for example , in subspace clustering applications , as well as in contemporary sparse modeling and dictionary learning tasks our main theoretical contributions are estimation error bounds for sparsity regularized maximum likelihood estimators for problems of this form , which are applicable to a number of different observation noise or corruption models several specific implications are examined , including scenarios where observations are corrupted by additive gaussian noise or additive heavier tailed \( laplace \) noise , poisson distributed observations , and highly quantized \( e g , one bit \) observations we also propose a simple algorithmic approach based on the alternating direction method of multipliers for these tasks , and provide experimental evidence to support our error analyses"
cs.IT,"network coding is a packet encoding technique which has recently been shown to improve network performance \( by reducing delays and increasing throughput \) in broadcast and multicast communications the cost for such an improvement comes in the form of increased decoding complexity \( and thus delay \) at the receivers end before delivering the file to higher layers , the receiver should first decode those packets in our work we consider the broadcast transmission of a large file to n wireless users the file is segmented into a number of blocks \( each containing k packets the coding window size \) the packets of each block are encoded using random linear network coding \( rlnc \) we obtain the minimum coding window size so that the completion time of the file transmission is upper bounded by a used defined delay constraint"
stat.ML,"in this paper , we study a fast approximation method for it large scale high dimensional sparse least squares regression problem by exploiting the johnson lindenstrauss \( jl \) transforms , which embed a set of high dimensional vectors into a low dimensional space in particular , we propose to apply the jl transforms to the data matrix and the target vector and then to solve a sparse least squares problem on the compressed data with a it slightly larger regularization parameter theoretically , we establish the optimization error bound of the learned model for two different sparsity inducing regularizers , i e , the elastic net and the ell 1 norm compared with previous relevant work , our analysis is it non asymptotic and exhibits more insights on the bound , the sample complexity and the regularization as an illustration , we also provide an error bound of the it dantzig selector under jl transforms"
cs.DS,"this paper studies the problem of testing if an input \( gamma , \) , where gamma is a finite set of unknown size and is a binary operation over gamma given as an oracle , is close to a specified class of groups friedl et al efficient testing of groups , stoc'05 have constructed an efficient tester using poly \( log gamma \) queries for the class of abelian groups we focus in this paper on subclasses of abelian groups , and show that these problems are much harder omega \( gamma 1 6 \) queries are necessary to test if the input is close to a cyclic group , and omega \( gamma c \) queries for some constant c are necessary to test more generally if the input is close to an abelian group generated by k elements , for any fixed integer k 0 we also show that knowledge of the size of the ground set gamma helps only for k 1 , in which case we construct an efficient tester using poly \( log gamma \) queries for any other value k 1 the query complexity remains omega \( gamma c \) all our upper and lower bounds hold for both the edit distance and the hamming distance these are , to the best of our knowledge , the first nontrivial lower bounds for such group theoretic problems in the property testing model and , in particular , they imply the first exponential separations between the classical and quantum query complexities of testing closeness to classes of groups"
cs.SI,"we study the networks formed by the directors of the most important swiss boards and the boards themselves for the year 2009 the networks are obtained by projection from the original bipartite graph we highlight a number of important statistical features of those networks such as degree distribution , weight distribution , and several centrality measures as well as their interrelationships while similar statistics were already known for other board systems , and are comparable here , we have extended the study with a careful investigation of director and board centrality , a k core analysis , and a simulation of the speed of information propagation and its relationships with the topological aspects of the network such as clustering and link weight and betweenness the overall picture that emerges is one in which the topological structure of the swiss board and director networks has evolved in such a way that special actors and links between actors play a fundamental role in the flow of information among distant parts of the network this is shown in particular by the centrality measures and by the simulation of a simple epidemic process on the directors network"
cs.DM,"let f v rightarrow 1 , ldots , k be a labeling of the vertices of a graph g \( v , e \) and denote with f \( n \( v \) \) the sum of the labels of all vertices adjacent to v the least value k for which a graph g admits a labeling satisfying f \( n \( u \) \) neq f \( n \( v \) \) for all \( u , v \) in e is called emph additive chromatic number of g and denoted eta \( g \) it was first presented by czerwi 'nski , grytczuk and zelazny who also proposed a conjecture that for every graph g , eta \( g \) leq chi \( g \) , where chi \( g \) is the chromatic number of g bounds of eta \( g \) are known for very few families of graphs in this work , we show that the conjecture holds for split graphs by giving an upper bound of the additive chromatic number and we present exact formulas for computing eta \( g \) when g is a fan , windmill , circuit , wheel , complete split , headless spider , cycle wheel complete sun , regular bipartite or complete multipartite observing that the conjecture is satisfied in all cases in addition , we propose an integer programming formulation which is used for checking the conjecture over all connected graphs up to 10 vertices"
cs.IT|cs.LG|stat.ML,"in the context of sparse recovery , it is known that most of existing regularizers such as ell 1 suffer from some bias incurred by some leading entries \( in magnitude \) of the associated vector to neutralize this bias , we propose a class of models with partial regularizers for recovering a sparse solution of a linear system we show that every local minimizer of these models is sufficiently sparse or the magnitude of all its nonzero entries is above a uniform constant depending only on the data of the linear system moreover , for a class of partial regularizers , any global minimizer of these models is a sparsest solution to the linear system we also establish some sufficient conditions for local or global recovery of the sparsest solution to the linear system , among which one of the conditions is weaker than the best known restricted isometry property \( rip \) condition for sparse recovery by ell 1 in addition , a first order feasible augmented lagrangian \( fal \) method is proposed for solving these models , in which each subproblem is solved by a nonmonotone proximal gradient \( npg \) method despite the complication of the partial regularizers , we show that each proximal subproblem in npg can be solved as a certain number of one dimensional optimization problems , which usually have a closed form solution we also show that any accumulation point of the sequence generated by fal is a first order stationary point of the models numerical results on compressed sensing and sparse logistic regression demonstrate that the proposed models substantially outperform the widely used ones in the literature in terms of solution quality"
cs.IT,"we propose an algorithm with expected complexity of bigo \( n log n \) arithmetic operations to solve a special shortest vector problem arising in computer and forward design , where n is the dimension of the channel vector this algorithm is more efficient than the best known algorithms with proved complexity"
cs.IT|cs.LG|stat.ML,"in this paper , we develop an approach to recursively estimate the quadratic risk for matrix recovery problems regularized with spectral functions toward this end , in the spirit of the sure theory , a key step is to compute the \( weak \) derivative and divergence of a solution with respect to the observations as such a solution is not available in closed form , but rather through a proximal splitting algorithm , we propose to recursively compute the divergence from the sequence of iterates a second challenge that we unlocked is the computation of the \( weak \) derivative of the proximity operator of a spectral function to show the potential applicability of our approach , we exemplify it on a matrix completion problem to objectively and automatically select the regularization parameter"
cs.IT,"component selection is considered one of hard tasks in component based software engineering \( cbse \) it is difficult to find the optimal component selection cbse is an approach that is used to develop a software system from pre existing software components appropriate software component selection plays an important role in cbse many approaches were suggested to solve component selection problem in this paper the component selection is done by improving the integrated component selection framework by including the pliability metric pliability is a flexible measure that assesses software quality in terms of its components quality the validation of this proposed solution is done through collecting a sample of people who answer an electronic questionnaire that composed of 20 questions the questionnaire is distributed through social sites such as twitter , facebook and emails the result of the validation showed that using the integrated component selection framework with pliability metric is suitable for component selection"
cs.IT,"this paper focusses on the sparse estimation in the situation where both the the sensing matrix and the measurement vector are corrupted by additive gaussian noises the performance bound of sparse estimation is analyzed and discussed in depth two types of lower bounds , the constrained cram ' e r rao bound \( ccrb \) and the hammersley chapman robbins bound \( hcrb \) , are discussed it is shown that the situation with sensing matrix perturbation is more complex than the one with only measurement noise for the ccrb , its closed form expression is deduced it demonstrates a gap between the maximal and nonmaximal support cases it is also revealed that a gap lies between the ccrb and the mse of the oracle pseudoinverse estimator , but it approaches zero asymptotically when the problem dimensions tend to infinity for a tighter bound , the hcrb , despite of the difficulty in obtaining a simple expression for general sensing matrix , a closed form expression in the unit sensing matrix case is derived for a qualitative study of the performance bound it is shown that the gap between the maximal and nonmaximal cases is eliminated for the hcrb numerical simulations are performed to verify the theoretical results in this paper"
cs.IT,"energy cost of cellular networks is ever increasing to match the surge of wireless data traffic , and the saving of this cost is important to reduce the operational expenditure \( opex \) of wireless operators in future the recent advancements of renewable energy integration and two way energy flow in smart grid provide potential new solutions to save the cost however , they also impose challenges , especially on how to use the stochastically and spatially distributed renewable energy harvested at cellular base stations \( bss \) to reliably supply time and space varying wireless traffic over cellular networks to overcome these challenges , in this article we present three approaches , namely , emph energy cooperation , communication cooperation , and joint energy and communication cooperation , in which different bss bidirectionally trade or share energy via the aggregator in smart grid , and or share wireless resources and shift loads with each other to reduce the total energy cost"
cs.LG,we explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data \( scalp eeg \) the input to the neural network is a set of 9 pre defined features extracted from 1 second non overlapping windows in each of 14 channels per patient selected for the experiment the models in our experiments achieve high sensitivity and specificity on patient records not used in the training process this is demonstrated using leave one out cross validation across patient records
cs.DS,"we give a simple deterministic constant round algorithm in the congested clique model for reducing the number of edges in a graph to n 1 varepsilon while preserving the minimum spanning forest , where varepsilon 0 is any constant this implies that in the congested clique model , it is sufficient to improve mst and other connectivity algorithms on graphs with slightly superlinear number of edges to obtain a general improvement as a byproduct , we also obtain a simple alternative proof showing that mst can be computed deterministically in o \( log log n \) rounds"
cs.IT,"wireless industry nowadays is facing two major challenges 1 \) how to support the vertical industry applications so that to expand the wireless industry market and 2 \) how to further enhance device capability and user experience in this paper , we propose a technology framework to address these challenges the proposed technology framework is based on end to end vertical and horizontal slicing , where vertical slicing enables vertical industry and services and horizontal slicing improves system capacity and user experience the technology development on vertical slicing has already started in late 4g and early 5g and is mostly focused on slicing the core network we envision this trend to continue with the development of vertical slicing in the radio access network and the air interface moving beyond vertical slicing , we propose to horizontally slice the computation and communication resources to form virtual computation platforms for solving the network capacity scaling problem and enhancing device capability and user experience in this paper , we explain the concept of vertical and horizontal slicing and illustrate the slicing techniques in the air interface , the radio access network , the core network and the computation platform this paper aims to initiate the discussion on the long range technology roadmap and spur development on the solutions for e2e network slicing in 5g and beyond"
cs.DS|cs.LG,"since its introduction by valiant in 1984 , pac learning of dnf expressions remains one of the central problems in learning theory we consider this problem in the setting where the underlying distribution is uniform , or more generally , a product distribution kalai , samorodnitsky and teng \( 2009 \) showed that in this setting a dnf expression can be efficiently approximated from its heavy low degree fourier coefficients alone this is in contrast to previous approaches where boosting was used and thus fourier coefficients of the target function modified by various distributions were needed this property is crucial for learning of dnf expressions over smoothed product distributions , a learning model introduced by kalai et al \( 2009 \) and inspired by the seminal smoothed analysis model of spielman and teng \( 2001 \) we introduce a new approach to learning \( or approximating \) a polynomial threshold functions which is based on creating a function with range 1 , 1 that approximately agrees with the unknown function on low degree fourier coefficients we then describe conditions under which this is sufficient for learning polynomial threshold functions our approach yields a new , simple algorithm for approximating any polynomial size dnf expression from its heavy low degree fourier coefficients alone our algorithm greatly simplifies the proof of learnability of dnf expressions over smoothed product distributions we also describe an application of our algorithm to learning monotone dnf expressions over product distributions building on the work of servedio \( 2001 \) , we give an algorithm that runs in time poly \( \( s cdot log \( s eps \) \) log \( s eps \) , n \) , where s is the size of the target dnf expression and eps is the accuracy this improves on poly \( \( s cdot log \( ns eps \) \) log \( s eps \) cdot log \( 1 eps \) , n \) bound of servedio \( 2001 \)"
cs.SI,"the proliferation of smart meters enables load serving entities to aggregate customers according to their consumption patterns we demonstrate a method for constructing groups of customers who will be the cheapest to service at wholesale market prices using smart meter data from a region in california , we show that by aggregating more of these customers together , their consumption can be forecasted more accurately , which allows an lse to mitigate financial risks in its wholesale market transactions we observe that the consumption of aggregates of customers with similar consumption patterns can be forecasted more accurately than that of random aggregates of customers"
cs.SI,"mobile p2p technology provides a scalable approach to content delivery to a large number of users on their mobile devices in this work , we study the dissemination of a emph single content \( e g , an item of news , a song or a video clip \) among a population of mobile nodes each node in the population is either a emph destination \( interested in the content \) or a potential emph relay \( not yet interested in the content \) there is an interest evolution process by which nodes not yet interested in the content \( i e , relays \) can become interested \( i e , become destinations \) on learning about the popularity of the content \( i e , the number of already interested nodes \) in our work , the interest in the content evolves under the emph linear threshold model the content is copied between nodes when they make random contact for this we employ a controlled epidemic spread model we model the joint evolution of the copying process and the interest evolution process , and derive the joint fluid limit ordinary differential equations we then study the selection of the parameters under the content provider 's control , for the optimization of various objective functions that aim at maximizing content popularity and efficient content delivery"
cs.IT,code division multiple access \( cdma \) is the most promising candidate for wideband data access this is due to the advantage of soft limit on the number of active mobile devices many wireless mesh systems impose an upper bound on the ber performance which restricts the increase in number of mobile users capacity is further reduced in multipath fading environment \( mfe \) this paper presents an effective method of improving the capacity of a cdma based mesh network by managing the transmitted powers of the mobile devices and using mmse based multiuser detection \( mud \) the proposed scheme improves the capacity two times as compared to the conventional cdma based mesh network simulation results have been presented to demonstrate the effectiveness of the proposed scheme
cs.IT,"this paper considers the problem of defining a measure of redundant information that quantifies how much common information two or more random variables specify about a target random variable we discussed desired properties of such a measure , and propose new measures with some desirable properties"
cs.LG|stat.ML,"the oscar \( octagonal selection and clustering algorithm for regression \) regularizer consists of a l 1 norm plus a pair wise l inf norm \( responsible for its grouping behavior \) and was proposed to encourage group sparsity in scenarios where the groups are a priori unknown the oscar regularizer has a non trivial proximity operator , which limits its applicability we reformulate this regularizer as a weighted sorted l 1 norm , and propose its grouping proximity operator \( gpo \) and approximate proximity operator \( apo \) , thus making state of the art proximal splitting algorithms \( psas \) available to solve inverse problems with oscar regularization the gpo is in fact the apo followed by additional grouping and averaging operations , which are costly in time and storage , explaining the reason why algorithms with apo are much faster than that with gpo the convergences of psas with gpo are guaranteed since gpo is an exact proximity operator although convergence of psas with apo is may not be guaranteed , we have experimentally found that apo behaves similarly to gpo when the regularization parameter of the pair wise l inf norm is set to an appropriately small value experiments on recovery of group sparse signals \( with unknown groups \) show that psas with apo are very fast and accurate"
cs.AI,"this paper presents comparison of several stochastic optimization algorithms developed by authors in their previous works for the solution of some problems arising in civil engineering the introduced optimization methods are the integer augmented simulated annealing \( iasa \) , the real coded augmented simulated annealing \( rasa \) , the differential evolution \( de \) in its original fashion developed by r storn and k price and simplified real coded differential genetic algorithm \( sade \) each of these methods was developed for some specific optimization problem namely the chebychev trial polynomial problem , the so called type 0 function and two engineering problems the reinforced concrete beam layout and the periodic unit cell problem respectively detailed and extensive numerical tests were performed to examine the stability and efficiency of proposed algorithms the results of our experiments suggest that the performance and robustness of rasa , iasa and sade methods are comparable , while the de algorithm performs slightly worse this fact together with a small number of internal parameters promotes the sade method as the most robust for practical use"
cs.IT,"wyner 's wiretap channel is extended to parallel broadcast channels and fading channels with multiple receivers in the first part of the paper , we consider the setup of parallel broadcast channels with one sender , multiple intended receivers , and one eavesdropper we study the situations where the sender broadcasts either a common message or independent messages to the intended receivers we derive upper and lower bounds on the common message secrecy capacity , which coincide when the users are reversely degraded for the case of independent messages we establish the secrecy sum capacity when the users are reversely degraded in the second part of the paper we apply our results to fading channels perfect channel state information of all intended receivers is known globally , whereas the eavesdropper channel is known only to her for the common message case , a somewhat surprising result is proven a positive rate can be achieved independently of the number of intended receivers for independent messages , an opportunistic transmission scheme is presented that achieves the secrecy sum capacity in the limit of large number of receivers our results are stated for a fast fading channel model extensions to the block fading model are also discussed"
cs.IT,"we study the throughput capacity region of the gaussian multi access \( mac \) fading channel with perfect channel state information \( csi \) at the receiver and at the transmitters , at low power regime we show that it has a multidimensional rectangle structure and thus is simply characterized by single user capacity points more specifically , we show that at low power regime , the boundary surface of the capacity region shrinks to a single point corresponding to the sum rate maximizer and that the coordinates of this point coincide with single user capacity bounds inspired by this result , we propose an on off scheme , compute its achievable rate , and show that this scheme achieves single user capacity bounds of the mac channel for a wide class of fading channels at asymptotically low power regime we argue that this class of fading encompasses all known wireless channels for which the capacity region of the mac channel has even a simpler expression in terms of users' average power constraints only using the duality of gaussian mac and broadcast channels \( bc \) , we deduce a simple characterization of the bc capacity region at low power regime and show that for a class of fading channels \( including rayleigh fading \) , time sharing is asymptotically optimal"
cs.LG|stat.ML,"we introduce online learning algorithms which are independent of feature scales , proving regret bounds dependent on the ratio of scales existent in the data rather than the absolute scale this has several useful effects there is no need to pre normalize data , the test time and test space complexity are reduced , and the algorithms are more robust"
cs.IT,"a malleable coding scheme considers not only compression efficiency but also the ease of alteration , thus encouraging some form of recycling of an old compressed version in the formation of a new one malleability cost is the difficulty of synchronizing compressed versions , and malleable codes are of particular interest when representing information and modifying the representation are both expensive we examine the trade off between compression efficiency and malleability cost under a malleability metric defined with respect to a string edit distance this problem introduces a metric topology to the compressed domain we characterize the achievable rates and malleability as the solution of a subgraph isomorphism problem this can be used to argue that allowing conditional entropy of the edited message given the original message to grow linearly with block length creates an exponential increase in code length"
cs.IT,"modern cellular networks in traditional frequency bands are notoriously interference limited especially in urban areas , where base stations are deployed in close proximity to one another the latest releases of long term evolution \( lte \) incorporate features for coordinating downlink transmissions as an efficient means of managing interference recent field trial results and theoretical studies of the performance of joint transmission \( jt \) coordinated multi point \( comp \) schemes revealed , however , that their gains are not as high as initially expected , despite the large coordination overhead these schemes are known to be very sensitive to defects in synchronization or information exchange between coordinating bases stations as well as uncoordinated interference in this article , we review recent advanced coordinated beamforming \( cb \) schemes as alternatives , requiring less overhead than jt comp while achieving good performance in realistic conditions by stipulating that , in certain lte scenarios of increasing interest , uncoordinated interference constitutes a major factor in the performance of comp techniques at large , we hereby assess the resilience of the state of the art cb to uncoordinated interference we also describe how these techniques can leverage the latest specifications of current cellular networks , and how they may perform when we consider standardized feedback and coordination this allows us to identify some key roadblocks and research directions to address as lte evolves towards the future of mobile communications"
cs.IT,"we propose a distributed sequential algorithm for quick detection of spectral holes in a cognitive radio set up two or more local nodes make decisions and inform the fusion centre \( fc \) over a reporting multiple access channel \( mac \) , which then makes the final decision the local nodes use energy detection and the fc uses mean change detection in the presence of heavy tailed electromagnetic interference \( emi \) and outliers different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the fc a recently developed entropy test is selected for the local nodes for energy detection , and a modified version of a well known t test at the fusion centre for mean change detection we show via simulations and analysis that the nonparametric distributed algorithms developed performs well in the presence of emi and outliers the algorithm is iterative in nature , unlike t test and rank test or their robust versions"
cs.IT,"in order to perform quantum cryptography procedures it is often essencial to ensure that the parties of the communication are authentic such task is accomplished by quantum authentication protocols which are distributed algorithms based on the intrinsic properties of quantum mechanics the choice of an authentication protocol must consider that quantum states are very delicate and that the channel is subject to eavesdropping however , even in face of the various existing definitions of quantum authentication protocols in the literature , little is known about them in this perspective , and this lack of knowledge may unfavor comparisons and wise choices in the attempt to overcome this limitation , in the present work we aim at showing an approach to evaluate quantum authentication protocols based on the determination of their quantum communication complexity based on our investigation , no similar methods to analyze quantum authentication protocols were found in the literature pursuing this further , our approach has advantages that need to be highlighted it characterizes a systematic procedure to evaluate quantum authentication protocols its evaluation is intuitive , based only on the protocol execution the resulting measure is a concise notation of what resources a quantum authentication protocol demands and how many communications are performed it allows comparisons between protocols it makes possible to analyze the communication effort when an eavesdropping occurs and , lastly , it is likely to be applied in almost any quantum authentication protocol to illustrate the proposed approach , we also bring results about its application in ten existing quantum authentication protocols \( data origin authentication and identity authentication \) such evaluations increase the knowledge about the existing protocols , presenting its advantages , limitations and contrasts"
cs.LG|stat.ML,"in a variety of disciplines such as social sciences , psychology , medicine and economics , the recorded data are considered to be noisy measurements of latent variables connected by some causal structure this corresponds to a family of graphical models known as the structural equation model with latent variables while linear non gaussian variants have been well studied , inference in nonparametric structural equation models is still underdeveloped we introduce a sparse gaussian process parameterization that defines a non linear structure connecting latent variables , unlike common formulations of gaussian process latent variable models the sparse parameterization is given a full bayesian treatment without compromising markov chain monte carlo efficiency we compare the stability of the sampling procedure and the predictive ability of the model against the current practice"
cs.AI,"we live in the information age , and information has become a critically important component of our life the success of the internet made huge amounts of it easily available and accessible to everyone to keep the flow of this information manageable , means for its faultless circulation and effective handling have become urgently required considerable research efforts are dedicated today to address this necessity , but they are seriously hampered by the lack of a common agreement about what is information \? in particular , what is visual information human 's primary input from the surrounding world the problem is further aggravated by a long lasting stance borrowed from the biological vision research that assumes human like information processing as an enigmatic mix of perceptual and cognitive vision faculties i am trying to find a remedy for this bizarre situation relying on a new definition of information , which can be derived from kolmogorov 's compexity theory and chaitin 's notion of algorithmic information , i propose a unifying framework for visual information processing , which explicitly accounts for the perceptual and cognitive image processing peculiarities i believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human like intelligent image processing"
cs.DS,"we investigate labeling schemes supporting adjacency , ancestry , sibling , and connectivity queries in forests in the course of more than 20 years , the existence of log n o \( log log \) labeling schemes supporting each of these functions was proven , with the most recent being ancestry fraigniaud and korman , stoc '10 several multi functional labeling schemes also enjoy lower or upper bounds of log n omega \( log log n \) or log n o \( log log n \) respectively notably an upper bound of log n 5 log log n for adjacency siblings and a lower bound of log n log log n for each of the functions siblings , ancestry , and connectivity alstrup et al , soda '03 we improve the constants hidden in the o notation in particular we show a log n 2 log log n lower bound for connectivity ancestry and connectivity siblings , as well as an upper bound of log n 3 log log n o \( log log log n \) for connectivity adjacency siblings by altering existing methods in the context of dynamic labeling schemes it is known that ancestry requires omega \( n \) bits cohen , et al pods '02 in contrast , we show upper and lower bounds on the label size for adjacency , siblings , and connectivity of 2 log n bits , and 3 log n to support all three functions there exist efficient adjacency labeling schemes for planar , bounded treewidth , bounded arboricity and interval graphs in a dynamic setting , we show a lower bound of omega \( n \) for each of those families"
cs.LG|cs.SI|stat.ML,"many algorithms have been proposed for fitting network models with communities , but most of them do not scale well to large networks , and often fail on sparse networks here we propose a new fast pseudo likelihood method for fitting the stochastic block model for networks , as well as a variant that allows for an arbitrary degree distribution by conditioning on degrees we show that the algorithms perform well under a range of settings , including on very sparse networks , and illustrate on the example of a network of political blogs we also propose spectral clustering with perturbations , a method of independent interest , which works well on sparse networks where regular spectral clustering fails , and use it to provide an initial value for pseudo likelihood we prove that pseudo likelihood provides consistent estimates of the communities under a mild condition on the starting value , for the case of a block model with two communities"
cs.SI,"in the past decade , blogging web sites have become more sophisticated and influential than ever much of this sophistication and influence follows from their network organization blogging social networks \( bsns \) allow individual bloggers to form contact lists , subscribe to other blogs , comment on blog posts , declare interests , and participate in collective blogs thus , a bsn is a bimodal venue , where users can engage in publishing \( post \) as well as in social \( make friends \) activities in this paper , we study the co evolution of both activities we observed a significant positive correlation between blogging and socializing in addition , we identified a number of user archetypes that correspond to mainly bloggers , mainly socializers , etc we analyzed a bsn at the level of individual posts and changes in contact lists and at the level of trajectories in the friendship publishing space both approaches produced consistent results the majority of bsn users are passive readers publishing is the dominant active behavior in a bsn and social activities complement blogging , rather than compete with it"
cs.IT,"the 1 bit compressed sensing framework enables the recovery of a sparse vector x from the sign information of each entry of its linear transformation discarding the amplitude information can significantly reduce the amount of data , which is highly beneficial in practical applications in this paper , we present a bayesian approach to signal reconstruction for 1 bit compressed sensing , and analyze its typical performance using statistical mechanics utilizing the replica method , we show that the bayesian approach enables better reconstruction than the l1 norm minimization approach , asymptotically saturating the performance obtained when the non zero entries positions of the signal are known we also test a message passing algorithm for signal reconstruction on the basis of belief propagation the results of numerical experiments are consistent with those of the theoretical analysis"
cs.IT,"an interleaver is a critical component for the channel coding performance of turbo codes algebraic constructions are of particular interest because they admit analytical designs and simple , practical hardware implementation contention free interleavers have been recently shown to be suitable for parallel decoding of turbo codes in this correspondence , it is shown that permutation polynomials generate maximum contention free interleavers , i e , every factor of the interleaver length becomes a possible degree of parallel processing of the decoder further , it is shown by computer simulations that turbo codes using these interleavers perform very well for the 3rd generation partnership project \( 3gpp \) standard"
cs.DM|cs.DS,"in this paper , we generalize spencer 's hyperbolic cosine algorithm to the matrix valued setting we apply the proposed algorithm to several problems by analyzing its computational efficiency under two special cases of matrices one in which the matrices have a group structure and an other in which they have rank one as an application of the former case , we present a deterministic algorithm that , given the multiplication table of a finite group of size n , it constructs an expanding cayley graph of logarithmic degree in near optimal o \( n 2 log 3 n \) time for the latter case , we present a fast deterministic algorithm for spectral sparsification of positive semi definite matrices , which implies an improved deterministic algorithm for spectral graph sparsification of dense graphs in addition , we give an elementary connection between spectral sparsification of positive semi definite matrices and element wise matrix sparsification as a consequence , we obtain improved element wise sparsification algorithms for diagonally dominant like matrices"
cs.LG|stat.ML,"the word2vec software of tomas mikolov and colleagues \( https code google com p word2vec \) has gained a lot of traction lately , and provides state of the art word embeddings the learning models behind the software are described in two research papers we found the description of the models in these papers to be somewhat cryptic and hard to follow while the motivations and presentation may be obvious to the neural networks language modeling crowd , we had to struggle quite a bit to figure out the rationale behind the equations this note is an attempt to explain equation \( 4 \) \( negative sampling \) in distributed representations of words and phrases and their compositionality by tomas mikolov , ilya sutskever , kai chen , greg corrado and jeffrey dean"
cs.IT,"unicast communication over a network of m parallel relays in the presence of an eavesdropper is considered the relay nodes , operating under individual power constraints , amplify and forward the signals received at their inputs the problem of the maximum secrecy rate achievable with af relaying is addressed previous work on this problem provides iterative algorithms based on semidefinite relaxation however , those algorithms result in suboptimal performance without any performance and convergence guarantees we address this problem for three specific network models , with real valued channel gains we propose a novel transformation that leads to convex optimization problems our analysis leads to \( i \) a polynomial time algorithm to compute the optimal secure af rate for two of the models and \( ii \) a closed form expression for the optimal secure rate for the other"
cs.IT,"in this paper we study the quantization stage that is implicit in any compressed sensing signal acquisition paradigm we propose using sigma delta quantization and a subsequent reconstruction scheme based on convex optimization we prove that the reconstruction error due to quantization decays polynomially in the number of measurements our results apply to arbitrary signals , including compressible ones , and account for measurement noise additionally , they hold for sub gaussian \( including gaussian and bernoulli \) random compressed sensing measurements , as well as for both high bit depth and coarse quantizers , and they extend to 1 bit quantization in the noise free case , when the signal is strictly sparse we prove that by optimizing the order of the quantization scheme one can obtain root exponential decay in the reconstruction error due to quantization"
cs.DM,"we say that a permutation p is 'merged' from permutations q and r , if we can color the elements of p red and blue so that the red elements are order isomorphic to q and the blue ones to r a 'permutation class' is a set of permutations closed under taking subpermutations a permutation class c is 'splittable' if it has two proper subclasses a and b such that every element of c can be obtained by merging an element of a with an element of b several recent papers use splittability as a tool in deriving enumerative results for specific permutation classes the goal of this paper is to study splittability systematically as our main results , we show that if q is a sum decomposable permutation of order at least four , then the class av \( q \) of all q avoiding permutations is splittable , while if q is a simple permutation , then av \( q \) is unsplittable we also show that there is a close connection between splittings of certain permutation classes and colorings of circle graphs of bounded clique size indeed , our splittability results can be interpreted as a generalization of a theorem of gy 'arf 'as stating that circle graphs of bounded clique size have bounded chromatic number"
cs.IT,"an overview of game theoretic approaches to energy efficient resource allocation in wireless networks is presented focusing on multiple access networks , it is demonstrated that game theory can be used as an effective tool to study resource allocation in wireless networks with quality of service \( qos \) constraints a family of non cooperative \( distributed \) games is presented in which each user seeks to choose a strategy that maximizes its own utility while satisfying its qos requirements the utility function considered here measures the number of reliable bits that are transmitted per joule of energy consumed and , hence , is particulary suitable for energy constrained networks the actions available to each user in trying to maximize its own utility are at least the choice of the transmit power and , depending on the situation , the user may also be able to choose its transmission rate , modulation , packet size , multiuser receiver , multi antenna processing algorithm , or carrier allocation strategy the best response strategy and nash equilibrium for each game is presented using this game theoretic framework , the effects of power control , rate control , modulation , temporal and spatial signal processing , carrier allocation strategy and delay qos constraints on energy efficiency and network capacity are quantified"
cs.IT,"in this paper , we study the downlink performance of two important 5g network architectures , i e massive multiple input multiple output \( m mimo \) and small cell densification we propose a comparative modeling for the two systems , where the user and antenna base station \( bs \) locations are distributed according to poisson point processes \( ppps \) we then leverage both the stochastic geometry results and large system analytical tool to study the sir distribution and the average shannon and outage rates of each network by comparing these results , we observe that for user average spectral efficiency , small cell densification is favorable in crowded areas with moderate to high user density and massive mimo with low user density however , small cell systems outperform m mimo in all cases when the performance metric is the energy efficiency the results of this paper are useful for the optimal design of practical 5g networks"
cs.IT,"phase retrieval arises in various fields of science and engineering and it is well studied in a finite dimensional setting in this paper , we consider an infinite dimensional phase retrieval problem to reconstruct real valued signals living in a shift invariant space from its phaseless samples taken either on the whole line or on a set with finite sampling rate we find the equivalence between nonseparability of signals in a linear space and its phase retrievability with phaseless samples taken on the whole line for a spline signal of order n , we show that it can be well approximated , up to a sign , from its noisy phaseless samples taken on a set with sampling rate 2n 1 we propose an algorithm to reconstruct nonseparable signals in a shift invariant space generated by a compactly supported continuous function the proposed algorithm is robust against bounded sampling noise and it could be implemented in a distributed manner"
cs.AI|cs.LG,"in this work we present a novel end to end framework for tracking and classifying a robot 's surroundings in complex , dynamic and only partially observable real world environments the approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations , along with their identity in both visible and occluded areas to achieve this we first train the network using unsupervised deep tracking , a recently proposed theoretical framework for end to end space occupancy prediction we show that by learning to track on a large amount of unsupervised data , the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it 's semantic classification as a result , we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real world robotics applications we demonstrate the tracking and classification performance of the method on real world data collected at a busy road junction our evaluation shows that the proposed end to end framework compares favourably to a state of the art , model free tracking solution and that it outperforms a conventional one shot training scheme for semantic classification"
cs.SI,"in social networks , control of rumor spread is an active area of research sir model is generally used to study the rumor dynamics in network while considering the rumor as an epidemic in disease spreading model , epidemic is controlled by removing central nodes in the network full network information is needed for such removal to have the information of complete network is difficult proposition as a consequence , the search of an algorithm that may control epidemic without needing global information is a matter of great interest in this paper , an immunization strategy is proposed that uses only local information available at a node , viz degree of the node and average degree of its neighbour nodes proposed algorithm has been evaluated for scale free network using sir model numerical results show that proposed method has less complexity and gives significantly better results in comparison with other strategies while using only local information"
cs.SI,"there has been much effort on studying how social media sites , such as twitter , help propagate information in different situations , including spreading alerts and sos messages in an emergency however , existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame to address this problem , we have developed two models \( i \) a feature based model that leverages peoples' exhibited social behavior , including the content of their tweets and social interactions , to characterize their willingness and readiness to propagate information on twitter via the act of retweeting and \( ii \) a wait time model based on a user 's previous retweeting wait times to predict her next retweeting time when asked based on these two models , we build a recommender system that predicts the likelihood of a stranger to retweet information when asked , within a specific time window , and recommends the top n qualified strangers to engage with our experiments , including live studies in the real world , demonstrate the effectiveness of our work"
cs.LG|stat.ML,"we study the problem of learning high dimensional regression models regularized by a structured sparsity inducing penalty that encodes prior structural information on either input or output sides we consider two widely adopted types of such penalties as our motivating examples 1 \) overlapping group lasso penalty , based on the l1 l2 mixed norm penalty , and 2 \) graph guided fusion penalty for both types of penalties , due to their non separability , developing an efficient optimization method has remained a challenging problem in this paper , we propose a general optimization approach , called smoothing proximal gradient method , which can solve the structured sparse regression problems with a smooth convex loss and a wide spectrum of structured sparsity inducing penalties our approach is based on a general smoothing technique of nesterov it achieves a convergence rate faster than the standard first order method , subgradient method , and is much more scalable than the most widely used interior point method numerical results are reported to demonstrate the efficiency and scalability of the proposed method"
cs.IT,"the ans family of arithmetic coders developed by jarek duda has the unique property that encoder and decoder are completely symmetric in the sense that a decoder reading bits will be in the exact same state that the encoder was in when writing those bits all buffering of information is explicitly part of the coder state and identical between encoder and decoder as a consequence , the output from multiple abs ans coders can be interleaved into the same bitstream without any additional metadata this allows for very efficient encoding and decoding on cpus supporting superscalar execution or simd instructions , as well as gpu implementations we also show how interleaving without additional metadata can be implemented for any entropy coder , at some increase in encoder complexity"
cs.DS|cs.LG,"in this paper we initiate the study of optimization of bandit type problems in scenarios where the feedback of a play is not immediately known this arises naturally in allocation problems which have been studied extensively in the literature , albeit in the absence of delays in the feedback we study this problem in the bayesian setting in presence of delays , no solution with provable guarantees is known to exist with sub exponential running time we show that bandit problems with delayed feedback that arise in allocation settings can be forced to have significant structure , with a slight loss in optimality this structure gives us the ability to reason about the relationship of single arm policies to the entangled optimum policy , and eventually leads to a o \( 1 \) approximation for a significantly general class of priors the structural insights we develop are of key interest and carry over to the setting where the feedback of an action is available instantaneously , and we improve all previous results in this setting as well"
cs.DM|cs.DS,"previous work identifying depth optimal n channel sorting networks for 9 leq n leq 16 is based on exploiting symmetries of the first two layers however , the naive generate and test approach typically applied does not scale this paper revisits the problem of generating two layer prefixes modulo symmetries an improved notion of symmetry is provided and a novel technique based on regular languages and graph isomorphism is shown to generate the set of non symmetric representations an empirical evaluation demonstrates that the new method outperforms the generate and test approach by orders of magnitude and easily scales until n 40"
cs.IT,"in this paper , an efficient distributed approach for implementing the approximate message passing \( amp \) algorithm , named distributed amp \( damp \) , is developed for compressed sensing \( cs \) recovery in sensor networks with the sparsity k unknown in the proposed damp , distributed sensors do not have to use or know the entire global sensing matrix , and the burden of computation and storage for each sensor is reduced to reduce communications among the sensors , a new data query algorithm , called global computation for amp \( gcamp \) , is proposed the proposed gcamp based damp approach has exactly the same recovery solution as the centralized amp algorithm , which is proved theoretically in the paper the performance of the damp approach is evaluated in terms of the communication cost saved by using gcamp for comparison purpose , thresholding algorithm \( ta \) , a well known distributed top k algorithm , is modified so that it also leads to the same recovery solution as the centralized amp numerical results demonstrate that the gcamp based damp outperforms the modified ta based damp , and reduces the communication cost significantly"
cs.IT,"polar codes were recently introduced by ar i kan they achieve the capacity of arbitrary symmetric binary input discrete memoryless channels under a low complexity successive cancellation decoding strategy the original polar code construction is closely related to the recursive construction of reed muller codes and is based on the 2 times 2 matrix bigl 1 0 1 1 bigr it was shown by ar i kan and telatar that this construction achieves an error exponent of frac12 , i e , that for sufficiently large blocklengths the error probability decays exponentially in the square root of the length it was already mentioned by ar i kan that in principle larger matrices can be used to construct polar codes a fundamental question then is to see whether there exist matrices with exponent exceeding frac12 we first show that any ell times ell matrix none of whose column permutations is upper triangular polarizes symmetric channels we then characterize the exponent of a given square matrix and derive upper and lower bounds on achievable exponents using these bounds we show that there are no matrices of size less than 15 with exponents exceeding frac12 further , we give a general construction based on bch codes which for large n achieves exponents arbitrarily close to 1 and which exceeds frac12 for size 16"
cs.AI,"tetravex is a widely played one person computer game in which you are given n 2 unit tiles , each edge of which is labelled with a number the objective is to place each tile within a n by n square such that all neighbouring edges are labelled with an identical number unfortunately , playing tetravex is computationally hard more precisely , we prove that deciding if there is a tiling of the tetravex board is np complete deciding where to place the tiles is therefore np hard this may help to explain why tetravex is a good puzzle this result compliments a number of similar results for one person games involving tiling for example , np completeness results have been shown for the offline version of tetris , kplumber \( which involves rotating tiles containing drawings of pipes to make a connected network \) , and shortest sliding puzzle problems it raises a number of open questions for example , is the infinite version turing complete \? how do we generate tetravex problems which are truly puzzling as random np complete problems are often surprising easy to solve \? can we observe phase transition behaviour \? what about the complexity of the problem when it is guaranteed to have an unique solution \? how do we generate puzzles with unique solutions \?"
cs.AI|cs.LG,"this paper addresses the task of zero shot image classification the key contribution of the proposed approach is to control the semantic embedding of images one of the main ingredients of zero shot learning by formulating it as a metric learning problem the optimized empirical criterion associates two types of sub task constraints metric discriminating capacity and accurate attribute prediction this results in a novel expression of zero shot learning not requiring the notion of class in the training phase only pairs of image attributes , augmented with a consistency indicator , are given as ground truth at test time , the learned model can predict the consistency of a test image with a given set of attributes , allowing flexible ways to produce recognition inferences despite its simplicity , the proposed approach gives state of the art results on four challenging datasets used for zero shot recognition evaluation"
cs.DM,"a popular method in combinatorial optimization is to express polytopes p , which may potentially have exponentially many facets , as solutions of linear programs that use few extra variables to reduce the number of constraints down to a polynomial after two decades of standstill , recent years have brought amazing progress in showing lower bounds for the so called extension complexity , which for a polytope p denotes the smallest number of inequalities necessary to describe a higher dimensional polytope q that can be linearly projected on p however , the central question in this field remained wide open can the perfect matching polytope be written as an lp with polynomially many constraints \? we answer this question negatively in fact , the extension complexity of the perfect matching polytope in a complete n node graph is 2 omega \( n \) by a known reduction this also improves the lower bound on the extension complexity for the tsp polytope from 2 omega \( n 1 2 \) to 2 omega \( n \)"
cs.IT,"in this paper , we consider physical layer security provisioning in multi cell massive multiple input multiple output \( mimo \) systems specifically , we consider secure downlink transmission in a multi cell massive mimo system with matched filter precoding and artificial noise \( an \) generation at the base station \( bs \) in the presence of a passive multi antenna eavesdropper we investigate the resulting achievable ergodic secrecy rate and the secrecy outage probability for the cases of perfect training and pilot contamination thereby , we consider two different an shaping matrices , namely , the conventional an shaping matrix , where the an is transmitted in the null space of the matrix formed by all user channels , and a random an shaping matrix , which avoids the complexity associated with finding the null space of a large matrix our analytical and numerical results reveal that in multi cell massive mimo systems employing matched filter precoding \( 1 \) an generation is required to achieve a positive ergodic secrecy rate if the user and the eavesdropper experience the same path loss , \( 2 \) even with an generation secure transmission may not be possible if the number of eavesdropper antennas is too large and not enough power is allocated to channel estimation , \( 3 \) for a given fraction of power allocated to an and a given number of users , in case of pilot contamination , the ergodic secrecy rate is not a monotonically increasing function of the number of bs antennas , and \( 4 \) random an shaping matrices provide a favourable performance complexity tradeoff and are an attractive alternative to conventional an shaping matrices"
cs.DS,in this note we study the greedy algorithm for combinatorial auctions with submodular bidders it is well known that this algorithm provides an approximation ratio of 2 for every order of the items we show that if the valuations are vertex cover functions and the order is random then the expected approximation ratio imrpoves to frac 7 4
cs.IT,"we consider a basic content distribution scenario consisting of a single origin server connected through a shared bottleneck link to a number of users each equipped with a cache of finite memory the users issue a sequence of content requests from a set of popular files , and the goal is to operate the caches as well as the server such that these requests are satisfied with the minimum number of bits sent over the shared link assuming a basic markov model for renewing the set of popular files , we characterize approximately the optimal long term average rate of the shared link we further prove that the optimal online scheme has approximately the same performance as the optimal offline scheme , in which the cache contents can be updated based on the entire set of popular files before each new request to support these theoretical results , we propose an online coded caching scheme termed coded least recently sent \( lrs \) and simulate it for a demand time series derived from the dataset made available by netflix for the netflix prize for this time series , we show that the proposed coded lrs algorithm significantly outperforms the popular least recently used \( lru \) caching algorithm"
cs.DS|cs.LG,"we study the problem of a seller dynamically pricing d distinct types of goods , when faced with the online arrival of buyers drawn independently from an unknown distribution the seller observes only the bundle of goods purchased at each day , but nothing else about the buyer 's valuation function when buyers have strongly concave , holder continuous valuation functions , we give a pricing scheme that finds a pricing that optimizes welfare \( including the seller 's cost of production \) in time and number of rounds that are polynomial in d and the accuracy parameter we are able to do this despite the fact that \( i \) welfare is a non concave function of the prices , and \( ii \) the welfare is not observable to the seller we also extend our results to a limited supply setting"
cs.IT|cs.LG,"one class classifiers offer valuable tools to assess the presence of outliers in data in this paper , we propose a design methodology for one class classifiers based on entropic spanning graphs the spanning graph is learned on the embedded input data , with the aim to generate a partition of the vertices the final partition is derived by exploiting a criterion based on mutual information minimization here , we compute the mutual information by using a convenient formulation provided in terms of the alpha jensen difference once training is completed , in order to associate a confidence level with the classifier decision , a graph based fuzzy model is constructed the fuzzification process is based only on topological information of the vertices of the entropic spanning graph as such , the proposed one class classifier is suitable also for datasets with complex geometric structures we provide experiments on well known benchmarking datasets containing both feature vectors and labeled graphs in addition , we apply the method on the problem of protein solubility recognition by considering several data representations for the samples experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state of the art approaches"
cs.IT,"inferring the coupling structure of complex systems from time series data in general by means of statistical and information theoretic techniques is a challenging problem in applied science the reliability of statistical inferences requires the construction of suitable information theoretic measures that take into account both direct and indirect influences , manifest in the form of information flows , between the components within the system in this work , we present an application of the optimal causation entropy \( ocse \) principle to identify the coupling structure of a synthetic biological system , the repressilator specifically , when the system reaches an equilibrium state , we use a stochastic perturbation approach to extract time series data that approximate a linear stochastic process then , we present and jointly apply the aggregative discovery and progressive removal algorithms based on the ocse principle to infer the coupling structure of the system from the measured data finally , we show that the success rate of our coupling inferences not only improves with the amount of available data , but it also increases with a higher frequency of sampling and is especially immune to false positives"
cs.LG,"direct quantile regression involves estimating a given quantile of a response variable as a function of input variables we present a new framework for direct quantile regression where a gaussian process model is learned , minimising the expected tilted loss function the integration required in learning is not analytically tractable so to speed up the learning we employ the expectation propagation algorithm we describe how this work relates to other quantile regression methods and apply the method on both synthetic and real data sets the method is shown to be competitive with state of the art methods whilst allowing for the leverage of the full gaussian process probabilistic framework"
cs.IT,"this paper investigates the interplay between cooperation and achievable rates in multi terminal networks cooperation refers to the process of nodes working together to relay data toward the destination there is an inherent tradeoff between achievable information transmission rates and the level of cooperation , which is determined by how many nodes are involved and how the nodes encode decode the data we illustrate this trade off by studying information theoretic decode forward based coding strategies for data transmission in multi terminal networks decode forward strategies are usually discussed in the context of omniscient coding , in which all nodes in the network fully cooperate with each other , both in encoding and decoding in this paper , we investigate myopic coding , in which each node cooperates with only a few neighboring nodes we show that achievable rates of myopic decode forward can be as large as that of omniscient decode forward in the low snr regime we also show that when each node has only a few cooperating neighbors , adding one node into the cooperation increases the transmission rate significantly furthermore , we show that myopic decode forward can achieve non zero rates as the network size grows without bound"
cs.IT,"motivated by the rank modulation scheme , a recent work by sala and dolecek explored the study of constraint codes for permutations the constraint studied by them is inherited by the inter cell interference phenomenon in flash memories , where high level cells can inadvertently increase the level of low level cells in this paper , the model studied by sala and dolecek is extended into two constraints a permutation sigma in s n satisfies the emph two neighbor k constraint if for all 2 leq i leq n 1 either sigma \( i 1 \) sigma \( i \) leq k or sigma \( i \) sigma \( i 1 \) leq k , and it satisfies the emph asymmetric two neighbor k constraint if for all 2 leq i leq n 1 , either sigma \( i 1 \) sigma \( i \) k or sigma \( i 1 \) sigma \( i \) k we show that the capacity of the first constraint is \( 1 epsilon \) 2 in case that k theta \( n epsilon \) and the capacity of the second constraint is 1 regardless to the value of k we also extend our results and study the capacity of these two constraints combined with error correction codes in the kendall 's tau metric"
cs.AI|cs.IT|stat.ML,"we propose a graphical model for representing networks of stochastic processes , the minimal generative model graph it is based on reduced factorizations of the joint distribution over time we show that under appropriate conditions , it is unique and consistent with another type of graphical model , the directed information graph , which is based on a generalization of granger causality we demonstrate how directed information quantifies granger causality in a particular sequential prediction setting we also develop efficient methods to estimate the topological structure from data that obviate estimating the joint statistics one algorithm assumes upper bounds on the degrees and uses the minimal dimension statistics necessary in the event that the upper bounds are not valid , the resulting graph is nonetheless an optimal approximation another algorithm uses near minimal dimension statistics when no bounds are known but the distribution satisfies a certain criterion analogous to how structure learning algorithms for undirected graphical models use mutual information estimates , these algorithms use directed information estimates we characterize the sample complexity of two plug in directed information estimators and obtain confidence intervals for the setting when point estimates are unreliable , we propose an algorithm that uses confidence intervals to identify the best approximation that is robust to estimation error lastly , we demonstrate the effectiveness of the proposed algorithms through analysis of both synthetic data and real data from the twitter network in the latter case , we identify which news sources influence users in the network by merely analyzing tweet times"
cs.LG|stat.ML,"hyperspectral instruments \( hsis \) measure the electromagnetic energy emitted by materials at high resolution \( hundreds to thousands of channels \) enabling material identification through spectroscopic analysis laser induced breakdown spectroscopy \( libs \) is used by the chemcam instrument on the curiosity rover to measure the emission spectra of surface materials on mars from orbit , hyperspectral instruments \( hsis \) on the crism instrument of the mars reconnaissance orbiter \( mro \) measure the electromagnetic energy emitted by materials at high resolution \( hundreds to thousands of channels \) enabling material identification through spectroscopic analysis the data received are noisy , high dimensional , and largely unlabeled the ability to accurately predict elemental and material compositions of surface samples as well as to simulate spectra from hypothetical compositions , collectively known as hyperspectral unmixing , is invaluable to the exploration process the nature of the problem allows us to construct deep \( semi supervised \) generative models to accomplish both these tasks while making use of a large unlabeled dataset our main technical contribution is an invertibility trick where we train our model in reverse"
cs.AI,"standard belief change assumes an underlying logic containing full classical propositional logic however , there are good reasons for considering belief change in less expressive logics as well in this paper we build on recent investigations by delgrande on contraction for horn logic we show that the standard basic form of contraction , partial meet , is too strong in the horn case this result stands in contrast to delgrande 's conjecture that orderly maxichoice is the appropriate form of contraction for horn logic we then define a more appropriate notion of basic contraction for the horn case , influenced by the convexity property holding for full propositional logic and which we refer to as infra contraction the main contribution of this work is a result which shows that the construction method for horn contraction for belief sets based on our infra remainder sets corresponds exactly to hansson 's classical kernel contraction for belief sets , when restricted to horn logic this result is obtained via a detour through contraction for belief bases we prove that kernel contraction for belief bases produces precisely the same results as the belief base version of infra contraction the use of belief bases to obtain this result provides evidence for the conjecture that horn belief change is best viewed as a hybrid version of belief set change and belief base change one of the consequences of the link with base contraction is the provision of a representation result for horn contraction for belief sets in which a version of the core retainment postulate features"
cs.DM|cs.DS,"in maxsat , we are given a cnf formula f with n variables and m clauses and asked to find a truth assignment satisfying the maximum number of clauses let r 1 , , r m be the number of literals in the clauses of f then asat \( f \) sum i 1 m \( 1 2 r i \) is the expected number of clauses satisfied by a random truth assignment \( the truth values to the variables are distributed uniformly and independently \) it is well known that , in polynomial time , one can find a truth assignment satisfying at least asat \( f \) clauses in the parameterized problem maxsat aa , we are to decide whether there is a truth assignment satisfying at least asat \( f \) k clauses , where k is the parameter we prove that maxsat aa is para np complete and , thus , maxsat aa is not fixed parameter tractable unless p np this is in sharp contrast to maxlin2 aa which was recently proved to be fixed parameter tractable by crowston et al \( arxiv 1104 1135v3 \) in fact , we consider a more refined version of sc maxsat aa , sc max r \( n \) sat aa , where r j le r \( n \) for each j alon em et al \( soda 2010 \) proved that if r r \( n \) is a constant , then sc max r sat aa is fixed parameter tractable we prove that sc max r \( n \) sat aa is para np complete for r \( n \) lceil log n rceil we also prove that assuming the exponential time hypothesis , sc max r \( n \) sat aa is not in xp already for any r \( n \) ge log log n phi \( n \) , where phi \( n \) is any unbounded strictly increasing function this lower bound on r \( n \) cannot be decreased much further as we prove that sc max r \( n \) sat aa is \( i \) in xp for any r \( n \) le log log n log log log n and \( ii \) fixed parameter tractable for any r \( n \) le log log n log log log n phi \( n \) , where phi \( n \) is any unbounded strictly increasing function the proof uses some results on sc maxlin2 aa"
cs.IT,"model based compressed sensing refers to compressed sensing with extra structure about the underlying sparse signal known a priori recent work has demonstrated that both for deterministic and probabilistic models imposed on the signal , this extra information can be successfully exploited to enhance recovery performance in particular , weighted ell 1 minimization with suitable choice of weights has been shown to improve performance in the so called non uniform sparse model of signals in this paper , we consider a full generalization of the non uniform sparse model with very mild assumptions we prove that when the measurements are obtained using a matrix with i i d gaussian entries , weighted ell 1 minimization successfully recovers the sparse signal from its measurements with overwhelming probability we also provide a method to choose these weights for any general signal model from the non uniform sparse class of signal models"
cs.LG,"this paper presents a new method for pre training neural networks that can decrease the total training time for a neural network while maintaining the final performance , which motivates its use on deep neural networks by partitioning the training task in multiple training subtasks with sub models , which can be performed independently and in parallel , it is shown that the size of the sub models reduces almost quadratically with the number of subtasks created , quickly scaling down the sub models used for the pre training the sub models are then merged to provide a pre trained initial set of weights for the original model the proposed method is independent of the other aspects of the training , such as architecture of the neural network , training method , and objective , making it compatible with a wide range of existing approaches the speedup without loss of performance is validated experimentally on mnist and on cifar10 data sets , also showing that even performing the subtasks sequentially can decrease the training time moreover , we show that larger models may present higher speedups and conjecture about the benefits of the method in distributed learning systems"
cs.IT,"this paper investigates spectrum power trading be tween a small cell \( sc \) and a macro cell \( mc \) , where the sc consumes power to serve the macro cell users \( mus \) in exchange for some bandwidth from the mc our goal is to maximize the system energy efficiency \( ee \) of the sc while guaranteeing the quality of service \( qos \) of each mu as well as small cell users \( sus \) specifically , given the minimum data rate requirement and the bandwidth provided by the mc , the sc jointly optimizes mu selection , bandwidth allocation , and power allocation while guaranteeing its own minimum required system data rate the problem is challenging due to the binary mu selection variables and the fractional form objective function we first show that in order to achieve the maximum system ee , the bandwidth of an mu is shared with at most one su in the sc then , for a given mu selection , the optimal bandwidth and power allocations are obtained by exploiting the fractional programming to perform mu selection , we first introduce the concept of trading ee then , we reveal a sufficient and necessary condition for serving an mu without considering the total power constraint and the minimum data rate constraint based on this insight , we propose a low computational complexity mu selection algorithm simulation results demonstrate the effectiveness of the proposed algorithms"
cs.LG|stat.ML,"in this paper , we prove a conjecture published in 1989 and also partially address an open problem announced at the conference on learning theory \( colt \) 2015 for an expected loss function of a deep nonlinear neural network , we prove the following statements under the independence assumption adopted from recent work 1 \) the function is non convex and non concave , 2 \) every local minimum is a global minimum , 3 \) every critical point that is not a global minimum is a saddle point , and 4 \) the property of saddle points differs for shallow networks \( with three layers \) and deeper networks \( with more than three layers \) moreover , we prove that the same four statements hold for deep linear neural networks with any depth , any widths and no unrealistic assumptions as a result , we present an instance , for which we can answer to the following question how difficult to directly train a deep model in theory \? it is more difficult than the classical machine learning models \( because of the non convexity \) , but not too difficult \( because of the nonexistence of poor local minima and the property of the saddle points \) we note that even though we have advanced the theoretical foundations of deep learning , there is still a gap between theory and practice"
cs.DS|cs.IT|cs.LG,"a poisson binomial distribution over n variables is the distribution of the sum of n independent bernoullis we provide a sample near optimal algorithm for testing whether a distribution p supported on 0 , , n to which we have sample access is a poisson binomial distribution , or far from all poisson binomial distributions the sample complexity of our algorithm is o \( n 1 4 \) to which we provide a matching lower bound we note that our sample complexity improves quadratically upon that of the naive learn followed by tolerant test approach , while instance optimal identity testing vv14 is not applicable since we are looking to simultaneously test against a whole family of distributions"
cs.AI|cs.DS,"disjunctive answer set programming \( asp \) is a powerful declarative programming paradigm whose main decision problems are located on the second level of the polynomial hierarchy identifying tractable fragments and developing efficient algorithms for such fragments are thus important objectives in order to complement the sophisticated asp systems available to date hard problems can become tractable if some problem parameter is bounded by a fixed constant such problems are then called fixed parameter tractable \( fpt \) while several fpt results for asp exist , parameters that relate to directed or signed graphs representing the program at hand have been neglected so far in this paper , we first give some negative observations showing that directed width measures on the dependency graph of a program do not lead to fpt results we then consider the graph parameter of signed clique width and present a novel dynamic programming algorithm that is fpt w r t this parameter clique width is more general than the well known treewidth , and , to the best of our knowledge , ours is the first fpt algorithm for bounded clique width for reasoning problems beyond sat"
cs.AI,"the coalitional manipulation \( cm \) problem has been studied extensively in the literature for many voting rules the cm problem , however , has been studied only in the complete information setting , that is , when the manipulators know the votes of the non manipulators a more realistic scenario is an incomplete information setting where the manipulators do not know the exact votes of the non manipulators but may have some partial knowledge of the votes in this paper , we study a setting where the manipulators know a partial order for each voter that is consistent with the vote of that voter in this setting , we introduce and study two natural computational problems \( 1 \) weak manipulation \( wm \) problem where the manipulators wish to vote in a way that makes their preferred candidate win in at least one extension of the partial votes of the non manipulators \( 2 \) strong manipulation \( sm \) problem where the manipulators wish to vote in a way that makes their preferred candidate win in all possible extensions of the partial votes of the non manipulators we study the computational complexity of the wm and the sm problems for commonly used voting rules such as plurality , veto , k approval , k veto , maximin , copeland , and bucklin our key finding is that , barring a few exceptions , manipulation becomes a significantly harder problem in the setting of incomplete votes"
cs.DM,"let g \( v 1 \( g \) , v 2 \( g \) , e \( g \) \) be a bipartite multigraph , and r subseteq v 1 \( g \) cup v 2 \( g \) a proper coloring of edges of g with the colors 1 , ldots , t is called interval \( respectively , continuous \) on r , if each color is used for at least one edge and the edges incident with each vertex x in r are colored by d \( x \) consecutive colors \( respectively , by the colors 1 , ldots , d \( x \) \) , where d \( x \) is a degree of the vertex x we denote by w 1 \( g \) and w 1 \( g \) , respectively , the least and the greatest values of t , for which there exists an interval on v 1 \( g \) coloring of the multigraph g with the colors 1 , ldots , t in the paper the following basic results are obtained textbf theorem 2 for an arbitrary k , w 1 \( g \) leq k leq w 1 \( g \) , there is an interval on v 1 \( g \) coloring of the multigraph g with the colors 1 , ldots , k textbf theorem 3 the problem of recognition of the existence of a continuous on v 1 \( g \) coloring of the multigraph g is np complete textbf theorem 4 if for any edge \( x , y \) in e \( g \) , where x in v 1 \( g \) , the inequality d \( x \) geq d \( y \) holds then there is a continuous on v 1 \( g \) coloring of the multigraph g textbf theorem 1 if g has no multiple edges and triangles , and there is an interval on v \( g \) coloring of the graph g with the colors 1 , ldots , k , then k leq v \( g \) 1"
cs.IT,"we study sigma delta \( sigma delta \) quantization of oversampled bandlimited functions we prove that digitally integrating blocks of bits and then down sampling , a process known as decimation , can efficiently encode the associated sigma delta bit stream it allows a large reduction in the bit rate while still permitting good approximation of the underlying bandlimited function via an appropriate reconstruction kernel specifically , in the case of stable r th order sigma delta schemes we show that the reconstruction error decays exponentially in the bit rate for example , this result applies to the 1 bit , greedy , first order sigma delta scheme"
cs.IT,"the reference scenario in this paper is a single cell in a long term evolution advanced \( lte a \) system , where multiple user equipments \( ues \) aim at uploading some data to a central server or to the cloud the traditional uploading technique used in cellular systems , i e , with separate links from each ue to the enodeb , is compared to innovative textit relay based schemes that exploit device to device \( d2d \) communications between two \( or more \) ues in proximity to each other differences in the channel quality experienced by the ues offer an opportunity to develop d2d based solutions , where textit \( i \) the ue with a poor direct link to the enodeb will forward data to a nearby ue over a high quality d2d link and textit \( ii \) the receiving ue then uploads its own generated data and the relayed data to the enodeb over a good uplink channel a straightforward gain in the data uploading time can be obtained for the first ue to extend the benefits , also to the relaying ue , enhanced d2d based solutions are proposed that decrease the uploading time of this ue based on the cooperative sharing of the resources allocated by the enodeb to the cooperating devices finally , preliminary results are also presented for a multihop study case , where a chain of devices exploits d2d communications to upload data to the enodeb"
cs.DS|cs.IT,"we develop novel data dissemination and collection algorithms for wireless sensor networks \( wsns \) in which we consider n sensor nodes distributed randomly in a certain field to measure a physical phenomena such sensors have limited energy , shortage coverage range , bandwidth and memory constraints we desire to disseminate nodes' data throughout the network such that a base station will be able to collect the sensed data by querying a small number of nodes we propose two data dissemination and collection algorithms \( dca 's \) to solve this problem data dissemination is achieved through dynamical selection of some nodes the selected nodes will be changed after a time slot t and may be repeated after a period t"
cs.DS,"given a graph g and an integer k , the feedback vertex set \( fvs \) problem asks if there is a vertex set t of size at most k that hits all cycles in the graph the fixed parameter tractability status of fvs in directed graphs was a long standing open problem until chen et al \( stoc '08 \) showed that it is fpt by giving a 4 k k ! n o \( 1 \) time algorithm in the subset versions of this problems , we are given an additional subset s of vertices \( resp , edges \) and we want to hit all cycles passing through a vertex of s \( resp an edge of s \) recently , the subset feedback vertex set in undirected graphs was shown to be fpt by cygan et al \( icalp '11 \) and independently by kakimura et al \( soda '12 \) we generalize the result of chen et al \( stoc '08 \) by showing that subset feedback vertex set in directed graphs can be solved in time 2 o \( k 3 \) n o \( 1 \) by our result , we complete the picture for feedback vertex set problems and their subset versions in undirected and directed graphs besides proving the fixed parameter tractability of directed subset feedback vertex set , we reformulate the random sampling of important separators technique in an abstract way that can be used for a general family of transversal problems moreover , we modify the probability distribution used in the technique to achieve better running time in particular , this gives an improvement from 2 2 o \( k \) to 2 o \( k 2 \) in the parameter dependence of the directed multiway cut algorithm of chitnis et al \( soda '12 \)"
cs.IT,"we propose an information theoretic framework for the secure two party function computation \( sfc \) problem and introduce the notion of sfc capacity we study and extend string oblivious transfer \( ot \) to sample wise ot we propose an efficient , perfectly private ot protocol utilizing the binary erasure channel or source we also propose the bootstrap string ot protocol which provides disjoint \( weakened \) privacy while achieving a multiplicative increase in rate , thus trading off security for rate finally , leveraging our ot protocol , we construct a protocol for sfc and establish a general lower bound on sfc capacity of the binary erasure channel and source"
cs.IT,"recently li and xia have proposed a transmission scheme for wireless relay networks based on the alamouti space time code and orthogonal frequency division multiplexing to combat the effect of timing errors at the relay nodes this transmission scheme is amazingly simple and achieves a diversity order of two for any number of relays motivated by its simplicity , this scheme is extended to a more general transmission scheme that can achieve full cooperative diversity for any number of relays the conditions on the distributed space time code \( dstc \) structure that admit its application in the proposed transmission scheme are identified and it is pointed out that the recently proposed full diversity four group decodable dstcs from precoded co ordinate interleaved orthogonal designs and extended clifford algebras satisfy these conditions it is then shown how differential encoding at the source can be combined with the proposed transmission scheme to arrive at a new transmission scheme that can achieve full cooperative diversity in asynchronous wireless relay networks with no channel information and also no timing error knowledge at the destination node finally , four group decodable distributed differential space time codes applicable in this new transmission scheme for power of two number of relays are also provided"
cs.LG|cs.SI|stat.ML,"this paper addresses the problem of distributed detection in multi agent networks agents receive private signals about an unknown state of the world the underlying state is globally identifiable , yet informative signals may be dispersed throughout the network using an optimization based framework , we develop an iterative local strategy for updating individual beliefs in contrast to the existing literature which focuses on asymptotic learning , we provide a finite time analysis furthermore , we introduce a kullback leibler cost to compare the efficiency of the algorithm to its centralized counterpart our bounds on the cost are expressed in terms of network size , spectral gap , centrality of each agent and relative entropy of agents' signal structures a key observation is that distributing more informative signals to central agents results in a faster learning rate furthermore , optimizing the weights , we can speed up learning by improving the spectral gap we also quantify the effect of link failures on learning speed in symmetric networks we finally provide numerical simulations which verify our theoretical results"
cs.AI|stat.ML,"a theoretical framework that supports automated construction of dynamic prime models purely from experimental time series data has been invented and developed , which can automatically generate \( construct \) data driven models of any time series data in seconds this has resulted in the formulation and formalisation of new reverse engineering and dynamic methods for automated systems modelling of complex systems , including complex biological , financial , control , and artificial neural network systems the systems model theory behind the invention has been formalised as a new , effective and robust system identification strategy complementary to process based modelling the proposed dynamic modelling and network inference solutions often involve tackling extremely difficult parameter estimation challenges , inferring unknown underlying network structures , and unsupervised formulation and construction of smart and intelligent ode models of complex systems in underdetermined conditions , i e , cases of dealing with how best to instantaneously and rapidly construct data consistent prime models of unknown \( or well studied \) complex system from small sized time series data , inference of unknown underlying network of interaction is more challenging this article reports a robust step by step mathematical and computational analysis of the entire prime model construction process that determines a model from data in less than a minute"
cs.LG,"we study optimization of finite sums of emph geodesically smooth functions on riemannian manifolds although variance reduction techniques for optimizing finite sum problems have witnessed a huge surge of interest in recent years , all existing work is limited to vector space problems we introduce emph riemannian svrg , a new variance reduced riemannian optimization method we analyze this method for both geodesically smooth emph convex and emph nonconvex functions our analysis reveals that riemannian svrg comes with advantages of the usual svrg method , but with factors depending on manifold curvature that influence its convergence to the best of our knowledge , ours is the first emph fast stochastic riemannian method moreover , our work offers the first non asymptotic complexity analysis for nonconvex riemannian optimization \( even for the batch setting \) our results have several implications for instance , they offer a riemannian perspective on variance reduced pca , which promises a short , transparent convergence analysis"
cs.IT,"we present a superposition coding scheme for communication over a network , which combines partial decode and forward and noisy network coding this hybrid scheme is termed as superposition noisy network coding the scheme is designed and analyzed for single relay channel , single source multicast network and multiple source multicast network the achievable rate region is determined for each case the special cases of gaussian single relay channel and two way relay channel are analyzed for superposition noisy network coding the achievable rate of the proposed scheme is higher than the existing schemes of noisy network coding and compress forward"
cs.IT,"in this paper , the transmission of an improper complex second order stationary data sequence is considered over a strictly band limited frequency selective channel it is assumed that the transmitter employs linear modulation and that the channel output is corrupted by additive proper complex cyclostationary noise under the average transmit power constraint , the problem of minimizing the mean squared error at the output of a widely linear receiver is formulated in the time domain to find the optimal transmit and receive waveforms the optimization problem is converted into a frequency domain problem by using the vectorized fourier transform technique and put into the form of a double minimization first , the widely linear receiver is optimized that requires , unlike the linear receiver design with only one waveform , the design of two receive waveforms then , the optimal transmit waveform for the linear modulator is derived by introducing the notion of the impropriety frequency function of a discrete time random process and by performing a line search combined with an iterative algorithm the optimal solution shows that both the periodic spectral correlation due to the cyclostationarity and the symmetric spectral correlation about the origin due to the impropriety are well exploited"
cs.IT,"wireless cellular networks evolve towards a heterogeneous infrastructure , featuring multiple types of base stations \( bss \) , such as femto bss \( fbss \) and macro bss \( mbss \) a wireless device observes multiple points \( bss \) through which it can access the infrastructure and it may choose to receive the downlink \( dl \) traffic from one bs and send uplink \( ul \) traffic through another bs such a situation is referred to as decoupled dl ul access using the framework of stochastic geometry , we derive the association probability for dl ul in order to maximize the average received power , as the relative density of fbss initially increases , a large fraction of devices chooses decoupled access , i e receive from a mbs in dl and transmit through a fbs in ul we analyze the impact that this type of association has on the average throughput in the system"
cs.LG|stat.ML,"this paper presents a spermwhale' localization architecture using jointly a bag of features \( bof \) approach and machine learning framework bof methods are known , especially in computer vision , to produce from a collection of local features a global representation invariant to principal signal transformations our idea is to regress supervisely from these local features two rough estimates of the distance and azimuth thanks to some datasets where both acoustic events and ground truth position are now available furthermore , these estimates can feed a particle filter system in order to obtain a precise spermwhale' position even in mono hydrophone configuration anti collision system and whale watching are considered applications of this work"
cs.AI,"we generalize shimizu et al 's \( 2006 \) ica based approach for discovering linear non gaussian acyclic \( lingam \) structural equation models \( sems \) from causally sufficient , continuous valued observational data by relaxing the assumption that the generating sem 's graph is acyclic , we solve the more general problem of linear non gaussian \( ling \) sem discovery ling discovery algorithms output the distribution equivalence class of sems which , in the large sample limit , represents the population distribution we apply a ling discovery algorithm to simulated data finally , we give sufficient conditions under which only one of the sems in the output class is 'stable'"
cs.LG,"while machine learning is currently very successful in several application domains , we are still very far from a real artificial intelligence in this paper , we study basic sequence prediction problems that are beyond the scope of what is learnable with popular methods such as recurrent networks we show that simple algorithms can be learnt from sequential data with a recurrent network associated with trainable stacks we focus our study on algorithmically generated sequences such as a n b n , that can only be learnt by models which have the capacity to count our study highlights certain topics in machine learning that deserve more attention , such as addressing the shortcomings of purely gradient based training of non convex models we achieve progress in this direction by incorporating search based strategy once trained , we show that our method is able generalize to sequences up to an arbitrary size"
cs.IT,"upper and lower bounds on the capacity of gaussian multicast relay channels are shown to be quasi concave in the receiver signal to noise ratios and the transmit correlation coefficient the bounds considered are the cut set bound , decode forward \( df \) rates , and quantize forward rates the df rates are shown to be quasi concave in the relay position and this property is used to optimize the relay position for several networks"
cs.AI,"an interesting research problem in our age of big data is that of determining provenance granular evaluation of provenance of physical goods e g tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods has often not been possible with today 's items that are produced and transported in complex , inter organizational , often internationally spanning supply chains recent adoption of internet of things and blockchain technologies give promise at better supply chain provenance we are particularly interested in the blockchain as many favoured use cases of blockchain are for provenance tracking we are also interested in applying ontologies as there has been some work done on knowledge provenance , traceability , and food provenance using ontologies in this paper , we make a case for why ontologies can contribute to blockchain design to support this case , we analyze a traceability ontology and translate some of its representations to smart contracts that execute a provenance trace and enforce traceability constraints on the ethereum blockchain platform"
cs.LG,"in this paper , we develop a framework for information theoretic learning based on infinitely divisible matrices we formulate an entropy like functional on positive definite matrices based on renyi 's axiomatic definition of entropy and examine some key properties of this functional that lead to the concept of infinite divisibility the proposed formulation avoids the plug in estimation of density and brings along the representation power of reproducing kernel hilbert spaces as an application example , we derive a supervised metric learning algorithm using a matrix based analogue to conditional entropy achieving results comparable with the state of the art"
cs.IT,"some new results are derived concerning random coding error exponents and expurgated exponents for list decoding with a deterministic list size l two asymptotic regimes are considered , the fixed list size regime , where l is fixed independently of the block length n , and the exponential list size , where l grows exponentially with n we first derive a general upper bound on the list decoding average error probability , which is suitable for both regimes this bound leads to more specific bounds in the two regimes in the fixed list size regime , the bound is related to known bounds and we establish its exponential tightness in the exponential list size regime , we establish the achievability of the well known sphere packing lower bound relations to guessing exponents are also provided an immediate byproduct of our analysis in both regimes is the universality of the maximum mutual information \( mmi \) list decoder in the error exponent sense finally , we consider expurgated bounds at low rates , both using gallager 's approach and the csisz 'ar k orner marton approach , which is , in general better \( at least for l 1 \) the latter expurgated bound , which involves the notion of it multi information , is also modified to apply to continuous alphabet channels , and in particular , to the gaussian memoryless channel , where the expression of the expurgated bound becomes quite explicit"
cs.IT,"in this paper we present a multi user cooperative protocol for wireless networks two sources transmit simultaneously their information blocks and relays employ opportunistically successive interference cancellation \( sic \) in an effort to decode them an adaptive decode amplify and forward scheme is applied at the relays to the decoded blocks or their sufficient statistic if decoding fails the main feature of the protocol is that sic is exploited in a network since more opportunities arise for each block to be decoded as the number of used relays nru is increased this feature leads to benefits in terms of diversity and multiplexing gains that are proven with the help of an analytical outage model and a diversity multiplexing tradeoff \( dmt \) analysis the performance improvements are achieved without any network synchronization and coordination in the final part of this work the closed form outage probability model is used by a novel approach for offline pre selection of the nru relays , that have the best sic performance , from a larger number of nr nodes the analytical results are corroborated with extensive simulations , while the protocol is compared with orthogonal and multi user protocols reported in the literature"
cs.AI|cs.IT|cs.SI,"we present a framework for representing and modeling data on graphs based on this framework , we study three typical classes of graph signals smooth graph signals , piecewise constant graph signals , and piecewise smooth graph signals for each class , we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties we then study how such graph dictionary works in two standard tasks approximation and sampling followed with recovery , both from theoretical as well as algorithmic perspectives finally , for each class , we present a case study of a real world problem by using the proposed methodology"
cs.IT,"two skew cyclic codes can be equivalent for the hamming metric only if they have the same length , and only the zero code is degenerate the situation is completely different for the rank metric , where lengths of codes correspond to the number of outgoing links from the source when applying the code on a network we study rank equivalences between skew cyclic codes of different lengths and , with the aim of finding the skew cyclic code of smallest length that is rank equivalent to a given one , we define different types of length for a given skew cyclic code , relate them and compute them in most cases we give different characterizations of rank degenerate skew cyclic codes using conventional polynomials and linearized polynomials some known results on the rank weight hierarchy of cyclic codes for some lengths are obtained as particular cases and extended to all lengths and to all skew cyclic codes finally , we prove that the smallest length of a linear code that is rank equivalent to a given skew cyclic code can be attained by a pseudo skew cyclic code throughout the paper , we find new relations between linear skew cyclic codes and their galois closures"
cs.LG|stat.ML,"manifold learning is a class of algorithms seeking a low dimensional non linear representation of high dimensional data thus manifold learning algorithms are , at least in theory , most applicable to high dimensional data and sample sizes to enable accurate estimation of the manifold despite this , most existing manifold learning implementations are not particularly scalable here we present a python package that implements a variety of manifold learning algorithms in a modular and scalable fashion , using fast approximate neighbors searches and fast sparse eigendecompositions the package incorporates theoretical advances in manifold learning , such as the unbiased laplacian estimator and the estimation of the embedding distortion by the riemannian metric method in benchmarks , even on a single core desktop computer , our code embeds millions of data points in minutes , and takes just 200 minutes to embed the main sample of galaxy spectra from the sloan digital sky survey consisting of 0 6 million samples in 3750 dimensions a task which has not previously been possible"
cs.AI,"this paper presents a solution to autonomous underwater vehicles \( auvs \) large scale route planning and task assignment joint problem given a set of constraints \( e g , time \) and a set of task priority values , the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints making use of the heuristic nature of genetic and swarm intelligence algorithms in solving np hard graph problems , particle swarm optimization \( pso \) and genetic algorithm \( ga \) are employed to find the optimum solution , where each individual in the population is a candidate solution \( route \) to evaluate the robustness of the proposed methods , the performance of the all ps and ga algorithms are examined and compared for a number of monte carlo runs simulation results suggest that the routes generated by both algorithms are feasible and reliable enough , and applicable for underwater motion planning however , the ga based route planner produces superior results comparing to the results obtained from the pso based route planner"
cs.LG|stat.ML,"in this paper , a bayesian inference technique based on taylor series approximation of the logarithm of the likelihood function is presented the proposed approximation is devised for the case , where the prior distribution belongs to the exponential family of distributions the logarithm of the likelihood function is linearized with respect to the sufficient statistic of the prior distribution in exponential family such that the posterior obtains the same exponential family form as the prior similarities between the proposed method and the extended kalman filter for nonlinear filtering are illustrated furthermore , an extended target measurement update for target models where the target extent is represented by a random matrix having an inverse wishart distribution is derived the approximate update covers the important case where the spread of measurement is due to the target extent as well as the measurement noise in the sensor"
cs.DM,"we introduce weak oddness omega textrm w , a new measure of uncolourability of cubic graphs , defined as the least number of odd components in an even factor for every bridgeless cubic graph g , rho \( g \) le omega textrm w \( g \) le omega \( g \) , where rho \( g \) denotes the resistance of g and omega \( g \) denotes the oddness of g , so this new measure is an approximation of both oddness and resistance we demonstrate that there are graphs g satisfying rho \( g \) omega textrm w \( g \) omega \( g \) , and that the difference between any two of those three measures can be arbitrarily large the construction implies that if we replace a vertex of a cubic graph with a triangle , then its oddness can decrease by an arbitrarily large amount"
cs.IT,"in orthogonal frequency division multiplexing \( ofdm \) systems , carrier and sampling frequency offsets \( cfo and sfo , respectively \) can destroy the orthogonality of the subcarriers and degrade system performance in the literature , nguyen le , le ngoc , and ko proposed a simple maximum likelihood \( ml \) scheme using two long training symbols for estimating the initial cfo and sfo of a recursive least squares \( rls \) estimation scheme however , the results of nguyen le 's ml estimation show poor performance relative to the cramer rao bound \( crb \) in this paper , we extend moose 's cfo estimation algorithm to joint ml estimation of cfo and sfo using two long training symbols in particular , we derive crbs for the mean square errors \( mses \) of cfo and sfo estimation simulation results show that the proposed ml scheme provides better performance than nguyen le 's ml scheme"
cs.IT,"in this paper , a novel cluster based approach for optimizing the energy efficiency of wireless small cell networks is proposed a dynamic mechanism based on the spectral clustering technique is proposed to dynamically form clusters of small cell base stations such clustering enables intra cluster coordination among the base stations for optimizing the downlink performance through load balancing , while satisfying users' quality of service requirements in the proposed approach , the clusters use an opportunistic base station sleep wake switching mechanism to strike a balance between delay and energy consumption the inter cluster interference affects the performance of the clusters and their choices of active or sleep state due to the lack of inter cluster communications , the clusters have to compete with each other to make decisions on improving the energy efficiency this competition is formulated as a noncooperative game among the clusters that seek to minimize a cost function which captures the tradeoff between energy expenditure and load to solve this game , a distributed learning algorithm is proposed using which the clusters autonomously choose their optimal transmission strategies simulation results show that the proposed approach yields significant performance gains in terms of reduced energy expenditures up to 40 and reduced load up to 23 compared to conventional approaches"
cs.DS|cs.LG|stat.ML,"we give a new approach to the dictionary learning \( also known as sparse coding \) problem of recovering an unknown n times m matrix a \( for m geq n \) from examples of the form y ax e , where x is a random vector in mathbb r m with at most tau m nonzero coordinates , and e is a random noise vector in mathbb r n with bounded magnitude for the case m o \( n \) , our algorithm recovers every column of a within arbitrarily good constant accuracy in time m o \( log m log \( tau 1 \) \) , in particular achieving polynomial time if tau m delta for any delta 0 , and time m o \( log m \) if tau is \( a sufficiently small \) constant prior algorithms with comparable assumptions on the distribution required the vector x to be much sparser at most sqrt n nonzero coordinates and there were intrinsic barriers preventing these algorithms from applying for denser x we achieve this by designing an algorithm for noisy tensor decomposition that can recover , under quite general conditions , an approximate rank one decomposition of a tensor t , given access to a tensor t' that is tau close to t in the spectral norm \( when considered as a matrix \) to our knowledge , this is the first algorithm for tensor decomposition that works in the constant spectral norm noise regime , where there is no guarantee that the local optima of t and t' have similar structures our algorithm is based on a novel approach to using and analyzing the sum of squares semidefinite programming hierarchy \( parrilo 2000 , lasserre 2001 \) , and it can be viewed as an indication of the utility of this very general and powerful tool for unsupervised learning problems"
cs.IT,"in this work we explore possibilities for coding when information worlds have different \( semantic \) values we introduce a loss function that expresses the overall performance of a coding scheme for discrete channels and exchange the usual goal of minimizing the error probability to that of minimizing the expected loss in this environment we explore the possibilities of using poset decoders to make a message wise unequal error protection \( uep \) , where the most valuable information is protected by placing in its proximity information words that differ by small valued information similar definitions and results are shortly presented also for signal constellations in euclidean space"
cs.IT,"this paper characterizes the capacity of a class of modulo additive noise relay channels , in which the relay observes a corrupted version of the noise and has a separate channel to the destination the capacity is shown to be strictly below the cut set bound in general and achievable using a quantize and forward strategy at the relay this result confirms a conjecture by ahlswede and han about the capacity of channels with rate limited state information at the destination for this particular class of channels"
cs.SI,"urban environments develop complex , non obvious structures that are often hard to represent in the form of maps or guides finding the right place to go often requires intimate familiarity with the location in question and cannot easily be deduced by visitors in this work , we exploit large scale samples of usage information , in the form of mobile phone traces and geo tagged twitter messages in order to automatically explore and annotate city maps via kernel density estimation our experiments are based on one year 's worth of mobile phone activity collected by nokia 's mobile data challenge \( mdc \) we show that usage information can be a strong predictor of semantic place categories , allowing us to automatically annotate maps based on the behavior of the local user base"
cs.DM,"this paper is concerned with the recognition of approximate graph products with respect to the cartesian product most graphs are prime , although they can have a rich product like structure the proposed algorithms are based on a local approach that covers a graph by small subgraphs , so called partial star products , and then utilizes this information to derive the global factors and an embedding of the graph under investigation into cartesian product graphs"
cs.AI|cs.DS,"we consider the task of obtaining the maximum a posteriori estimate of discrete pairwise random fields with arbitrary unary potentials and semimetric pairwise potentials for this problem , we propose an accurate hierarchical move making strategy where each move is computed efficiently by solving an st mincut problem unlike previous move making approaches , e g the widely used a expansion algorithm , our method obtains the guarantees of the standard linear programming \( lp \) relaxation for the important special case of metric labeling unlike the existing lp relaxation solvers , e g interior point algorithms or tree reweighted message passing , our method is significantly faster as it uses only the efficient st mincut algorithm in its design using both synthetic and real data experiments , we show that our technique outperforms several commonly used algorithms"
cs.IT,"the symbol error rate of the minimum distance detector for an arbitrary multi dimensional constellation impaired by additive white gaussian noise is characterized as the product of a completely monotone function with a non negative power of the signal to noise ratio this representation is also shown to apply to cases when the impairing noise is compound gaussian using this general result , it is proved that the symbol error rate is completely monotone if the rank of its constellation matrix is either one or two further , a necessary and sufficient condition for the complete monotonicity of the symbol error rate of a constellation of any dimension is also obtained applications to stochastic ordering of wireless system performance are also discussed"
cs.IT,"arithmetic coding \( ac \) is widely used for the entropy coding of text and video data it involves recursive partitioning of the range 0 , 1 \) in accordance with the relative probabilities of occurrence of the input symbols a data \( image or video \) encryption scheme based on arithmetic coding called as chaotic arithmetic coding \( cac \) has been presented in previous works in cac , a large number of chaotic maps can be used to perform coding , each achieving shannon optimal compression performance the exact choice of map is governed by a key cac has the effect of scrambling the intervals without making any changes to the width of interval in which the codeword must lie , thereby allowing encryption without sacrificing any coding efficiency in this paper , we use a redundancy in cac procedure for secure multicast of videos where multiple users are distributed with different keys to decode same encrypted file by encrypting once , we can generate multiple keys , either of which can be used to decrypt the encoded file this is very suitable for video distribution over internet where a single video can be distributed to multiple clients in a privacy preserving manner"
cs.AI|cs.LG,"autonomous robots need to be able to adapt to unforeseen situations and to acquire new skills through trial and error reinforcement learning in principle offers a suitable methodological framework for this kind of autonomous learning however current computational reinforcement learning agents mostly learn each individual skill entirely from scratch how can we enable artificial agents , such as robots , to acquire some form of generic knowledge , which they could leverage for the learning of new skills \? this paper argues that , like the brain , the cognitive system of artificial agents has to develop a world model to support adaptive behavior and learning inspiration is taken from two recent developments in the cognitive science literature predictive processing theories of cognition , and the sensorimotor contingencies theory of perception based on these , a hypothesis is formulated about what the content of information might be that is encoded in an internal world model , and how an agent could autonomously acquire it a computational model is described to formalize this hypothesis , and is evaluated in a series of simulation experiments"
cs.IT,"estimating the probability that a sum of random variables \( rvs \) exceeds a given threshold is a well known challenging problem closed form expression of the sum distribution is usually intractable and presents an open problem a crude monte carlo \( mc \) simulation is the standard technique for the estimation of this type of probability however , this approach is computationally expensive especially when dealing with rare events \( i e events with very small probabilities \) importance sampling \( is \) is an alternative approach which effectively improves the computational efficiency of the mc simulation in this paper , we develop a general framework based on is approach for the efficient estimation of the probability that the sum of independent and not necessarily identically distributed heavy tailed rvs exceeds a given threshold the proposed is approach is based on constructing a new sampling distribution by twisting the hazard rate of the original underlying distribution of each component in the summation a minmax approach is carried out for the determination of the twisting parameter , for any given threshold moreover , using this minmax optimal choice , the estimation of the probability of interest is shown to be asymptotically optimal as the threshold goes to infinity we also offer some selected simulation results illustrating first the efficiency of the proposed is approach compared to the naive mc simulation the near optimality of the minmax approach is then numerically analyzed"
cs.LG|stat.ML,"central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements this paper presents a gaussian process localization \( gp localize \) algorithm that , in contrast to existing works , can exploit the spatially correlated field measurements taken during a robot 's exploration \( instead of relying on prior training data \) for efficiently and scalably learning the gp observation model online through our proposed novel online sparse gp as a result , gp localize is capable of achieving constant time and memory \( i e , independent of the size of the data \) per filtering step , which demonstrates the practical feasibility of using gps for persistent robot localization and autonomy empirical evaluation via simulated experiments with real world datasets and a real robot experiment shows that gp localize outperforms existing gp localization algorithms"
cs.LG|stat.ML,"singular value decomposition \( svd \) has been used successfully in recent years in the area of recommender systems in this paper we present how this model can be extended to consider both user ratings and information from wikipedia by mapping items to wikipedia pages and quantifying their similarity , we are able to use this information in order to improve recommendation accuracy , especially when the sparsity is high another advantage of the proposed approach is the fact that it can be easily integrated into any other svd implementation , regardless of additional parameters that may have been added to it preliminary experimental results on the movielens dataset are encouraging"
cs.IT,"this paper shows how to decode errors and erasures with gabidulin codes in sub quadratic time in the code length , improving previous algorithms which had at least quadratic complexity the complexity reduction is achieved by accelerating operations on linearized polynomials in particular , we present fast algorithms for division , multi point evaluation and interpolation of linearized polynomials and show how to efficiently compute minimal subspace polynomials"
cs.DS,"we consider the problem of computing a maximal independent set \( mis \) in an extremely harsh broadcast model that relies only on carrier sensing the model consists of an anonymous broadcast network in which nodes have no knowledge about the topology of the network or even an upper bound on its size furthermore , it is assumed that an adversary chooses at which time slot each node wakes up at each time slot a node can either beep , that is , emit a signal , or be silent at a particular time slot , beeping nodes receive no feedback , while silent nodes can only differentiate between none of its neighbors beeping , or at least one of its neighbors beeping we start by proving a lower bound that shows that in this model , it is not possible to locally converge to an mis in sub polynomial time we then study four different relaxations of the model which allow us to circumvent the lower bound and find an mis in polylogarithmic time first , we show that if a polynomial upper bound on the network size is known , it is possible to find an mis in o \( log 3 n \) time second , if we assume sleeping nodes are awoken by neighboring beeps , then we can also find an mis in o \( log 3 n \) time third , if in addition to this wakeup assumption we allow sender side collision detection , that is , beeping nodes can distinguish whether at least one neighboring node is beeping concurrently or not , we can find an mis in o \( log 2 n \) time finally , if instead we endow nodes with synchronous clocks , it is also possible to find an mis in o \( log 2 n \) time"
cs.IT,"this paper studies the problem of recovering a signal vector and the corrupted noise vector from a collection of corrupted linear measurements through the solution of a l1 minimization , where the sensing matrix is a partial fourier matrix whose rows are selected randomly and uniformly from rows of a full fourier matrix after choosing the parameter in the l1 minimization appropriately , we show that the recovery can be successful even when a constant fraction of the measurements are arbitrarily corrupted , moreover , the proportion of corrupted measurement can grows arbitrarily close to 1 , provided that the signal vector is sparse enough the upper bound on the sparsity of the signal vector required in this paper is asymptotically optimal and is better than those achieved by recent literatures 1 , 2 by a ln \( n \) factor furthermore , the assumptions we impose on the signal vector and the corrupted noise vector are loosest comparing to the existing literatures 1 3 , which lenders our recovery guarantees are more applicable extensive numerical experiments based on synthesis as well as real world data are presented to verify the conclusion of the proposed theorem and to demonstrate the potential of the l1 minimization framework"
cs.IT,"a quantum communication channel can be put to many uses it can transmit classical information , private classical information , or quantum information it can be used alone , with shared entanglement , or together with other channels for each of these settings there is a capacity that quantifies a channel 's potential for communication in this short review , i summarize what is known about the various capacities of a quantum channel , including a discussion of the relevant additivity questions i also give some indication of potentially interesting directions for future research"
cs.IT,"we study synthetic aperture radar \( sar \) imaging and motion estimation of complex scenes consisting of stationary and moving targets we use the classic sar setup with a single antenna emitting signals and receiving the echoes from the scene the known motion estimation methods for such setups work only in simple cases , with one or a few targets in the same motion we propose to extend the applicability of these methods to complex scenes , by complementing them with a data pre processing step intended to separate the echoes from the stationary targets and the moving ones we present two approaches the first is an iteration designed to subtract the echoes from the stationary targets one by one it estimates the location of each stationary target from a preliminary image , and then uses it to define a filter that removes its echo from the data the second approach is based on the robust principle component analysis \( pca \) method the key observation is that with appropriate pre processing and windowing , the discrete samples of the stationary target echoes form a low rank matrix , whereas the samples of a few moving target echoes form a high rank sparse matrix the robust pca method is designed to separate the low rank from the sparse part , and thus can be used for the sar data separation we present a brief analysis of the two methods and explain how they can be combined to improve the data separation for extended and complex imaging scenes we also assess the performance of the methods with extensive numerical simulations"
cs.IT,"we consider binary systematic network codes and investigate their capability of decoding a source message either in full or in part we carry out a probability analysis , derive closed form expressions for the decoding probability and show that systematic network coding outperforms conventional network coding we also develop an algorithm based on gaussian elimination that allows progressive decoding of source packets simulation results show that the proposed decoding algorithm can achieve the theoretical optimal performance furthermore , we demonstrate that systematic network codes equipped with the proposed algorithm are good candidates for progressive packet recovery owing to their overall decoding delay characteristics"
cs.IT,"a majority logic decoder made of unreliable logic gates , whose failures are transient and datadependent , is analyzed based on a combinatorial representation of fault configurations a closed form expression for the average bit error rate for an one step majority logic decoder is derived , for a regular low density parity check \( ldpc \) code ensemble and the proposed failure model the presented analysis framework is then used to establish bounds on the one step majority logic decoder performance under the simplified probabilistic gate output switching model based on the expander property of tanner graphs of ldpc codes , it is proven that a version of the faulty parallel bit flipping decoder can correct a fixed fraction of channel errors in the presence of data dependent gate failures the results are illustrated with numerical examples of finite geometry codes"
cs.SI,"we study an extension of duncan watts' 2002 model of information cascades in social networks where edge weights are taken to be random , an innovation motivated by recent applications of cascade analysis to systemic risk in financial networks the main result is a probabilistic analysis that characterizes the cascade in an infinite network as the fixed point of a vector valued mapping , explicit in terms of convolution integrals that can be efficiently evaluated numerically using the fast fourier transform algorithm a second result gives an approximate probabilistic analysis of cascades on real world networks , finite networks based on a fixed deterministic graph extensive cross testing with monte carlo estimates shows that this approximate analysis performs surprisingly well , and provides a flexible microscope that can be used to investigate properties of information cascades in real world networks over a wide range of model parameters"
cs.AI,"in the context of csps , a strong backdoor is a subset of variables such that every complete assignment yields a residual instance guaranteed to have a specified property if the property allows efficient solving , then a small strong backdoor provides a reasonable decomposition of the original instance into easy instances an important challenge is the design of algorithms that can find quickly a small strong backdoor if one exists we present a systematic study of the parameterized complexity of backdoor detection when the target property is a restricted type of constraint language defined by means of a family of polymorphisms in particular , we show that under the weak assumption that the polymorphisms are idempotent , the problem is unlikely to be fpt when the parameter is either r \( the constraint arity \) or k \( the size of the backdoor \) unless p np or fpt w 2 when the parameter is k r , however , we are able to identify large classes of languages for which the problem of finding a small backdoor is fpt"
cs.IT,"the following textit network computing problem is considered source nodes in a directed acyclic network generate independent messages and a single receiver node computes a target function f of the messages the objective is to maximize the average number of times f can be computed per network usage , i e , the ``computing capacity'' the textit network coding problem for a single receiver network is a special case of the network computing problem in which all of the source messages must be reproduced at the receiver for network coding with a single receiver , routing is known to achieve the capacity by achieving the network textit min cut upper bound we extend the definition of min cut to the network computing problem and show that the min cut is still an upper bound on the maximum achievable rate and is tight for computing \( using coding \) any target function in multi edge tree networks and for computing linear target functions in any network we also study the bound 's tightness for different classes of target functions in particular , we give a lower bound on the computing capacity in terms of the steiner tree packing number and a different bound for symmetric functions we also show that for certain networks and target functions , the computing capacity can be less than an arbitrarily small fraction of the min cut bound"
cs.IT,"in this paper , a tutorial software to learn information theory basics in a practical way is reported the software , called it tutor uv , makes use of a modern existing spanish corpus for the modeling of the source both the source and the channel coding are also included in this educational tool as part of the learning experience entropy values of the spanish language obtained with the it tutor uv are discussed and compared to others that were previously calculated under limited conditions"
cs.IT,"this paper studies a layered erasure model for two user interference channels , which can be viewed as a simplified version of gaussian fading interference channel it is assumed that channel state information \( csi \) is only available at receivers but not at transmitters under such assumption , an outer bound is derived for the capacity region of such interference channel the new outer bound is tight in many circumstances for the remaining open cases , the outer bound extends previous results"
cs.AI,"over the last few years , more and more heuristic decision making techniques have been inspired by nature , e g evolutionary algorithms , ant colony optimisation and simulated annealing more recently , a novel computational intelligence technique inspired by immunology has emerged , called artificial immune systems \( ais \) this immune system inspired technique has already been useful in solving some computational problems in this keynote , we will very briefly describe the immune system metaphors that are relevant to ais we will then give some illustrative real world problems suitable for ais use and show a step by step algorithm walkthrough a comparison of ais to other well known algorithms and areas for future work will round this keynote off it should be noted that as ais is still a young and evolving field , there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from the examples given here"
cs.DM,"let w be a word in alphabet x , d with m x 's and n d 's interpreting x as multiplication by x , and d as differentiation with respect to x , the identity wf \( x \) x m n sum k s w \( k \) x k d k f \( x \) , valid for any smooth function f \( x \) , defines a sequence \( s w \( k \) \) k , the terms of which we refer to as the em stirling numbers \( of the second kind \) of w the nomenclature comes from the fact that when w \( xd \) n , we have s w \( k \) n brace k , the ordinary stirling number of the second kind explicit expressions for , and identities satisfied by , the s w \( k \) have been obtained by numerous authors , and combinatorial interpretations have been presented here we provide a new combinatorial interpretation that retains the spirit of the familiar interpretation of n brace k as a count of partitions specifically , we associate to each w a quasi threshold graph g w , and we show that s w \( k \) enumerates partitions of the vertex set of g w into classes that do not span an edge of g w we also discuss some relatives of , and consequences of , our interpretation , including q analogs and bijections between families of labelled forests and sets of restricted partitions"
cs.IT,"the capacity region of the interference channel in which one transmitter non causally knows the message of the other , termed the cognitive interference channel , has remained open since its inception in 2005 a number of subtly differing achievable rate regions and outer bounds have been derived , some of which are tight under specific conditions in this work we present a new unified inner bound for the discrete memoryless cognitive interference channel we show explicitly how it encompasses all known discrete memoryless achievable rate regions as special cases the presented achievable region was recently used in deriving the capacity region of the general deterministic cognitive interference channel , and thus also the linear high snr deterministic approximation of the gaussian cognitive interference channel the high snr deterministic approximation was then used to obtain the capacity of the gaussian cognitive interference channel to within 1 87 bits"
cs.IT,"in this paper we investigate the achievable rate of a system that includes a nomadic transmitter with several antennas , which is received by multiple agents , exhibiting independent channel gains and additive circular symmetric complex gaussian noise in the nomadic regime , we assume that the agents do not have any decoding ability these agents process their channel observations and forward them to the final destination through lossless links with a fixed capacity we propose new achievable rates based on elementary compression and also on a wyner ziv \( ceo like \) processing , for both fast fading and block fading channels , as well as for general discrete channels the simpler two agents scheme is solved , up to an implicit equation with a single variable limiting the nomadic transmitter to a circular symmetric complex gaussian signalling , new upper bounds are derived for both fast and block fading , based on the vector version of the entropy power inequality these bounds are then compared to the achievable rates in several extreme scenarios the asymptotic setting with numbers of agents and transmitter 's antennas taken to infinity is analyzed in addition , the upper bounds are analytically shown to be tight in several examples , while numerical calculations reveal a rather small gap in a finite 2 times2 setting the advantage of the wyner ziv approach over elementary compression is shown where only the former can achieve the full diversity multiplexing tradeoff we also consider the non nomadic setting , with agents that can decode here we give an achievable rate , over fast fading channel , which combines broadcast with dirty paper coding and the decentralized reception , which was introduced for the nomadic setting"
cs.IT,"a transmitter without channel state information \( csi \) wishes to send a delay limited gaussian source over a slowly fading channel the source is coded in superimposed layers , with each layer successively refining the description in the previous one the receiver decodes the layers that are supported by the channel realization and reconstructs the source up to a distortion the expected distortion is minimized by optimally allocating the transmit power among the source layers for two source layers , the allocation is optimal when power is first assigned to the higher layer up to a power ceiling that depends only on the channel fading distribution all remaining power , if any , is allocated to the lower layer for convex distortion cost functions with convex constraints , the minimization is formulated as a convex optimization problem in the limit of a continuum of infinite layers , the minimum expected distortion is given by the solution to a set of linear differential equations in terms of the density of the fading distribution as the bandwidth ratio b \( channel uses per source symbol \) tends to zero , the power distribution that minimizes expected distortion converges to the one that maximizes expected capacity while expected distortion can be improved by acquiring csi at the transmitter \( csit \) or by increasing diversity from the realization of independent fading paths , at high snr the performance benefit from diversity exceeds that from csit , especially when b is large"
cs.LG|cs.SI,"with the emergence of social networking services , researchers enjoy the increasing availability of large scale heterogenous datasets capturing online user interactions and behaviors traditional analysis of techno social systems data has focused mainly on describing either the dynamics of social interactions , or the attributes and behaviors of the users however , overwhelming empirical evidence suggests that the two dimensions affect one another , and therefore they should be jointly modeled and analyzed in a multi modal framework the benefits of such an approach include the ability to build better predictive models , leveraging social network information as well as user behavioral signals to this purpose , here we propose the constrained latent space model \( clsm \) , a generalized framework that combines mixed membership stochastic blockmodels \( mmsb \) and latent dirichlet allocation \( lda \) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities we derive an efficient inference algorithm based on variational expectation maximization that has a computational cost linear in the size of the network , thus making it feasible to analyze massive social datasets we validate the proposed framework on two problems prediction of social interactions from user attributes and behaviors , and behavior prediction exploiting network information we perform experiments with a variety of multi modal social systems , spanning location based social networks \( gowalla \) , social media services \( instagram , orkut \) , e commerce and review sites \( amazon , ciao \) , and finally citation networks \( cora \) the results indicate significant improvement in prediction accuracy over state of the art methods , and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi modal social data"
cs.IT,"this paper introduces a new scheme of lt codes , named multiple configurations in multiple configurations lt codes \( mc lt codes \) , multiple sets of output symbols are simultaneously provided to receivers for recovering the source data each receiver , without the need to send information back to the sender , is capable of receiving the output symbols generated by some configuration chosen according to its own decoding phase aiming at the broadcasting scenarios without feedback channels , the proposed mc lt codes are shown to outperform the optimal pure lt codes at the cost of encoding and transmitting units in this paper , the inspiration of mc lt codes is presented , how mc lt codes work is described by giving examples , in which the optimal pure lt codes are outperformed , and a practical design of mc lt codes , which is analytically proved to have at least the same performance bound as the pure lt codes , is proposed the results of numerical simulation experiments demonstrate that the proposed practical design of mc lt codes can deliver better performance than the lt codes in comparison in summary , this paper creates new potential research directions for lt codes , and mc lt codes are a promising variant of lt codes , especially for broadcasting scenarios"
cs.IT,"in this paper , we propose novel state based algorithms which dynamically control the random access network based on its current state such as channel states of wireless links and backlog states of the queues after formulating the problem , corresponding algorithms with diverse control functions are proposed consequently , it will be shown that the proposed state based schemes for control of the random access networks , results in significant performance gains in comparison with previously proposed control algorithms in order to select an appropriate control function , performances of the state based control algorithms are compared for a wide range of traffic scenarios it is also shown that even an approximate knowledge of network statistics helps in selecting the proper state dependent control function"
cs.DS,"this work studies the generalized moran process , as introduced by lieberman et al nature , 433 312 316 , 2005 , where the individuals of a population reside on the vertices of an undirected connected graph the initial population has a single mutant of a fitness value r , residing at some vertex v , while every other individual has initially fitness 1 the main quantity of interest is the fixation probability , i e the probability that eventually the whole graph is occupied by descendants of the mutant in this work we concentrate on the fixation probability when the mutant is initially on a specific vertex v we then aim at finding graphs that have many strong starts \( or many weak starts \) for the mutant thus we introduce a parameterized notion of selective amplifiers suppressors , i e graphs with at least some h \( n \) vertices \( starting points of the mutant \) , which fixate the graph with large \( resp small \) probability we prove the existence of strong selective amplifiers and of quite strong selective suppressors regarding the traditional notion of fixation probability from a random start , we provide the first non trivial upper and lower bounds first we demonstrate the non existence of strong universal amplifiers , i e we prove that for any graph the fixation probability from a random start is at most 1 frac c \( r \) n 3 4 then we prove the thermal theorem , stating that for any graph , when the mutant starts at vertex v , the fixation probability is at least \( r 1 \) \( r frac deg v deg min \) this implies the first nontrivial lower bound for the usual notion of fixation probability , which is almost tight this theorem extends the isothermal theorem of lieberman et al for regular graphs our proof techniques are original and are based on new domination arguments which may be of general interest in markov processes that are of the general birth death type"
cs.DS,"in this paper , we study a parallel version of galton watson processes for the random generation of tree shaped structures random trees are useful in many situations \( testing , binary search , simulation of physics phenomena , \) as attests more than 49000 citations on google scholar using standard analytic combinatorics , we first give a theoretical , average case study of the random process in order to evaluate how parallelism can be extracted from this process , and we deduce a parallel generation algorithm then we present how it can be implemented in a task based parallel paradigm for shared memory \( here , intel cilk \) this implementation faces several challenges , among which efficient , thread safe random bit generation , memory management and algorithmic modifications for small grain parallelism finally , we evaluate the performance of our implementation and the impact of different choices and parameters we obtain a significant efficiency improvement for the generation of big trees we also conduct empirical and theoretical studies of the average behaviour of our algorithm"
stat.ML,"we present a simple and fast geometric method for modeling data by a union of affine subspaces the method begins by forming a collection of local best fit affine subspaces , i e , subspaces approximating the data in local neighborhoods the correct sizes of the local neighborhoods are determined automatically by the jones' beta 2 numbers \( we prove under certain geometric conditions that our method finds the optimal local neighborhoods \) the collection of subspaces is further processed by a greedy selection procedure or a spectral method to generate the final model we discuss applications to tracking based motion segmentation and clustering of faces under different illuminating conditions we give extensive experimental evidence demonstrating the state of the art accuracy and speed of the suggested algorithms on these problems and also on synthetic hybrid linear data as well as the mnist handwritten digits data and we demonstrate how to use our algorithms for fast determination of the number of affine subspaces"
cs.IT|stat.ML,"information divergence functions play a critical role in statistics and information theory in this paper we introduce a divergence function between distributions and describe a number of properties that make it appealing for classification applications based on an extension of a multivariate two sample test , we identify a nonparametric estimator of the divergence that does not impose strong assumptions on the data distribution furthermore , we show that this measure bounds the minimum binary classification error for the case when the training and test data are drawn from the same distribution and for the case where there exists some mismatch between training and test distributions we confirm the theoretical results by designing feature selection algorithms using the criteria from these bounds and evaluating the algorithms on a series of pathological speech classification tasks"
cs.IT,"the compressed sensing paradigm allows to efficiently represent sparse signals by means of their linear measurements however , the problem of transmitting these measurements to a receiver over a channel potentially prone to packet losses has received little attention so far in this paper , we propose novel methods to generate multiple descriptions from compressed sensing measurements to increase the robustness over unreliable channels in particular , we exploit the democracy property of compressive measurements to generate descriptions in a simple manner by partitioning the measurement vector and properly allocating bit rate , outperforming classical methods like the multiple description scalar quantizer in addition , we propose a modified version of the basis pursuit denoising recovery procedure that is specifically tailored to the proposed methods experimental results show significant performance gains with respect to existing methods"
cs.LG|stat.ML,"the profusion of online news articles makes it difficult to find interesting articles , a problem that can be assuaged by using a recommender system to bring the most relevant news stories to readers however , news recommendation is challenging because the most relevant articles are often new content seen by few users in addition , they are subject to trends and preference changes over time , and in many cases we do not have sufficient information to profile the reader in this paper , we introduce a class of news recommendation systems based on context trees they can provide high quality news recommendation to anonymous visitors based on present browsing behaviour we show that context tree recommender systems provide good prediction accuracy and recommendation novelty , and they are sufficiently flexible to capture the unique properties of news articles"
cs.AI,"we present an algorithm that identifies the reasoning patterns of agents in a game , by iteratively examining the graph structure of its multi agent influence diagram \( maid \) representation if the decision of an agent participates in no reasoning patterns , then we can effectively ignore that decision for the purpose of calculating a nash equilibrium for the game in some cases , this can lead to exponential time savings in the process of equilibrium calculation moreover , our algorithm can be used to enumerate the reasoning patterns in a game , which can be useful for constructing more effective computerized agents interacting with humans"
cs.IT,"we consider a two user state dependent multiaccess channel in which the states of the channel are known non causally to one of the encoders and only strictly causally to the other encoder both encoders transmit a common message and , in addition , the encoder that knows the states non causally transmits an individual message we find explicit characterizations of the capacity region of this communication model in both discrete memoryless and memoryless gaussian cases the analysis also reveals optimal ways of exploiting the knowledge of the state only strictly causally at the encoder that sends only the common message when such a knowledge is beneficial the encoders collaborate to convey to the decoder a lossy version of the state , in addition to transmitting the information messages through a generalized gel'fand pinsker binning particularly important in this problem are the questions of 1 \) optimal ways of performing the state compression and 2 \) whether or not the compression indices should be decoded uniquely we show that both compression `a la noisy network coding , i e , with no binning , and compression using wyner ziv binning are optimal the scheme that uses wyner ziv binning shares elements with cover and el gamal original compress and forward , but differs from it mainly in that backward decoding is employed instead of forward decoding and the compression indices are not decoded uniquely finally , by exploring the properties of our outer bound , we show that , although not required in general , the compression indices can in fact be decoded uniquely essentially without altering the capacity region , but at the expense of larger alphabets sizes for the auxiliary random variables"
cs.AI|cs.LG|stat.ML,"a serious problem in learning probabilistic models is the presence of hidden variables these variables are not observed , yet interact with several of the observed variables detecting hidden variables poses two problems determining the relations to other variables in the model and determining the number of states of the hidden variable in this paper , we address the latter problem in the context of bayesian networks we describe an approach that utilizes a score based agglomerative state clustering as we show , this approach allows us to efficiently evaluate models with a range of cardinalities for the hidden variable we show how to extend this procedure to deal with multiple interacting hidden variables we demonstrate the effectiveness of this approach by evaluating it on synthetic and real life data we show that our approach learns models with hidden variables that generalize better and have better structure than previous approaches"
cs.LG,"we show that learning a convex body in rr d , given random samples from the body , requires 2 omega \( sqrt d eps \) samples by learning a convex body we mean finding a set having at most eps relative symmetric difference with the input body to prove the lower bound we construct a hard to learn family of convex bodies our construction of this family is very simple and based on error correcting codes"
cs.LG|stat.ML,"in this work we address the problem of approximating high dimensional data with a low dimensional representation we make the following contributions we propose an inverse regression method which exchanges the roles of input and response , such that the low dimensional variable becomes the regressor , and which is tractable we introduce a mixture of locally linear probabilistic mapping model that starts with estimating the parameters of inverse regression , and follows with inferring closed form solutions for the forward parameters of the high dimensional regression problem of interest moreover , we introduce a partially latent paradigm , such that the vector valued response variable is composed of both observed and latent entries , thus being able to deal with data contaminated by experimental artifacts that cannot be explained with noise models the proposed probabilistic formulation could be viewed as a latent variable augmentation of regression we devise expectation maximization \( em \) procedures based on a data augmentation strategy which facilitates the maximum likelihood search over the model parameters we propose two augmentation schemes and we describe in detail the associated em inference procedures that may well be viewed as generalizations of a number of em regression , dimension reduction , and factor analysis algorithms the proposed framework is validated with both synthetic and real data we provide experimental evidence that our method outperforms several existing regression techniques"
cs.IT,this paper gives the linear complexity of binary ding helleseth generalized cyclotomic sequences of any order
cs.IT,"we consider a communication problem in which an update of the source message needs to be conveyed to one or more distant receivers that are interested in maintaining specific linear functions of the source message the setting is one in which the updates are sparse in nature , and where neither the source nor the receiver \( s \) is aware of the exact em difference vector , but only know the amount of sparsity that is present in the difference vector under this setting , we are interested in devising linear encoding and decoding schemes that minimize the communication cost involved we show that the optimal solution to this problem is closely related to the notion of maximally recoverable codes \( mrcs \) , which were originally introduced in the context of coding for storage systems in the context of storage , mrcs guarantee optimal erasure protection when the system is partially constrained to have local parity relations among the storage nodes in our problem , we show that optimal solutions exist if and only if mrcs of certain kind \( identified by the desired linear functions \) exist we consider point to point and broadcast versions of the problem , and identify connections to mrcs under both these settings"
cs.DM,"we study the robustness the invariance under definition changes of the cluster class cl p hhkw05 this class contains each p function that is computed by a balanced turing machine whose accepting paths always form a cluster with respect to some length respecting total order with efficient adjacency checks the definition of cl p is heavily influenced by the defining paper 's focus on \( global \) orders in contrast , we define a cluster class , clu p , to capture what seems to us a more natural model of cluster computing we prove that the naturalness is costless cl p clu p then we exploit the more natural , flexible features of clu p to prove new robustness results for cl p and to expand what is known about the closure properties of cl p the complexity of recognizing edges of an ordered collection of computation paths or of a cluster of accepting computation paths is central to this study most particularly , our proofs exploit the power of unique discovery of edges the ability of nondeterministic functions to , in certain settings , discover on exactly one \( in some cases , on at most one \) computation path a critical piece of information regarding edges of orderings or clusters"
cs.IT,"a directed acyclic graph \( dag \) partially represents the conditional independence structure among observations of a system if the local markov condition holds , that is , if every variable is independent of its non descendants given its parents in general , there is a whole class of dags that represents a given set of conditional independence relations we are interested in properties of this class that can be derived from observations of a subsystem only to this end , we prove an information theoretic inequality that allows for the inference of common ancestors of observed parts in any dag representing some unknown larger system more explicitly , we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information within the causal interpretation of dags our result can be seen as a quantitative extension of reichenbach 's principle of common cause to more than two variables our conclusions are valid also for non probabilistic observations such as binary strings , since we state the proof for an axiomatized notion of mutual information that includes the stochastic as well as the algorithmic version"
cs.IT,"the problem of constrained finite impulse response \( fir \) filter design is central to signal processing and arises in a variety of disciplines this paper surveys the design of such filters using projection onto convex sets \( pocs \) and discusses certain commonly encountered time and frequency domain constraints we study in particular the design of nyquist filters and propose a simple extension to the work carried out by haddad , stark , and galatsanos in 1 the flexibility and the ease that this design method provides in terms of accommodating constraints is one of its outstanding features"
cs.IT,"we consider one hop communication in wireless networks with random connections in the random connection model , the channel powers between different nodes are drawn from a common distribution in an i i d manner an scheme achieving the throughput scaling of order n 1 3 delta , for any delta 0 , is proposed , where n is the number of nodes such achievable throughput , along with the order n 1 3 upper bound derived by cui et al , characterizes the throughput capacity of one hop schemes for the class of connection models with finite mean and variance"
cs.IT,"we study the outage probability of opportunistic relay selection in decode and forward relaying with secrecy constraints we derive the closed form expression for the outage probability based on the analytical result , the asymptotic performance is then investigated the accuracy of our performance analysis is verified by the simulation results"
cs.SI,"in this paper we extend our previous and only study on the dynamics of the chilean web this new study doubles the time period and to the best of our knowledge is the only study of its type known about any country in the web the new results corroborate the trends found before , in particular the exponential growth of the web , and reinforce the conclusion that the web is more chaotic than we would like hence , modeling most web characteristics is not trivial"
cs.IT,"we further study the keyless authentication problem in a noisy model in our previous work , where no secret setup is available for sender alice and receiver bob while there is dmc w 1 from alice to bob and a two way noiseless but insecure channel between them we propose a construction such that the message length over dmc w 1 does not depend on the size of the source space if the source space is cal s and the number of channel w 1 uses is n , then our protocol only has a round complexity of log cal s log n 4 in addition , we show that the round complexity of any secure protocol in our model is lower bounded by log cal s log n 5 we also obtain a lower bound on the success probability when the message size on dmc w 1 is given finally , we derive the capacity for a non interactive authentication protocol under general dmcs , which extends the result under bscs in our previous work"
cs.AI,"as computer clusters become more common and the size of the problems encountered in the field of ai grows , there is an increasing demand for efficient parallel inference algorithms we consider the problem of parallel inference on large factor graphs in the distributed memory setting of computer clusters we develop a new efficient parallel inference algorithm , dbrsplash , which incorporates over segmented graph partitioning , belief residual scheduling , and uniform work splash operations we empirically evaluate the dbrsplash algorithm on a 120 processor cluster and demonstrate linear to super linear performance gains on large factor graph models"
cs.IT,"in this paper , designs and analyses of compressive recognition systems are discussed , and also a method of establishing a dual connection between designs of good communication codes and designs of recognition systems is presented pattern recognition systems based on compressed patterns and compressed sensor measurements can be designed using low density matrices we examine truncation encoding where a subset of the patterns and measurements are stored perfectly while the rest is discarded we also examine the use of ldpc parity check matrices for compressing measurements and patterns we show how more general ensembles of good linear codes can be used as the basis for pattern recognition system design , yielding system design strategies for more general noise models"
cs.IT,"in this paper , the filter and forward \( ff \) relay design for orthogonal frequency division multiplexing \( ofdm \) transmission systems is considered to improve the system performance over simple amplify and forward \( af \) relaying unlike conventional ofdm relays performing ofdm demodulation and remodulation , to reduce processing complexity , the proposed ff relay directly filters the incoming signal in time domain with a finite impulse response \( fir \) and forwards the filtered signal to the destination three design criteria are considered to optimize the relay filter the first criterion is the minimization of the relay transmit power subject to per subcarrier signal to noise ratio \( snr \) constraints , the second is the maximization of the worst subcarrier channel snr subject to source and relay transmit power constraints , and the third is the maximization of data rate subject to source and relay transmit power constraints it is shown that the first problem reduces to a semi definite programming \( sdp \) problem by semi definite relaxation and the solution to the relaxed sdp problem has rank one under a mild condition for the latter two problems , the problem of joint source power allocation and relay filter design is considered and an efficient algorithm is proposed for each problem based on alternating optimization and the projected gradient method \( pgm \) numerical results show that the proposed ff relay significantly outperforms simple af relays with insignificant increase in complexity thus , the proposed ff relay provides a practical alternative to the af relaying scheme for ofdm transmission"
cs.AI,"in support of art investigation , we propose a new source separation method that unmixes a single x ray scan acquired from double sided paintings in this problem , the x ray signals to be separated have similar morphological characteristics , which brings previous source separation methods to their limits our solution is to use photographs taken from the front and back side of the panel to drive the separation process the crux of our approach relies on the coupling of the two imaging modalities \( photographs and x rays \) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations the common component models features shared by the multi modal images , whereas the innovation component captures modality specific information as such , our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the x rays our dictionary learning framework can be tailored both to a single and a multi scale framework , with the latter leading to a significant performance improvement moreover , to improve further on the visual quality of the separated images , we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure experimentation on synthetic and real data taken from digital acquisition of the ghent altarpiece \( 1432 \) confirms the superiority of our method against the state of the art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation"
cs.AI|cs.DS,"the computation of the global minimum energy conformation \( gmec \) is an important and challenging topic in structure based computational protein design in this paper , we propose a new protein design algorithm based on the and or branch and bound \( aobb \) search , which is a variant of the traditional branch and bound search algorithm , to solve this combinatorial optimization problem by integrating with a powerful heuristic function , aobb is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process tests on real protein data show that our new protein design algorithm is able to solve many prob lems that were previously unsolvable by the traditional exact search algorithms , and for the problems that can be solved with traditional provable algorithms , our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation \( gmec \) solution"
cs.LG|stat.ML,"it is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power however , inspecting input output relationships of those models using visual analytics , while treating them as black box , can help to understand the reasoning behind outcomes without sacrificing predictive quality we identify a space of possible solutions and provide two examples of where such techniques have been successfully used in practice"
cs.IT,"we consider the problem of optimally allocating a given total storage budget in a distributed storage system a source has a data object which it can code and store over a set of storage nodes it is allowed to store any amount of coded data in each node , as long as the total amount of storage used does not exceed the given budget a data collector subsequently attempts to recover the original data object by accessing each of the nodes independently with some constant probability by using an appropriate code , successful recovery occurs when the total amount of data in the accessed nodes is at least the size of the original data object the goal is to find an optimal storage allocation that maximizes the probability of successful recovery this optimization problem is challenging because of its discrete nature and nonconvexity , despite its simple formulation symmetric allocations \( in which all nonempty nodes store the same amount of data \) , though intuitive , may be suboptimal the problem is nontrivial even if we optimize over only symmetric allocations our main result shows that the symmetric allocation that spreads the budget maximally over all nodes is asymptotically optimal in a regime of interest specifically , we derive an upper bound for the suboptimality of this allocation and show that the performance gap vanishes asymptotically in the specified regime further , we explicitly find the optimal symmetric allocation for a variety of cases our results can be applied to distributed storage systems and other problems dealing with reliability under uncertainty , including delay tolerant networks \( dtns \) and content delivery networks \( cdns \)"
cs.AI|cs.LG,"data quantization learns encoding results of data with certain requirements , and provides a broad perspective of many real world applications to data handling nevertheless , the results of encoder is usually limited to multivariate inputs with the random mapping , and side information of binary codes are hardly to mostly depict the original data patterns as possible in the literature , cosine based random quantization has attracted much attentions due to its intrinsic bounded results nevertheless , it usually suffers from the uncertain outputs , and information of original data fails to be fully preserved in the reduced codes in this work , a novel binary embedding method , termed adaptive training quantization \( atq \) , is proposed to learn the ideal transform of random encoder , where the limitation of cosine random mapping is tackled as an adaptive learning idea , the reduced mapping is adaptively calculated with idea of data group , while the bias of random transform is to be improved to hold most matching information experimental results show that the proposed method is able to obtain outstanding performance compared with other random quantization methods"
cs.AI,"currently the dempster shafer based algorithm and uniform random probability based algorithm are the preferred method of resolving security games , in which defenders are able to identify attackers and only strategy remained ambiguous however this model is inefficient in situations where resources are limited and both the identity of the attackers and their strategies are ambiguous the intent of this study is to find a more effective algorithm to guide the defenders in choosing which outside agents with which to cooperate given both ambiguities we designed an experiment where defenders were compelled to engage with outside agents in order to maximize protection of their targets we introduced two important notions the behavior of each agent in target protection and the tolerance threshold in the target protection process from these , we proposed an algorithm that was applied by each defender to determine the best potential assistant \( s \) with which to cooperate our results showed that our proposed algorithm is safer than the dempster shafer based algorithm"
cs.DS,"we study the phase diagram and the algorithmic hardness of the random `locked' constraint satisfaction problems , and compare them to the commonly studied 'non locked' problems like satisfiability of boolean formulas or graph coloring the special property of the locked problems is that clusters of solutions are isolated points this simplifies significantly the determination of the phase diagram , which makes the locked problems particularly appealing from the mathematical point of view on the other hand we show empirically that the clustered phase of these problems is extremely hard from the algorithmic point of view the best known algorithms all fail to find solutions our results suggest that the easy hard transition \( for currently known algorithms \) in the locked problems coincides with the clustering transition these should thus be regarded as new benchmarks of really hard constraint satisfaction problems"
cs.LG|stat.ML,"cardinality potentials are a generally useful class of high order potential that affect probabilities based on how many of d binary variables are active maximum a posteriori \( map \) inference for cardinality potential models is well understood , with efficient computations taking o \( dlogd \) time yet efficient marginalization and sampling have not been addressed as thoroughly in the machine learning community we show that there exists a simple algorithm for computing marginal probabilities and drawing exact joint samples that runs in o \( dlog2 d \) time , and we show how to frame the algorithm as efficient belief propagation in a low order tree structured model that includes additional auxiliary variables we then develop a new , more general class of models , termed recursive cardinality models , which take advantage of this efficiency finally , we show how to do efficient exact inference in models composed of a tree structure and a cardinality potential we explore the expressive power of recursive cardinality models and empirically demonstrate their utility"
cs.LG,this paper suggests the use of intelligent network aware processing agents in wireless local area network drivers to generate metrics for bandwidth estimation based on real time channel statistics to enable wireless multimedia application adaptation various configurations in the wireless digital home are studied and the experimental results with performance variations are presented
cs.IT,"we study a cooperation model where the positions of base stations follow a poisson point process distribution and where voronoi cells define the planar areas associated with them for the service of each user , either one or two base stations are involved if two , these cooperate by exchange of user data and reduced channel information \( channel phase , second neighbour interference \) with conferencing over some backhaul link the total user transmission power is split between them and a common message is encoded , which is coherently transmitted by the stations the decision for a user to choose service with or without cooperation is directed by a family of geometric policies the suggested policies further control the shape of coverage contours in favor of cell edge areas analytic expressions based on stochastic geometry are derived for the coverage probability in the network their numerical evaluation shows benefits from cooperation , which are enhanced when dirty paper coding is applied to eliminate the second neighbour interference"
cs.IT,"we present the construction of a new family of erasure correcting codes for distributed storage that yield low repair bandwidth and low repair complexity the construction is based on two classes of parity symbols the primary goal of the first class of symbols is to provide good erasure correcting capability , while the second class facilitates node repair , reducing the repair bandwidth and the repair complexity we compare the proposed codes with other codes proposed in the literature"
cs.IT,"timing side channels in two user schedulers are studied when two users share a scheduler , one user may learn the other user 's behavior from patterns of service timings we measure the information leakage of the resulting timing side channel in schedulers serving a legitimate user and a malicious attacker , using a privacy metric defined as the shannon equivocation of the user 's job density we show that the commonly used first come first serve \( fcfs \) scheduler provides no privacy as the attacker is able to to learn the user 's job pattern completely furthermore , we introduce an scheduling policy , accumulate and serve scheduler , which services jobs from the user and attacker in batches after buffering them the information leakage in this scheduler is mitigated at the price of service delays , and the maximum privacy is achievable when large delays are added"
cs.LG,"it has long been conjectured that hypothesis spaces suitable for data that is compositional in nature , such as text or images , may be more efficiently represented with deep hierarchical architectures than with shallow ones despite the vast empirical evidence , formal arguments to date are limited and do not capture the kind of networks used in practice using tensor factorization , we derive a universal hypothesis space implemented by an arithmetic circuit over functions applied to local data structures \( e g image patches \) the resulting networks first pass the input through a representation layer , and then proceed with a sequence of layers comprising sum followed by product pooling , where sum corresponds to the widely used convolution operator the hierarchical structure of networks is born from factorizations of tensors based on the linear weights of the arithmetic circuits we show that a shallow network corresponds to a rank 1 decomposition , whereas a deep network corresponds to a hierarchical tucker \( ht \) decomposition log space computation for numerical stability transforms the networks into simnets in its basic form , our main theoretical result shows that the set of polynomially sized rank 1 decomposable tensors has measure zero in the parameter space of polynomially sized ht decomposable tensors in deep learning terminology , this amounts to saying that besides a negligible set , all functions that can be implemented by a deep network of polynomial size , require an exponential size if one wishes to implement \( or approximate \) them with a shallow network our construction and theory shed new light on various practices and ideas employed by the deep learning community , and in that sense bear a paradigmatic contribution as well"
cs.IT,"orthogonal frequency division multiplexing \( ofdm \) is an emerging research field of wireless communication it is one of the most proficient multi carrier transmission techniques widely used today as broadband wired wireless applications having several attributes such as provides greater immunity to multipath fading impulse noise , eliminating inter symbol interference \( isi \) , inter carrier interference \( ici \) the need for equalizers ofdm signals have a general problem of high peak to average power ratio \( papr \) which is defined as the ratio of the peak power to the average power of the ofdm signal the drawback of high papr is that the dynamic range of the power amplifier \( pa \) and digital to analog converter \( dac \) in this paper , an improved scheme of amplitude clipping filtering method is proposed and implemented which shows the significant improvement in case of papr reduction while increasing slight ber compare to an existing method also , the comparative studies of different parameters will be covered"
cs.AI,"past experiences under the designation of swarm paintings conducted in 2001 , not only confirmed the possibility of realizing an artificial art \( thus non human \) , as introduced into the process the questioning of creative migration , specifically from the computer monitors to the canvas via a robotic harm in more recent self organized based research we seek to develop and profound the initial ideas by using a swarm of autonomous robots \( artsbot project 2002 03 \) , that live avoiding the purpose of being merely a simple perpetrator of order streams coming from an external computer , but instead , that actually co evolve within the canvas space , acting \( that is , laying ink \) according to simple inner threshold stimulus response functions , reacting simultaneously to the chromatic stimulus present in the canvas environment done by the passage of their team mates , as well as by the distributed feedback , affecting their future collective behaviour in parallel , and in what respects to certain types of collective systems , we seek to confirm , in a physically embedded way , that the emergence of order \( even as a concept \) seems to be found at a lower level of complexity , based on simple and basic interchange of information , and on the local dynamic of parts , who , by self organizing mechanisms tend to form an lived whole , innovative and adapting , allowing for emergent open ended creative and distributed production keywords artsbots project , swarm intelligence , stigmergy , unmanned art , symbiotic art , swarm paintings , robot paintings , non human art , painting emergence and cooperation , art and complexity , artbots the robot talent show"
cs.LG,"learning structured models using maximum margin techniques has become an indispensable tool for com puter vision researchers , as many computer vision applications can be cast naturally as an image labeling problem pixel based or superpixel based conditional random fields are particularly popular examples typ ically , neighborhood graphs , which contain a large number of cycles , are used as exact inference in loopy graphs is np hard in general , learning these models without approximations is usually deemed infeasible in this work we show that , despite the theoretical hardness , it is possible to learn loopy models exactly in practical applications to this end , we analyze the use of multiple approximate inference techniques together with cutting plane training of structural svms we show that our proposed method yields exact solutions with an optimality guarantees in a computer vision application , for little additional computational cost we also propose a dynamic caching scheme to accelerate training further , yielding runtimes that are comparable with approximate methods we hope that this insight can lead to a reconsideration of the tractability of loopy models in computer vision"
cs.SI,"influence maximization is the problem of finding a set of influential users in a social network such that the expected spread of influence under a certain propagation model is maximized much of the previous work has neglected the important distinction between social influence and actual product adoption however , as recognized in the management science literature , an individual who gets influenced by social acquaintances may not necessarily adopt a product \( or technology \) , due , e g , to monetary concerns in this work , we distinguish between influence and adoption by explicitly modeling the states of being influenced and of adopting a product we extend the classical linear threshold \( lt \) model to incorporate prices and valuations , and factor them into users' decision making process of adopting a product we show that the expected profit function under our proposed model maintains submodularity under certain conditions , but no longer exhibits monotonicity , unlike the expected influence spread function to maximize the expected profit under our extended lt model , we employ an unbudgeted greedy framework to propose three profit maximization algorithms the results of our detailed experimental study on three real world datasets demonstrate that of the three algorithms , textsf page , which assigns prices dynamically based on the profit potential of each candidate seed , has the best performance both in the expected profit achieved and in running time"
cs.IT,"this paper proposes a new framework for citation content analysis \( cca \) , for syntactic and semantic analysis of citation content that can be used to better analyze the rich sociocultural context of research behavior the framework could be considered the next generation of citation analysis this paper briefly reviews the history and features of content analysis in traditional social sciences , and its previous application in library and information science based on critical discussion of the theoretical necessity of a new method as well as the limits of citation analysis , the nature and purposes of cca are discussed , and potential procedures to conduct cca , including principles to identify the reference scope , a two dimensional \( citing and cited \) and two modular \( syntactic and semantic modules \) codebook , are provided and described future works and implications are also suggested"
cs.DM|cs.DS,"we consider the problem of finding a low discrepancy coloring for sparse set systems where each element lies in at most t sets we give an efficient algorithm that finds a coloring with discrepancy o \( \( t log n \) 1 2 \) , matching the best known non constructive bound for the problem due to banaszczyk the previous algorithms only achieved an o \( t 1 2 log n \) bound the result also extends to the more general koml ' o s setting and gives an algorithmic o \( log 1 2 n \) bound"
cs.IT,"millimeter wave \( mmwave \) communication is a promising technology for 5g cellular systems to compensate for the severe path loss in mmwave systems , large antenna arrays are generally used to achieve significant beamforming gains however , due to the high hardware and power consumption cost associated with radio frequency \( rf \) chains , it is desirable to achieve the large antenna gains , but with only limited number of rf chains for mmwave communications to this end , we study in this paper a new lens antenna array enabled mmwave mimo communication system we first show that the array response of the proposed lens antenna array at the receiver transmitter follows a sinc function , where the antenna with the peak response is determined by the angle of arrival \( aoa \) departure \( aod \) of the received transmitted signal by exploiting this unique property of lens antenna arrays along with the multi path sparsity of mmwave channels , we propose a novel low cost and capacity achieving mimo transmission scheme , termed emph orthogonal path division multiplexing \( opdm \) for channels with insufficiently separated aoas and or aods , we also propose a simple emph path grouping technique with group based small scale mimo processing to mitigate the inter path interference numerical results are provided to compare the performance of the proposed lens antenna arrays for mmwave mimo system against that of conventional arrays , under different practical setups it is shown that the proposed system achieves significant throughput gain as well as complexity and hardware cost reduction , both making it an appealing new paradigm for mmwave mimo communications"
cs.LG|stat.ML,"every organism in an environment , whether biological , robotic or virtual , must be able to predict certain aspects of its environment in order to survive or perform whatever task is intended it needs a model that is capable of estimating the consequences of possible actions , so that planning , control , and decision making become feasible for scientific purposes , such models are usually created in a problem specific manner using differential equations and other techniques from control and system theory in contrast to that , we aim for an unsupervised approach that builds up the desired model in a self organized fashion inspired by slow feature analysis \( sfa \) , our approach is to extract sub signals from the input , that behave as predictable as possible these predictable features are highly relevant for modeling , because predictability is a desired property of the needed consequence estimating model by definition in our approach , we measure predictability with respect to a certain prediction model we focus here on the solution of the arising optimization problem and present a tractable algorithm based on algebraic methods which we call predictable feature analysis \( pfa \) we prove that the algorithm finds the globally optimal signal , if this signal can be predicted with low error to deal with cases where the optimal signal has a significant prediction error , we provide a robust , heuristically motivated variant of the algorithm and verify it empirically additionally , we give formal criteria a prediction model must meet to be suitable for measuring predictability in the pfa setting and also provide a suitable default model along with a formal proof that it meets these criteria"
cs.IT,"this paper studies the coordinated beamforming \( cobf \) design for the multiple input single output interference channel , provided that only channel distribution information is known to the transmitters the problem under consideration is a probabilistically constrained optimization problem which maximizes a predefined system utility subject to constraints on rate outage probability and power budget of each transmitter our recent analysis has shown that the outage constrained cobf problem is intricately difficult , e g , np hard therefore , the focus of this paper is on suboptimal but computationally efficient algorithms specifically , by leveraging on the block successive upper bound minimization \( bsum \) method in optimization , we propose a gauss seidel type algorithm , called distributed bsum algorithm , which can handle differentiable , monotone and concave system utilities by exploiting a weighted minimum mean square error \( wmmse \) reformulation , we further propose a jocobi type algorithm , called distributed wmmse algorithm , which can optimize the weighted sum rate utility in a fully parallel manner to provide a performance benchmark , a relaxed approximation method based on polyblock outer approximation is also proposed simulation results show that the proposed algorithms are significantly superior to the existing successive convex approximation method in both performance and computational efficiency , and can yield promising approximation performance"
cs.SI,"the history of phishing traces back in important ways to the mid 1990s when hacking software facilitated the mass targeting of people in password stealing scams on america online \( aol \) the first of these software programs was mine , called aohell , and it was where the word phishing was coined the software provided an automated password and credit card stealing mechanism starting in january 1995 though the practice of tricking users in order to steal passwords or information possibly goes back to the earliest days of computer networking , aohell 's phishing system was the first automated tool made publicly available for this purpose the program influenced the creation of many other automated phishing systems that were made over a number of years these tools were available to amateurs who used them to engage in a countless number of phishing attacks by the later part of the decade , the activity moved from aol to other networks and eventually grew to involve professional criminals on the internet what began as a scheme by rebellious teenagers to steal passwords evolved into one of the top computer security threats affecting people , corporations , and governments"
cs.IT,"information embedding \( ie \) is the transmission of information within a host signal subject to a distortion constraint there are two types of embedding methods , namely irreversible ie and reversible ie , depending upon whether or not the host , as well as the message , is recovered at the decoder in irreversible ie , only the embedded message is recovered at the decoder , and in reversible ie , both the message and the host are recovered at the decoder this paper considers combinations of irreversible and reversible ie in multiple access channels \( mac \) and physically degraded broadcast channels \( bc \)"
cs.IT,"in this paper we discuss the use of cooperative game theory for analyzing interference channels we extend our previous work , to games with n players as well as frequency selective channels and joint tdm fdm strategies we show that the nash bargaining solution can be computed using convex optimization techniques we also show that the same results are applicable to interference channels where only statistical knowledge of the channel is available moreover , for the special case of two players 2 times k frequency selective channel \( with k frequency bins \) we provide an o \( k log 2 k \) complexity algorithm for computing the nash bargaining solution under mask constraint and using joint fdm tdm strategies simulation results are also provided"
cs.IT,"this paper addresses the problem of channel estimation in multi cell interference limited cellular networks we consider systems employing multiple antennas and are interested in both the finite and large scale antenna number regimes \( so called massive mimo \) such systems deal with the multi cell interference by way of per cell beamforming applied at each base station channel estimation in such networks , which is known to be hampered by the pilot contamination effect , constitute a major bottleneck for overall performance we present a novel approach which tackles this problem by enabling a low rate coordination between cells during the channel estimation phase itself the coordination makes use of the additional second order statistical information about the user channels , which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences importantly , we demonstrate analytically that in the large number of antennas regime , the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes"
cs.DM,"a path in an edge colored graph g , where adjacent edges may be colored the same , is called a rainbow path if no two edges of g are colored the same for a kappa connected graph g and an integer k with 1 leq k leq kappa , the rainbow k connectivity rc k \( g \) of g is defined as the minimum integer j for which there exists a j edge coloring of g such that every two distinct vertices of g are connected by k internally disjoint rainbow paths let g be a complete \( ell 1 \) partite graph with ell parts of size r and one part of size p where 0 leq p r \( in the case p 0 , g is a complete ell partite graph with each part of size r \) this paper is to investigate the rainbow k connectivity of g we show that for every pair of integers k geq 2 and r geq 1 , there is an integer f \( k , r \) such that if ell geq f \( k , r \) , then rc k \( g \) 2 as a consequence , we improve the upper bound of f \( k \) from \( k 1 \) 2 to ck 3 2 c , where 0 c 1 , c o \( k 3 2 \) , and f \( k \) is the integer such that if n geq f \( k \) then rc k \( k n \) 2"
cs.DM,"in the recursive lamination of the disk , one tries to add chords one after another at random a chord is kept and inserted if it does not intersect any of the previously inserted ones curien and le gall ann probab 39 \( 2011 \) 2224 2270 have proved that the set of chords converges to a limit triangulation of the disk encoded by a continuous process mathscr m based on a new approach resembling ideas from the so called contraction method in function spaces , we prove that , when properly rescaled , the planar dual of the discrete lamination converges almost surely in the gromov hausdorff sense to a limit real tree mathscr t , which is encoded by mathscr m this confirms a conjecture of curien and le gall"
cs.DM|cs.IT,"continuous time signals are well known for not being perfectly localized in both time and frequency domains conversely , a signal defined over the vertices of a graph can be perfectly localized in both vertex and frequency domains we derive the conditions ensuring the validity of this property and then , building on this theory , we provide the conditions for perfect reconstruction of a graph signal from its samples next , we provide a finite step algorithm for the reconstruction of a band limited signal from its samples and then we show the effect of sampling a non perfectly band limited signal and show how to select the bandwidth that minimizes the mean square reconstruction error"
cs.DS,"we consider the problem of exact and inexact matching of weighted undirected graphs , in which a bijective correspondence is sought to minimize a quadratic weight disagreement this computationally challenging problem is often relaxed as a convex quadratic program , in which the space of permutations is replaced by the space of doubly stochastic matrices however , the applicability of such a relaxation is poorly understood we define a broad class of friendly graphs characterized by an easily verifiable spectral property we prove that for friendly graphs , the convex relaxation is guaranteed to find the exact isomorphism or certify its inexistence this result is further extended to approximately isomorphic graphs , for which we develop an explicit bound on the amount of weight disagreement under which the relaxation is guaranteed to find the globally optimal approximate isomorphism we also show that in many cases , the graph matching problem can be further harmlessly relaxed to a convex quadratic program with only n separable linear equality constraints , which is substantially more efficient than the standard relaxation involving 2n equality and n 2 inequality constraints finally , we show that our results are still valid for unfriendly graphs if additional information in the form of seeds or attributes is allowed , with the latter satisfying an easy to verify spectral characteristic"
cs.DS,"let p be a path graph of n vertices embedded in a metric space we consider the problem of adding a new edge to p such that the diameter of the resulting graph is minimized previously \( in icalp 2015 \) the problem was solved in o \( n log 3 n \) time in this paper , based on new observations and different algorithmic techniques , we present an o \( n log n \) time algorithm"
cs.IT,"a new dictionary for sparse representation of chirp echo in broadband radar is put forward in this paper different with chirplet decomposition which decomposes echo in time frequency plane , the dictionary transforms the sparsity of target observed by radar in distance range to the sparsity in frequency domain by stretch processing and the sparse representation of echo is realized using strict deduction with mathematics , the sparsity of echo in dictionary is proved and the dictionary is orthogonal in the application property , the construction of dictionary is simple , the parameters that are needed for dictionary can be obtained conveniently and the dictionary is convenient to use furthermore , the object of application can be expanded to the echo of multi component chirps with single freedom degree"
cs.IT,"in this article , we study the problem of secret key generation in the multiterminal source model , where the terminals have access to correlated gaussian sources we assume that the sources form a markov chain on a tree we give a nested lattice based key generation scheme whose computational complexity is polynomial in the number , n , of independent and identically distributed samples observed by each source we also compute the achievable secret key rate and give a class of examples where our scheme is optimal in the fine quantization limit however , we also give examples that show that our scheme is not always optimal in the limit of fine quantization"
cs.IT,"a two user discrete memoryless compound multiple access channel with a common message and conferencing decoders is considered the capacity region is characterized in the special cases of physically degraded channels and unidirectional cooperation , and achievable rate regions are provided for the general case the results are then extended to the corresponding gaussian model in the gaussian setup , the provided achievable rates are shown to lie within some constant number of bits from the boundary of the capacity region in several special cases an alternative model , in which the encoders are connected by conferencing links rather than having a common message , is studied as well , and the capacity region for this model is also determined for the cases of physically degraded channels and unidirectional cooperation numerical results are also provided to obtain insights about the potential gains of conferencing at the decoders and encoders"
cs.LG,"theoretical analyses of the dendritic cell algorithm \( dca \) have yielded several criticisms about its underlying structure and operation as a result , several alterations and fixes have been suggested in the literature to correct for these findings a contribution of this work is to investigate the effects of replacing the classification stage of the dca \( which is known to be flawed \) with a traditional machine learning technique this work goes on to question the merits of those unique properties of the dca that are yet to be thoroughly analysed if none of these properties can be found to have a benefit over traditional approaches , then fixing the dca is arguably less efficient than simply creating a new algorithm this work examines the dynamic filtering property of the dca and questions the utility of this unique feature for the anomaly detection problem it is found that this feature , while advantageous for noisy , time ordered classification , is not as useful as a traditional static filter for processing a synthetic dataset it is concluded that there are still unique features of the dca left to investigate areas that may be of benefit to the artificial immune systems community are suggested"
cs.IT,"the design of the channel part of a digital communication system \( e g , error correction , modulation \) is heavily based on the assumption that the data to be transmitted forms a fair bit stream however , simple source encoders such as short huffman codes generate bit streams that poorly match this assumption as a result , the channel input distribution does not match the original design criteria in this work , a simple method called half huffman coding \( halfhc \) is developed halfhc transforms a huffman code into a source code whose output is more similar to a fair bit stream this is achieved by permuting the codewords such that the frequency of 1s at the output is close to 0 5 the permutations are such that the optimality in terms of achieved compression ratio is preserved halfhc is applied in a practical example , and the resulting overall system performs better than when conventional huffman coding is used"
cs.DS,"discrete energy minimization is widely used in computer vision and machine learning for problems such as map inference in graphical models the problem , in general , is notoriously intractable , and finding the global optimal solution is known to be np hard however , is it possible to approximate this problem with a reasonable ratio bound on the solution quality in polynomial time \? we show in this paper that the answer is no specifically , we show that general energy minimization , even in the 2 label pairwise case , and planar energy minimization with three or more labels are exp apx complete this finding rules out the existence of any approximation algorithm with a sub exponential approximation ratio in the input size for these two problems , including constant factor approximations moreover , we collect and review the computational complexity of several subclass problems and arrange them on a complexity scale consisting of three major complexity classes po , apx , and exp apx , corresponding to problems that are solvable , approximable , and inapproximable in polynomial time problems in the first two complexity classes can serve as alternative tractable formulations to the inapproximable ones this paper can help vision researchers to select an appropriate model for an application or guide them in designing new algorithms"
cs.AI|cs.LG,"in the big data era , scalability has become a crucial requirement for any useful computational model probabilistic graphical models are very useful for mining and discovering data insights , but they are not scalable enough to be suitable for big data problems bayesian networks particularly demonstrate this limitation when their data is represented using few random variables while each random variable has a massive set of values with hierarchical data data that is arranged in a treelike structure with several levels one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels when modeling this kind of hierarchical data across large data sets , bayesian networks become infeasible for representing the probability distributions for the following reasons i \) each level represents a single random variable with hundreds of thousands of values , ii \) the number of levels is usually small , so there are also few random variables , and iii \) the structure of the network is predefined since the dependency is modeled top down from each parent to each of its child nodes , so the network would contain a single linear path for the random variables from each parent to each child node in this paper we present a scalable probabilistic graphical model to overcome these limitations for massive hierarchical data we believe the proposed model will lead to an easily scalable , more readable , and expressive implementation for problems that require probabilistic based solutions for massive amounts of hierarchical data we successfully applied this model to solve two different challenging probabilistic based problems on massive hierarchical data sets for different domains , namely , bioinformatics and latent semantic discovery over search logs"
cs.DM,"in directed tree width , j combin theory ser b 82 \( 2001 \) , 138 154 we introduced the notion of tree width of directed graphs and presented a conjecture , formulated during discussions with noga alon and bruce reed , stating that a digraph of huge tree width has a large cylindrical grid minor here we prove the conjecture for planar digraphs , but many steps of the proof work in general this is an unedited and unpolished manuscript from october 2001 since many people asked for copies we are making it available in the hope that it may be useful the conjecture was proved by kawarabayashi and kreutzer in arxiv 1411 5681"
cs.DS,"we introduce the notion of a network 's conduciveness , a probabilistically interpretable measure of how the network 's structure allows it to be conducive to roaming agents , in certain conditions , from one portion of the network to another we exemplify its use through an application to the two problems in combinatorial optimization that , given an undirected graph , ask that its so called chromatic and independence numbers be found though np hard , when solved on sequences of expanding random graphs there appear marked transitions at which optimal solutions can be obtained substantially more easily than right before them we demonstrate that these phenomena can be understood by resorting to the network that represents the solution space of the problems for each graph and examining its conduciveness between the non optimal solutions and the optimal ones at the said transitions , this network becomes strikingly more conducive in the direction of the optimal solutions than it was just before them , while at the same time becoming less conducive in the opposite direction we believe that , besides becoming useful also in other areas in which network theory has a role to play , network conduciveness may become instrumental in helping clarify further issues related to np hardness that remain poorly understood"
cs.AI|cs.LG,"we demonstrate that any physical object , as long as its volume is conserved when coupled with suitable operations , provides a sophisticated decision making capability we consider the problem of finding , as accurately and quickly as possible , the most profitable option from a set of options that gives stochastic rewards these decisions are made as dictated by a physical object , which is moved in a manner similar to the fluctuations of a rigid body in a tug of war game our analytical calculations validate statistical reasons why our method exhibits higher efficiency than conventional algorithms"
cs.LG|stat.ML,"traditional relation extraction predicts relations within some fixed and finite target schema machine learning approaches to this task require either manual annotation or , in the case of distant supervision , existing structured sources of the same schema the need for existing datasets can be avoided by using a universal schema the union of all involved schemas \( surface form predicates as in openie , and relations in the schemas of pre existing databases \) this schema has an almost unlimited set of relations \( due to surface forms \) , and supports integration with existing structured data \( through the relation types of existing databases \) to populate a database of such schema we present a family of matrix factorization models that predict affinity between database tuples and relations we show that this achieves substantially higher accuracy than the traditional classification approach more importantly , by operating simultaneously on relations observed in text and in pre existing structured dbs such as freebase , we are able to reason about unstructured and structured data in mutually supporting ways by doing so our approach outperforms state of the art distant supervision systems"
cs.SI,"individuals change their behavior during an epidemic in response to whether they and or those they interact with are healthy or sick healthy individuals are concerned about contracting a disease from their sick contacts and may utilize protective measures sick individuals may be concerned with spreading the disease to their healthy contacts and adopt preemptive measures yet , in practice both protective and preemptive changes in behavior come with costs this paper proposes a stochastic network disease game model that captures the self interests of individuals during the spread of a susceptible infected susceptible \( sis \) disease where individuals react to current risk of disease spread , and their reactions together with the current state of the disease stochastically determine the next stage of the disease we show that there is a critical level of concern , i e , empathy , by the sick individuals above which disease is eradicated fast furthermore , we find that if the network and disease parameters are above the epidemic threshold , the risk averse behavior by the healthy individuals cannot eradicate the disease without the preemptive measures of the sick individuals this imbalance in the role played by the response of the infected versus the susceptible individuals in disease eradication affords critical policy insights"
cs.IT,"in this letter , we analyze the ergodic capacity of bidirectional amplify and forward relay selection \( rs \) with imperfect channel state information \( csi \) , i e , outdated csi and imperfect channel estimation practically , the optimal rs scheme in maximizing the ergodic capacity cannot be achieved , due to the imperfect csi therefore , two suboptimal rs schemes are discussed and analyzed , in which the first rs scheme is based on the imperfect channel coefficients , and the second rs scheme is based on the predicted channel coefficients the lower bound of the ergodic capacity with imperfect csi is derived in a closed form , which matches tightly with the simulation results the results reveal that once csi is imperfect , the ergodic capacity of bidirectional rs degrades greatly , whereas the rs scheme based on the predicted channel has better performance , and it approaches infinitely to the optimal performance , when the prediction length is sufficiently large"
cs.DS,"we consider the problem of performing a random walk in a distributed network given bandwidth constraints , the goal of the problem is to minimize the number of rounds required to obtain a random walk sample das sarma et al podc'10 show that a random walk of length ell on a network of diameter d can be performed in tilde o \( sqrt ell d d \) time a major question left open is whether there exists a faster algorithm , especially whether the multiplication of sqrt ell and sqrt d is necessary in this paper , we show a tight unconditional lower bound on the time complexity of distributed random walk computation specifically , we show that for any n , d , and d leq ell leq \( n \( d 3 log n \) \) 1 4 , performing a random walk of length theta \( ell \) on an n node network of diameter d requires omega \( sqrt ell d d \) time this bound is em unconditional , i e , it holds for any \( possibly randomized \) algorithm to the best of our knowledge , this is the first lower bound that the diameter plays a role of multiplicative factor our bound shows that the algorithm of das sarma et al is time optimal our proof technique introduces a new connection between em bounded round communication complexity and distributed algorithm lower bounds with d as a trade off parameter , strengthening the previous study by das sarma et al stoc'11 in particular , we make use of the bounded round communication complexity of the pointer chasing problem our technique can be of independent interest and may be useful in showing non trivial lower bounds on the complexity of other fundamental distributed computing problems"
cs.DM,"distance hereditary graphs form an important class of graphs , from the theoretical point of view , due to the fact that they are the totally decomposable graphs for the split decomposition the previous best enumerative result for these graphs is from nakano et al \( j comp sci tech , 2007 \) , who have proven that the number of distance hereditary graphs on n vertices is bounded by 2 lceil 3 59n rceil in this paper , using classical tools of enumerative combinatorics , we improve on this result by providing an exact enumeration of distance hereditary graphs , which allows to show that the number of distance hereditary graphs on n vertices is tightly bounded by \( 7 24975 ldots \) n opening the perspective such graphs could be encoded on 3n bits we also provide the exact enumeration and asymptotics of an important subclass , the 3 leaf power graphs our work illustrates the power of revisiting graph decomposition results through the framework of analytic combinatorics"
cs.SI,"an active line of research has considered games played on networks in which payoffs depend on both a player 's individual decision and also the decisions of her neighbors such games have been used to model issues including the formation of opinions and the adoption of technology a basic question that has remained largely open in this area is to consider games where the strategies available to the players come from a fixed , discrete set , and where players may have different intrinsic preferences among the possible strategies it is natural to model the tension among these different preferences by positing a distance function on the strategy set that determines a notion of similarity among strategies a player 's payoff is determined by the distance from her chosen strategy to her preferred strategy and to the strategies chosen by her network neighbors even when there are only two strategies available , this framework already leads to natural open questions about a version of the classical battle of the sexes problem played on a graph we develop a set of techniques for analyzing this class of games , which we refer to as discrete preference games we parametrize the games by the relative extent to which a player takes into account the effect of her preferred strategy and the effect of her neighbors' strategies , allowing us to interpolate between network coordination games and unilateral decision making when these two effects are balanced , we show that the price of stability is equal to 1 for any discrete preference game in which the distance function on the strategies is a tree metric as a special case , this includes the battle of the sexes on a graph we also show that trees form the maximal family of metrics for which the price of stability is 1 , and produce a collection of metrics on which the price of stability converges to a tight bound of 2"
cs.IT,"in this paper , a new class of circulant matrices built from deterministic sequences is proposed for convolution based compressed sensing \( cs \) in contrast to random convolution , the coefficients of the underlying filter are given by the discrete fourier transform of a deterministic sequence with good autocorrelation both uniform recovery and non uniform recovery of sparse signals are investigated , based on the coherence parameter of the proposed sensing matrices many examples of the sequences are investigated , particularly the frank zadoff chu \( fzc \) sequence , the textit m sequence and the golay sequence a salient feature of the proposed sensing matrices is that they can not only handle sparse signals in the time domain , but also those in the frequency and or or discrete cosine transform \( dct \) domain"
cs.SI,"we derive rigorous bounds for well defined community structure in complex networks for a stochastic block model \( sbm \) benchmark in particular , we analyze the effect of inter community noise \( inter community edges \) on any community detection algorithm 's ability to correctly group nodes assigned to a planted partition , a problem which has been proven to be np complete in a standard rendition our result does not rely on the use of any one particular algorithm nor on the analysis of the limitations of inference rather , we turn the problem on its head and work backwards to examine when , in the first place , well defined structure may exist in sbms the method that we introduce here could potentially be applied to other computational problems the objective of community detection algorithms is to partition a given network into optimally disjoint subgraphs \( or communities \) similar to k sat and other combinatorial optimization problems , community detection exhibits different phases networks that lie in the unsolvable phase lack well defined structure and thus have no partition that is meaningful solvable systems splinter into two disparate phases those in the hard phase and those in the easy phase as befits its name , within the easy phase , a partition is easy to achieve by known algorithms when a network lies in the hard phase , it still has an underlying structure yet finding a meaningful partition which can be checked in polynomial time requires an exhaustive computational effort that rapidly increases with the size of the graph when taken together , \( i \) the rigorous results that we report here on when graphs have an underlying structure and \( ii \) recent results concerning the limits of rather general algorithms , suggest bounds on the hard phase"
cs.SI,"this paper presents a two dimensional modal logic for reasoning about the changing patterns of knowledge and social relationships in networks organised on the basis of a symmetric 'friendship' relation , providing a precise language for exploring 'logic in the community' 11 agents are placed in the model , allowing us to express such indexical facts as 'i am your friend' and 'you , my friends , are in danger' the technical framework for this work is general dynamic dynamic logic \( gddl \) 4 , which provides a general method for extending modal logics with dynamic operators for reasoning about a wide range of model transformations , starting with those definable in propositional dynamic logic \( pdl \) and extended to allow for the more subtle operators involved in , for example , private communication , as represented in dynamic epistemic logic \( del \) and related systems we provide a hands on introduction to gddl , introducing elements of the formalism as we go , but leave the reader to consult 4 for technical details instead , the purpose of this paper is to investigate a number of conceptual issues that arise when considering communication between agents in such networks , both from one agent to another , and broadcasts to socially defined groups of agents , such as the group of my friends"
cs.SI,"the recent availability of data describing social networks is changing our understanding of the microscopic structure of a social tie a social tie indeed is an aggregated outcome of many social interactions such as face to face conversations or phone calls analysis of data on face to face interactions shows that such events , as many other human activities , are bursty , with very heterogeneous durations in this paper we present a model for social interactions at short time scales , aimed at describing contexts such as conference venues in which individuals interact in small groups we present a detailed anayltical and numerical study of the model 's dynamical properties , and show that it reproduces important features of empirical data the model allows for many generalizations toward an increasingly realistic description of social interactions in particular in this paper we investigate the case where the agents have intrinsic heterogeneities in their social behavior , or where dynamic variations of the local number of individuals are included finally we propose this model as a very flexible framework to investigate how dynamical processes unfold in social networks"
cs.IT,"in this paper we prove threshold saturation for spatially coupled turbo codes \( sc tcs \) and braided convolutional codes \( bccs \) over the binary erasure channel we introduce a compact graph representation for the ensembles of sc tc and bcc codes which simplifies their description and the analysis of the message passing decoding we demonstrate that by few assumptions in the ensembles of these codes , it is possible to rewrite their vector recursions in a form which places these ensembles under the category of scalar admissible systems this allows us to define potential functions and prove threshold saturation using the proof technique introduced by yedla et al"
cs.IT,"the squashed entanglement of a quantum channel is an additive function of quantum channels , which finds application as an upper bound on the rate at which secret key and entanglement can be generated when using a quantum channel a large number of times in addition to unlimited classical communication this quantity has led to an upper bound of log \( \( 1 eta \) \( 1 eta \) \) on the capacity of an optical communication channel for such a task , where eta is the average fraction of photons that make it from the input to the output of the channel the purpose of the present paper is to extend these results beyond the single sender single receiver setting to the more general case of a single sender and multiple receivers \( a quantum broadcast channel \) we employ multipartite generalizations of the squashed entanglement to constrain the rates at which secret key and entanglement can be generated between any subset of the users of such a channel , along the way developing several new properties of these measures we apply our results to the case of an optical broadcast channel with one sender and two receivers"
cs.IT,"integer forcing \( if \) is a new framework , based on compute and forward , for decoding multiple integer linear combinations from the output of a gaussian multiple input multiple output channel this work applies the if approach to arrive at a new low complexity scheme , if source coding , for distributed lossy compression of correlated gaussian sources under a minimum mean squared error distortion measure all encoders use the same nested lattice codebook each encoder quantizes its observation using the fine lattice as a quantizer and reduces the result modulo the coarse lattice , which plays the role of binning rather than directly recovering the individual quantized signals , the decoder first recovers a full rank set of judiciously chosen integer linear combinations of the quantized signals , and then inverts it in general , the linear combinations have smaller average powers than the original signals this allows to increase the density of the coarse lattice , which in turn translates to smaller compression rates we also propose and analyze a one shot version of if source coding , that is simple enough to potentially lead to a new design principle for analog to digital converters that can exploit spatial correlations between the sampled signals"
cs.IT,"the problem of securing a network coding communication system against a wiretapper adversary is considered the network implements linear network coding to deliver n packets from source to each receiver , and the wiretapper can eavesdrop on mu arbitrarily chosen links a coding scheme is proposed that can achieve the maximum possible rate of k n mu packets that are information theoretically secure from the adversary a distinctive feature of our scheme is that it is universal it can be applied on top of any communication network without requiring knowledge of or any modifications on the underlying network code in fact , even a randomized network code can be used our approach is based on rouayheb soljanin 's formulation of a wiretap network as a generalization of the ozarow wyner wiretap channel of type ii essentially , the linear mds code in ozarow wyner 's coset coding scheme is replaced by a maximum rank distance code over an extension of the field in which linear network coding operations are performed"
cs.IT,"in this paper , the concept of the it broken diagonal pair in the chess like square board is used to define some well structured block designs whose incidence matrices can be considered as the parity check matrices of some high rate cycle codes with girth 12 the structure of the proposed parity check matrices significantly reduces the complexity of encoding and decoding interestingly , the constructed regular cycle codes with row weights t , 3 leq t leq 20 , t neq 7 , 15 , 16 , have the best lengths among the known regular girth 12 cycle codes in addition , the proposed cycle codes can be easily extended to some high rate column weight 3 ldpc codes with girth 6 simulation results show that the constructed codes achieve excellent performances , specially the constructed column weight 3 ldpc codes outperform ldpc codes based on steiner triple systems \( sts \)"
cs.IT,"this paper presents 28 ghz and 73 ghz empirically derived large scale and small scale channel model parameters that characterize average temporal and angular properties of multipaths omnidirectional azimuth scans at both the transmitter and receiver used high gain directional antennas , from which the mean global azimuth and zenith spreads of arrival were found to be 33 6 degrees and 5 9 degrees at 28 ghz , and 33 4 degrees and 3 6 degrees at 73 ghz , respectively , in non line of sight \( nlos \) small scale measurements at 28 ghz reveal a mean cross polar ratio for individual multipath components of 29 7 db and 16 7 db in line of sight and nlos , respectively small scale parameters extracted using the kpowermeans algorithm yielded on average 5 3 and 4 6 clusters at 28 ghz and 73 ghz , respectively , in nlos an alternative physically based binning procedure uses temporal clusters and spatial lobes that faithfully reproduces first and second order statistics of measured millimeter wave channels"
cs.IT,"we consider power allocations in downlink cellular wireless systems where the basestations are equipped with multiple transmit antennas and the mobile users are equipped with single receive antennas such systems can be modeled as multiuser miso systems we assume that the multi antenna transmitters employ some fixed beamformers to transmit data , and the objective is to optimize the power allocation for different users to satisfy certain qos constraints , with imperfect transmitter side channel state information \( csi \) specifically , for miso interference channels , we consider the transmit power minimization problem and the max min sinr problem for miso broadcast channels , we consider the mse constrained transmit power minimization problem all these problems are formulated as probability constrained optimization problems we make use of the bernstein approximation to conservatively transform the probabilistic constraints into deterministic ones , and consequently convert the original stochastic optimization problems into convex optimization problems however , the transformed problems cannot be straightforwardly solved using standard solver , since one of the constraints is itself an optimization problem we employ the long step logarithmic barrier cutting plane \( llbcp \) algorithm to overcome difficulty extensive simulation results are provided to demonstrate the effectiveness of the proposed method , and the performance advantage over some existing methods"
cs.IT,"this paper proves the existence of fundamental relations between information theory and estimation theory for network coded flows when the network is represented by a directed graph g \( v , e \) and under the assumption of uncorrelated noise over information flows between the directed links connecting transmitters , switches \( relays \) , and receivers we unveil that there yet exist closed form relations for the gradient of the mutual information with respect to different components of the system matrix m on the one hand , this result casts further insights into effects of the network topology , topological changes when nodes are mobile , and the impact of errors and delays in certain links into the network capacity which can be further studied in scenarios where one source multi sinks multicasts and multi source multicast where the inevertability and the rank of matrix m plays a significant role in the decoding process and therefore , on the network capacity on the other hand , it opens further research questions of finding precoding solutions adapted to the network level"
cs.IT,"the order of letters is not always relevant in a communication task this paper discusses the implications of order irrelevance on source coding , presenting results in several major branches of source coding theory lossless coding , universal lossless coding , rate distortion , high rate quantization , and universal lossy coding the main conclusions demonstrate that there is a significant rate savings when order is irrelevant in particular , lossless coding of n letters from a finite alphabet requires theta \( log n \) bits and universal lossless coding requires n o \( n \) bits for many countable alphabet sources however , there are no universal schemes that can drive a strong redundancy measure to zero results for lossy coding include distribution free expressions for the rate savings from order irrelevance in various high rate quantization schemes rate distortion bounds are given , and it is shown that the analogue of the shannon lower bound is loose at all finite rates"
cs.IT,"this paper considers the optimization of multi edge type low density parity check \( metldpc \) codes to maximize the decoding threshold we propose an algorithm to jointly optimize the node degree distribution and the multi edge structure of met ldpc codes for given values of the maximum number of edge types and maximum node degrees this joint optimization is particularly important for met ldpc codes as it is not clear a priori which structures will be good using several examples , we demonstrate that the met ldpc codes designed by the proposed joint optimization algorithm exhibit improved decoding thresholds compared to previously reported met ldpc codes"
cs.IT,"in this paper we propose a heuristic technique for distributing points on the surface of a unit n dimensional euclidean sphere , generated as the orbit of a finite cyclic subgroup of orthogonal matrices , the so called cyclic group codes massive numerical experiments were done and many new cyclic group codes have been obtained in several dimensions at various rate the obtained results assure that the heuristic approach have performance comparable to a brute force search technique with the advantage of having low complexity , allowing for designing codes with a large number of points in higher dimensions"
cs.IT,"allowing the input auxiliary random variables to be correlated and using the binning scheme , the han kobayashi \( hk \) rate region for general interference channel is partially improved the obtained partially new achievable rate region \( i \) is compared to the hk region and its simplified description , i e , chong motani garg \( cmg \) region , in a detailed and favorable manner , by considering different versions of the regions , and \( ii \) has an interesting and easy interpretation as expected , any rate in our region has generally two additional terms in comparison with the hk region \( one due to the input correlation and the other as a result of the binning scheme \) keywords interference channel , input correlation , binning scheme"
cs.IT,a random construction of bipolar sensing matrices based on binary linear codes is introduced and its rip \( restricted isometry property \) is analyzed based on an argument on the ensemble average of the weight distribution of binary linear codes
cs.IT,"spectrum refarming \( sr \) refers to a radio resource management technique which allows different generations of cellular networks to operate in the same radio spectrum in this paper , an underlay sr model is proposed , in which an orthogonal frequency division multiple access \( ofdma \) system refarms the spectrum of a code division multiple access \( cdma \) system through intelligently exploiting the interference margin provided by the cdma system we investigate the mutual effect of the two systems by evaluating the asymptotic signal to interference plus noise ratio \( sinr \) of the users , based on which the interference margin tolerable by the cdma system is determined by using the interference margin together with the transmit power constraints , the uplink resource allocation problem of ofdma system is formulated and solved through dual decomposition method simulation results have verified our theoretical analysis , and validated the effectiveness of the proposed resource allocation algorithm and its capability to protect the legacy cdma users the proposed sr system requires the least information flow from the cdma system to the ofdma system , and importantly , no upgrading of legacy cdma system is needed thus it can be deployed by telecom operators to maximize the spectral efficiency of their cellular networks"
cs.IT,"buffer aided relaying has recently attracted a lot of attention due to the improvement in the system throughput however , a side effect usually deemed is that buffering at relay nodes results in the increase in packet delays in this paper , we study the effect of buffering relays on the end to end delay of users' data , from the time they arrive at source until delivery to the destination we use simple discussions to provide an insight on the overall waiting time of the packets in the system by studying the bernoulli distributed channel conditions , and using intuitive generalizations , we conclude that the use of buffers at relays improves not only throughput , but ironically the end to end delay as well computer simulations in the settings of practical systems confirm the above results"
cs.DM,"a graph h is t apex if h x is planar for some subset x of v \( h \) of size t for any integer t 0 and a fixed t apex graph h , we give a polynomial time algorithm to decide whether a \( t 3 \) connected h minor free graph is colorable from a given assignment of lists of size t 4 the connectivity requirement is the best possible in the sense that for every t 1 , there exists a t apex graph h such that testing \( t 4 \) colorability of \( t 2 \) connected h minor free graphs is np complete similarly , the size of the lists cannot be decreased \( unless p np \) , since for every t 1 , testing \( t 3 \) list colorability of \( t 3 \) connected k t 4 minor free graphs is np complete"
cs.LG|stat.ML,"in this manuscript we review and discuss some theoretical aspects of amari 's natural gradient method , provide a unifying picture of the many different versions of it which have appeared over the years , and offer some new insights and perspectives regarding the method and its relationship to other optimization methods among our various contributions is the identification of a general condition under which the fisher information matrix and schraudolph 's generalized gauss newton matrix are equivalent this equivalence implies that optimization methods which use the latter matrix , such as the hessian free optimization approach of martens , are actually natural gradient methods in disguise it also lets us view natural gradient methods as approximate newton methods , justifying the application of various update damping techniques to them , which are designed to compensate for break downs in local quadratic approximations additionally , we analyze the parameterization invariance possessed by the natural gradient method in the idealized setting of infinitesimally small update steps , and consider the extent to which it holds for practical versions of the method which take large discrete steps we go on to show that parameterization invariance is not possessed by the classical newton raphson method \( even in the idealized setting \) , and then give a general characterization of gradient based methods which do possess it"
cs.IT,"we propose a quantized decoding algorithm for low density parity check codes where the variable node update rule of the standard min sum algorithm is replaced with a look up table \( lut \) that is designed using an information theoretic criterion we show that even with message resolutions as low as 3 bits , the proposed algorithm can achieve better error rates than a floating point min sum decoder moreover , we study in detail the effect of different decoder design parameters , like the design snr and the lut tree structure on the performance of our decoder , and we propose some complexity reduction techniques , such as lut re use and message alphabet downsizing"
cs.IT,"smart grid is an emerging technology which is able to control the power load via price signaling the communication between the power supplier and power customers is a key issue in smart grid performance degradation like delay or outage may cause significant impact on the stability of the pricing based control and thus the reward of smart grid therefore , a qos mechanism is proposed for the communication system in smart grid , which incorporates the derivation of qos requirement and applies qos routing in the communication network for deriving the qos requirement , the dynamics of power load and the load price mapping are studied the corresponding impacts of different qos metrics like delay are analyzed then , the qos is derived via an optimization problem that maximizes the total revenue based on the derived qos requirement , a simple greedy qos routing algorithm is proposed for the requirement of high speed routing in smart grid it is also proven that the proposed greedy algorithm is a k approximation numerical simulation shows that the proposed mechanism and algorithm can effectively derive and secure the communication qos in smart grid"
cs.IT,"current generation flash devices experience significant read channel degradation from damage to the oxide layer during program and erase operations information about the read channel degradation drives advanced signal processing methods in flash to mitigate its effect in this context , channel estimation must be ongoing since channel degradation evolves over time and as a function of the number of program erase \( p e \) cycles this paper proposes a framework for ongoing model based channel estimation using limited channel measurements \( reads \) this paper uses a channel model characterizing degradation resulting from retention time and the amount of charge programmed and erased for channel histogram measurements , bin selection to achieve approximately equal probability bins yields a good approximation to the original distribution using only ten bins \( i e nine reads \) with the channel model and binning strategy in place , this paper explores candidate numerical least squares algorithms and ultimately demonstrates the effectiveness of the levenberg marquardt algorithm which provides both speed and accuracy"
cs.LG,"in this paper , we present a novel approach that uses deep learning techniques for colorizing grayscale images by utilizing a pre trained convolutional neural network , which is originally designed for image classification , we are able to separate content and style of different images and recombine them into a single image we then propose a method that can add colors to a grayscale image by combining its content with style of a color image having semantic similarity with the grayscale one as an application , to our knowledge the first of its kind , we use the proposed method to colorize images of ukiyo e a genre of japanese painting \? and obtain interesting results , showing the potential of this method in the growing field of computer assisted art"
cs.AI,"the rapid development of autonomous vehicles spurred a careful investigation of the potential benefits of all autonomous transportation networks most studies conclude that autonomous systems can enable drastic improvements in performance a widely studied concept is all autonomous , collision free intersections , where vehicles arriving in a traffic intersection with no traffic light adjust their speeds to cross safely through the intersection as quickly as possible in this paper , we propose a coordination control algorithm for this problem , assuming stochastic models for the arrival times of the vehicles the proposed algorithm provides provable guarantees on safety and performance more precisely , it is shown that no collisions occur surely , and moreover a rigorous upper bound is provided for the expected wait time the algorithm is also demonstrated in simulations the proposed algorithms are inspired by polling systems in fact , the problem studied in this paper leads to a new polling system where customers are subject to differential constraints , which may be interesting in its own right"
cs.LG|stat.ML,"we analyze the problem of regression when both input covariates and output responses are functions from a nonparametric function class function to function regression \( ffr \) covers a large range of interesting applications including time series prediction problems , and also more general tasks like studying a mapping between two separate types of distributions however , previous nonparametric estimators for ffr type problems scale badly computationally with the number of input output pairs in a data set given the complexity of a mapping between general functions it may be necessary to consider large data sets in order to achieve a low estimation risk to address this issue , we develop a novel scalable nonparametric estimator , the triple basis estimator \( 3be \) , which is capable of operating over datasets with many instances to the best of our knowledge , the 3be is the first nonparametric ffr estimator that can scale to massive datasets we analyze the 3be 's risk and derive an upperbound rate furthermore , we show an improvement of several orders of magnitude in terms of prediction speed and a reduction in error over previous estimators in various real world data sets"
cs.AI|cs.LG|stat.ML,"loopy and generalized belief propagation are popular algorithms for approximate inference in markov random fields and bayesian networks fixed points of these algorithms correspond to extrema of the bethe and kikuchi free energy however , belief propagation does not always converge , which explains the need for approaches that explicitly minimize the kikuchi bethe free energy , such as cccp and ups here we describe a class of algorithms that solves this typically nonconvex constrained minimization of the kikuchi free energy through a sequence of convex constrained minimizations of upper bounds on the kikuchi free energy intuitively one would expect tighter bounds to lead to faster algorithms , which is indeed convincingly demonstrated in our simulations several ideas are applied to obtain tight convex bounds that yield dramatic speed ups over cccp"
cs.AI|cs.SI|stat.ML,"large unweighted directed graphs are commonly used to capture relations between entities a fundamental problem in the analysis of such networks is to properly define the similarity or dissimilarity between any two vertices despite the significance of this problem , statistical characterization of the proposed metrics has been limited we introduce and develop a class of techniques for analyzing random walks on graphs using stochastic calculus using these techniques we generalize results on the degeneracy of hitting times and analyze a metric based on the laplace transformed hitting time \( ltht \) the metric serves as a natural , provably well behaved alternative to the expected hitting time we establish a general correspondence between hitting times of the brownian motion and analogous hitting times on the graph we show that the ltht is consistent with respect to the underlying metric of a geometric graph , preserves clustering tendency , and remains robust against random addition of non geometric edges tests on simulated and real world data show that the ltht matches theoretical predictions and outperforms alternatives"
cs.DM,"in this paper , we show that all fat hoffman graphs with smallest eigenvalue at least 1 tau , where tau is the golden ratio , can be described by a finite set of fat \( 1 tau \) irreducible hoffman graphs in the terminology of woo and neumaier , we mean that every fat hoffman graph with smallest eigenvalue at least 1 tau is an h line graph , where h is the set of isomorphism classes of maximal fat \( 1 tau \) irreducible hoffman graphs it turns out that there are 37 fat \( 1 tau \) irreducible hoffman graphs , up to isomorphism"
cs.IT,"joseph miller 16 and independently andre nies , frank stephan and sebastiaan terwijn 18 gave a complexity characterization of 2 random sequences in terms of plain kolmogorov complexity c they are sequences that have infinitely many initial segments with o \( 1 \) maximal plain complexity \( among the strings of the same length \) later miller 17 showed that prefix complexity k can also be used in a similar way a sequence is 2 random if and only if it has infinitely many initial segments with o \( 1 \) maximal prefix complexity \( which is n k \( n \) for strings of length n \) the known proofs of these results are quite involved in this paper we provide simple direct proofs for both of them in 16 miller also gave a quantitative version of the first result the 0' randomness deficiency of a sequence omega equals lim inf n c \( omega 1 omega n \) o \( 1 \) \( our simplified proof can also be used to prove this \) we show \( and this seems to be a new result \) that a similar quantitative result is also true for prefix complexity 0' randomness deficiency equals lim inf n k \( n \) k \( omega 1 omega n \) o \( 1 \)"
cs.IT,"csma \( carrier sense multiple access \) , which resolves contentions over wireless networks in a fully distributed fashion , has recently gained a lot of attentions since it has been proved that appropriate control of csma parameters guarantees optimality in terms of stability \( i e , scheduling \) and system wide utility \( i e , scheduling and congestion control \) most csma based algorithms rely on the popular mcmc \( markov chain monte carlo \) technique , which enables one to find optimal csma parameters through iterative loops of `simulation and update' however , such a simulation based approach often becomes a major cause of exponentially slow convergence , being poorly adaptive to flow topology changes in this paper , we develop distributed iterative algorithms which produce approximate solutions with convergence in polynomial time for both stability and utility maximization problems in particular , for the stability problem , the proposed distributed algorithm requires , somewhat surprisingly , only one iteration among links our approach is motivated by the bethe approximation \( introduced by yedidia , freeman and weiss in 2005 \) allowing us to express approximate solutions via a certain non linear system with polynomial size our polynomial convergence guarantee comes from directly solving the non linear system in a distributed manner , rather than multiple simulation and update loops in existing algorithms we provide numerical results to show that the algorithm produces highly accurate solutions and converges much faster than the prior ones"
cs.IT,"for the k user , single input single output \( siso \) , frequency selective interference channel , a new low complexity transmit beamforming design that improves the achievable sum rate is presented jointly employing the interference alignment \( ia \) scheme presented by cadambe and jafar in 1 and linear minimum mean square error \( mmse \) decoding at the transmitters and receivers , respectively , the new ia precoding design improves the average sum rate while preserving the achievable degrees of freedom of the cadambe and jafar scheme , k 2"
cs.AI,"the aim of this paper is to study the fusion at feature extraction level for face and fingerprint biometrics the proposed approach is based on the fusion of the two traits by extracting independent feature pointsets from the two modalities , and making the two pointsets compatible for concatenation moreover , to handle the problem of curse of dimensionality , the feature pointsets are properly reduced in dimension different feature reduction techniques are implemented , prior and after the feature pointsets fusion , and the results are duly recorded the fused feature pointset for the database and the query face and fingerprint images are matched using techniques based on either the point pattern matching , or the delaunay triangulation comparative experiments are conducted on chimeric and real databases , to assess the actual advantage of the fusion performed at the feature extraction level , in comparison to the matching score level"
cs.IT,"a novel inter frame coding approach to the problem of varying channel state conditions in broadcast wireless communication is developed in this paper this problem causes the appropriate code rate to vary across different transmitted frames and different receivers as well the main aspect of the proposed approach is that it incorporates an iterative rate matching process into the decoding of the received set of frames , such that throughout inter frame decoding , the code rate of each frame is progressively lowered to or below the appropriate value , prior to applying or re applying conventional physical layer channel decoding on it this iterative rate matching process is asymptotically analyzed in this paper it is shown to be optimal , in the sense defined in the paper consequently , the data rates achievable by the proposed scheme are derived overall , it is concluded that , compared to the existing solutions , inter frame coding presents a better complexity versus data rate tradeoff in terms of complexity , the overhead of inter frame decoding includes operations that are similar in type and scheduling to those employed in the relatively simple iterative erasure decoding in terms of data rates , compared to the state of the art two stage scheme involving both error correcting and erasure coding , inter frame coding increases the data rate by a factor that reaches up to 1 55x"
cs.AI,"we introduce novel mathematical models and algorithms to generate \( shortest or k different \) explanations for biomedical queries , using answer set programming we implement these algorithms and integrate them in bioquery asp we illustrate the usefulness of these methods with some complex biomedical queries related to drug discovery , over the biomedical knowledge resources pharmgkb , drugbank , biogrid , ctd , sider , disease ontology and orphadata to appear in theory and practice of logic programming \( tplp \)"
stat.ML,"we consider a network of agents that aim to learn some unknown state of the world using private observations and exchange of beliefs at each time , agents observe private signals generated based on the true unknown state each agent might not be able to distinguish the true state based only on her private observations this occurs when some other states are observationally equivalent to the true state from the agent 's perspective to overcome this shortcoming , agents must communicate with each other to benefit from local observations we propose a model where each agent selects one of her neighbors randomly at each time then , she refines her opinion using her private signal and the prior of that particular neighbor the proposed rule can be thought of as a bayesian agent who cannot recall the priors based on which other agents make inferences this learning without recall approach preserves some aspects of the bayesian inference while being computationally tractable by establishing a correspondence with a random walk on the network graph , we prove that under the described protocol , agents learn the truth exponentially fast in the almost sure sense the asymptotic rate is expressed as the sum of the relative entropies between the signal structures of every agent weighted by the stationary distribution of the random walk"
cs.SI,"exploiting the information about members of a social network \( sn \) represents one of the most attractive and dwelling subjects for both academic and applied scientists the community of complexity science and especially those researchers working on multiplex social systems are devoting increasing efforts to outline general laws , models , and theories , to the purpose of predicting emergent phenomena in sn 's \( e g success of a product \) on the other side the semantic web community aims at engineering a new generation of advanced services tailored to specific people needs this implies defining constructs , models and methods for handling the semantic layer of sns we combined models and techniques from both the former fields to provide a hybrid approach to understand a basic \( yet complex \) phenomenon the propagation of individual interests along the social networks since information may move along different social networks , one should take into account a multiplex structure therefore we introduced the notion of semantic multiplex in this paper we analyse two different semantic social networks represented by authors publishing in the computer science and those in the american physical society journals the comparison allows to outline common and specific features"
cs.LG|stat.ML,"many problems in machine learning and statistics can be formulated as \( generalized \) eigenproblems in terms of the associated optimization problem , computing linear eigenvectors amounts to finding critical points of a quadratic function subject to quadratic constraints in this paper we show that a certain class of constrained optimization problems with nonquadratic objective and constraints can be understood as nonlinear eigenproblems we derive a generalization of the inverse power method which is guaranteed to converge to a nonlinear eigenvector we apply the inverse power method to 1 spectral clustering and sparse pca which can naturally be formulated as nonlinear eigenproblems in both applications we achieve state of the art results in terms of solution quality and runtime moving beyond the standard eigenproblem should be useful also in many other applications and our inverse power method can be easily adapted to new problems"
cs.AI,"although researchers often comment on the rising popularity of nature inspired meta heuristics \( nim \) , there has been a paucity of data to directly support the claim that nim are growing in prominence compared to other optimization techniques this study presents evidence that the use of nim is not only growing , but indeed appears to have surpassed mathematical optimization techniques \( mot \) in several important metrics related to academic research activity \( publication frequency \) and commercial activity \( patenting frequency \) motivated by these findings , this article discusses some of the possible origins of this growing popularity i review different explanations for nim popularity and discuss why some of these arguments remain unsatisfying i argue that a compelling and comprehensive explanation should directly account for the manner in which most nim success has actually been achieved , e g through hybridization and customization to different problem environments by taking a problem lifecycle perspective , this paper offers a fresh look at the hypothesis that nature inspired meta heuristics derive much of their utility from being flexible i discuss global trends within the business environments where optimization algorithms are applied and i speculate that highly flexible algorithm frameworks could become increasingly popular within our diverse and rapidly changing world"
cs.LG,"recurrent neural networks are powerful models for sequential data , able to represent complex dependencies in the sequence that simpler models such as hidden markov models cannot handle yet they are notoriously hard to train here we introduce a training procedure using a gradient ascent in a riemannian metric this produces an algorithm independent from design choices such as the encoding of parameters and unit activities this metric gradient ascent is designed to have an algorithmic cost close to backpropagation through time for sparsely connected networks we use this procedure on gated leaky neural networks \( glnns \) , a variant of recurrent neural networks with an architecture inspired by finite automata and an evolution equation inspired by continuous time networks glnns trained with a riemannian gradient are demonstrated to effectively capture a variety of structures in synthetic problems basic block nesting as in context free grammars \( an important feature of natural languages , but difficult to learn \) , intersections of multiple independent markov type relations , or long distance relationships such as the distant xor problem this method does not require adjusting the network structure or initial parameters the network used is a sparse random graph and the initialization is identical for all problems considered"
cs.IT,"recent experimental results have shown that full duplex communication is possible for short range communications however , extending full duplex to long range communication remains a challenge , primarily due to residual self interference even with a combination of passive suppression and active cancellation methods in this paper , we investigate the root cause of performance bottlenecks in current full duplex systems we first classify all known full duplex architectures based on how they compute their cancelling signal and where the cancelling signal is injected to cancel self interference based on the classification , we analytically explain several published experimental results the key bottleneck in current systems turns out to be the phase noise in the local oscillators in the transmit and receive chain of the full duplex node as a key by product of our analysis , we propose signal models for wideband and mimo full duplex systems , capturing all the salient design parameters , and thus allowing future analytical development of advanced coding and signal design for full duplex systems"
cs.AI,"the model of cognition developed in \( smolensky and legendre , 2006 \) seeks to unify two levels of description of the cognitive process the connectionist and the symbolic the theory developed brings together these two levels into the integrated connectionist symbolic cognitive architecture \( ics \) clark and pulman \( 2007 \) draw a parallel with semantics where meaning may be modelled on both distributional and symbolic levels , developed by coecke et al , 2010 into the distributional compositional \( disco \) model of meaning in the current work , we revisit smolensky and legendre \( s l \) 's model we describe the disco framework , summarise the key ideas in s l 's architecture , and describe how their description of harmony as a graded measure of grammaticality may be applied in the disco model"
cs.IT,"the minimum mean square error of the estimation of a signal where observed from the additive white gaussian noise \( wgn \) channel 's output , is analyzed it is assumed that the channel input 's signal is composed of a \( normalized \) sum of n narrowband , mutually independent waves it is shown that if n goes to infinity , then for any fixed signal energy to noise energy ratio \( no mater how big \) both the causal minimum mean square error cmmse and the non causal minimum mean square error mmse converge to the signal energy at a rate which is proportional to 1 n"
cs.IT,"principal component analysis \( pca \) is a classical method for dimensionality reduction based on extracting the dominant eigenvectors of the sample covariance matrix however , pca is well known to behave poorly in the ``large p , small n '' setting , in which the problem dimension p is comparable to or larger than the sample size n this paper studies pca in this high dimensional regime , but under the additional assumption that the maximal eigenvector is sparse , say , with at most k nonzero components we consider a spiked covariance model in which a base matrix is perturbed by adding a k sparse maximal eigenvector , and we analyze two computationally tractable methods for recovering the support set of this maximal eigenvector , as follows \( a \) a simple diagonal thresholding method , which transitions from success to failure as a function of the rescaled sample size theta mathrm dia \( n , p , k \) n k 2 log \( p k \) and \( b \) a more sophisticated semidefinite programming \( sdp \) relaxation , which succeeds once the rescaled sample size theta mathrm sdp \( n , p , k \) n k log \( p k \) is larger than a critical threshold in addition , we prove that no method , including the best method which has exponential time complexity , can succeed in recovering the support if the order parameter theta mathrm sdp \( n , p , k \) is below a threshold our results thus highlight an interesting trade off between computational and statistical efficiency in high dimensional inference"
cs.IT,"this paper presents an analytical characterization of the ergodic capacity of amplify and forward \( af \) mimo dual hop relay channels , assuming that the channel state information is available at the destination terminal only in contrast to prior results , our expressions apply for arbitrary numbers of antennas and arbitrary relay configurations we derive an expression for the exact ergodic capacity , simplified closed form expressions for the high snr regime , and tight closed form upper and lower bounds these results are made possible to employing recent tools from finite dimensional random matrix theory to derive new closed form expressions for various statistical properties of the equivalent af mimo dual hop relay channel , such as the distribution of an unordered eigenvalue and certain random determinant properties based on the analytical capacity expressions , we investigate the impact of the system and channel characteristics , such as the antenna configuration and the relay power gain we also demonstrate a number of interesting relationships between the dual hop af mimo relay channel and conventional point to point mimo channels in various asymptotic regimes"
cs.DS,"a path from s to t on a polyhedral terrain is descending if the height of a point p never increases while we move p along the path from s to t no efficient algorithm is known to find a shortest descending path \( sdp \) from s to t in a polyhedral terrain we give a simple approximation algorithm that solves the sdp problem on general terrains our algorithm discretizes the terrain with o \( n 2 x e \) steiner points so that after an o \( n 2 x e log \( n x e \) \) time preprocessing phase for a given vertex s , we can determine a \( 1 e \) approximate sdp from s to any point v in o \( n \) time if v is either a vertex of the terrain or a steiner point , and in o \( n x e \) time otherwise here n is the size of the terrain , and x is a parameter of the geometry of the terrain"
cs.IT,"we propose a method to increase the capacity achieved by uniform prior in discrete memoryless channels \( dmc \) with high input cardinality it consists in appropriately reducing the input set different design criteria of the input subset are discussed we develop an efficient algorithm to solve this problem based on the maximization of the cut off rate the method is applied to a mono bit transceiver mimo system , and it is shown that the capacity can be approached within tenths of a db by employing standard binary codes while avoiding the use of distribution shapers"
cs.DM,"we describe random generation algorithms for a large class of random combinatorial objects called schur processes , which are sequences of random \( integer \) partitions subject to certain interlacing conditions this class contains several fundamental combinatorial objects as special cases , such as plane partitions , tilings of aztec diamonds , pyramid partitions and more generally steep domino tilings of the plane our algorithm , which is of polynomial complexity , is both exact \( i e the output follows exactly the target probability law , which is either boltzmann or uniform in our case \) , and entropy optimal \( i e it reads a minimal number of random bits as an input \) the algorithm encompasses previous growth procedures for special schur processes related to the primal and dual rsk algorithm , as well as the famous domino shuffling algorithm for domino tilings of the aztec diamond it can be easily adapted to deal with symmetric schur processes and general schur processes involving infinitely many parameters it is more concrete and easier to implement than borodin 's algorithm , and it is entropy optimal at a technical level , it relies on unified bijective proofs of the different types of cauchy and littlewood identities for schur functions , and on an adaptation of fomin 's growth diagram description of the rsk algorithm to that setting simulations performed with this algorithm suggest interesting limit shape phenomena for the corresponding tiling models , some of which are new"
cs.IT,"we consider the cryptographic problem of constructing an invertible random permutation from a public random function \( i e , which can be accessed by the adversary \) this goal is formalized by the notion of indifferentiability of maurer et al \( tcc 2004 \) this is the natural extension to the public setting of the well studied problem of building random permutations from random functions , which was first solved by luby and rackoff \( siam j comput , '88 \) using the so called feistel construction the most important implication of such a construction is the equivalence of the random oracle model \( bellare and rogaway , ccs '93 \) and the ideal cipher model , which is typically used in the analysis of several constructions in symmetric cryptography coron et al \( crypto 2008 \) gave a rather involved proof that the six round feistel construction with independent random round functions is indifferentiable from an invertible random permutation also , it is known that fewer than six rounds do not suffice for indifferentiability the first contribution \( and starting point \) of our paper is a concrete distinguishing attack which shows that the indifferentiability proof of coron et al is not correct in addition , we provide supporting evidence that an indifferentiability proof for the six round feistel construction may be very hard to find to overcome this gap , our main contribution is a proof that the feistel construction with eigthteen rounds is indifferentiable from an invertible random permutation the approach of our proof relies on assigning to each of the rounds in the construction a unique and specific role needed in the proof this avoids many of the problems that appear in the six round case"
cs.IT,"we establish new upper bounds about symmetric bilinear complexity in any extension of finite fields note that these bounds are not asymptotical but uniform moreover , we discuss the validity of certain published bounds"
cs.IT,"this paper investigates the enumeration , rate region computation , and hierarchy of general multi source multi sink hyperedge networks under network coding , which includes multiple network models , such as independent distributed storage systems and index coding problems , as special cases a notion of minimal networks and a notion of network equivalence under group action are defined an efficient algorithm capable of directly listing single minimal canonical representatives from each network equivalence class is presented and utilized to list all minimal canonical networks with up to 5 sources and hyperedges computational tools are then applied to obtain the rate regions of all of these canonical networks , providing exact expressions for 744 , 119 newly solved network coding rate regions corresponding to more than 2 trillion isomorphic network coding problems in order to better understand and analyze the huge repository of rate regions through hierarchy , several embedding and combination operations are defined so that the rate region of the network after operation can be derived from the rate regions of networks involved in the operation the embedding operations enable the definition and determination of a list of forbidden network minors for the sufficiency of classes of linear codes the combination operations enable the rate regions of some larger networks to be obtained as the combination of the rate regions of smaller networks the integration of both the combinations and embedding operators is then shown to enable the calculation of rate regions for many networks not reachable via combination operations alone"
cs.IT,"in this paper , we introduce a new framework for robust multiple signal classification \( music \) the proposed framework , called robust measure transformed \( mt \) music , is based on applying a transform to the probability distribution of the received signals , i e , transformation of the probability measure defined on the observation space in robust mt music , the sample covariance is replaced by the empirical mt covariance by judicious choice of the transform we show that 1 \) the resulting empirical mt covariance is b robust , with bounded influence function that takes negligible values for large norm outliers , and 2 \) under the assumption of spherically contoured noise distribution , the noise subspace can be determined from the eigendecomposition of the mt covariance furthermore , we derive a new robust measure transformed minimum description length \( mdl \) criterion for estimating the number of signals , and extend the mt music framework to the case of coherent signals the proposed approach is illustrated in simulation examples that show its advantages as compared to other robust music and mdl generalizations"
cs.IT,"this paper considers a cooperative ofdma based cognitive radio network where the primary system leases some of its subchannels to the secondary system for a fraction of time in exchange for the secondary users \( sus \) assisting the transmission of primary users \( pus \) as relays our aim is to determine the cooperation strategies among the primary and secondary systems so as to maximize the sum rate of sus while maintaining quality of service \( qos \) requirements of pus we formulate a joint optimization problem of pu transmission mode selection , su \( or relay \) selection , subcarrier assignment , power control , and time allocation by applying dual method , this mixed integer programming problem is decomposed into parallel per subcarrier subproblems , with each determining the cooperation strategy between one pu and one su we show that , on each leased subcarrier , the optimal strategy is to let a su exclusively act as a relay or transmit for itself this result is fundamentally different from the conventional spectrum leasing in single channel systems where a su must transmit a fraction of time for itself if it helps the pu 's transmission we then propose a subgradient based algorithm to find the asymptotically optimal solution to the primal problem in polynomial time simulation results demonstrate that the proposed algorithm can significantly enhance the network performance"
cs.IT,"we draw relationships between the generalized data processing theorems of zakai and ziv \( 1973 and 1975 \) and the dynamical version of the second law of thermodynamics , a k a the boltzmann h theorem , which asserts that the shannon entropy , h \( x t \) , pertaining to a finite state markov process x t , is monotonically non decreasing as a function of time t , provided that the steady state distribution of this process is uniform across the state space \( which is the case when the process designates an isolated system \) it turns out that both the generalized data processing theorems and the boltzmann h theorem can be viewed as special cases of a more general principle concerning the monotonicity \( in time \) of a certain generalized information measure applied to a markov process this gives rise to a new look at the generalized data processing theorem , which suggests to exploit certain degrees of freedom that may lead to better bounds , for a given choice of the convex function that defines the generalized mutual information"
cs.DM,"the sand pile model \( spm \) and its generalization , the ice pile model \( ipm \) , originate from physics and have various applications in the description of the evolution of granular systems in this article , we deal with the enumeration and the exhaustive generation of the accessible configuration of the system our work is based on a new recursive decomposition theorem for spm configurations using the notion of staircase bases based on this theorem , we provide a recursive formula for the enumeration of spm \( n \) and a constant amortized time \( cat \) algorithm for the generation of all spm \( n \) configurations the extension of the same approach to the ice pile model is also discussed"
cs.IT,"this paper considers the problem of perfectly secure communication on a modified version of wyner 's wiretap channel ii where both the main and wiretapper 's channels have some erasures a secret message is to be encoded into n channel symbols and transmitted the main channel is such that the legitimate receiver receives the transmitted codeword with exactly n nu erasures , where the positions of the erasures are random additionally , an eavesdropper \( wire tapper \) is able to observe the transmitted codeword with n mu erasures in a similar fashion this paper studies the maximum achievable information rate with perfect secrecy on this channel and gives a coding scheme using nested codes that achieves the secrecy capacity"
cs.IT,"recently , a new structure called butterfly introduced by perrin et at has very good cryptographic properties the differential uniformity is at most equal to 4 and algebraic degree is also very good when exponent e 3 however , the nonlinearity has not been proved in general it is conjecture that the nonlinearity is also optimal in this paper , we further study the butterfly structure and show that these structure with exponent e 2 i 1 have also very good cryptographic properties more importantly , we prove in theory the nonlinearity is optimal for every odd k , which completely solve the open problem finally , we study the butter structure with trivial coefficient and show these butterflies have also optimal nonlinearity furthermore , we show that the closed butterfly with trivial coefficient is bijective as well , which also can be used to serve as a cryptographic primitive"
cs.DM|cs.DS,"testing whether there is an induced path in a graph spanning k given vertices is already np complete in general graphs when k 3 we show how to solve this problem in polynomial time on claw free graphs , when k is not part of the input but an arbitrarily fixed integer"
cs.LG|stat.ML,"in supervised learning , an inductive learning algorithm extracts general rules from observed training instances , then the rules are applied to test instances we show that this splitting of training and application arises naturally , in the classical setting , from a simple independence requirement with a physical interpretation of being non signalling thus , two seemingly different definitions of inductive learning happen to coincide this follows from very specific properties of classical information , which break down in the quantum setup we prove a quantum de finetti theorem for quantum channels , which shows that in the quantum case , the equivalence holds in the asymptotic setting \( for large number of test instances \) this reveals a natural analogy between classical learning protocols and their quantum counterparts , thus allowing to naturally enquire about standard elements in computational learning theory , such as structural risk minimization , model and sample complexity"
cs.IT,"just as the hamming weight spectrum of a linear block code sheds light on the performance of a maximum likelihood decoder , the pseudo weight spectrum provides insight into the performance of a linear programming decoder using properties of polyhedral cones , we find the pseudo weight spectrum of some short codes we also present two general lower bounds on the minimum pseudo weight the first bound is based on the column weight of the parity check matrix the second bound is computed by solving an optimization problem in some cases , this bound is more tractable to compute than previously known bounds and thus can be applied to longer codes"
cs.DM|cs.DS,"faults and viruses often spread in networked environments by propagating from site to neighboring site we model this process of em network contamination by graphs consider a graph g \( v , e \) , whose vertex set is contaminated and our goal is to decontaminate the set v \( g \) using mobile decontamination agents that traverse along the edge set of g temporal immunity tau \( g \) ge 0 is defined as the time that a decontaminated vertex of g can remain continuously exposed to some contaminated neighbor without getting infected itself the emph immunity number of g , iota k \( g \) , is the least tau that is required to decontaminate g using k agents we study immunity number for some classes of graphs corresponding to network topologies and present upper bounds on iota 1 \( g \) , in some cases with matching lower bounds variations of this problem have been extensively studied in literature , but proposed algorithms have been restricted to em monotone strategies , where a vertex , once decontaminated , may not be recontaminated we exploit nonmonotonicity to give bounds which are strictly better than those derived using monotone strategies"
cs.IT,"we suggest a new approach to hypothesis testing for ergodic and stationary processes in contrast to standard methods , the suggested approach gives a possibility to make tests , based on any lossless data compression method even if the distribution law of the codeword lengths is not known we apply this approach to the following four problems goodness of fit testing \( or identity testing \) , testing for independence , testing of serial independence and homogeneity testing and suggest nonparametric statistical tests for these problems it is important to note that practically used so called archivers can be used for suggested testing"
stat.ML,"i propose a novel method for practical joint processing dl comp implementation in lte lte a systems using a supervised machine learning technique dl comp has not been thoroughly studied in previous work although cluster formation and interference mitigation have been studied extensively in this paper , i attempt to improve the cell edge user data rate served by a heterogeneous network cluster by means of dynamically changing the dl sinr threshold at which dl comp is triggered i do so by allowing the base stations to derive a threshold on the basis of machine learning inference the simulation results show an improved user throughput at the cell edge of 40 and a 6 4 improvement to the average cell throughput compared to the baseline of static triggering"
cs.DS,"the aim of the paper to answer a long standing open problem on the relationship between np and bqp the paper shows that bqp contains np by proposing a bqp quantum algorithm for the max e3 sat problem which is a fundamental np hard problem given an e3 cnf boolean formula , the aim of the max e3 sat problem is to find the variable assignment that maximizes the number of satisfied clauses the proposed algorithm runs in o \( m 2 \) for an e3 cnf boolean formula with m clauses and in the worst case runs in o \( n 6 \) for an e3 cnf boolean formula with n inputs the proposed algorithm maximizes the set of satisfied clauses using a novel iterative partial negation and partial measurement technique the algorithm is shown to achieve an arbitrary high probability of success of 1 epsilon for small epsilon 0 using a polynomial resources in addition to solving the max e3 sat problem , the proposed algorithm can also be used to decide if an e3 cnf boolean formula is satisfiable or not , which is an np complete problem , based on the maximum number of satisfied clauses"
cs.IT,"on the multi antenna broadcast channel , the spatial degrees of freedom support simultaneous transmission to multiple users the optimal multiuser transmission , known as dirty paper coding , is not directly realizable moreover , close to optimal solutions such as tomlinson harashima precoding are sensitive to csi inaccuracy this paper considers a more practical design called per user unitary and rate control \( pu2rc \) , which has been proposed for emerging cellular standards pu2rc supports multiuser simultaneous transmission , enables limited feedback , and is capable of exploiting multiuser diversity its key feature is an orthogonal beamforming \( or precoding \) constraint , where each user selects a beamformer \( or precoder \) from a codebook of multiple orthonormal bases in this paper , the asymptotic throughput scaling laws for pu2rc with a large user pool are derived for different regimes of the signal to noise ratio \( snr \) in the multiuser interference limited regime , the throughput of pu2rc is shown to scale logarithmically with the number of users in the normal snr and noise limited regimes , the throughput is found to scale double logarithmically with the number of users and also linearly with the number of antennas at the base station in addition , numerical results show that pu2rc achieves higher throughput and is more robust against csi quantization errors than the popular alternative of zero forcing beamforming if the number of users is sufficiently large"
cs.IT,"the evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity any data service is instantly accessible everywhere with each generation of cellular networks , we have moved closer to this wireless dream first by delivering wireless access to voice communications , then by providing wireless data services , and recently by delivering a wifi like experience with wide area coverage and user mobility management the support for high data rates has been the main objective in recent years , as seen from the academic focus on sum rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in imt advanced in contrast , a variety of metrics objectives are put forward in the technological preparations for 5g networks higher peak rates , improved coverage with uniform user experience , higher reliability and lower latency , better energy efficiency , lower cost user devices and services , better scalability with number of devices , etc these multiple objectives are coupled , often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives hence , the design of future networks calls for new optimization tools that properly handle the existence and tradeoffs between multiple objectives in this article , we provide a review of multi objective optimization \( moo \) , which is a mathematical framework to solve design problems with multiple conflicting objectives \( \) we provide a survey of the basic definitions , properties , and algorithmic tools in moo this reveals how signal processing algorithms are used to visualize the inherent conflicts between 5g performance objectives , thereby allowing the network designer to understand the possible operating points and how to balance the objectives in an efficient and satisfactory way for clarity , we provide a case study on massive mimo"
cs.LG|stat.ML,"in probabilistic approaches to classification and information extraction , one typically builds a statistical model of words under the assumption that future data will exhibit the same regularities as the training data in many data sets , however , there are scope limited features whose predictive power is only applicable to a certain subset of the data for example , in information extraction from web pages , word formatting may be indicative of extraction category in different ways on different web pages the difficulty with using such features is capturing and exploiting the new regularities encountered in previously unseen data in this paper , we propose a hierarchical probabilistic model that uses both local scope limited features , such as word formatting , and global features , such as word content the local regularities are modeled as an unobserved random parameter which is drawn once for each local data set this random parameter is estimated during the inference process and then used to perform classification with both the local and global features a procedure which is akin to automatically retuning the classifier to the local regularities on each newly encountered web page exact inference is intractable and we present approximations via point estimates and variational methods empirical results on large collections of web data demonstrate that this method significantly improves performance from traditional models of global features alone"
cs.IT,"a distributed inference scheme which uses bounded transmission functions over a gaussian multiple access channel is considered when the sensor measurements are decreasingly reliable as a function of the sensor index , the conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized for the distributed estimation problem , an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variance of the noise samples are bounded and that the transmission function is one to one the proposed estimation scheme is compared with the amplify and forward technique and its robustness to impulsive sensing noise distributions is highlighted in contrast to amplify and forward schemes , it is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity for the distributed detection problem , similar results are obtained by studying the deflection coefficient simulations corroborate our analytical results"
cs.IT,"the problem of three user multiple access channel \( mac \) with correlated sources is investigated an extension to the cover el gamal salehi \( ces \) scheme is introduced we use a combination of this scheme with linear codes and propose a new coding strategy we derive new sufficient conditions to transmit correlated sources reliably we consider an example of three user mac with binary inputs using this example , we show strict improvements over the ces scheme"
cs.AI,"autonomous intelligent agent research is a domain situated at the forefront of artificial intelligence interest based negotiation \( ibn \) is a form of negotiation in which agents exchange information about their underlying goals , with a view to improve the likelihood and quality of a offer in this paper we model and verify a multi agent argumentation scenario of resource sharing mechanism to enable resource sharing in a distributed system we use ibn in our model wherein agents express their interests to the others in the society to gain certain resources"
cs.SI,"most of the conventional models for opinion dynamics mainly account for a fully local influence , where myopic agents decide their actions after they interact with other agents that are adjacent to them for example , in the case of social interactions , this includes family , friends , and other strong social ties the model proposed in this contribution , embodies a global influence as well where , by global , we mean that each node also observes a sample of the average behavior of the entire population \( in the social example , people observe other people on the streets , subway , and other social venues \) we consider a case where nodes have dichotomous states \( examples include elections with two major parties , whether or not to adopt a new technology or product , and any yes no opinion such as in voting on a referendum \) the dynamics of states on a network with arbitrary degree distribution are studied for a given initial condition , we find the probability to reach consensus on each state and the expected time reach to consensus the effect of an exogenous bias on the average orientation of the system is investigated , to model mass media to do so , we add an external field to the model that favors one of the states over the other this field interferes with the regular decision process of each node and creates a constant probability to lean towards one of the states we solve for the average state of the system as a function of time for given initial conditions then anti conformists \( stubborn nodes who never revise their states \) are added to the network , in an effort to circumvent the external bias we find necessary conditions on the number of these defiant nodes required to cancel the effect of the external bias our analysis is based on a mean field approximation of the agent opinions"
cs.LG,"the problem of rare and unknown words is an important issue that can potentially effect the performance of many nlp systems , including both traditional count based models and deep learning models we propose a novel way to deal with the rare and unseen words for the neural network models with attention our model uses two softmax layers in order to predict the next word in conditional language models one of the softmax layers predicts the location of a word in the source sentence , and the other softmax layer predicts a word in the shortlist vocabulary the decision of which softmax layer to use at each timestep is adaptively made by an mlp which is conditioned on the context we motivate this work from a psychological evidence that humans naturally have a tendency to point towards objects in the context or the environment when the name of an object is not known using our proposed model , we observe improvements in two tasks , neural machine translation on the europarl english to french parallel corpora and text summarization on the gigaword dataset"
cs.IT,"a new interpolation based decoding principle for interleaved gabidulin codes is presented the approach consists of two steps first , a multi variate linearized polynomial is constructed which interpolates the coefficients of the received word and second , the roots of this polynomial have to be found due to the specific structure of the interpolation polynomial , both steps \( interpolation and root finding \) can be accomplished by solving a linear system of equations this decoding principle can be applied as a list decoding algorithm \( where the list size is not necessarily bounded polynomially \) as well as an efficient probabilistic unique decoding algorithm for the unique decoder , we show a connection to known unique decoding approaches and give an upper bound on the failure probability finally , we generalize our approach to incorporate not only errors , but also row and column erasures"
cs.LG|stat.ML,"in the paradigm of multi task learning , mul tiple related prediction tasks are learned jointly , sharing information across the tasks we propose a framework for multi task learn ing that enables one to selectively share the information across the tasks we assume that each task parameter vector is a linear combi nation of a finite number of underlying basis tasks the coefficients of the linear combina tion are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these our model is based on on the assumption that task pa rameters within a group lie in a low dimen sional subspace but allows the tasks in differ ent groups to overlap with each other in one or more bases experimental results on four datasets show that our approach outperforms competing methods"
cs.IT,"the problem of obtaining network coding maps for the physical layer network coded two way relay channel is considered , using the denoise and forward forward protocol it is known that network coding maps used at the relay node which ensure unique decodability at the end nodes form a latin square also , it is known that minimum distance of the effective constellation at the relay node becomes zero , when the ratio of the fade coefficients from the end node to the relay node , belongs to a finite set of complex numbers determined by the signal set used , called the singular fade states furthermore , it has been shown recently that the problem of obtaining network coding maps which remove the harmful effects of singular fade states , reduces to the one of obtaining latin squares , which satisfy certain constraints called textit singularity removal constraints in this paper , it is shown that the singularity removal constraints along with the row and column exclusion conditions of a latin square , can be compactly represented by a graph called the textit singularity removal graph determined by the singular fade state and the signal set used it is shown that a latin square which removes a singular fade state can be obtained from a proper vertex coloring of the corresponding singularity removal graph the minimum number of symbols used to fill in a latin square which removes a singular fade state is equal to the chromatic number of the singularity removal graph it is shown that for any square m qam signal set , there exists singularity removal graphs whose chromatic numbers exceed m and hence require more than m colors for vertex coloring also , it is shown that for any 2 lambda psk signal set , lambda geq 3 , all the singularity removal graphs can be colored using 2 lambda colors"
cs.IT,"in a two user broadcast channel where one user has full csir and the other has none , a recent result showed that tdma is strictly suboptimal and a product superposition requiring non coherent signaling achieves dof gains under many antenna configurations this work introduces product superposition in the domain of coherent signaling with pilots , demonstrates the advantages of product superposition in low snr as well as high snr , and establishes dof gains in a wider set of receiver antenna configurations two classes of decoders , with and without interference cancellation , are studied achievable rates are established by analysis and illustrated by simulations"
cs.IT,"deterministic constructions of measurement matrices in compressed sensing \( cs \) are considered in this paper the constructions are inspired by the recent discovery of dimakis , smarandache and vontobel which says that parity check matrices of good low density parity check \( ldpc \) codes can be used as provably good measurement matrices for compressed sensing under ell 1 minimization the performance of the proposed binary measurement matrices is mainly theoretically analyzed with the help of the analyzing methods and results from \( finite geometry \) ldpc codes particularly , several lower bounds of the spark \( i e , the smallest number of columns that are linearly dependent , which totally characterizes the recovery performance of ell 0 minimization \) of general binary matrices and finite geometry matrices are obtained and they improve the previously known results in most cases simulation results show that the proposed matrices perform comparably to , sometimes even better than , the corresponding gaussian random matrices moreover , the proposed matrices are sparse , binary , and most of them have cyclic or quasi cyclic structure , which will make the hardware realization convenient and easy"
cs.IT,"unified performance analysis is carried out for amplify and forward \( af \) multiple input multiple output \( mimo \) beamforming \( bf \) two way relay networks in rayleigh fading with five different relaying protocols including two novel protocols for better performance as a result , a novel closed form sum bit error rate \( ber \) expression is presented in a unified expression for all protocols a new closed form high signal to noise ratio \( snr \) performance is also obtained in a single expression , and an analytical high snr gap expression between the five protocols is provided we compare the performance of the five relaying protocols with respect to sum ber with appropriately normalized rate and power , and show that the proposed protocol with four time slots outperforms other protocols when transmit powers from two sources are sufficiently different , and the one with three time slots dominates other protocols when multiple relay antennas are used , at high snr"
cs.DS,"the single allocation ordered median hub location problem is a recent hub model introduced in puerto et al \( 2011 \) that provides a unifying analysis of a wide class of hub location mod els in this paper , we deal with the capacitated version of this problem , presenting two formulations as well as some preprocessing phases for fixing variables in addition , a strengthening of one of these formulations is also studied through the use of some fami lies of valid inequalities a battery of test problems with data taken from the ap library are solved where it is shown that the running times have been significantly reduced with the improvements presented in the paper"
cs.AI,"we introduce two hierarchies of clause sets , slur k and uc k , based on the classes slur \( single lookahead unit refutation \) , introduced in 1995 , and uc \( unit refutation complete \) , introduced in 1994 the class slur , introduced in annexstein et al , 1995 , is the class of clause sets for which unit clause propagation \( denoted by r 1 \) detects unsatisfiability , or where otherwise iterative assignment , avoiding obviously false assignments by look ahead , always yields a satisfying assignment it is natural to consider how to form a hierarchy based on slur such investigations were started in cepek et al , 2012 and balyo et al , 2012 we present what we consider the limit hierarchy slur k , based on generalising r 1 by r k , that is , using generalised unit clause propagation introduced in kullmann , 1999 , 2004 the class uc , studied in del val , 1994 , is the class of unit refutation complete clause sets , that is , those clause sets for which unsatisfiability is decidable by r 1 under any falsifying assignment for unsatisfiable clause sets f , the minimum k such that r k determines unsatisfiability of f is exactly the hardness of f , as introduced in ku 99 , 04 for satisfiable f we use now an extension mentioned in ansotegui et al , 2008 the hardness is the minimum k such that after application of any falsifying partial assignments , r k determines unsatisfiability the class uc k is given by the clause sets which have hardness k we observe that uc 1 is exactly uc uc k has a proof theoretic character , due to the relations between hardness and tree resolution , while slur k has an algorithmic character the correspondence between r k and k times nested input resolution \( or tree resolution using clause space k 1 \) means that r k has a dual nature both algorithmic and proof theoretic this corresponds to a basic result of this paper , namely slur k uc k"
cs.IT,"a distributed detection problem over fading gaussian multiple access channels is considered sensors observe a phenomenon and transmit their observations to a fusion center using the amplify and forward scheme the fusion center has multiple antennas with different channel models considered between the sensors and the fusion center , and different cases of channel state information are assumed at the sensors the performance is evaluated in terms of the error exponent for each of these cases , where the effect of multiple antennas at the fusion center is studied it is shown that for zero mean channels between the sensors and the fusion center when there is no channel information at the sensors , arbitrarily large gains in the error exponent can be obtained with sufficient increase in the number of antennas at the fusion center in stark contrast , when there is channel information at the sensors , the gain in error exponent due to having multiple antennas at the fusion center is shown to be no more than a factor of \( 8 pi \) for rayleigh fading channels between the sensors and the fusion center , independent of the number of antennas at the fusion center , or correlation among noise samples across sensors scaling laws for such gains are also provided when both sensors and antennas are increased simultaneously simple practical schemes and a numerical method using semidefinite relaxation techniques are presented that utilize the limited possible gains available simulations are used to establish the accuracy of the results"
cs.LG|stat.ML,"we introduce deep variational bayes filters \( dvbf \) , a new method for unsupervised learning of latent markovian state space models leveraging recent advances in stochastic gradient variational bayes , dvbf can overcome intractable inference distributions by means of variational inference thus , it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding this also enables realistic long term prediction"
cs.AI,"we study a problem of allocating divisible jobs , arriving online , to workers in a crowdsourcing setting which involves learning two parameters of strategically behaving workers each job is split into a certain number of tasks that are then allocated to workers each arriving job has to be completed within a deadline and each task has to be completed satisfying an upper bound on probability of failure the job population is homogeneous while the workers are heterogeneous in terms of costs , completion times , and times to failure the job completion time and time to failure of each worker are stochastic with fixed but unknown means the requester is faced with the challenge of learning two separate parameters of each \( strategically behaving \) worker simultaneously , namely , the mean job completion time and the mean time to failure the time to failure of a worker depends on the duration of the task handled by the worker assuming non strategic workers to start with , we solve this biparameter learning problem by applying the robust ucb algorithm then , we non trivially extend this algorithm to the setting where the workers are strategic about their costs our proposed mechanism is dominant strategy incentive compatible and ex post individually rational with asymptotically optimal regret performance"
cs.SI,"complex systems are usually illustrated by networks which captures the topology of the interactions between the entities to better understand the roles played by the entities in the system one needs to uncover the underlying community structure of the system in recent years , systems with interactions that have various types or can change over time between the entities have attracted an increasing research attention however , algorithms aiming to solve the key problem community detection in multilayer networks are still limited in this work , we first introduce the multilayer network model representation with multiple aspects , which is flexible to a variety of networks then based on this model , we naturally derive the multilayer modularity a widely adopted objective function of community detection in networks from a static perspective as an evaluation metric to evaluate the quality of the communities detected in multilayer networks it enables us to better understand the essence of the modularity by pointing out the specific kind of communities that will lead to a high modularity score we also propose a spectral method called mspec for the optimization of the proposed modularity function based on the supra adjacency representation of the multilayer networks experiments on the electroencephalograph network and the comparison results on several empirical multilayer networks demonstrate the feasibility and reliable performance of the proposed method"
cs.AI,"multi person event recognition is a challenging task , often with many people active in the scene but only a small subset contributing to an actual event in this paper , we propose a model which learns to detect events in such videos while automatically attending to the people responsible for the event our model does not use explicit annotations regarding who or where those people are during training and testing in particular , we track people in videos and use a recurrent neural network \( rnn \) to represent the track features we learn time varying attention weights to combine these features at each time instant the attended features are then processed using another rnn for event detection classification since most video datasets with multiple people are restricted to a small number of videos , we also collected a new basketball dataset comprising 257 basketball games with 14k event annotations corresponding to 11 event classes our model outperforms state of the art methods for both event classification and detection on this new dataset additionally , we show that the attention mechanism is able to consistently localize the relevant players"
cs.IT,"demixing is the problem of identifying multiple structured signals from a superimposed , undersampled , and noisy observation this work analyzes a general framework , based on convex optimization , for solving demixing problems when the constituent signals follow a generic incoherence model , this analysis leads to precise recovery guarantees these results admit an attractive interpretation each signal possesses an intrinsic degrees of freedom parameter , and demixing can succeed if and only if the dimension of the observation exceeds the total degrees of freedom present in the observation"
cs.IT,"we derive optimal snr based transmit antenna selection rules at the source and relay for the nonregenerative half duplex mimo relay channel while antenna selection is a suboptimal form of beamforming , it has the advantage that the optimization is tractable and can be implemented with only a few bits of feedback from the destination to the source and relay we compare the bit error rate of optimal antenna selection at both the source and relay to other proposed beamforming techniques and propose methods for performing the necessary limited feedback"
cs.DS,"this paper has been divided into three papers arxiv 0809 3232 , arxiv 0808 4134 , arxiv cs 0607105"
cs.IT,"the network , the nodes of which are concepts \( people 's names , companies' names , etc \) , extracted from web publications , is considered a working algorithm of extracting such concepts is presented edges of the network under consideration refer to the reference frequency which depends on the fact how many times the concepts , which correspond to the nodes , are mentioned in the same documents web documents being published within a period of time together form an information flow , which defines the dynamics of the network studied the phenomenon of its structure stability , when the number of web publications , constituting its formation bases , increases , is discussed"
cs.AI,"we study the problem of rewriting an ontology o1 expressed in a dl l1 into an ontology o2 in a horn dl l2 such that o1 and o2 are equisatisfiable when extended with an arbitrary dataset ontologies that admit such rewritings are amenable to reasoning techniques ensuring tractability in data complexity after showing undecidability whenever l1 extends alcf , we focus on devising efficiently checkable conditions that ensure existence of a horn rewriting by lifting existing techniques for rewriting disjunctive datalog programs into plain datalog to the case of arbitrary first order programs with function symbols , we identify a class of ontologies that admit horn rewritings of polynomial size our experiments indicate that many real world ontologies satisfy our sufficient conditions and thus admit polynomial horn rewritings"
cs.SI|stat.ML,"multidimensional scaling \( mds \) is a class of projective algorithms traditionally used in euclidean space to produce two or three dimensional visualizations of datasets of multidimensional points or point distances more recently however , several authors have pointed out that for certain datasets , hyperbolic target space may provide a better fit than euclidean space in this paper we develop pd mds , a metric mds algorithm designed specifically for the poincare disk \( pd \) model of the hyperbolic plane emphasizing the importance of proceeding from first principles in spite of the availability of various black box optimizers , our construction is based on an elementary hyperbolic line search and reveals numerous particulars that need to be carefully addressed when implementing this as well as more sophisticated iterative optimization methods in a hyperbolic space model"
cs.AI|cs.LG,"we consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility in these environments , agents must learn communication protocols in order to share information that is needed to solve the tasks by embracing deep neural networks , we are able to demonstrate end to end learning of protocols in complex environments inspired by communication riddles and multi agent computer vision problems with partial observability we propose two approaches for learning in these domains reinforced inter agent learning \( rial \) and differentiable inter agent learning \( dial \) the former uses deep q learning , while the latter exploits the fact that , during learning , agents can propagate error derivatives through \( noisy \) communication channels hence , this approach uses centralised learning but decentralised execution our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains"
cs.IT,"one of the key open problems in network information theory is to obtain the capacity region for the two user interference channel \( ic \) in this paper , new results are derived for this channel as a first result , a noisy interference regime is given for the general ic where the sum rate capacity is achieved by treating interference as noise at the receivers to obtain this result , a single letter outer bound in terms of some auxiliary random variables is first established for the sum rate capacity of the general ic and then those conditions under which this outer bound is reduced to the achievable sum rate given by the simple treating interference as noise strategy are specified the main benefit of this approach is that it is applicable for any two user ic \( potentially non gaussian \) for the special case of gaussian channel , our result is reduced to the noisy interference regime that was previously obtained next , some results are given on the han kobayashi \( hk \) achievable rate region the evaluation of this rate region is in general difficult in this paper , a simple characterization of the hk rate region is derived for some special cases , specifically , for a novel very weak interference regime as a remarkable characteristic , it is shown that for this very weak interference regime , the achievable sum rate due to the hk region is identical to the one given by the simple treating interference as noise strategy"
cs.LG|stat.ML,"we present a formal model of human decision making in explore exploit tasks using the context of multi armed bandit problems , where the decision maker must choose among multiple options with uncertain rewards we address the standard multi armed bandit problem , the multi armed bandit problem with transition costs , and the multi armed bandit problem on graphs we focus on the case of gaussian rewards in a setting where the decision maker uses bayesian inference to estimate the reward values we model the decision maker 's prior knowledge with the bayesian prior on the mean reward we develop the upper credible limit \( ucl \) algorithm for the standard multi armed bandit problem and show that this deterministic algorithm achieves logarithmic cumulative expected regret , which is optimal performance for uninformative priors we show how good priors and good assumptions on the correlation structure among arms can greatly enhance decision making performance , even over short time horizons we extend to the stochastic ucl algorithm and draw several connections to human decision making behavior we present empirical data from human experiments and show that human performance is efficiently captured by the stochastic ucl algorithm with appropriate parameters for the multi armed bandit problem with transition costs and the multi armed bandit problem on graphs , we generalize the ucl algorithm to the block ucl algorithm and the graphical block ucl algorithm , respectively we show that these algorithms also achieve logarithmic cumulative expected regret and require a sub logarithmic expected number of transitions among arms we further illustrate the performance of these algorithms with numerical examples"
cs.IT,"this article proposes a novel iterative algorithm based on low density parity check \( ldpc \) codes for compression of correlated sources at rates approaching the slepian wolf bound the setup considered in the article looks at the problem of compressing one source at a rate determined based on the knowledge of the mean source correlation at the encoder , and employing the other correlated source as side information at the decoder which decompresses the first source based on the estimates of the actual correlation we demonstrate that depending on the extent of the actual source correlation estimated through an iterative paradigm , significant compression can be obtained relative to the case the decoder does not use the implicit knowledge of the existence of correlation"
cs.LG,"in intrusion detection systems , classifiers still suffer from several drawbacks such as data dimensionality and dominance , different network feature types , and data impact on the classification in this paper two significant enhancements are presented to solve these drawbacks the first enhancement is an improved feature selection using sequential backward search and information gain this , in turn , extracts valuable features that enhance positively the detection rate and reduce the false positive rate the second enhancement is transferring nominal network features to numeric ones by exploiting the discrete random variable and the probability mass function to solve the problem of different feature types , the problem of data dominance , and data impact on the classification the latter is combined to known normalization methods to achieve a significant hybrid normalization approach finally , an intensive and comparative study approves the efficiency of these enhancements and shows better performance comparing to other proposed methods"
cs.IT,"motivated by streaming applications with stringent delay constraints , we consider the design of online network coding algorithms with timely delivery guarantees assuming that the sender is providing the same data to multiple receivers over independent packet erasure channels , we focus on the case of perfect feedback and heterogeneous erasure probabilities based on a general analytical framework for evaluating the decoding delay , we show that existing arq schemes fail to ensure that receivers with weak channels are able to recover from packet losses within reasonable time to overcome this problem , we re define the encoding rules in order to break the chains of linear combinations that cannot be decoded after one of the packets is lost our results show that sending uncoded packets at key times ensures that all the receivers are able to meet specific delay requirements with very high probability"
cs.DM,"daligault , rao and thomass 'e proposed in 2010 a fascinating conjecture connecting two seemingly unrelated notions clique width and well quasi ordering they asked if the clique width of graphs in a hereditary class which is well quasi ordered under labelled induced subgraphs is bounded by a constant this is equivalent to asking whether every hereditary class of unbounded clique width has a labelled infinite antichain we believe the answer to this question is positive and propose a stronger conjecture stating that every minimal hereditary class of graphs of unbounded clique width has a canonical labelled infinite antichain to date , only two hereditary classes are known to be minimal with respect to clique width and each of them is known to contain a canonical antichain in the present paper , we discover two more minimal hereditary classes of unbounded clique width and show that both of them contain canonical antichains"
cs.IT,"mimo processing techniques in fiber optical communications have been proposed as a promising approach to meet increasing demand for information throughput in this context , the multiple channels correspond to the multiple modes and or multiple cores in the fiber in this paper we characterize the distribution of the mutual information with gaussian input in a simple channel model for this system assuming significant cross talk between cores , negligible backscattering and near lossless propagation in the fiber , we model the transmission channel as a random complex unitary matrix the loss in the transmission may be parameterized by a number of unutilized channels in the fiber we analyze the system in a dual fashion first , we evaluate a closed form expression for the outage probability , which is handy for small matrices we also apply the asymptotic approach , in particular the coulomb gas method from statistical mechanics , to obtain closed form results for the ergodic mutual information , its variance as well as the outage probability for gaussian input in the limit of large number of cores modes by comparing our analytic results to simulations , we see that , despite the fact that this method is nominally valid for large number of modes , our method is quite accurate even for small to modest number of channels"
cs.AI,"wildfires are frequent , devastating events in australia that regularly cause significant loss of life and widespread property damage fire weather indices are a widely adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution localized wireless sensor networks , on the other hand , gather continuous sensor data measuring variables such as air temperature , relative humidity , rainfall and wind speed at high resolutions however , using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues , lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices within the scope of this paper , we propose a standardized approach to calculating fire weather indices \( a k a fire danger ratings \) and overcome a number of the challenges by applying semantic web technologies to the processing of data streams from a wireless sensor network deployed in the springbrook region of south east queensland this paper describes the underlying ontologies , the semantic reasoning and the semantic fire weather index \( sfwi \) system that we have developed to enable domain experts to specify and adapt rules for calculating fire weather indices we also describe the web based mapping interface that we have developed , that enables users to improve their understanding of how fire weather indices vary over time within a particular region finally , we discuss our evaluation results that indicate that the proposed system outperforms state of the art techniques in terms of accuracy , precision and query performance"
cs.IT|cs.LG,"this tutorial provides an overview of and introduction to rissanen 's minimum description length \( mdl \) principle the first chapter provides a conceptual , entirely non technical introduction to the subject it serves as a basis for the technical introduction given in the second chapter , in which all the ideas of the first chapter are made mathematically precise the main ideas are discussed in great conceptual and technical detail this tutorial is an extended version of the first two chapters of the collection advances in minimum description length theory and application \( edited by p grunwald , i j myung and m pitt , to be published by the mit press , spring 2005 \)"
cs.LG,"in this paper we study the problem of answering cloze style questions over short documents we introduce a new attention mechanism which uses multiplicative interactions between the query embedding and intermediate states of a recurrent neural network reader this enables the reader to build query specific representations of tokens in the document which are further used for answer selection our model , the gated attention reader , outperforms all state of the art models on several large scale benchmark datasets for this task the cnn dailymail news stories and children 's book test we also provide a detailed analysis of the performance of our model and several baselines over a subset of questions manually annotated with certain linguistic features the analysis sheds light on the strengths and weaknesses of several existing models"
cs.IT,"how should you choose a good set of \( say \) 48 planes in four dimensions \? more generally , how do you find packings in grassmannian spaces \? in this article i give a brief introduction to the work that i have been doing on this problem in collaboration with a r calderbank , j h conway , r h hardin , e m rains and p w shor we have found many nice examples of specific packings \( 70 4 spaces in 8 space , for instance \) , several general constructions , and an embedding theorem which shows that a packing in grassmannian space g \( m , n \) is a subset of a sphere in r d , where d \( m 2 \) \( m 1 \) 2 , and leads to a proof that many of our packings are optimal there are a number of interesting unsolved problems"
cs.AI,"we introduce utility directed procedures for mediating the flow of potentially distracting alerts and communications to computer users we present models and inference procedures that balance the context sensitive costs of deferring alerts with the cost of interruption we describe the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications after introducing principles of attention sensitive alerting , we focus on the problem of guiding alerts about email messages we dwell on the problem of inferring the expected criticality of email and discuss work on the priorities system , centering on prioritizing email by criticality and modulating the communication of notifications to users about the presence and nature of incoming email"
cs.DS,"this document collects the lecture notes from my course communication complexity \( for algorithm designers \) , '' taught at stanford in the winter quarter of 2015 the two primary goals of the course are 1 learn several canonical problems that have proved the most useful for proving lower bounds \( disjointness , index , gap hamming , etc \) 2 learn how to reduce lower bounds for fundamental algorithmic problems to communication complexity lower bounds along the way , we 'll also 3 get exposure to lots of cool computational models and some famous results about them data streams and linear sketches , compressive sensing , space query time trade offs in data structures , sublinear time algorithms , and the extension complexity of linear programs 4 scratch the surface of techniques for proving communication complexity lower bounds \( fooling sets , corruption bounds , etc \)"
cs.LG,"we investigate the issues of inductive problem solving and learning by doxastic agents we provide topological characterizations of solvability and learnability , and we use them to prove that agm style belief revision is universal , i e , that every solvable problem is solvable by agm conditioning"
cs.IT,"as two major players in terrestrial wireless communications , wi fi systems and cellular networks have different origins and have largely evolved separately motivated by the exponentially increasing wireless data demand , cellular networks are evolving towards a heterogeneous and small cell network architecture , wherein small cells are expected to provide very high capacity however , due to the limited licensed spectrum for cellular networks , any effort to achieve capacity growth through network densification will face the challenge of severe inter cell interference in view of this , recent standardization developments have started to consider the opportunities for cellular networks to use the unlicensed spectrum bands , including the 2 4 ghz and 5 ghz bands that are currently used by wi fi , zigbee and some other communication systems in this article , we look into the coexistence of wi fi and 4g cellular networks sharing the unlicensed spectrum we introduce a network architecture where small cells use the same unlicensed spectrum that wi fi systems operate in without affecting the performance of wi fi systems we present an almost blank subframe \( abs \) scheme without priority to mitigate the co channel interference from small cells to wi fi systems , and propose an interference avoidance scheme based on small cells estimating the density of nearby wi fi access points to facilitate their coexistence while sharing the same unlicensed spectrum simulation results show that the proposed network architecture and interference avoidance schemes can significantly increase the capacity of 4g heterogeneous cellular networks while maintaining the service quality of wi fi systems"
cs.IT,"data compression has become a necessity not only the in the field of communication but also in various scientific experiments the data that is being received is more and the processing time required has also become more a significant change in the algorithms will help to optimize the processing speed with the invention of technologies like iot and in technologies like machine learning there is a need to compress data for example training an artificial neural network requires a lot of data that should be processed and trained in small interval of time for which compression will be very helpful there is a need to process the data faster and quicker in this paper we present a method that reduces the data size this method is known as optimized huffmans coding in the huffmans coding we encode the messages so as to reduce the data and here in the optimized huffmans coding we compress the data to a great extent which helps in various signal processing algorithms and has advantages in many applications here in this paper we have presented the optimized huffmans coding method for text compression this method but has advantages over the normal huffmans coding this algorithm presented here says that every letter can be grouped together and encoded which not only reduces the size but also the huffmans tree data that is required for decoding , hence reducing the data size this method has huge scientific applications"
cs.AI|cs.LG,"time series classification has attracted considerable research attention due to the various domains where time series data are observed , ranging from medicine to econometrics traditionally , the focus of time series classification has been on short time series data composed of a unique pattern with intraclass pattern distortions and variations , while recently there have been attempts to focus on longer series composed of various local patterns this study presents a novel method which can detect local patterns in long time series via fitting local polynomial functions of arbitrary degrees the coefficients of the polynomial functions are converted to symbolic words via equivolume discretizations of the coefficients' distributions the symbolic polynomial words enable the detection of similar local patterns by assigning the same words to similar polynomials moreover , a histogram of the frequencies of the words is constructed from each time series' bag of words each row of the histogram enables a new representation for the series and symbolize the existence of local patterns and their frequencies experimental evidence demonstrates outstanding results of our method compared to the state of art baselines , by exhibiting the best classification accuracies in all the datasets and having statistically significant improvements in the absolute majority of experiments"
cs.DM|cs.IT,"non adaptive group testing involves grouping arbitrary subsets of n items into different pools each pool is then tested and defective items are identified a fundamental question involves minimizing the number of pools required to identify at most d defective items motivated by applications in network tomography , sensor networks and infection propagation , a variation of group testing problems on graphs is formulated unlike conventional group testing problems , each group here must conform to the constraints imposed by a graph for instance , items can be associated with vertices and each pool is any set of nodes that must be path connected in this paper , a test is associated with a random walk in this context , conventional group testing corresponds to the special case of a complete graph on n vertices for interesting classes of graphs a rather surprising result is obtained , namely , that the number of tests required to identify d defective items is substantially similar to what is required in conventional group testing problems , where no such constraints on pooling is imposed specifically , if t \( n \) corresponds to the mixing time of the graph g , it is shown that with m o \( d 2t 2 \( n \) log \( n d \) \) non adaptive tests , one can identify the defective items consequently , for the erdos renyi random graph g \( n , p \) , as well as expander graphs with constant spectral gap , it follows that m o \( d 2 log 3n \) non adaptive tests are sufficient to identify d defective items next , a specific scenario is considered that arises in network tomography , for which it is shown that m o \( d 3 log 3n \) non adaptive tests are sufficient to identify d defective items noisy counterparts of the graph constrained group testing problem are considered , for which parallel results are developed we also briefly discuss extensions to compressive sensing on graphs"
cs.IT,"the capacity region of a multiple input multiple output interference channel \( mimo ic \) where the channel matrices are square and invertible is studied the capacity region for strong interference is established where the definition of strong interference parallels that of scalar channels moreover , the sum rate capacity for z interference , noisy interference , and mixed interference is established these results generalize known results for the scalar gaussian ic"
cs.DS,"as we add rigid bars between points in the plane , at what point is there a giant \( linear sized \) rigid component , which can be rotated and translated , but which has no internal flexibility \? if the points are generic , this depends only on the combinatorics of the graph formed by the bars we show that if this graph is an erdos renyi random graph g \( n , c n \) , then there exists a sharp threshold for a giant rigid component to emerge for c c 2 , w h p all rigid components span one , two , or three vertices , and when c c 2 , w h p there is a giant rigid component the constant c 2 approx 3 588 is the threshold for 2 orientability , discovered independently by fernholz and ramachandran and cain , sanders , and wormald in soda'07 we also give quantitative bounds on the size of the giant rigid component when it emerges , proving that it spans a \( 1 o \( 1 \) \) fraction of the vertices in the \( 3 2 \) core informally , the \( 3 2 \) core is maximal induced subgraph obtained by starting from the 3 core and then inductively adding vertices with 2 neighbors in the graph obtained so far"
cs.IT,"we consider scheduled message communication over a discrete memoryless degraded broadcast channel the framework we consider here models both the random message arrivals and the subsequent reliable communication by suitably combining techniques from queueing theory and information theory the channel from the transmitter to each of the receivers is quasi static , flat , and with independent fades across the receivers requests for message transmissions are assumed to arrive according to an i i d arrival process then , \( i \) we derive an outer bound to the region of message arrival vectors achievable by the class of stationary scheduling policies , \( ii \) we show for any message arrival vector that satisfies the outerbound , that there exists a stationary ``state independent'' policy that results in a stable system for the corresponding message arrival process , and \( iii \) under two asymptotic regimes , we show that the stability region of nat arrival rate vectors has information theoretic capacity region interpretation"
cs.IT|stat.ML,"approximate message passing \( amp \) has been shown to be a superior method for inference problems , such as the recovery of signals from sets of noisy , lower dimensionality measurements , both in terms of reconstruction accuracy and in computational efficiency however , amp suffers from serious convergence issues in contexts that do not exactly match its assumptions we propose a new approach to stabilizing amp in these contexts by applying amp updates to individual coefficients rather than in parallel our results show that this change to the amp iteration can provide theoretically expected , but hitherto unobtainable , performance for problems on which the standard amp iteration diverges additionally , we find that the computational costs of this swept coefficient update scheme is not unduly burdensome , allowing it to be applied efficiently to signals of large dimensionality"
cs.IT,"this paper studies the structure of downlink sum rate maximizing selective decentralized feedback policies for opportunistic beamforming under finite feedback constraints on the average number of mobile users feeding back firstly , it is shown that any sum rate maximizing selective decentralized feedback policy must be a threshold feedback policy this result holds for all fading channel models with continuous distribution functions secondly , the resulting optimum threshold selection problem is analyzed in detail this is a non convex optimization problem over finite dimensional euclidean spaces by utilizing the theory of majorization , an underlying schur concave structure in the sum rate function is identified , and the sufficient conditions for the optimality of homogenous threshold feedback policies are obtained applications of these results are illustrated for well known fading channel models such as rayleigh , nakagami and rician fading channels , along with various engineering and design insights rather surprisingly , it is shown that using the same threshold value at all mobile users is not always a rate wise optimal feedback strategy , even for a network with identical mobile users experiencing statistically the same channel conditions for the rayleigh fading channel model , on the other hand , homogenous threshold feedback policies are proven to be rate wise optimal if multiple orthonormal data carrying beams are used to communicate with multiple mobile users simultaneously"
cs.DS,"in this paper we study emph many to one boundary labeling with backbone leaders in this new many to one model , a horizontal backbone reaches out of each label into the feature enclosing rectangle feature points that need to be connected to this label are linked via vertical line segments to the backbone we present dynamic programming algorithms for label number and total leader length minimization of crossing free backbone labelings when crossings are allowed , we aim to obtain solutions with the minimum number of crossings this can be achieved efficiently in the case of fixed label order , however , in the case of flexible label order we show that minimizing the number of leader crossings is np hard"
cs.IT,"in heterogeneous cellular networks \( hcns \) , it is desirable to offload mobile users to small cells , which are typically significantly less congested than the macrocells to achieve sufficient load balancing , the offloaded users often have much lower sinr than they would on the macrocell this sinr degradation can be partially alleviated through interference avoidance , for example time or frequency resource partitioning , whereby the macrocell turns off in some fraction of such resources naturally , the optimal offloading strategy is tightly coupled with resource partitioning the optimal amount of which in turn depends on how many users have been offloaded in this paper , we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two tier cellular network with it , we are able to derive the downlink rate distribution over the entire network , and an optimal strategy for joint resource partitioning and offloading we show that load balancing , by itself , is insufficient , and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co channel heterogeneous networks"
cs.DS,"we introduce a cost sharing framework for ridesharing that explicitly takes into account the inconvenience costs of passengers due to detours we introduce the notion of sequential individual rationality \( sir \) that requires that the disutility of existing passengers is non increasing as additional passengers are picked up , and show that these constraints induce a natural limit on the incremental detours permissible as the ride progresses we provide an exact characterization of all routes for which there exists some cost sharing scheme that is sir on that route , and under these constraints , for realistic scenarios , we also show a theta \( sqrt n \) upper bound and a theta \( log n \) lower bound on the total detour experienced by the passengers as a fraction of the direct distance to their destination next , we observe that under any budget balanced cost sharing scheme that is sir on a route , the total amount by which the passengers' disutilities decrease \( which can be viewed as the total incremental benefit due to ridesharing \) is a constant this observation inspires a dual notion of viewing cost sharing schemes as benefit sharing schemes , under which we introduce a natural definition of sequential fairness the total incremental benefit due to the addition of a new passenger is \( partly \) shared among the existing passengers in proportion to the incremental inconvenience costs they suffer we then provide an exact characterization of sequentially fair cost sharing schemes , which brings out several useful structural properties , including a strong requirement that passengers must compensate each other for the detour inconveniences that they cause finally , we conclude with an extended discussion of new algorithmic problems related to and motivated by sir , and important future work"
cs.DM,"in this paper , we prove that every planar 4 connected graph has a cz representation a string representation using paths in a rectangular grid that contain at most one vertical segment furthermore , two paths representing vertices u , v intersect precisely once whenever there is an edge between u and v the required size of the grid is n times 2n"
cs.IT,"an information reconciliation method for continuous variable quantum key distribution with gaussian modulation that is based on non binary low density parity check \( ldpc \) codes is presented sets of regular and irregular ldpc codes with different code rates over the galois fields gf \( 8 \) , gf \( 16 \) , gf \( 32 \) , and gf \( 64 \) have been constructed we have performed simulations to analyze the efficiency and the frame error rate using the sum product algorithm the proposed method achieves an efficiency between 0 94 and 0 98 if the signal to noise ratio is between 4 db and 24 db"
cs.IT,the capacity regions of multiple input multiple output gaussian z interference channels are established for the very strong interference and aligned strong interference cases the sum rate capacity of such channels is established under noisy interference these results generalize known results for scalar gaussian z interference channels
cs.LG,"we introduce a novel technique for verification and model synthesis of sequential programs our technique is based on learning a regular model of the set of feasible paths in a program , and testing whether this model contains an incorrect behavior exact learning algorithms require checking equivalence between the model and the program , which is a difficult problem , in general undecidable our learning procedure is therefore based on the framework of probably approximately correct \( pac \) learning , which uses sampling instead and provides correctness guarantees expressed using the terms error probability and confidence besides the verification result , our procedure also outputs the model with the said correctness guarantees obtained preliminary experiments show encouraging results , in some cases even outperforming mature software verifiers"
cs.IT,"distributed source coding is traditionally viewed in the block coding context all the source symbols are known in advance at the encoders this paper instead considers a streaming setting in which iid source symbol pairs are revealed to the separate encoders in real time and need to be reconstructed at the decoder with some tolerable end to end delay using finite rate noiseless channels a sequential random binning argument is used to derive a lower bound on the error exponent with delay and show that both ml decoding and universal decoding achieve the same positive error exponents inside the traditional slepian wolf rate region the error events are different from the block coding error events and give rise to slightly different exponents because the sequential random binning scheme is also universal over delays , the resulting code eventually reconstructs every source symbol correctly with probability 1"
cs.AI|cs.LG,"electricity theft is a major problem around the world in both developed and developing countries and may range up to 40 of the total electricity distributed more generally , electricity theft belongs to non technical losses \( ntl \) , which are losses that occur during the distribution of electricity in power grids in this paper , we build features from the neighborhood of customers we first split the area in which the customers are located into grids of different sizes for each grid cell we then compute the proportion of inspected customers and the proportion of ntl found among the inspected customers we then analyze the distributions of features generated and show why they are useful to predict ntl in addition , we compute features from the consumption time series of customers we also use master data features of customers , such as their customer class and voltage of their connection we compute these features for a big data base of 31m meter readings , 700k customers and 400k inspection results we then use these features to train four machine learning algorithms that are particularly suitable for big data sets because of their parallelizable structure logistic regression , k nearest neighbors , linear support vector machine and random forest using the neighborhood features instead of only analyzing the time series has resulted in appreciable results for big data sets for varying ntl proportions of 1 90 this work can therefore be deployed to a wide range of different regions around the world"
cs.SI,"one of the most widely used methods for community detection in networks is the maximization of the quality function known as modularity of the many maximization techniques that have been used in this context , some of the most conceptually attractive are the spectral methods , which are based on the eigenvectors of the modularity matrix spectral algorithms have , however , been limited by and large to the division of networks into only two or three communities , with divisions into more than three being achieved by repeated two way division here we present a spectral algorithm that can directly divide a network into any number of communities the algorithm makes use of a mapping from modularity maximization to a vector partitioning problem , combined with a fast heuristic for vector partitioning we compare the performance of this spectral algorithm with previous approaches and find it to give superior results , particularly in cases where community sizes are unbalanced we also give demonstrative applications of the algorithm to two real world networks and find that it produces results in good agreement with expectations for the networks studied"
cs.AI|cs.LG|stat.ML,"in this paper we propose an approach to preference elicitation that is suitable to large configuration spaces beyond the reach of existing state of the art approaches our setwise max margin method can be viewed as a generalization of max margin learning to sets , and can produce a set of diverse items that can be used to ask informative queries to the user moreover , the approach can encourage sparsity in the parameter space , in order to favor the assessment of utility towards combinations of weights that concentrate on just few features we present a mixed integer linear programming formulation and show how our approach compares favourably with bayesian preference elicitation alternatives and easily scales to realistic datasets"
cs.AI,"this note deals with a class of variables that , if conditioned on , tends to amplify confounding bias in the analysis of causal effects this class , independently discovered by bhattacharya and vogt \( 2007 \) and wooldridge \( 2009 \) , includes instrumental variables and variables that have greater influence on treatment selection than on the outcome we offer a simple derivation and an intuitive explanation of this phenomenon and then extend the analysis to non linear models we show that 1 the bias amplifying potential of instrumental variables extends over to non linear models , though not as sweepingly as in linear models 2 in non linear models , conditioning on instrumental variables may introduce new bias where none existed before 3 in both linear and non linear models , instrumental variables have no effect on selection induced bias"
cs.LG,"the purported black box nature of neural networks is a barrier to adoption in applications where interpretability is essential here we present deeplift \( learning important features \) , an efficient and effective method for computing importance scores in a neural network deeplift compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference we apply deeplift to models trained on natural images and genomic data , and show significant advantages over gradient based methods"
cs.IT,"this paper studies the positioning problem based on two way time of arrival \( tw toa \) measurements in asynchronous wireless sensor networks since the optimal estimator for this problem involves difficult nonconvex optimization , we propose two suboptimal estimators based on squared range least squares and least absolute mean of residual errors the former approach is formulated as a general trust region subproblem which can be solved exactly under mild conditions the latter approach is formulated as a difference of convex functions programming \( dcp \) , which can be solved using a concave convex procedure simulation results illustrate the high performance of the proposed techniques , especially for the dcp approach"
cs.DS,"ge and stefankovic have recently introduced a novel two variable graph polynomial when specialised to a bipartite graphs g and evaluated at the point \( 1 2 , 1 \) this polynomial gives the number of independent sets in the graph inspired by this polynomial , they also introduced a markov chain which , if rapidly mixing , would provide an efficient sampling procedure for independent sets in g this sampling procedure in turn would imply the existence of efficient approximation algorithms for a number of significant counting problems whose complexity is so far unresolved the proposed markov chain is promising , in the sense that it overcomes the most obvious barrier to mixing however , we show here , by exhibiting a sequence of counterexamples , that the mixing time of their markov chain is exponential in the size of the input when the input is chosen from a particular infinite family of bipartite graphs"
cs.DS|cs.IT,"the noise model of deletions poses significant challenges in coding theory , with basic questions like the capacity of the binary deletion channel still being open in this paper , we study the harder model of worst case deletions , with a focus on constructing efficiently decodable codes for the two extreme regimes of high noise and high rate specifically , we construct polynomial time decodable codes with the following trade offs \( for any eps 0 \) \( 1 \) codes that can correct a fraction 1 eps of deletions with rate poly \( eps \) over an alphabet of size poly \( 1 eps \) \( 2 \) binary codes of rate 1 o \( sqrt \( eps \) \) that can correct a fraction eps of deletions and \( 3 \) binary codes that can be list decoded from a fraction \( 1 2 eps \) of deletions with rate poly \( eps \) our work is the first to achieve the qualitative goals of correcting a deletion fraction approaching 1 over bounded alphabets , and correcting a constant fraction of bit deletions with rate aproaching 1 the above results bring our understanding of deletion code constructions in these regimes to a similar level as worst case errors"
cs.IT,"to meet the growing spectrum demands , future cellular systems are expected to share the spectrum of other services such as radar in this paper , we consider a network multiple input multiple output \( mimo \) with partial cooperation model where radar stations cooperate with cellular base stations \( bs \) s to deliver messages to intended mobile users so the radar stations act as bss in the cellular system however , due to the high power transmitted by radar stations for detection of far targets , the cellular receivers could burnout when receiving these high radar powers therefore , we propose a new projection method called small singular values space projection \( ssvsp \) to mitigate these harmful high power and enable radar stations to collaborate with cellular base stations in addition , we formulate the problem into a mimo interference channel with general constraints \( mimo ifc gc \) finally , we provide a solution to minimize the weighted sum mean square error minimization problem \( wsmmse \) with enforcing power constraints on both radar and cellular stations"
cs.IT,"departing from traditional communication theory where decoding algorithms are assumed to perform without error , a system where noise perturbs both computational devices and communication channels is considered here this paper studies limits in processing noisy signals with noisy circuits by investigating the effect of noise on standard iterative decoders for low density parity check codes concentration of decoding performance around its average is shown to hold when noise is introduced into message passing and local computation density evolution equations for simple faulty iterative decoders are derived in one model , computing nonlinear estimation thresholds shows that performance degrades smoothly as decoder noise increases , but arbitrarily small probability of error is not achievable probability of error may be driven to zero in another system model the decoding threshold again decreases smoothly with decoder noise as an application of the methods developed , an achievability result for reliable memory systems constructed from unreliable components is provided"
cs.IT,"biometrics are an important and widely used class of methods for identity verification and access control biometrics are attractive because they are inherent properties of an individual they need not be remembered like passwords , and are not easily lost or forged like identifying documents at the same time , bio metrics are fundamentally noisy and irreplaceable there are always slight variations among the measurements of a given biometric , and , unlike passwords or identification numbers , biometrics are derived from physical characteristics that cannot easily be changed the proliferation of biometric usage raises critical privacy and security concerns that , due to the noisy nature of biometrics , cannot be addressed using standard cryptographic methods in this article we present an overview of secure biometrics , also referred to as biometric template protection , an emerging class of methods that address these concerns"
cs.DS,"in a balloon drawing of a tree , all the children under the same parent are placed on the circumference of the circle centered at their parent , and the radius of the circle centered at each node along any path from the root reflects the number of descendants associated with the node among various styles of tree drawings reported in the literature , the balloon drawing enjoys a desirable feature of displaying tree structures in a rather balanced fashion for each internal node in a balloon drawing , the ray from the node to each of its children divides the wedge accommodating the subtree rooted at the child into two sub wedges depending on whether the two sub wedge angles are required to be identical or not , a balloon drawing can further be divided into two types even sub wedge and uneven sub wedge types in the most general case , for any internal node in the tree there are two dimensions of freedom that affect the quality of a balloon drawing \( 1 \) altering the order in which the children of the node appear in the drawing , and \( 2 \) for the subtree rooted at each child of the node , flipping the two sub wedges of the subtree in this paper , we give a comprehensive complexity analysis for optimizing balloon drawings of rooted trees with respect to angular resolution , aspect ratio and standard deviation of angles under various drawing cases depending on whether the tree is of even or uneven sub wedge type and whether \( 1 \) and \( 2 \) above are allowed it turns out that some are np complete while others can be solved in polynomial time we also derive approximation algorithms for those that are intractable in general"
cs.IT,"we study the asymptotic outage performance of incremental redundancy automatic repeat request \( inr arq \) transmission over the multiple input multiple output \( mimo \) block fading channels with discrete input constellations we first show that transmission with random codes using a discrete signal constellation across all transmit antennas achieves the optimal outage diversity given by the singleton bound we then analyze the optimal snr exponent and outage diversity of inr arq transmission over the mimo block fading channel we show that a significant gain in outage diversity is obtained by providing more than one bit feedback at each arq round thus , the outage performance of inr arq transmission can be remarkably improved with minimal additional overhead a suboptimal feedback and power adaptation rule , which achieves the optimal outage diversity , is proposed for mimo inr arq , demonstrating the benefits provided by multi bit feedback"
cs.AI|cs.LG|stat.ML,"the accuracy of machine learning systems is a widely studied research topic established techniques such as cross validation predict the accuracy on unseen data of the classifier produced by applying a given learning method to a given training data set however , they do not predict whether incurring the cost of obtaining more data and undergoing further training will lead to higher accuracy in this paper we investigate techniques for making such early predictions we note that when a machine learning algorithm is presented with a training set the classifier produced , and hence its error , will depend on the characteristics of the algorithm , on training set 's size , and also on its specific composition in particular we hypothesise that if a number of classifiers are produced , and their observed error is decomposed into bias and variance terms , then although these components may behave differently , their behaviour may be predictable we test our hypothesis by building models that , given a measurement taken from the classifier created from a limited number of samples , predict the values that would be measured from the classifier produced when the full data set is presented we create separate models for bias , variance and total error our models are built from the results of applying ten different machine learning algorithms to a range of data sets , and tested with unseen algorithms and datasets we analyse the results for various numbers of initial training samples , and total dataset sizes results show that our predictions are very highly correlated with the values observed after undertaking the extra training finally we consider the more complex case where an ensemble of heterogeneous classifiers is trained , and show how we can accurately estimate an upper bound on the accuracy achievable after further training"
cs.IT,"this paper investigates the benefits of amplify and forward \( af \) relaying in the setup of multi antenna wireless networks the concept of random sequential \( rs \) relaying is previously introduced in the literature and showed that it achieves the maximum diversity gain in a general multi antenna network here , we show that random unitary matrix multiplication at the relay nodes empowers the rs scheme to achieve a better diversity multiplexing tradeoff \( dmt \) as compared to the traditional af relaying first , we study the case of a multi antenna full duplex single relay two hop network , for which we show that the rs achieves the optimum dmt applying this result , we derive a new achievable dmt for the case of multi antenna half duplex parallel relay network interestingly , it turns out that the dmt of the rs scheme is optimum for the case of multi antenna two parallel non interfering half duplex relays next , we show that random unitary matrix multiplication also improves the dmt of the non orthogonal af relaying scheme in the case of a multi antenna single relay channel finally , we study the general case of multi antenna full duplex relay networks and derive a new lower bound on its dmt using the rs scheme"
cs.AI,"coalition formation is a fundamental type of interaction that involves the creation of coherent groupings of distinct , autonomous , agents in order to efficiently achieve their individual or collective goals forming effective coalitions is a major research challenge in the field of multi agent systems central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal this usually requires calculating a value for every possible coalition , known as the coalition value , which indicates how beneficial that coalition would be if it was formed once these values are calculated , the agents usually need to find a combination of coalitions , in which every agent belongs to exactly one coalition , and by which the overall outcome of the system is maximized however , this coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined , which grows exponentially with the number of agents involved to date , therefore , many algorithms have been proposed to solve this problem using different techniques ranging from dynamic programming , to integer programming , to stochastic search all of which suffer from major limitations relating to execution time , solution quality , and memory requirements with this in mind , we develop an anytime algorithm to solve the coalition structure generation problem specifically , the algorithm uses a novel representation of the search space , which partitions the space of possible solutions into sub spaces such that it is possible to compute upper and lower bounds on the values of the best coalition structures in them these bounds are then used to identify the sub spaces that have no potential of containing the optimal solution so that they can be pruned the algorithm , then , searches through the remaining sub spaces very efficiently using a branch and bound technique to avoid examining all the solutions within the searched subspace \( s \) in this setting , we prove that our algorithm enumerates all coalition structures efficiently by avoiding redundant and invalid solutions automatically moreover , in order to effectively test our algorithm we develop a new type of input distribution which allows us to generate more reliable benchmarks compared to the input distributions previously used in the field given this new distribution , we show that for 27 agents our algorithm is able to find solutions that are optimal in 0 175 of the time required by the fastest available algorithm in the literature the algorithm is anytime , and if interrupted before it would have normally terminated , it can still provide a solution that is guaranteed to be within a bound from the optimal one moreover , the guarantees we provide on the quality of the solution are significantly better than those provided by the previous state of the art algorithms designed for this purpose for example , for the worst case distribution given 25 agents , our algorithm is able to find a 90 efficient solution in around 10 of time it takes to find the optimal solution"
cs.IT,"in this paper , we investigate the design of artificial noise aided secure multi antenna transmission in slow fading channels the primary design concerns include the transmit power allocation and the rate parameters of the wiretap code we consider two scenarios with different complexity levels i \) the design parameters are chosen to be fixed for all transmissions , ii \) they are adaptively adjusted based on the instantaneous channel feedback from the intended receiver in both scenarios , we provide explicit design solutions for achieving the maximal throughput subject to a secrecy constraint , given by a maximum allowable secrecy outage probability we then derive accurate approximations for the maximal throughput in both scenarios in the high signal to noise ratio region , and give new insights into the additional power cost for achieving a higher security level , whilst maintaining a specified target throughput in the end , the throughput gain of adaptive transmission over non adaptive transmission is also quantified and analyzed"
cs.IT,"with the successful worldwide deployment of 3rd generation mobile communication , security aspects are ensured partly researchers are now looking for 4g mobile for its deployment with high data rate , enhanced security and reliability so that world should look for calm , continuous air interface for long and medium range communication this calm will be a reliable high data rate secured mobile communication to be deployed for car to car communication \( c2c \) for safety application this paper reviewed the wimax , 60 ghz rf carrier for c2c the system is tested at smit laboratory with multimedia transmission and reception with proper deployment of this 60 ghz system on vehicles , the existing commercial products for 802 11p will be required to be replaced or updated soon"
cs.LG,"this study proposes a general , scalable method to learn control oriented thermal models of buildings that could enable wide scale deployment of cost effective predictive controls an unscented kalman filter augmented for parameter and disturbance estimation is shown to accurately learn and predict a building 's thermal response recent studies of heating , ventilating , and air conditioning \( hvac \) systems have shown significant energy savings with advanced model predictive control \( mpc \) a scalable cost effective method to readily acquire accurate , robust models of individual buildings' unique thermal envelopes has historically been elusive and hindered the widespread deployment of prediction based control systems continuous commissioning and lifetime performance of these thermal models requires deployment of on line data driven system identification and parameter estimation routines we propose a novel gray box approach using an unscented kalman filter based on a multi zone thermal network and validate it with energyplus simulation data the filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24 hour energy predictions this study extends our initial investigation by formalizing parameter and disturbance estimation routines and demonstrating results across a year long study"
cs.SI,"community structure in networks is often a consequence of homophily , or assortative mixing , based on some attribute of the vertices for example , researchers may be grouped into communities corresponding to their research topic this is possible if vertex attributes have discrete values , but many networks exhibit assortative mixing by some continuous valued attribute , such as age or geographical location in such cases , no discrete communities can be identified we consider how the notion of community structure can be generalized to networks that are based on continuous valued attributes in general , a network may contain discrete communities which are ordered according to their attribute values we propose a method of generating synthetic ordered networks and investigate the effect of ordered community structure on the spread of infectious diseases we also show that community detection algorithms fail to recover community structure in ordered networks , and evaluate an alternative method using a layout algorithm to recover the ordering"
cs.IT,"several exact recovery criteria \( erc \) ensuring that orthogonal matching pursuit \( omp \) identifies the correct support of sparse signals have been developed in the last few years these erc rely on the restricted isometry property \( rip \) , the associated restricted isometry constant \( ric \) and sometimes the restricted orthogonality constant \( roc \) in this paper , three of the most recent erc for omp are examined the contribution is to show that these erc remain valid for a generalization of omp , entitled simultaneous orthogonal matching pursuit \( somp \) , that is capable to process several measurement vectors simultaneously and return a common support estimate for the underlying sparse vectors the sharpness of the bounds is also briefly discussed in light of previous works focusing on omp"
cs.LG|stat.ML,"mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations the current practice for estimating the parameters of such models relies on local search heuristics \( e g , the em algorithm \) which are prone to failure , and existing consistent methods are unfavorable due to their high computational and sample complexity which typically scale exponentially with the number of mixture components this work develops an efficient method of moments approach to parameter estimation for a broad class of high dimensional mixture models with many components , including multi view mixtures of gaussians \( such as mixtures of axis aligned gaussians \) and hidden markov models the new method leads to rigorous unsupervised learning results for mixture models that were not achieved by previous works and , because of its simplicity , it offers a viable alternative to em for practical deployment"
cs.IT,"the network traffic matrix is widely used in network operation and management it is therefore of crucial importance to analyze the components and the structure of the network traffic matrix , for which several mathematical approaches such as principal component analysis \( pca \) were proposed in this paper , we first argue that pca performs poorly for analyzing traffic matrix that is polluted by large volume anomalies , and then propose a new decomposition model for the network traffic matrix according to this model , we carry out the structural analysis by decomposing the network traffic matrix into three sub matrices , namely , the deterministic traffic , the anomaly traffic and the noise traffic matrix , which is similar to the robust principal component analysis \( rpca \) problem previously studied in 13 based on the relaxed principal component pursuit \( relaxed pcp \) method and the accelerated proximal gradient \( apg \) algorithm , we present an iterative approach for decomposing a traffic matrix , and demonstrate its efficiency and flexibility by experimental results finally , we further discuss several features of the deterministic and noise traffic our study develops a novel method for the problem of structural analysis of the traffic matrix , which is robust against pollution of large volume anomalies"
cs.IT|cs.LG|stat.ML,"one of the key challenges in sensor networks is the extraction of information by fusing data from a multitude of distinct , but possibly unreliable sensors recovering information from the maximum number of dependable sensors while specifying the unreliable ones is critical for robust sensing this sensing task is formulated here as that of finding the maximum number of feasible subsystems of linear equations , and proved to be np hard useful links are established with compressive sampling , which aims at recovering vectors that are sparse in contrast , the signals here are not sparse , but give rise to sparse residuals capitalizing on this form of sparsity , four sensing schemes with complementary strengths are developed the first scheme is a convex relaxation of the original problem expressed as a second order cone program \( socp \) it is shown that when the involved sensing matrices are gaussian and the reliable measurements are sufficiently many , the socp can recover the optimal solution with overwhelming probability the second scheme is obtained by replacing the initial objective function with a concave one the third and fourth schemes are tailored for noisy sensor data the noisy case is cast as a combinatorial problem that is subsequently surrogated by a \( weighted \) socp interestingly , the derived cost functions fall into the framework of robust multivariate linear regression , while an efficient block coordinate descent algorithm is developed for their minimization the robust sensing capabilities of all schemes are verified by simulated tests"
cs.IT,"we construct classes of permutation polynomials over f q 2 by exhibiting classes of low degree rational functions over f q 2 which induce bijections on the set of \( q 1 \) th roots of unity in f q 2 as a consequence , we prove two conjectures about permutation trinomials from a recent paper by tu , zeng , hu and li"
cs.LG,"visualizing high dimensional data by projecting them into two or three dimensional space is one of the most effective ways to intuitively understand the data 's underlying characteristics , for example their class neighborhood structure while data visualization in low dimensional space can be efficient for revealing the data 's underlying characteristics , classifying a new sample in the reduced dimensional space is not always beneficial because of the loss of information in expressing the data it is possible to classify the data in the high dimensional space , while visualizing them in the low dimensional space , but in this case , the visualization is often meaningless because it fails to illustrate the underlying characteristics that are crucial for the classification process in this paper , the performance preserving property of the previously proposed restricted radial basis function network in reducing the dimension of labeled data is explained here , it is argued through empirical experiments that the internal representation of the restricted radial basis function network , which during the supervised learning process organizes a visualizable two dimensional map , does not only preserve the topographical structure of high dimensional data but also captures their class neighborhood structures that are important for classifying them hence , unlike many of the existing dimension reduction methods , the restricted radial basis function network offers two dimensional visualization that is strongly correlated with the classification process"
cs.AI|stat.ML,"the adaboost algorithm was designed to combine many weak hypotheses that perform slightly better than random guessing into a strong hypothesis that has very low error we study the rate at which adaboost iteratively converges to the minimum of the exponential loss unlike previous work , our proofs do not require a weak learning assumption , nor do they require that minimizers of the exponential loss are finite our first result shows that at iteration t , the exponential loss of adaboost 's computed parameter vector will be at most epsilon more than that of any parameter vector of ell 1 norm bounded by b in a number of rounds that is at most a polynomial in b and 1 epsilon we also provide lower bounds showing that a polynomial dependence on these parameters is necessary our second result is that within c epsilon iterations , adaboost achieves a value of the exponential loss that is at most epsilon more than the best possible value , where c depends on the dataset we show that this dependence of the rate on epsilon is optimal up to constant factors , i e , at least omega \( 1 epsilon \) rounds are necessary to achieve within epsilon of the optimal exponential loss"
cs.DM,"a path in an edge colored graph is said to be a rainbow path if no two edges on the path have the same color an edge colored graph is \( strongly \) rainbow connected if there exists a \( geodesic \) rainbow path between every pair of vertices the \( strong \) rainbow connectivity of a graph g , denoted by \( src \( g \) , respectively \) rc \( g \) is the smallest number of colors required to edge color the graph such that the graph is \( strong \) rainbow connected it is known that for emph even k to decide whether the rainbow connectivity of a graph is at most k or not is np hard it was conjectured that for all k , to decide whether rc \( g \) leq k is np hard in this paper we prove this conjecture we also show that it is np hard to decide whether src \( g \) leq k or not even when g is a bipartite graph"
cs.IT,"in this article , we discuss the problem of establishing relations between information measures assessed for network structures two types of entropy based measures namely , the shannon entropy and its generalization , the r ' e nyi entropy have been considered for this study our main results involve establishing formal relationship , in the form of implicit inequalities , between these two kinds of measures when defined for graphs further , we also state and prove inequalities connecting the classical partition based graph entropies and the functional based entropy measures in addition , several explicit inequalities are derived for special classes of graphs"
cs.SI,"a person 's interests exist as an internal state and are difficult to define since only external actions are observable , a proxy must be used that represents someone 's interests techniques like collaborative filtering , behavioral targeting , and hashtag analysis implicitly model an individual 's interests i argue that these models are limited to shallow , temporary interests , which do not reflect people 's deeper interests or passions i propose an alternative model of interests that takes advantage of a user 's social graph the basic principle is that people only follow those that interest them , so the social graph is an effective and robust proxy for people 's interests"
cs.IT,"folding a sequence s into a multidimensional box is a well known method which is used as a multidimensional coding technique the operation of folding is generalized in a way that the sequence s can be folded into various shapes and not just a box the new definition of folding is based on a lattice tiling for the given shape cs and a direction in the d dimensional integer grid necessary and sufficient conditions that a lattice tiling for cs combined with a direction define a folding of a sequence into cs are derived the immediate and most impressive application is some new lower bounds on the number of dots in two dimensional synchronization patterns this can be also generalized for multidimensional synchronization patterns the technique and its application for two dimensional synchronization patterns , raise some interesting problems in discrete geometry we will also discuss these problems it is also shown how folding can be used to construct multidimensional error correcting codes finally , by using the new definition of folding , multidimensional pseudo random arrays with various shapes are generated"
cs.DS,"a vertex subset graph problem q defines which subsets of the vertices of an input graph are feasible solutions a reconfiguration variant of a vertex subset problem asks , given two feasible solutions s and t of size k , whether it is possible to transform s into t by a sequence of vertex additions and deletions such that each intermediate set is also a feasible solution of size bounded by k we study reconfiguration variants of two classical vertex subset problems , namely independent set and dominating set we denote the former by isr and the latter by dsr both isr and dsr are pspace complete on graphs of bounded bandwidth and w 1 hard parameterized by k on general graphs we show that isr is fixed parameter tractable parameterized by k when the input graph is of bounded degeneracy or nowhere dense as a corollary , we answer positively an open question concerning the parameterized complexity of the problem on graphs of bounded treewidth moreover , our techniques generalize recent results showing that isr is fixed parameter tractable on planar graphs and graphs of bounded degree for dsr , we show the problem fixed parameter tractable parameterized by k when the input graph does not contain large bicliques , a class of graphs which includes graphs of bounded degeneracy and nowhere dense graphs"
cs.AI,"making sense of incomplete and conflicting narrative knowledge in the presence of abnormalities , unobservable processes , and other real world considerations is a challenge and crucial requirement for cognitive robotics systems an added challenge , even when suitably specialised action languages and reasoning systems exist , is practical integration and application within large scale robot control frameworks in the backdrop of an autonomous wheelchair robot control task , we report on application driven work to realise postdiction triggered abnormality detection and re planning for real time robot control \( a \) narrative based knowledge about the environment is obtained via a larger smart environment framework and \( b \) abnormalities are postdicted from stable models of an answer set program corresponding to the robot 's epistemic model the overall reasoning is performed in the context of an approximate epistemic action theory based planner implemented via a translation to answer set programming"
cs.AI|cs.LG|stat.ML,this paper presents a framework for exact discovery of the most interesting sequential patterns it combines \( 1 \) a novel definition of the expected support for a sequential pattern a concept on which most interestingness measures directly rely with \( 2 \) skopus a new branch and bound algorithm for the exact discovery of top k sequential patterns under a given measure of interest we carry out experiments on both synthetic data with known patterns and real world datasets both experiments confirm the consistency and relevance of our approach
cs.IT,"in this work , we present a simplified successive cancellation list decoder that uses a chase like decoding process to achieve a six time improvement in speed compared to successive cancellation list decoding while maintaining the same error correction performance advantage over standard successive cancellation polar decoders we discuss the algorithm and detail the data structures and methods used to obtain this speed up we also propose an adaptive decoding algorithm that significantly improves the throughput while retaining the error correction performance simulation results over the additive white gaussian noise channel are provided and show that the proposed system is up to 16 times faster than an ldpc decoder of the same frame size , code rate , and similar error correction performance , making it more suitable for use as a software decoding solution"
cs.LG|cs.SI,"we present an asymptotically exact analysis of the problem of detecting communities in sparse random networks our results are also applicable to detection of functional modules , partitions , and colorings in noisy planted models using a cavity method analysis , we unveil a phase transition from a region where the original group assignment is undetectable to one where detection is possible in some cases , the detectable region splits into an algorithmically hard region and an easy one our approach naturally translates into a practical algorithm for detecting modules in sparse networks , and learning the parameters of the underlying model"
cs.DM|cs.DS,"the pi vertex deletion problem has as input an undirected graph g \( v , e \) and an integer k and asks whether there is a set of at most k vertices that can be deleted such that the resulting graph is a member of the graph class pi by a classic result of lewis and yannakakis j comput syst sci '80 , pi vertex deletion is np hard for all hereditary properties pi we adapt the original np hardness construction to show that under the exponential time hypothesis \( eth \) tight complexity results can be obtained we show that pi vertex deletion does not admit a 2 o \( n \) time algorithm where n is the number of vertices in g we also obtain a dichotomy for the number m of edges in the input graph if pi contains all independent sets , then there is no 2 o \( m \) time algorithm for pi vertex deletion if there is a fixed independent set that is not contained in pi , then pi vertex deletion can be solved in 2 o \( m \) time finally , we consider the special case that g is planar and obtain that pi vertex deletion cannot be solved in 2 o \( sqrt n \) time for all hereditary pi containing and excluding infinitely many planar graphs"
cs.IT,"generalized spatial modulation \( gsm \) uses n antenna elements but fewer radio frequency \( rf \) chains \( r \) at the transmitter spatial modulation and spatial multiplexing are special cases of gsm with r 1 and r n , respectively in gsm , apart from conveying information bits through r modulation symbols , information bits are also conveyed through the indices of the r active transmit antennas in this paper , we derive lower and upper bounds on the the capacity of a \( n , m , r \) gsm mimo system , where m is the number of receive antennas further , we propose a computationally efficient gsm encoding \( i e , bits to signal mapping \) method and a message passing based low complexity detection algorithm suited for large scale gsm mimo systems"
cs.LG|stat.ML,"bayesian sequence prediction is a simple technique for predicting future symbols sampled from an unknown measure on infinite sequences over a countable alphabet while strong bounds on the expected cumulative error are known , there are only limited results on the distribution of this error we prove tight high probability bounds on the cumulative error , which is measured in terms of the kullback leibler \( kl \) divergence we also consider the problem of constructing upper confidence bounds on the kl and hellinger errors similar to those constructed from hoeffding like bounds in the i i d case the new results are applied to show that bayesian sequence prediction can be used in the knows what it knows \( kwik \) framework with bounds that match the state of the art"
cs.IT,"in this paper , we analyze the performance of regularized channel inversion \( rci \) precoding in multiple input single output \( miso \) broadcast channels with confidential messages under transmit side channel correlation we derive a deterministic equivalent for the achievable per user secrecy rate which is almost surely exact as the number of transmit antennas and the number of users grow to infinity in a fixed ratio , and we determine the optimal regularization parameter that maximizes the secrecy rate furthermore , we obtain deterministic equivalents for the secrecy rates achievable by \( i \) zero forcing precoding and \( ii \) single user beamforming the accuracy of our analysis is validated by simulations of finite size systems"
cs.AI|cs.LG|stat.ML,"inverse optimal control , also known as inverse reinforcement learning , is the problem of recovering an unknown reward function in a markov decision process from expert demonstrations of the optimal policy we introduce a probabilistic inverse optimal control algorithm that scales gracefully with task dimensionality , and is suitable for large , continuous domains where even computing a full policy is impractical by using a local approximation of the reward function , our method can also drop the assumption that the demonstrations are globally optimal , requiring only local optimality this allows it to learn from examples that are unsuitable for prior methods"
cs.DS,"modern networks are large , highly complex and dynamic add to that the mobility of the agents comprising many of these networks it is difficult or even impossible for such systems to be managed centrally in an efficient manner it is imperative for such systems to attain a degree of self management self healing i e the capability of a system in a good state to recover to another good state in face of an attack , is desirable for such systems in this paper , we discuss the self healing model for dynamic reconfigurable systems in this model , an omniscient adversary inserts or deletes nodes from a network and the algorithm responds by adding a limited number of edges in order to maintain invariants of the network we look at some of the results in this model and argue for their applicability and further extensions of the results and the model we also look at some of the techniques we have used in our earlier work , in particular , we look at the idea of maintaining virtual graphs mapped over the existing network and assert that this may be a useful technique to use in many problem domains"
cs.IT,"we consider wireless powered amplify and forward and decode and forward relaying in cooperative communications , where an energy constrained relay node first harvests energy through the received radio frequency signal from the source and then uses the harvested energy to forward the source information to the destination node we propose time switching based energy harvesting \( eh \) and information transmission \( it \) protocols with two modes of eh at the relay for continuous time eh , the eh time can be any percentage of the total transmission block time for discrete time eh , the whole transmission block is either used for eh or it the proposed protocols are attractive because they do not require channel state information at the transmitter side and enable relay transmission with preset fixed transmission power we derive analytical expressions of the achievable throughput for the proposed protocols the derived expressions are verified by comparison with computer simulations and allow the system performance to be determined as a function of the system parameters finally , we show that the proposed protocols outperform the existing fixed time duration eh protocols in the literature , since they intelligently track the level of the harvested energy to switch between eh and it in an online fashion , allowing efficient use of resources"
cs.DM,"this work establishes the complexity class of several instances of the s packing coloring problem for a graph g , a positive integer k and a non decreasing list of integers s \( s 1 , , s k \) , g is s colorable , if its vertices can be partitioned into sets s i , i 1 , , k , where each s i being a s i packing \( a set of vertices at pairwise distance greater than s i \) for a list of three integers , a dichotomy between np complete problems and polynomial time solvable problems is determined for subcubic graphs moreover , for an unfixed size of list , the complexity of the s packing coloring problem is determined for several instances of the problem these properties are used in order to prove a dichotomy between np complete problems and polynomial time solvable problems for lists of at most four integers"
cs.AI|cs.LG|cs.SI,"social media channels such as twitter have emerged as popular platforms for crowds to respond to public events such as speeches , sports and debates while this promises tremendous opportunities to understand and make sense of the reception of an event from the social media , the promises come entwined with significant technical challenges in particular , given an event and an associated large scale collection of tweets , we need approaches to effectively align tweets and the parts of the event they refer to this in turn raises questions about how to segment the event into smaller yet meaningful parts , and how to figure out whether a tweet is a general one about the entire event or specific one aimed at a particular segment of the event in this work , we present et lda , an effective method for aligning an event and its tweets through joint statistical modeling of topical influences from the events and their associated tweets the model enables the automatic segmentation of the events and the characterization of tweets into two categories \( 1 \) episodic tweets that respond specifically to the content in the segments of the events , and \( 2 \) steady tweets that respond generally about the events we present an efficient inference method for this model , and a comprehensive evaluation of its effectiveness over existing methods in particular , through a user study , we demonstrate that users find the topics , the segments , the alignment , and the episodic tweets discovered by et lda to be of higher quality and more interesting as compared to the state of the art , with improvements in the range of 18 41"
cs.IT,"many analytic results for the connectivity , coverage , and capacity of wireless networks have been reported for the case where the number of nodes , n , tends to infinity \( large scale networks \) the majority of these results have not been extended for small or moderate values of n whereas in many practical networks , n is not very large in this paper , we consider finite \( small scale \) wireless sensor networks we first show that previous asymptotic results provide poor approximations for such networks we provide a set of differences between small scale and large scale analysis and propose a methodology for analysis of finite sensor networks furthermore , we consider two models for such networks unreliable sensor grids , and sensor networks with random node deployment we provide easily computable expressions for bounds on the coverage and connectivity of these networks with validation from simulations , we show that the derived analytic expressions give very good estimates of such quantities for finite sensor networks our investigation confirms the fact that small scale networks possesses unique characteristics different from the large scale counterparts , necessitating the development of a new framework for their analysis and design"
cs.IT,"it has been shown recently by geng et al that in a k user gaussian interference network , if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user \( all signal strengths measured in db scale \) , then power control and treating interference as noise \( tin \) is sufficient to achieve the entire generalized degrees of freedom \( gdof \) region motivated by the intuition that the deterministic model of avestimehr et al \( adt deterministic model \) is particularly suited for exploring the optimality of tin , the results of geng et al are first re visited under the adt deterministic model , and are shown to directly translate between the gaussian and deterministic settings next , we focus on the extension of these results to parallel interference networks , from a sum capacity sum gdof perspective to this end , we interpret the explicit characterization of the sum capacity sum gdof of a tin optimal network \( without parallel channels \) as a minimum weighted matching problem in combinatorial optimization , and obtain a simple characterization in terms of a partition of the interference network into vertex disjoint cycles aided by insights from the cyclic partition , the sum capacity optimality of tin for k user parallel interference networks is characterized for the adt deterministic model , leading ultimately to corresponding gdof results for the gaussian setting in both cases , subject to a mild invertibility condition the optimality of tin is shown to extend to parallel networks in a separable fashion"
cs.DM|cs.DS,"in order to formulate mathematical conjectures likely to be true , a number of base cases must be determined however , many combinatorial problems are np hard and the computational complexity makes this research approach difficult using a standard brute force approach on a typical computer one sample problem explored is that of finding a minimum identifying code to work around the computational issues , a variety of methods are explored and consist of a parallel computing approach using matlab , a quantum annealing approach using the d wave computer , and lastly using satisfiability modulo theory \( smt \) and corresponding smt solvers each of these methods requires the problem to be formulated in a unique manner in this paper , we address the challenges of computing solutions to this np hard problem with respect to each of these methods"
cs.LG,"this paper proposes an efficient technique for partitioning large biometric database during identification in this technique feature vector which comprises of global and local descriptors extracted from offline signature are used by fuzzy clustering technique to partition the database as biometric features posses no natural order of sorting , thus it is difficult to index them alphabetically or numerically hence , some supervised criteria is required to partition the search space at the time of identification the fuzziness criterion is introduced to find the nearest clusters for declaring the identity of query sample the system is tested using bin miss rate and performs better in comparison to traditional k means approach"
cs.SI,"ever since the proposal of first epidemic model , scientists have been attempting to estimate the growth of a disease contagion while in its premature stage despite being the focus of researchers for a long time , understanding epidemiology remains as error prone as a weather forecast , mainly because of the unavailability of large amount of data an epidemic spread is analogous to the diffusion of memes in social networking sites diffusion of memes can be easily studied provided large datasets and computational powers to extract information from online networks so , studying a meme spreading pattern can help us in understanding epidemiology in this paper , we analyse the impact of the topology of a social network , specifically its meso scale properties community structure and core periphery structure , on a meme traversing over it we propose a meme propagation model for synthetic scale free graphs which resemble real world graphs and observe the process of a meme going viral on such a network we also validate our model for real world information propagation"
cs.IT,"this paper investigates the capacity of a channel in which information is conveyed by the timing of consecutive packets passing through a queue with independent and identically distributed service times such timing channels are commonly studied under the assumption of a work conserving queue in contrast , this paper studies the case of a bufferless queue that drops arriving packets while a packet is in service under this bufferless model , the paper provides upper bounds on the capacity of timing channels and establishes achievable rates for the case of bufferless m m 1 and m g 1 queues in particular , it is shown that a bufferless m m 1 queue at worst suffers less than 10 reduction in capacity when compared to an m m 1 work conserving queue"
cs.IT,"in this work , the performance analysis of a dual hop relay transmission system composed of asymmetric radio frequency \( rf \) free space optical \( fso \) links with pointing errors is presented more specifically , we build on the system model presented in 1 to derive new exact closed form expressions for the cumulative distribution function , probability density function , moment generating function , and moments of the end to end signal to noise ratio in terms of the meijer 's g function we then capitalize on these results to offer new exact closed form expressions for the higher order amount of fading , average error rate for binary and m ary modulation schemes , and the ergodic capacity , all in terms of meijer 's g functions our new analytical results were also verified via computer based monte carlo simulation results"
cs.IT,"we consider the analysis and design of space time trellis codes \( sttcs \) for a cooperative relay channel operating in amplify and forward \( af \) mode assuming the source and destination nodes are equipped with multiple antennas but the relay node has single antenna we derive a pairwise error probability \( pep \) expression for the performance of sttcs in this type of channels a simple upper bound on pep is then derived and is maximized to find the optimum sttcs we show that the designed sttcs based on the derived criterion achieve full diversity in the af relay channels especially at high signal to noise ratios \( snrs \) the maximum achievable diversity in relay channels with single antenna relay is bounded by min \( m , n \) where m and n are respectively the number of antennas in source and destination nodes simulation results confirm that the proposed codes achieve the maximum diversity and also provide an appealing coding gain"
cs.DS,"in recent years the cache oblivious model of external memory computation has provided an attractive theoretical basis for the analysis of algorithms on massive datasets much progress has been made in discovering algorithms that are asymptotically optimal or near optimal however , to date there are still relatively few successful experimental studies in this paper we compare two different cache oblivious priority queues based on the funnel and bucket heap and apply them to the single source shortest path problem on graphs with positive edge weights our results show that when ram is limited and data is swapping to external storage , the cache oblivious priority queues achieve orders of magnitude speedups over standard internal memory techniques however , for the single source shortest path problem both on simulated and real world graph data , these speedups are markedly lower due to the time required to access the graph adjacency list itself"
cs.LG|stat.ML,"we present a novel approach for semi supervised domain adaptation that is based upon the probabilistic framework of gaussian processes \( gps \) specifically , we introduce domain specific gps as local experts the adaptation is facilitated in a probabilistic fashion by conditioning the available target domain data on multiple source experts the target expert accounts for the target classifier solely a single and confident classifier is then obtained by combining the predictions from multiple experts based on their predictive confidence learning of the model is efficient and requires no retraining reweighting of the source classifiers we evaluate the proposed approach on two publicly available datasets for multi class \( multipie \) and multi label \( disfa \) classification of expressive facial images to this end , we perform adaptation of two contextual factors where \( view \) and who \( subject \) we show in our experiments that the proposed approach consistently outperforms both source and target classifiers , while using as few as 30 target examples it also outperforms the state of the art approaches for semi supervised domain adaptation"
cs.IT,"the discovery of algebraic geometric codes constructed on curves led to generalizing this construction on higher dimensional varieties in this paper , we use a theorem of b poonen to show that the codes obtained from higher dimensional varieties can be realized as codes on curves one of the important consequences of this result is that the search for good codes on varieties that beat the existing bounds can be restricted to the case of curves"
cs.IT,"we propose a computationally efficient multilevel coding scheme to achieve the capacity of an isi channel using layers of binary inputs the transmitter employs multilevel coding with linear mapping the receiver uses multistage decoding where each stage performs a separate linear minimum mean square error \( lmmse \) equalization and decoding the optimality of the scheme is due to the fact that the lmmse equalizer is information lossless in an isi channel when signal to noise ratio is sufficiently low the computational complexity is low and scales linearly with the length of the channel impulse response and the number of layers the decoder at each layer sees an equivalent awgn channel , which makes coding straightforward"
cs.IT,"this paper proposes an adaptive morphological dilation image coding with context weights prediction the new dilation method is not to use fixed models , but to decide whether a coefficient needs to be dilated or not according to the coefficient 's predicted significance degree it includes two key dilation technologies 1 \) controlling dilation process with context weights to reduce the output of insignificant coefficients , and 2 \) using variable length group test coding with context weights to adjust the coding order and cost as few bits as possible to present the events with large probability moreover , we also propose a novel context weight strategy to predict coefficient 's significance degree more accurately , which serves for two dilation technologies experimental results show that our proposed method outperforms the state of the art image coding algorithms available today"
cs.IT,"in this paper , we revisit the forward , backward and bidirectional bahl cocke jelinek raviv \( bcjr \) soft input soft output \( siso \) maximum a posteriori probability \( map \) decoding process of rate 1 binary convolutional codes from this we establish some interesting explicit relationships between encoding and decoding of rate 1 convolutional codes we observe that the forward and backward bcjr siso map decoders can be simply represented by their dual siso channel encoders using shift registers in the complex number field similarly , the bidirectional map decoding can be implemented by linearly combining the shift register contents of the dual siso encoders of the respective forward and backward decoders the dual encoder structures for various recursive and non recursive rate 1 convolutional codes are derived"
cs.DM,"the famous braess paradox describes the following phenomenon it might happen that the improvement of resources , like building a new street within a congested network , may in fact lead to larger costs for the players in an equilibrium in this paper we consider general nonatomic congestion games and give a characterization of the maximal combinatorial property of strategy spaces for which braess paradox does not occur in a nutshell , bases of matroids are exactly this maximal structure we prove our characterization by two novel sensitivity results for convex separable optimization problems over polymatroid base polyhedra which may be of independent interest"
cs.AI,"here we describe the share system , a web service based framework for distributed querying and reasoning on the semantic web the main innovations of share are \( 1 \) the extension of a sparql query engine to perform on demand data retrieval from web services , and \( 2 \) the extension of an owl reasoner to test property restrictions by means of web service invocations in addition to enabling queries across distributed datasets , the system allows for a target dataset that is significantly larger than is possible under current , centralized approaches although the architecture is equally applicable to all types of data , the share system targets bioinformatics , due to the large number of interoperable web services that are already available in this area share is built entirely on semantic web standards , and is the successor of the biomoby project"
cs.AI,"classification is one of the major issues in data mining research fields the classification problems in medical area often classify medical dataset based on the result of medical diagnosis or description of medical treatment by the medical practitioner this research work discusses the classification process of gene expression data for three different cancers which are breast cancer , lung cancer and leukemia cancer with two classes which are cancerous stage and non cancerous stage we have applied a fuzzy soft set similarity based classifier to enhance the accuracy to predict the stages among cancer genes and the informative genes are selected by using entopy filtering"
cs.DS,"we study combinatorial auctions for the secondary spectrum market in this market , short term licenses shall be given to wireless nodes for communication in their local neighborhood in contrast to the primary market , channels can be assigned to multiple bidders , provided that the corresponding devices are well separated such that the interference is sufficiently low interference conflicts are described in terms of a conflict graph in which the nodes represent the bidders and the edges represent conflicts such that the feasible allocations for a channel correspond to the independent sets in the conflict graph in this paper , we suggest a novel lp formulation for combinatorial auctions with conflict graph using a non standard graph parameter , the so called inductive independence number taking into account this parameter enables us to bypass the well known lower bound of omega \( n 1 epsilon \) on the approximability of independent set in general graphs with n nodes \( bidders \) we achieve significantly better approximation results by showing that interference constraints for wireless networks yield conflict graphs with bounded inductive independence number our framework covers various established models of wireless communication , e g , the protocol or the physical model for the protocol model , we achieve an o \( sqrt k \) approximation , where k is the number of available channels for the more realistic physical model , we achieve an o \( sqrt k log 2 n \) approximation based on edge weighted conflict graphs combining our approach with the the lp based framework of lavi and swamy , we obtain incentive compatible mechanisms for general bidders with arbitrary valuations on bundles of channels specified in terms of demand oracles"
cs.IT,"we investigate channel equalization for rayleigh fading channels under bounded channel uncertainties we analyze three robust methods to estimate an unknown signal transmitted through a rayleigh fading channel , where we avoid directly tuning the equalizer parameters to the available inaccurate channel information these methods are based on minimizing certain mean square error criteria that incorporate the channel uncertainties into the problem formulations we present closed form solutions to the channel equalization problems for each method and for both zero mean and nonzero mean signals we illustrate the performances of the equalization methods through simulations"
cs.IT,"in this part of the two part paper , the claim in part i , that superimposed pilots are superior at mitigating pilot contamination in massive multiple input multiple output \( mimo \) systems , is bolstered through additional performance metrics such as mean squared error \( mse \) and downlink signal to interference plus noise ratio \( sinr \) for a system employing superimposed pilots , it is shown that the mse of the channel estimate diminishes with increasing number of antennas at the base station \( bs \) , with the mse achieving its cramer rao lower bound asymptotically the downlink \( dl \) sinr is also shown to increase without bound at a rate proportional to the square root of the number of antennas at the bs furthermore , we extend the concept of the hybrid system and propose a framework for partitioning users into those that employ superimposed pilots and to those that employ time multiplexed pilots and data the superiority in the dl sinr performance of the channel estimator based on superimposed pilots and the higher throughput of the hybrid system are demonstrated by means of simulations"
cs.LG|stat.ML,"low rank tensor learning , such as tensor completion and multilinear multitask learning , has received much attention in recent years in this paper , we propose higher order matching pursuit for low rank tensor learning problems with a convex or a nonconvex cost function , which is a generalization of the matching pursuit type methods at each iteration , the main cost of the proposed methods is only to compute a rank one tensor , which can be done efficiently , making the proposed methods scalable to large scale problems moreover , storing the resulting rank one tensors is of low storage requirement , which can help to break the curse of dimensionality the linear convergence rate of the proposed methods is established in various circumstances along with the main methods , we also provide a method of low computational complexity for approximately computing the rank one tensors , with provable approximation ratio , which helps to improve the efficiency of the main methods and to analyze the convergence rate experimental results on synthetic as well as real datasets verify the efficiency and effectiveness of the proposed methods"
cs.IT,"before establishing a communication link in a cellular network , the user terminal must activate a synchronization procedure called initial cell search in order to acquire specific information about the serving base station to accomplish this task , the primary synchronization signal \( pss \) and secondary synchronization signal \( sss \) are periodically transmitted in the downlink of a long term evolution \( lte \) network since sss detection can be performed only after successful identification of the primary signal , in this work we present a robust scheme for joint pss detection , sector index identification and integer frequency offset \( ifo \) recovery in an lte system the proposed algorithm relies on the maximum likelihood \( ml \) estimation criterion and exploits a suitable reduced rank representation of the channel frequency response to take multipath distortions into account we show that some pss detection methods that were originally introduced through heuristic reasoning can be derived from our ml framework by selecting an appropriate model for the channel gains over the pss subcarriers numerical simulations indicate that the proposed scheme can be effectively applied in the presence of severe multipath propagation , where existing alternatives provide unsatisfactory performance"
cs.IT,"in this paper , we derive optimal transmission policies for energy harvesting sensors to maximize the utility obtained over a finite horizon first , we consider a single energy harvesting sensor , with discrete energy arrival process , and a discrete energy consumption policy under this model , we show that the optimal finite horizon policy is a threshold policy , and explicitly characterize the thresholds , and the thresholds can be precomputed using a recursion next , we address the case of multiple sensors , with only one of them allowed to transmit at any given time to avoid interference , and derive an explicit optimal policy for this scenario as well"
cs.DS|cs.LG,"we investigate the approximability of several classes of real valued functions by functions of a small number of variables \( em juntas \) our main results are tight bounds on the number of variables required to approximate a function f 0 , 1 n rightarrow 0 , 1 within ell 2 error epsilon over the uniform distribution 1 if f is submodular , then it is epsilon close to a function of o \( frac 1 epsilon 2 log frac 1 epsilon \) variables this is an exponential improvement over previously known results we note that omega \( frac 1 epsilon 2 \) variables are necessary even for linear functions 2 if f is fractionally subadditive \( xos \) it is epsilon close to a function of 2 o \( 1 epsilon 2 \) variables this result holds for all functions with low total ell 1 influence and is a real valued analogue of friedgut 's theorem for boolean functions we show that 2 omega \( 1 epsilon \) variables are necessary even for xos functions as applications of these results , we provide learning algorithms over the uniform distribution for xos functions , we give a pac learning algorithm that runs in time 2 poly \( 1 epsilon \) poly \( n \) for submodular functions we give an algorithm in the more demanding pmac learning model \( balcan and harvey , 2011 \) which requires a multiplicative 1 gamma factor approximation with probability at least 1 epsilon over the target distribution our uniform distribution algorithm runs in time 2 poly \( 1 \( gamma epsilon \) \) poly \( n \) this is the first algorithm in the pmac model that over the uniform distribution can achieve a constant approximation factor arbitrarily close to 1 for all submodular functions as follows from the lower bounds in \( feldman et al , 2013 \) both of these algorithms are close to optimal we also give applications for proper learning , testing and agnostic learning with value queries of these classes"
cs.IT,"in contrast to a maximum likelihood decoder , it is often desirable to use an incomplete decoder that can detect its decoding errors with high probability one common choice is the bounded distance decoder bounds are derived for the total word error rate , pw , and the undetected error rate , pu excellent agreement is found with simulation results for a small code , and the bounds are shown to be tractable for a larger code"
cs.IT,"this paper considers a two hop interference network , where two users transmit independent messages to their respective receivers with the help of two relay nodes the transmitters do not have direct links to the receivers instead , two relay nodes serve as intermediaries between the transmitters and receivers each hop , one from the transmitters to the relays and the other from the relays to the receivers , is modeled as a gaussian interference channel , thus the network is essentially a cascade of two interference channels for this network , achievable symmetric rates for different parameter regimes under decode and forward relaying and amplify and forward relaying are proposed and the corresponding coding schemes are carefully studied numerical results are also provided"
cs.IT,"a new variant of bit interleaved coded modulation \( bicm \) is proposed in the new scheme , called parallel bicm , l identical binary codes are used in parallel using a mapper , a newly proposed finite length interleaver and a binary dither signal as opposed to previous approaches , the scheme does not rely on any assumptions of an ideal , infinite length interleaver over a memoryless channel , the new scheme is proven to be equivalent to a binary memoryless channel therefore the scheme enables one to easily design coded modulation schemes using a simple binary code that was designed for that binary channel the overall performance of the coded modulation scheme is analytically evaluated based on the performance of the binary code over the binary channel the new scheme is analyzed from an information theoretic viewpoint , where the capacity , error exponent and channel dispersion are considered the capacity of the scheme is identical to the bicm capacity the error exponent of the scheme is numerically compared to a recently proposed mismatched decoding exponent analysis of bicm"
cs.IT,"the bandwidth of the sampling systems , especially for time interleaved analog to digital converters , needs to be extended along with the rapid increase of the sampling rate a digitally assisted technique becomes a feasible approach to extend the analog bandwidth , as it is impractical to implement the extension in analog circuits this paper derives accurate order estimation formulas for the bandwidth extension filter , which is designed in the minimax sense with the ripple constraints as the design criteria the derived filter order estimation is significant in evaluating the computational complexity from the viewpoint of the top level system design moreover , with the proposed order estimates , one can conveniently obtain the minimal order that satisfies the given ripple constraints , which contributes to reducing the design time both the performance of the extension filter and its order estimation are illustrated and demonstrated through simulation examples"
cs.IT,"this paper presents a tutorial on stochastic geometry \( sg \) based analysis for cellular networks this tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks the paper starts by modeling and analyzing the baseband interference in a basic cellular network model then , it characterizes signal to interference plus noise ratio \( sinr \) and its related performance metrics in particular , a unified approach to conduct error probability , outage probability , and rate analysis is presented although the main focus of the paper is on cellular networks , the presented unified approach applies for other types of wireless networks that impose interference protection around receivers the paper then extends the baseline unified approach to capture cellular network characteristics \( e g , frequency reuse , multiple antenna , power control , etc \) it also presents numerical examples associated with demonstrations and discussions finally , we point out future research directions"
cs.LG|stat.ML,"methods for automated discovery of causal relationships from non interventional data have received much attention recently a widely used and well understood model family is given by linear acyclic causal models \( recursive structural equation models \) for gaussian data both constraint based methods \( spirtes et al , 1993 pearl , 2000 \) \( which output a single equivalence class \) and bayesian score based methods \( geiger and heckerman , 1994 \) \( which assign relative scores to the equivalence classes \) are available on the contrary , all current methods able to utilize non gaussianity in the data \( shimizu et al , 2006 hoyer et al , 2008 \) always return only a single graph or a single equivalence class , and so are fundamentally unable to express the degree of certainty attached to that output in this paper we develop a bayesian score based approach able to take advantage of non gaussianity when estimating linear acyclic causal models , and we empirically demonstrate that , at least on very modest size networks , its accuracy is as good as or better than existing methods we provide a complete code package \( in r \) which implements all algorithms and performs all of the analysis provided in the paper , and hope that this will further the application of these methods to solving causal inference problems"
cs.IT,"information on the future state of time varying frequency selective channels can significantly enhance the effectiveness of feedback in adaptive and limited feedback mimo ofdm systems this paper investigates the parametric extrapolation of wideband mimo channels using variations of the double directional mimo model we propose three predictors which estimate parameters of the channel using 4d , 3d and 2d extensions of the esprit algorithm and predict future states of the channel using the models furthermore , using the vector formulation of the cramer rao lower bound for functions of parameters , we derive a bound on the prediction error in wideband mimo channels numerical simulations are used to evaluate the performance of the proposed algorithms under different channel and transmission conditions , and a comparison is made with the derived error bound"
cs.LG,"we propose a new riemannian geometry for fixed rank matrices that is specifically tailored to the low rank matrix completion problem exploiting the degree of freedom of a quotient space , we tune the metric on our search space to the particular least square cost function at one level , it illustrates in a novel way how to exploit the versatile framework of optimization on quotient manifold at another level , our algorithm can be considered as an improved version of lmafit , the state of the art gauss seidel algorithm we develop necessary tools needed to perform both first order and second order optimization in particular , we propose gradient descent schemes \( steepest descent and conjugate gradient \) and trust region algorithms we also show that , thanks to the simplicity of the cost function , it is numerically cheap to perform an exact linesearch given a search direction , which makes our algorithms competitive with the state of the art on standard low rank matrix completion instances"
cs.IT,"radio frequency \( rf \) impairments in the transceiver hardware of communication systems \( e g , phase noise \( pn \) , high power amplifier \( hpa \) nonlinearities , or in phase quadrature phase \( i q \) imbalance \) can severely degrade the performance of traditional multiple input multiple output \( mimo \) systems although calibration algorithms can partially compensate these impairments , the remaining distortion still has substantial impact despite this , most prior works have not analyzed this type of distortion in this paper , we investigate the impact of residual transceiver hardware impairments on the mimo system performance in particular , we consider a transceiver impairment model , which has been experimentally validated , and derive analytical ergodic capacity expressions for both exact and high signal to noise ratios \( snrs \) we demonstrate that the capacity saturates in the high snr regime , thereby creating a finite capacity ceiling we also present a linear approximation for the ergodic capacity in the low snr regime , and show that impairments have only a second order impact on the capacity furthermore , we analyze the effect of transceiver impairments on large scale mimo systems interestingly , we prove that if one increases the number of antennas at one side only , the capacity behaves similar to the finite dimensional case on the contrary , if the number of antennas on both sides increases with a fixed ratio , the capacity ceiling vanishes thus , impairments cause only a bounded offset in the capacity compared to the ideal transceiver hardware case"
cs.AI,"searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases recommendation systems help users find items of interest of a particular type , such as movies or restaurants , but are still somewhat awkward to use our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems , creating personalized aides we present a system the adaptive place advisor that treats item selection as an interactive , conversational process , with the program inquiring about item attributes and the user responding individual , long term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user we present a novel user model that influences both item search and the questions asked during a conversation we demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item , as compared to a control group of users interacting with a non adaptive version of the system"
cs.IT,"this letter is on the performance of the turbo signal recovery \( tsr \) algorithm for partial discrete fourier transform \( dft \) matrices based compressed sensing based on state evolution analysis , we prove that tsr with a partial dft sensing matrix outperforms the well known approximate message passing \( amp \) algorithm with an independent identically distributed \( iid \) sensing matrix"
cs.LG|stat.ML,"we describe a unified and coherent syntactic framework for supporting a semantically informed syntactic approach to statistical machine translation semantically enriched syntactic tags assigned to the target language training texts improved translation quality the resulting system significantly outperformed a linguistically naive baseline model \( hiero \) , and reached the highest scores yet reported on the nist 2009 urdu english translation task this finding supports the hypothesis \( posed by many researchers in the mt community , e g , in darpa gale \) that both syntactic and semantic information are critical for improving translation quality and further demonstrates that large gains can be achieved for low resource languages with different word order than english"
cs.AI,an updated version will be uploaded later
cs.SI,"the advent of social media has provided an extraordinary , if imperfect , 'big data' window into the form and evolution of social networks based on nearly 40 million message pairs posted to twitter between september 2008 and february 2009 , we construct and examine the revealed social network structure and dynamics over the time scales of days , weeks , and months at the level of user behavior , we employ our recently developed hedonometric analysis methods to investigate patterns of sentiment expression we find users' average happiness scores to be positively and significantly correlated with those of users one , two , and three links away we strengthen our analysis by proposing and using a null model to test the effect of network topology on the assortativity of happiness we also find evidence that more well connected users write happier status updates , with a transition occurring around dunbar 's number more generally , our work provides evidence of a social sub network structure within twitter and raises several methodological points of interest with regard to social network reconstructions"
cs.DM,"let b be a finite collection of geometric \( not necessarily convex \) bodies in the plane clearly , this class of geometric objects naturally generalizes the class of disks , lines , ellipsoids , and even convex polygons we consider geometric intersection graphs gb where each body of the collection b is represented by a vertex , and two vertices of gb are adjacent if the intersection of the corresponding bodies is non empty for such graph classes and under natural restrictions on their maximum degree or subgraph exclusion , we prove that the relation between their treewidth and the maximum size of a grid minor is linear these combinatorial results vastly extend the applicability of all the meta algorithmic results of the bidimensionality theory to geometrically defined graph classes"
cs.IT,"consider a two way communication scenario where two single antenna nodes , operating under full duplex mode , exchange information to one another through the aid of a \( full duplex \) multi antenna relay , and there is another single antenna node who intends to eavesdrop the relay employs artificial noise \( an \) to interfere the eavesdropper 's channel , and amplify forward \( af \) alamouti based rank two beamforming to establish the two way communication links of the legitimate nodes our problem is to optimize the rank two beamformer and an covariance for sum secrecy rate maximization \( ssrm \) this ssrm problem is nonconvex , and we develop an efficient solution approach using semidefinite relaxation \( sdr \) and minorization maximization \( mm \) we prove that sdr is tight for the ssrm problem and thus introduces no loss also , we consider an inexact mm method where an approximately but computationally cheap mm solution update is used in place of the exact update in conventional mm we show that this inexact mm method guarantees convergence to a stationary solution to the ssrm problem the effectiveness of our proposed approach is further demonstrated by an energy harvesting scenario extension , and by extensive simulation results"
cs.SI,"a patient centric approach to healthcare leads to an informal social network among medical professionals this chapter presents a research framework to identify the collaboration structure among physicians that is effective and efficient for patients , discover effective structural attributes of a collaboration network that evolves during the course of providing care , and explore the impact of socio demographic characteristics of healthcare professionals , patients , and hospitals on collaboration structures , from the point of view of measurable outcomes such as cost and quality of care the framework uses illustrative examples drawn from a data set of patients undergoing hip replacement surgery"
cs.LG|stat.ML,"we study an equivalence of \( i \) deterministic pathwise statements appearing in the online learning literature \( termed emph regret bounds \) , \( ii \) high probability tail bounds for the supremum of a collection of martingales \( of a specific form arising from uniform laws of large numbers for martingales \) , and \( iii \) in expectation bounds for the supremum by virtue of the equivalence , we prove exponential tail bounds for norms of banach space valued martingales via deterministic regret bounds for the online mirror descent algorithm with an adaptive step size we extend these results beyond the linear structure of the banach space we define a notion of emph martingale type for general classes of real valued functions and show its equivalence \( up to a logarithmic factor \) to various sequential complexities of the class \( in particular , the sequential rademacher complexity and its offset version \) for classes with the general martingale type 2 , we exhibit a finer notion of variation that allows partial adaptation to the function indexing the martingale our proof technique rests on sequential symmetrization and on certifying the emph existence of regret minimization strategies for certain online prediction problems"
cs.AI,"causal models defined in terms of a collection of equations , as defined by pearl , are axiomatized here axiomatizations are provided for three successively more general classes of causal models \( 1 \) the class of recursive theories \( those without feedback \) , \( 2 \) the class of theories where the solutions to the equations are unique , \( 3 \) arbitrary theories \( where the equations may not have solutions and , if they do , they are not necessarily unique \) it is shown that to reason about causality in the most general third class , we must extend the language used by galles and pearl in addition , the complexity of the decision procedures is characterized for all the languages and classes of models considered"
cs.IT,"to decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks \( c rans \) , a superimposed segment training design is proposed the core idea of the proposal is that each mobile station superimposes a periodic training sequence on the data signal , and each remote radio heads prepends a separate pilot to the received signal before forwarding it to the centralized base band unit pool moreover , a complex exponential basis expansion model based channel estimation algorithm to maximize a posteriori probability is developed , where the basis expansion model coefficients of access links \( als \) and the channel fading of wireless backhaul links are first obtained , after which the time domain channel samples of als are restored in terms of maximizing the average effective signal to noise ratio \( aesnr \) simulation results show that the proposed channel estimation algorithm can effectively decrease the estimation mean square error and increase the aesnr in c rans , thus significantly outperforming the existing solutions"
cs.IT,"we consider a set up where a file of size m is stored in n distributed storage nodes , using an \( n , k \) minimum storage regenerating \( msr \) code , i e , a maximum distance separable \( mds \) code that also allows efficient exact repair of any failed node the problem of interest in this paper is to minimize the repair bandwidth b for exact regeneration of a single failed node , i e , the minimum data to be downloaded by a new node to replace the failed node by its exact replica previous work has shown that a bandwidth of b m \( n 1 \) k \( n k \) is necessary and sufficient for functional \( not exact \) regeneration it has also been shown that if k max \( n 2 , 3 \) , then there is no extra cost of exact regeneration over functional regeneration the practically relevant setting of low redundancy , i e , k n 1 2 remains open for k 3 and it has been shown that there is an extra bandwidth cost for exact repair over functional repair in this case in this work , we adopt into the distributed storage context an asymptotically optimal interference alignment scheme previously proposed by cadambe and jafar for large wireless interference networks with this scheme we solve the problem of repair bandwidth minimization for \( n , k \) exact msr codes for all \( n , k \) values including the previously open case of k max \( n 2 , 3 \) our main result is that , for any \( n , k \) , and sufficiently large file sizes , there is no extra cost of exact regeneration over functional regeneration in terms of the repair bandwidth per bit of regenerated data more precisely , we show that in the limit as m approaches infinity , the ratio b m \( n 1 \) \( k \( n k \) \)"
cs.IT,"the binary primitive triple error correcting bch code is a cyclic code of minimum distance 7 with generator polynomial having zeros alpha , alpha 3 and alpha 5 where alpha is a primitive root of unity the zero set of the code is said to be 1 , 3 , 5 in the 1970 's kasami showed that one can construct similar triple error correcting codes using zero sets consisting of different triples than the bch codes furthermore , in 2000 chang et al found new triples leading to triple error correcting codes in this paper a new such triple is presented in addition a new method is presented that may be of interest in finding further such triples"
cs.DM|cs.IT,"we present kovalenko 's full rank limit as a tight lower bound for decoding error probability of ldpc codes and lt codes over bec from the limit , we derive a full rank overhead as a lower bound for stable overheads for successful maximum likelihood decoding of the codes"
cs.DS,"the paging problem is that of deciding which pages to keep in a memory of k pages in order to minimize the number of page faults this paper introduces the marking algorithm , a simple randomized on line algorithm for the paging problem , and gives a proof that its performance guarantee \( competitive ratio \) is o \( log k \) in contrast , no deterministic on line algorithm can have a performance guarantee better than k"
cs.AI,"the uniform one dimensional fragment of first order logic , u1 , is a recently introduced formalism that extends two variable logic in a natural way to contexts with relations of all arities we survey properties of u1 and investigate its relationship to description logics designed to accommodate higher arity relations , with particular attention given to dlr reg we also define a description logic version of a variant of u1 and prove a range of new results concerning the expressivity of u1 and related logics"
cs.LG,"this note provides a family of classification problems , indexed by a positive integer k , where all shallow networks with fewer than exponentially \( in k \) many nodes exhibit error at least 1 3 , whereas a deep network with 2 nodes in each of 2k layers achieves zero error , as does a recurrent network with 3 distinct nodes iterated k times the proof is elementary , and the networks are standard feedforward networks with relu \( rectified linear unit \) nonlinearities"
cs.LG|stat.ML,"we propose a novel general algorithm lhac that efficiently uses second order information to train a class of large scale l1 regularized problems our method executes cheap iterations while achieving fast local convergence rate by exploiting the special structure of a low rank matrix , constructed via quasi newton approximation of the hessian of the smooth loss function a greedy active set strategy , based on the largest violations in the dual constraints , is employed to maintain a working set that iteratively estimates the complement of the optimal active set this allows for smaller size of subproblems and eventually identifies the optimal active set empirical comparisons confirm that lhac is highly competitive with several recently proposed state of the art specialized solvers for sparse logistic regression and sparse inverse covariance matrix selection"
cs.DM|cs.DS,"we study the parameterized complexity of the connected version of the vertex cover problem , where the solution set has to induce a connected subgraph although this problem does not admit a polynomial kernel for general graphs \( unless np is a subset of conp poly \) , for planar graphs guo and niedermeier icalp'08 showed a kernel with at most 14k vertices , subsequently improved by wang et al mfcs'11 to 4k the constant 4 here is so small that a natural question arises could it be already an optimal value for this problem \? in this paper we answer this quesion in negative we show a \( 11 3 \) k vertex kernel for connected vertex cover in planar graphs we believe that this result will motivate further study in search for an optimal kernel"
cs.IT,"in this letter , we analyze power and rate adaptation in a point to point link with rayleigh fading and impulsive interference we model the impulsive interference as a bernoulli gaussian random process adaptation is used to maximize the average spectral efficiency by changing power and rate of the transmission subject to an average power and instantaneous probability of error constraints without impulsive interference , it is well known that water filling is optimal for block fading we provide two simple schemes that show that the conventional water filling algorithm is not optimal in an impulsive interference channel"
cs.LG,"we propose a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways specifically , we model document collections using a class of graphical models called hierarchical latent tree models \( hltms \) the variables at the bottom level of an hltm are observed binary variables that represent the presence absence of words in a document the variables at other levels are discrete latent variables , with those at the second level representing word co occurrence patterns and those at higher levels representing co occurrence of patterns at the level below each latent variable gives a soft partition of the documents , and document clusters in the partitions are interpreted as topics latent variables at high levels of the hierarchy capture long range word co occurrence patterns and hence give thematically more general topics , while those at low levels of the hierarchy capture short range word co occurrence patterns and give thematically more specific topics compared with lda based topic models , a key advantage of hltms is that they , as graphical models , explicitly model the dependence and independence structure among topics and words , which is conducive to the discovery of meaningful topics and topic hierarchies"
cs.LG|stat.ML,"a popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary while this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing , there have only been a few theoretical arguments supporting these evidences in particular , sparse coding , or sparse dictionary learning , relies on a non convex procedure whose local minima have not been fully analyzed yet in this paper , we consider a probabilistic model of sparse signals , and show that , with high probability , sparse coding admits a local minimum around the reference dictionary generating the signals our study takes into account the case of over complete dictionaries , noisy signals , and possible outliers , thus extending previous work limited to noiseless settings and or under complete dictionaries the analysis we conduct is non asymptotic and makes it possible to understand how the key quantities of the problem , such as the coherence or the level of noise , can scale with respect to the dimension of the signals , the number of atoms , the sparsity and the number of observations"
cs.IT,"in this paper , we investigate the spectral efficiency \( se \) of massive multiple input multiple output \( mimo \) systems with a large number of antennas at the base station \( bs \) accounting for physical space constraints in contrast to the vast body of related literature , which considers fixed inter element spacing , we elaborate on a practical topology in which an increase in the number of antennas in a fixed total space induces an inversely proportional decrease in the inter antenna distance for this scenario , we derive exact and approximate expressions , as well as simplified upper lower bounds , for the se of maximum ratio combining \( mrc \) , zero forcing \( zf \) and minimum mean squared error receivers \( mmse \) receivers in particular , our analysis shows that the mrc receiver is non optimal for space constrained massive mimo topologies on the other hand , zf and mmse receivers can still deliver an increasing se as the number of bs antennas grows large numerical results corroborate our analysis and show the effect of the number of antennas , the number of users , and the total antenna array space on the sum se performance"
cs.SI,"despite large scale availability of social data , our understanding of the basic laws governing human behaviour remains limited , owing to the lack of a proper framework which can capture the interplay of various interdependent factors affecting social interactions in the recent years , multilayer networks has increasingly been realized to provide an efficient framework for understanding the intricacies of complex real world systems the present study encompasses the multilayer network analysis of bollywood , the largest film industry of the world , comprising of a massive time varying social data making around 1500 films annually , bollywood has emerged as a globally recognized and appreciated platform for cultural exchange this film industry acts as a mirror of the society and the rapidly changing nature of the society is reflected in the depictions of films this renders this model system to provide a ripe platform to understand social behaviour by analyzing the patterns of evolution and the success of individuals in the society similarity in the degree distribution across the individual layers validates our basis of multilayering while the degree degree correlations of the whole networks reveal that in general nodes do not exhibit any particular preference for pairing up , multilayer framework indicates the gradual restoration of cooperation in the recent dataset working in more number of genres comes up as an intrinsic property of lead nodes further , versatility in pairs and triads has been shown to demonstrate its impact on the success of lead nodes while repeated cooperation in pairs of dissimilar types of nodes has been shown to yield success to the lead nodes , triads of similar types of nodes turn out to be more successful weak ties analysis emphasize on the importance of every type of node in the society"
cs.LG|stat.ML,"topic modeling based on latent dirichlet allocation \( lda \) has been a framework of choice to perform scene recognition and annotation recently , a new type of topic model called the document neural autoregressive distribution estimator \( docnade \) was proposed and demonstrated state of the art performance for document modeling in this work , we show how to successfully apply and extend this model to the context of visual scene modeling specifically , we propose supdocnade , a supervised extension of docnade , that increases the discriminative power of the hidden topic features by incorporating label information into the training objective of the model we also describe how to leverage information about the spatial position of the visual words and how to embed additional image annotations , so as to simultaneously perform image classification and annotation we test our model on the scene15 , labelme and uiuc sports datasets and show that it compares favorably to other topic models such as the supervised variant of lda"
cs.LG,"research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy we believe that there are other factors that should also play a role in the evaluation of bias one such factor is the stability of the algorithm in other words , the repeatability of the results if we obtain two sets of data from the same phenomenon , with the same underlying probability distribution , then we would like our learning algorithm to induce approximately the same concepts from both sets of data this paper introduces a method for quantifying stability , based on a measure of the agreement between concepts we also discuss the relationships among stability , predictive accuracy , and bias"
cs.AI,"correct inference of genetic regulations inside a cell is one of the greatest challenges in post genomic era for the biologist and researchers several intelligent techniques and models were already proposed to identify the regulatory relations among genes from the biological database like time series microarray data recurrent neural network \( rnn \) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes in this paper , bat algorithm \( ba \) was applied to optimize the model parameters of rnn model of gene regulatory network \( grn \) initially the proposed method is tested against small artificial network without any noise and the efficiency was observed in term of number of iteration , number of population and ba optimization parameters the model was also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset in the next phase of this research , ba based rnn is applied to real world benchmark time series microarray dataset of e coli the results shown that it can able to identify the maximum true positive regulation but also include some false positive regulations therefore , ba is very suitable for identifying biological plausible grn with the help rnn model"
cs.IT,"this paper considers downlink multicast transmit beamforming for secure layered transmission systems with wireless simultaneous information and power transfer we study the power allocation algorithm design for minimizing the total transmit power in the presence of passive eavesdroppers and energy harvesting receivers the algorithm design is formulated as a non convex optimization problem our problem formulation promotes the dual use of energy signals in providing secure communication and facilitating efficient energy transfer besides , we take into account a minimum required power for energy harvesting at the idle receivers and heterogeneous quality of service \( qos \) requirements for the multicast video receivers in light of the intractability of the problem , we reformulate the considered problem by replacing a non convex probabilistic constraint with a convex deterministic constraint then , a semidefinite programming relaxation \( sdr \) approach is adopted to obtain an upper solution for the reformulated problem subsequently , sufficient conditions for the global optimal solution of the reformulated problem are revealed furthermore , we propose two suboptimal power allocation schemes based on the upper bound solution simulation results demonstrate the excellent performance and significant transmit power savings achieved by the proposed schemes compared to isotropic energy signal generation"
cs.IT,"we consider a simple information theoretic model of communication , in which two species of bacteria have the option of exchanging information about their environment , thereby improving their chances of survival for this purpose , we model a system consisting of two species whose dynamics in the world are modelled by a bet hedging strategy it is well known that such models lend themselves to elegant information theoretical interpretations by relating their respective long term growth rate to the information the individual species has about its environment we are specifically interested in modelling how this dynamics are affected when the species interact cooperatively or in an antagonistic way in a scenario with limited resources for this purpose , we consider the exchange of environmental information between the two species in the framework of a game our results show that a transition from a cooperative to an antagonistic behaviour in a species results as a response to a change in the availability of resources species cooperate in abundance of resources , while they behave antagonistically in scarcity"
cs.AI|cs.DS,"we observe that certain large clique graph triangulations can be useful to reduce both computational and space requirements when making queries on mixed stochastic deterministic graphical models we demonstrate that many of these large clique triangulations are non minimal and are thus unattainable via the variable elimination algorithm we introduce ancestral pairs as the basis for novel triangulation heuristics and prove that no more than the addition of edges between ancestral pairs need be considered when searching for state space optimal triangulations in such graphs empirical results on random and real world graphs show that the resulting triangulations that yield significant speedups are almost always non minimal we also give an algorithm and correctness proof for determining if a triangulation can be obtained via elimination , and we show that the decision problem associated with finding optimal state space triangulations in this mixed stochastic deterministic setting is np complete"
cs.LG,"we report the results of the first experiments with learning proof dependencies from the formalizations done with the coq system we explain the process of obtaining the dependencies from the coq proofs , the characterization of formulas that is used for the learning , and the evaluation method various machine learning methods are compared on a dataset of 5021 toplevel coq proofs coming from the corn repository the best resulting method covers on average 75 of the needed proof dependencies among the first 100 predictions , which is a comparable performance of such initial experiments on other large theory corpora"
cs.AI,"we show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment , log2 \( n \) 1 experiments are sufficient and in the worst case necessary to determine the causal relations among n 2 variables when no latent variables , no sample selection bias and no feedback cycles are present for all k , 0 k 1 \( 2n \) we provide an upper bound on the number experiments required to determine causal structure when each experiment simultaneously randomizes k variables for large n , these bounds are significantly lower than the n 1 bound required when each experiment randomizes at most one variable for kmax n 2 , we show that \( n kmax 1 \) n \( 2kmax \) log2 \( kmax \) experiments aresufficient and in the worst case necessary we over a conjecture as to the minimal number of experiments that are in the worst case sufficient to identify all causal relations among n observed variables that are a subset of the vertices of a dag"
cs.SI,"in social network analysis , there is a common perception that influence is relevant to determine the global behavior of the society and thus it can be used to enforce cooperation by targeting an adequate initial set of individuals or to analyze global choice processes here we propose centrality measures that can be used to analyze the relevance of the actors in process related to spread of influence in 39 it was considered a multiagent system in which the agents are eager to perform a collective task depending on the perception of the willingness to perform the task of other individuals the setting is modeled using a notion of simple games called influence games those games are defined on graphs were the nodes are labeled by their influence threshold and the spread of influence between its nodes is used to determine whether a coalition is winning or not influence games provide tools to measure the importance of the actors of a social network by means of classic power indices and provide a framework to consider new centrality criteria in this paper we consider two of the most classical power indices , i e , banzhaf and shapley shubik indices , as centrality measures for social networks in influence games although there is some work related to specific scenarios of game theoretic networks , here we use such indices as centrality measures in any social network where the spread of influence phenomenon can be applied further , we define new centrality measures such as satisfaction and effort that , as far as we know , have not been considered so far we also perform a comparison of the proposed measures with other three classic centrality measures , degree , closeness and betweenness , considering three social networks we show that in some cases our measurements provide centrality hierarchies similar to those of other measures , while in other cases provide different hierarchies"
cs.AI,"we examine carefully the rationale underlying the approaches to belief change taken in the literature , and highlight what we view as methodological problems we argue that to study belief change carefully , we must be quite explicit about the ``ontology'' or scenario underlying the belief change process this is something that has been missing in previous work , with its focus on postulates our analysis shows that we must pay particular attention to two issues that have often been taken for granted the first is how we model the agent 's epistemic state \( do we use a set of beliefs , or a richer structure , such as an ordering on worlds \? and if we use a set of beliefs , in what language are these beliefs are expressed \? \) we show that even postulates that have been called ``beyond controversy'' are unreasonable when the agent 's beliefs include beliefs about her own epistemic state as well as the external world the second is the status of observations \( are observations known to be true , or just believed \? in the latter case , how firm is the belief \? \) issues regarding the status of observations arise particularly when we consider iterated belief revision , and we must confront the possibility of revising by p and then by not p"
cs.DM,"in this article , i present a conjecture on the number of independent sets on graph covers i also show that the conjecture implies that the partition function of a binary pairwise attractive model is greater than that of the bethe approximation"
cs.IT,"a new random linear network coding scheme for reliable communications for time division duplexing channels is proposed the setup assumes a packet erasure channel and that nodes cannot transmit and receive information simultaneously the sender transmits coded data packets back to back before stopping to wait for the receiver to acknowledge \( ack \) the number of degrees of freedom , if any , that are required to decode correctly the information we provide an analysis of this problem to show that there is an optimal number of coded data packets , in terms of mean completion time , to be sent before stopping to listen this number depends on the latency , probabilities of packet erasure and ack erasure , and the number of degrees of freedom that the receiver requires to decode the data this scheme is optimal in terms of the mean time to complete the transmission of a fixed number of data packets we show that its performance is very close to that of a full duplex system , while transmitting a different number of coded packets can cause large degradation in performance , especially if latency is high also , we study the throughput performance of our scheme and compare it to existing half duplex go back n and selective repeat arq schemes numerical results , obtained for different latencies , show that our scheme has similar performance to the selective repeat in most cases and considerable performance gain when latency and packet error probability is high"
cs.SI,"as a significant factor in urban planning , traffic forecasting and prediction of epidemics , modeling patterns of human mobility draws intensive attention from researchers for decades power law distribution and its variations are observed from quite a few real world human mobility datasets such as the movements of banking notes , trackings of cell phone users' locations and trajectories of vehicles in this paper , we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in beijing in contrast to most models observed in human mobility data , the taxis' traveling displacements in urban areas tend to follow an exponential distribution instead of a power law similarly , the elapsed time can also be well approximated by an exponential distribution worth mentioning , analysis of the interevent time indicates the bursty nature of human mobility , similar to many other human activities"
cs.IT,"this paper addresses robust communication on a fading relay channel in which the relay is connected to the decoder via an out of band digital link of limited capacity both the source to relay and the source to destination links are subject to fading gains , which are generally unknown to the encoder prior to transmission to overcome this impairment , a hybrid automatic retransmission request \( harq \) protocol is combined with multi layer broadcast transmission , thus allowing for variable rate decoding moreover , motivated by cloud radio access network applications , the relay operation is limited to compress and forward the aim is maximizing the throughput performance as measured by the average number of successfully received bits per channel use , under either long term static channel \( ltsc \) or short term static channel \( stsc \) models in order to opportunistically leverage better channel states based on the harq feedback from the decoder , an adaptive compression strategy at the relay is also proposed numerical results confirm the effectiveness of the proposed strategies"
cs.IT,"achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices this paper presents a novel solution that seamlessly integrates two technologies , mobile cloud computing and microwave power transfer \( mpt \) , to enable computation in passive low complexity devices such as sensors and wearable computing devices specifically , considering a single user system , a base station \( bs \) either transfers power to or offloads computation from a mobile to the cloud the mobile uses harvested energy to compute given data either locally or by offloading a framework for energy efficient computing is proposed that comprises a set of policies for controlling cpu cycles for the mode of local computing , time division between mpt and offloading for the other mode of offloading , and mode selection given the cpu cycle statistics information and channel state information \( csi \) , the policies aim at maximizing the probability of successfully computing given data , called computing probability , under the energy harvesting and deadline constraints the policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory the structures of the resultant policies are characterized in closed form furthermore , given non causal csi , the said analytical framework is further developed to support computation load allocation over multiple channel realizations , which further increases computing probability last , simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control"
cs.DS|cs.LG,"we study \( constrained \) least squares regression as well as multiple response least squares regression and ask the question of whether a subset of the data , a coreset , suffices to compute a good approximate solution to the regression we give deterministic , low order polynomial time algorithms to construct such coresets with approximation guarantees , together with lower bounds indicating that there is not much room for improvement upon our results"
cs.IT,"the approximate message passing \( amp \) algorithm shows advantage over conventional convex optimization methods in recovering under sampled sparse signals amp is analytically tractable and has a much lower complexity however , it requires that the true parameters of the input and output channels are known in this paper , we propose an amp algorithm with built in parameter estimation that jointly estimates the sparse signals along with the parameters by treating them as unknown random variables with simple priors specifically , the maximum a posterior \( map \) parameter estimation is presented and shown to produce estimations that converge to the true parameter values experiments on sparse signal recovery show that the performance of the proposed approach matches that of the oracle amp algorithm where the true parameter values are known"
cs.IT,"using gaussian inputs and treating interference as noise at the receivers has recently been shown to be sum capacity achieving for the two user single input single output \( siso \) gaussian interference channel in a low interference regime , where the interference levels are below certain thresholds in this paper , such a low interference regime is characterized for multiple input multiple output \( mimo \) gaussian interference channels conditions are provided on the direct and cross channel gain matrices under which using gaussian inputs and treating interference as noise at the receivers is sum capacity achieving for the special cases of the symmetric multiple input single output \( miso \) and single input multiple output \( simo \) gaussian interference channels , more explicit expressions for the low interference regime are derived in particular , the threshold on the interference levels that characterize low interference regime is related to the input snr and the angle between the direct and cross channel gain vectors it is shown that the low interference regime can be quite significant for mimo interference channels , with the low interference threshold being at least as large as the sine of the angle between the direct and cross channel gain vectors for the miso and simo cases"
cs.SI,"fractal structures emerge from statistical and hierarchical processes in urban development or network evolution in a class of efficient and robust geographical networks , we derive the size distribution of layered areas , and estimate the fractal dimension by using the distribution without huge computations this method can be applied to self similar tilings based on a stochastic process"
cs.IT,"in this paper we introduce a definition for nonanticipative rate distortion function \( rdf \) on abstract alphabets , and we invoke weak convergence of probability measures to show various of its properties , such as , existence of the optimal reproduction conditional distribution , compactness of the fidelity set , lower semicontinuity of the rdf functional , etc further , we derive the closed form expression of the optimal nonstationary reproduction distribution this expression is computed recursively backward in time throughout the paper we point out an operational meaning of the nonanticipative rdf by recalling the coding theorem derive in cite tatikonda2000 , and we state relations to gorbunov pinsker 's nonanticipatory epsilon entropy cite gorbunov pinsker"
cs.IT,"optimal zero delay coding \( quantization \) of a vector valued markov source driven by a noise process is considered using a stochastic control problem formulation , the existence and structure of optimal quantization policies are studied for a finite horizon problem with bounded per stage distortion measure , the existence of an optimal zero delay quantization policy is shown provided that the quantizers allowed are ones with convex codecells the bounded distortion assumption is relaxed to cover cases that include the linear quadratic gaussian problem for the infinite horizon problem and a stationary markov source the optimality of deterministic markov coding policies is shown the existence of optimal stationary markov quantization policies is also shown provided randomization that is shared by the encoder and the decoder is allowed"
cs.LG,"the human brain processes information showing learning and prediction abilities but the underlying neuronal mechanisms still remain unknown recently , many studies prove that neuronal networks are able of both generalizations and associations of sensory inputs in this paper , following a set of neurophysiological evidences , we propose a learning framework with a strong biological plausibility that mimics prominent functions of cortical circuitries we developed the inductive conceptual network \( icn \) , that is a hierarchical bio inspired network , able to learn invariant patterns by variable order markov models implemented in its nodes the outputs of the top most node of icn hierarchy , representing the highest input generalization , allow for automatic classification of inputs we found that the icn clusterized mnist images with an error of 5 73 and usps images with an error of 12 56"
cs.DM|cs.DS,"for graphs g and h , a homomorphism from g to h is a function varphi colon v \( g \) to v \( h \) , which maps vertices adjacent in g to adjacent vertices of h a homomorphism is locally injective if no two vertices with a common neighbor are mapped to a single vertex in h many cases of graph homomorphism and locally injective graph homomorphism are np complete , so there is little hope to design polynomial time algorithms for them in this paper we present an algorithm for graph homomorphism and locally injective homomorphism working in time mathcal o \( \( b 2 \) v \( g \) \) , where b is the bandwidth of the complement of h"
cs.DS|cs.IT,"design of it online algorithms for assigning mobile users to basestations is considered with the objective of maximizing the sum rate , when all users associated to any one basestation equally share each basestation 's resources each user on its arrival reveals the rates it can obtain if connected to each of the basestations , and the problem is to assign each user to any one basestation irrevocably so that the sum rate is maximized at the end of all user arrivals , without knowing the future user arrival or rate information or its statistics at each user arrival online algorithms with constant factor loss in comparison to offline algorithms \( that know both the user arrival and user rates profile in advance \) are derived the proposed online algorithms are motivated from the famous online k secretary problem and online maximum weight matching problem"
cs.AI,"we present ideas about creating a next generation intrusion detection system based on the latest immunological theories the central challenge with computer security is determining the difference between normal and potentially harmful activity for half a century , developers have protected their systems by coding rules that identify and block specific events however , the nature of current and future threats in conjunction with ever larger it systems urgently requires the development of automated and adaptive defensive tools a promising solution is emerging in the form of artificial immune systems the human immune system can detect and defend against harmful and previously unseen invaders , so can we not build a similar intrusion detection system for our computers"
cs.SI,"the recent wave of mobilizations in the arab world and across western countries has generated much discussion on how digital media is connected to the diffusion of protests we examine that connection using data from the surge of mobilizations that took place in spain in may 2011 we study recruitment patterns in the twitter network and find evidence of social influence and complex contagion we identify the network position of early participants \( i e the leaders of the recruitment process \) and of the users who acted as seeds of message cascades \( i e the spreaders of information \) we find that early participants cannot be characterized by a typical topological position but spreaders tend to me more central to the network these findings shed light on the connection between online networks , social contagion , and collective dynamics , and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action"
cs.IT,"the mceliece cryptosystem is a public key cryptosystem based on coding theory that has successfully resisted cryptanalysis for thirty years the original version , based on goppa codes , is able to guarantee a high level of security , and is faster than competing solutions , like rsa despite this , it has been rarely considered in practical applications , due to two major drawbacks i \) large size of the public key and ii \) low transmission rate low density parity check \( ldpc \) codes are state of art forward error correcting codes that permit to approach the shannon limit while ensuring limited complexity quasi cyclic \( qc \) ldpc codes are a particular class of ldpc codes , able to join low complexity encoding of qc codes with high performing and low complexity decoding of ldpc codes in a previous work it has been proposed to adopt a particular family of qc ldpc codes in the mceliece cryptosystem to reduce the key size and increase the transmission rate recently , however , new attacks have been found that are able to exploit a flaw in the transformation from the private key to the public one such attacks can be effectively countered by changing the form of some constituent matrices , without altering the system parameters this work gives an overview of the qc ldpc codes based mceliece cryptosystem and its cryptanalysis two recent versions are considered , and their ability to counter all the currently known attacks is discussed a third version able to reach a higher security level is also proposed finally , it is shown that the new qc ldpc codes based cryptosystem scales favorably with the key length"
cs.IT,we investigate properties of a channel coding scheme leading to the minimum possible frame error ratio when transmitting over a memoryless channel with rate r c the results are compared to the well known properties of a channel coding scheme leading to minimum bit error ratio it is concluded that these two optimization requests are contradicting a valuable application of the derived results is presented
cs.LG|stat.ML,we derive generalization error bounds for stationary univariate autoregressive \( ar \) models we show that imposing stationarity is enough to control the gaussian complexity without further regularization this lets us use structural risk minimization for model selection we demonstrate our methods by predicting interest rate movements
cs.LG,"recurrent neural networks \( rnns \) , including long short term memory \( lstm \) rnns , have produced state of the art results on a variety of speech recognition tasks however , these models are often too large in size for deployment on mobile devices with memory and latency constraints in this work , we study mechanisms for learning compact rnns and lstms via low rank factorizations and parameter sharing schemes our goal is to investigate redundancies in recurrent architectures where compression can be admitted without losing performance a hybrid strategy of using structured matrices in the bottom layers and shared low rank factors on the top layers is found to be particularly effective , reducing the parameters of a standard lstm by 75 , at a small cost of 0 3 increase in wer , on a 2 , 000 hr english voice search task"
cs.IT,"broadband wireless channel is a time dispersive and becomes strongly frequency selective in most cases , the channel is composed of a few dominant coefficients and a large part of coefficients is approximately zero or zero to exploit the sparsity of multi path channel \( mpc \) , there are various methods have been proposed they are , namely , greedy algorithms , iterative algorithms , and convex program the former two algorithms are easy to be implemented but not stable on the other hand , the last method is stable but difficult to be implemented as practical channel estimation problems because of computational complexity in this paper , we proposed a novel channel estimation strategy by using modified smoothed \( msl0 \) algorithm which combines stable and low complexity computer simulations confirm the effectiveness of the introduced algorithm comparisons with the existing methods we also give"
cs.IT,"we address in this paper decoding aspects of the compute and forward \( cf \) physical layer network coding strategy it is known that the original decoder for the cf is asymptotically optimal however , its performance gap to optimal decoders in practical settings are still not known in this work , we develop and assess the performance of novel decoding algorithms for the cf operating in the multiple access channel for the fading channel , we analyze the ml decoder and develop a novel diophantine approximation based decoding algorithm showed numerically to outperform the original cf decoder for the gaussian channel , we investigate the maximum a posteriori \( map \) decoder we derive a novel map decoding metric and develop practical decoding algorithms proved numerically to outperform the original one"
cs.IT,"power talk is a novel concept for communication among control units in microgrids \( mgs \) , carried out without a dedicated modem , but by using power electronics that interface the common bus the information is transmitted by modulating the parameters of the primary control , incurring subtle power deviations that can be detected by other units in this paper , we develop power talk communication strategies for dc mg systems with arbitrary number of control units that carry out all to all communication we investigate two multiple access strategies 1 \) tdma , where only one unit transmits at a time , and 2 \) full duplex , where all units transmit and receive simultaneously we introduce the notions of signaling space , where the power talk symbol constellations are constructed , and detection space , where the demodulation of the symbols is performed the proposed communication technique is challenged by the random changes of the bus parameters due to load variations in the system to this end , we employ a solution based on training sequences , which re establishes the signaling and detection spaces and thus enables reliable information exchange the presented results show that power talk is an effective solution for reliable communication among units in dc mg systems"
cs.DS|cs.IT,"we consider the problem of throughput optimal broadcast ing in time varying wireless networks , whose underlying topology is restricted to directed acyclic graphs \( dag \) previous broadcast algorithms route packets along spanning trees in large networks with time varying connectivities , these trees are difficult to compute and maintain in this paper we propose a new online throughput optimal broadcast algorithm which makes packet by packet scheduling and routing decisions , obviating the need for maintaining any global topological structures , such as spanning trees our algorithm relies on system state information for making transmission decisions and hence , may be thought of as a generalization of the well known back pressure algorithm which makes point to point unicast transmission decisions based on queue length information , without requiring knowledge of end to end paths technically , the back pressure algorithm is derived by stochastically stabilizing the network queues however , because of packet duplications associated with broadcast , the work conservation principle is violated and queuing processes are difficult to define in the broadcast problem to address this fundamental issue , we identify certain state variables which behave like virtual queues in the broadcast setting by stochastically stabilizing these virtual queues , we devise a throughput optimal broadcast policy we also derive new characterizations of the broadcast capacity of time varying wireless dags and derive an efficient algorithm to compute the capacity exactly under certain assumptions of interference model , and a poly time approximation algorithm for computing the capacity under less restrictive assumptions"
cs.IT,"in the monograph kounchev , o i , multivariate polysplines applications to numerical and wavelet analysis , academic press , san diego london , 2001 , and in the paper kounchev o , render , h , cardinal interpolation with polysplines on annuli , journal of approximation theory 137 \( 2005 \) 89 107 , we have introduced and studied a new paradigm for cardinal interpolation which is related to the theory of multivariate polysplines in the present paper we show that this is related to a new sampling paradigm in the multivariate case , whereas we obtain a shannon type function s \( x \) and the following shannon type formula f \( r theta \) sum j infty infty int qtr bbb s n 1 s \( e j r theta \) f \( e j theta \) d theta this formula relies upon infinitely many shannon type formulas for the exponential splines arising from the radial part of the polyharmonic operator delta p for fixed p geq 1 acknowledgement the first and the second author have been partially supported by the institutes partnership project with the alexander von humboldt foundation the first has been partially sponsored by the greek bulgarian bilateral project bgr 17 , and the second author by grant mtm2006 13000 c03 03 of the d g i of spain"
cs.LG|stat.ML,"we study two layer belief networks of binary random variables in which the conditional probabilities pr childlparents depend monotonically on weighted sums of the parents in large networks where exact probabilistic inference is intractable , we show how to compute upper and lower bounds on many probabilities of interest in particular , using methods from large deviation theory , we derive rigorous bounds on marginal probabilities such as pr children and prove rates of convergence for the accuracy of our bounds as a function of network size our results apply to networks with generic transfer function parameterizations of the conditional probability tables , such as sigmoid and noisy or they also explicitly illustrate the types of averaging behavior that can simplify the problem of inference in large networks"
cs.IT,"we study a distributed antenna system where l antenna terminals \( ats \) are connected to a central processor \( cp \) via digital error free links of finite capacity r 0 , and serve k user terminals \( uts \) we contribute to the subject in the following ways 1 \) for the uplink , we apply the compute and forward \( cof \) approach and examine the corresponding system optimization at finite snr 2 \) for the downlink , we propose a novel precoding scheme nicknamed reverse compute and forward \( rcof \) 3 \) in both cases , we present low complexity versions of cof and rcof based on standard scalar quantization at the receivers , that lead to discrete input discrete output symmetric memoryless channel models for which near optimal performance can be achieved by standard single user linear coding 4 \) for the case of large r 0 , we propose a novel integer forcing beamforming \( ifb \) scheme that generalizes the popular zero forcing beamforming and achieves sum rate performance close to the optimal gaussian dirty paper coding the proposed uplink and downlink system optimization focuses specifically on the ats and uts selection problem we present low complexity ats and uts selection schemes and demonstrate , through monte carlo simulation in a realistic environment with fading and shadowing , that the proposed schemes essentially eliminate the problem of rank deficiency of the system matrix and greatly mitigate the non integer penalty affecting cof rcof at high snr comparison with other state of the art information theoretic schemes , such as quantize remap and forward for the uplink and compressed dirty paper coding for the downlink , show competitive performance of the proposed approaches with significantly lower complexity"
cs.IT,"the deluge of date rate in today 's networks poses a cost burden on the backhaul network design designing cost efficient backhaul solutions becomes an interesting , yet challenging , problem traditional technologies for backhaul networks include either radio frequency backhauls \( rf \) or optical fibers \( of \) while rf is a cost effective solution as compared to of , it supports lower data rate requirements another promising backhaul solution that may combine both a high data rate and a relatively low cost is the free space optics \( fso \) fso , however , is sensitive to nature conditions \( e g rain , fog , line of sight , etc \) a more reliable alternative is therefore to combine rf and fso solutions through a hybrid structure called hybrid rf fso consider a backhaul network , where the base stations \( bs \) can be connected to each other either via of or via hybrid rf fso backhaul links the paper addresses the problem of minimizing the cost of backhaul planning under connectivity and data rates constraints , so as to choose the appropriate cost effective backhaul type between bss \( i e , either of or hybrid rf fso \) the paper solves the problem using graph theory techniques by introducing the corresponding planning graph it shows that under a specified realistic assumption about the cost of of and hybrid rf fso links , the problem is equivalent to a maximum weight clique problem , which can can be solved with moderate complexity simulation results show that our proposed solution shows a close to optimal performance , especially for practical prices of the hybrid rf fso"
cs.SI,"understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks , with broad implications on the system 's functioning , health , and associated costs this can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures one important ingredient to consider is the pattern of disease transmission contacts among the elements , however lack of data or delays in providing updated records may hinder its use , especially for time varying patterns here we explore to what extent it is possible to use past temporal data of a system 's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak , in absence of updated data we focus on two real world temporal systems a livestock displacements trade network among animal holdings , and a network of sexual encounters in high end prostitution we define the node 's loyalty as a local measure of its tendency to maintain contacts with the same elements over time , and uncover important non trivial correlations with the node 's epidemic risk we show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks high accuracy of our predictions is recovered across different settings , while the amount of possible predictions is system specific the proposed method can provide crucial information for the setup of targeted intervention strategies"
cs.IT,"the crushing demand for wireless data services will soon exceed the capability of the current homogeneous cellular architecture an emerging solution is to overlay small cell networks with the macro cell networks in this paper , we propose an amplitude space sharing \( ass \) method among the macro cell user and small cell users by transmit layer design and data rate optimization , the signals and interferences are promised to be separable at each receiver and the network sum rate is maximized the han koboyashi coding is employed and optimal power allocation is derived for the one small cell scenario , and a simple ass transmission scheme is developed for the multiple small cells scenarios simulation results show great superiority over other interference management schemes"
cs.IT,"network coding can significantly improve the transmission rate of communication networks with packet loss compared with routing however , using network coding usually incurs high computational and storage costs in the network devices and terminals for example , some network coding schemes require the computational and or storage capacities of an intermediate network node to increase linearly with the number of packets for transmission , making such schemes difficult to be implemented in a router like device that has only constant computational and storage capacities in this paper , we introduce batched sparse code \( bats code \) , which enables a digital fountain approach to resolve the above issue bats code is a coding scheme that consists of an outer code and an inner code the outer code is a matrix generation of a fountain code it works with the inner code that comprises random linear coding at the intermediate network nodes bats codes preserve such desirable properties of fountain codes as ratelessness and low encoding decoding complexity the computational and storage capacities of the intermediate network nodes required for applying bats codes are independent of the number of packets for transmission almost capacity achieving bats code schemes are devised for unicast networks , two way relay networks , tree networks , a class of three layer networks , and the butterfly network for general networks , under different optimization criteria , guaranteed decoding rates for the receiving nodes can be obtained"
cs.LG|stat.ML,"kalman filters are one of the most influential models of time varying phenomena they admit an intuitive probabilistic interpretation , have a simple functional form , and enjoy widespread adoption in a variety of disciplines motivated by recent variational methods for learning deep generative models , we introduce a unified algorithm to efficiently learn a broad spectrum of kalman filters of particular interest is the use of temporal generative models for counterfactual inference we investigate the efficacy of such models for counterfactual inference , and to that end we introduce the healing mnist dataset where long term structure , noise and actions are applied to sequences of digits we show the efficacy of our method for modeling this dataset we further show how our model can be used for counterfactual inference for patients , based on electronic health record data of 8 , 000 patients over 4 5 years"
cs.LG,"this paper presents the systems developed by lium and cvc for the wmt16 multimodal machine translation challenge we explored various comparative methods , namely phrase based systems and attentional recurrent neural networks models trained using monomodal or multimodal data we also performed a human evaluation in order to estimate the usefulness of multimodal data for human machine translation and image description generation our systems obtained the best results for both tasks according to the automatic evaluation metrics bleu and meteor"
cs.IT,"we consider the discrete time intersymbol interference \( isi \) channel model , with additive gaussian noise and fixed i i d inputs in this setting , we investigate the expression put forth by shamai and laroia as a conjectured lower bound for the input output mutual information after application of a mmse dfe receiver a low snr expansion is used to prove that the conjectured bound does not hold under general conditions , and to characterize inputs for which it is particularly ill suited one such input is used to construct a counterexample , indicating that the shamai laroia expression does not always bound even the achievable rate of the channel , thus excluding a natural relaxation of the original conjectured bound however , this relaxed bound is then shown to hold for any finite entropy input and isi channel , when the snr is sufficiently high finally , new simple bounds for the achievable rate are proven , and compared to other known bounds information estimation relations and estimation theoretic bounds play a key role in establishing our results"
cs.SI|stat.ML,"the problem of detecting communities in a graph is maybe one the most studied inference problems , given its simplicity and widespread diffusion among several disciplines a very common benchmark for this problem is the stochastic block model or planted partition problem , where a phase transition takes place in the detection of the planted partition by changing the signal to noise ratio optimal algorithms for the detection exist which are based on spectral methods , but we show these are extremely sensible to slight modification in the generative model recently javanmard , montanari and ricci tersenghi \( arxiv 1511 08769 \) have used statistical physics arguments , and numerical simulations to show that finding communities in the stochastic block model via semidefinite programming is quasi optimal further , the resulting semidefinite relaxation can be solved efficiently , and is very robust with respect to changes in the generative model in this paper we study in detail several practical aspects of this new algorithm based on semidefinite programming for the detection of the planted partition the algorithm turns out to be very fast , allowing the solution of problems with o \( 10 5 \) variables in few second on a laptop computer"
cs.IT,"in this paper , we study the graphical structure of elementary trapping sets \( ets \) of variable regular low density parity check \( ldpc \) codes etss are known to be the main cause of error floor in ldpc coding schemes for the set of ldpc codes with a given variable node degree d l and girth g , we identify all the non isomorphic structures of an arbitrary class of \( a , b \) etss , where a is the number of variable nodes and b is the number of odd degree check nodes in the induced subgraph of the ets our study leads to a simple characterization of dominant classes of etss \( those with relatively small values of a and b \) based on short cycles in the tanner graph of the code for such classes of etss , we prove that any set cal s in the class is a layered superset \( lss \) of a short cycle , where the term layered is used to indicate that there is a nested sequence of etss that starts from the cycle and grows , one variable node at a time , to generate cal s this characterization corresponds to a simple search algorithm that starts from the short cycles of the graph and finds all the etss with lss property in a guaranteed fashion specific results on the structure of etss are presented for d l 3 , 4 , 5 , 6 , g 6 , 8 and a , b leq 10 in this paper the results of this paper can be used for the error floor analysis and for the design of ldpc codes with low error floors"
cs.IT,"we analyze by means of granger causality the effect of synergy and redundancy in the inference \( from time series data \) of the information flow between subsystems of a complex network whilst we show that fully conditioned granger causality is not affected by synergy , the pairwise analysis fails to put in evidence synergetic effects in cases when the number of samples is low , thus making the fully conditioned approach unfeasible , we show that partially conditioned granger causality is an effective approach if the set of conditioning variables is properly chosen we consider here two different strategies \( based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences \) for partially conditioned granger causality and show that depending on the data structure either one or the other might be valid on the other hand , we observe that fully conditioned approaches do not work well in presence of redundancy , thus suggesting the strategy of separating the pairwise links in two subsets those corresponding to indirect connections of the fully conditioned granger causality \( which should thus be excluded \) and links that can be ascribed to redundancy effects and , together with the results from the fully connected approach , provide a better description of the causality pattern in presence of redundancy we finally apply these methods to two different real datasets first , analyzing electrophysiological data from an epileptic brain , we show that synergetic effects are dominant just before seizure occurrences second , our analysis applied to gene expression time series from hela culture shows that the underlying regulatory networks are characterized by both redundancy and synergy"
cs.IT,"this work considers distributed sensing and transmission of sporadic random samples lower bounds are derived for the reconstruction error of a single normally or uniformly distributed finite dimensional vector imperfectly measured by a network of sensors and transmitted with finite energy to a common receiver via an additive white gaussian noise asynchronous multiple access channel transmission makes use of a perfect causal feedback link to the encoder connected to each sensor a retransmission protocol inspired by the classical scheme in 1 applied to the transmission of single and bi variate analog samples analyzed in 2 and 3 is extended to the more general network scenario , for which asymptotic upper bounds on the reconstruction error are provided both the upper and lower bounds show that collaboration can be achieved through energy accumulation under certain circumstances in order to investigate the practical performance of the proposed retransmission protocol we provide a numerical evaluation of the upper bounds in the non asymptotic energy regime using low order quantization in the sensors the latter includes a minor modification of the protocol to improve reconstruction fidelity numerical results show that an increase in the size of the network brings benefit in terms of performance , but that the gain in terms of energy efficiency diminishes quickly at finite energies due to a non coherent combining loss"
cs.LG,"the importance of unsupervised clustering and topic modeling is well recognized with ever increasing volumes of text data in this paper , we propose a fast method for hierarchical clustering and topic modeling called hiernmf2 our method is based on fast rank 2 nonnegative matrix factorization \( nmf \) that performs binary clustering and an efficient node splitting rule further utilizing the final leaf nodes generated in hiernmf2 and the idea of nonnegative least squares fitting , we propose a new clustering topic modeling method called flatnmf2 that recovers a flat clustering topic modeling result in a very simple yet significantly more effective way than any other existing methods we describe highly optimized open source software in c for both hiernmf2 and flatnmf2 for hierarchical and partitional clustering topic modeling of document data sets substantial experimental tests are presented that illustrate significant improvements both in computational time as well as quality of solutions we compare our methods to other clustering methods including k means , standard nmf , and cluto , and also topic modeling methods including latent dirichlet allocation \( lda \) and recently proposed algorithms for nmf with separability constraints overall , we present efficient tools for analyzing large scale data sets , and techniques that can be generalized to many other data analytics problem domains"
cs.LG|stat.ML,"we consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period , under a constraint that the expected average sampling cost does not exceed an upper bound the outcome distributions are not known we construct a class of consistent adaptive policies , under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means we also compare the rate of convergence for various policies in this class using simulation"
cs.IT,"we introduce the idea of distortion side information , which does not directly depend on the source but instead affects the distortion measure we show that such distortion side information is not only useful at the encoder , but that under certain conditions , knowing it at only the encoder is as good as knowing it at both encoder and decoder , and knowing it at only the decoder is useless thus distortion side information is a natural complement to the signal side information studied by wyner and ziv , which depends on the source but does not involve the distortion measure furthermore , when both types of side information are present , we characterize the penalty for deviating from the configuration of encoder only distortion side information and decoder only signal side information , which in many cases is as good as full side information knowledge"
cs.LG|stat.ML,"this work considers an estimation task in compressive sensing , where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre measurement noise \( interference , or clutter \) as well as post measurement noise , in the specific setting where some \( perhaps limited \) prior knowledge on the signal , interference , and noise is available the specific aim here is to devise a strategy for incorporating this prior information into the design of an appropriate compressive measurement strategy here , the prior information is interpreted as statistics of a prior distribution on the relevant quantities , and an approach based on bayesian experimental design is proposed experimental results on synthetic data demonstrate that the proposed approach outperforms traditional random compressive measurement designs , which are agnostic to the prior information , as well as several other knowledge enhanced sensing matrix designs based on more heuristic notions"
cs.IT,"in this paper , we study the impact of aliased adjacent band blocker signal \( aabs \) on the information sum rate of massive multiple input multiple output \( mimo \) uplink , with both perfect and imperfect channel estimates , in order to determine the required adjacent band attenuation in rf bandpass filters \( bpfs \) with imperfect channel estimates , our study reveals that as the number of base station \( bs \) antennas \( m \) increases , with m to infty , the required attenuation at the bpfs increases as mathcal o \( sqrt m \) , provided the desired information sum rate \( both in the presence and absence of aabs \) remains fixed this implies a practical limit on the number of bs antennas due to the increase in bpf design complexity and power consumption with increasing m"
cs.DM,"given a finite simple graph g \( v , e \) with chromatic number c and chromatic polynomial c \( x \) every vertex graph coloring f of g defines an index i f \( x \) satisfying the poincare hopf theorem sum x i f \( x \) chi \( g \) as a variant to the index expectation result we prove that e i f \( x \) is equal to curvature k \( x \) satisfying gauss bonnet sum x k \( x \) chi \( g \) , where the expectation is the average over the finite probability space containing the c \( c \) possible colorings with c colors , for which each coloring has the same probability"
cs.AI|cs.LG|stat.ML,"structured prediction applications often involve complex inference problems that require the use of approximate methods approximations based on linear programming \( lp \) relaxations have proved particularly successful in this setting , with both theoretical and empirical support despite the general intractability of inference , it has been observed that in many real world applications the lp relaxation is often tight in this work we propose a theoretical explanation to this striking observation in particular , we show that learning with lp relaxed inference encourages tightness of training instances we complement this result with a generalization bound showing that tightness generalizes from train to test data"
cs.DM,"it is shown that in star free graphs the maximum independent set problem , the minimum dominating set problem and the minimum independent dominating set problem are approximable up to constant factor by any maximal independent set"
cs.IT,"we show how gabidulin codes can be decoded by using a parametrization approach our decoding algorithm computes a list of all closest codewords to a given received word we consider a certain module , called the interpolation module , over the ring of linearized polynomials with respect to composition of polynomials we consider minimal bases of this interpolation module and show that the predictable leading monomial \( plm \) property holds in this setting we show that any such minimal basis gives rise to a parametrization , using the plm property as a key ingredient in our proof two subalgorithms are presented to compute such a basis , one iterative , the other an extended euclidean algorithm both of these subalgorithms have polynomial time complexity the complexity of the overall algorithm , using the parametrization , becomes exponential as soon as the closest codewords are beyond the unique decoding radius however , for high rate codes , this complexity beats the complexity of exhaustive search"
cs.IT,"this paper develops upper bounds on the end to end transmission capacity of multi hop wireless networks potential source destination paths are dynamically selected from a pool of randomly located relays , from which a closed form lower bound on the outage probability is derived in terms of the expected number of potential paths this is in turn used to provide an upper bound on the number of successful transmissions that can occur per unit area , which is known as the transmission capacity the upper bound results from assuming independence among the potential paths , and can be viewed as the maximum diversity case a useful aspect of the upper bound is its simple form for an arbitrary sized network , which allows insights into how the number of hops and other network parameters affect spatial throughput in the non asymptotic regime the outage probability analysis is then extended to account for retransmissions with a maximum number of allowed attempts in contrast to prevailing wisdom , we show that predetermined routing \( such as nearest neighbor \) is suboptimal , since more hops are not useful once the network is interference limited our results also make clear that randomness in the location of relay sets and dynamically varying channel states is helpful in obtaining higher aggregate throughput , and that dynamic route selection should be used to exploit path diversity"
cs.IT,"density evolution \( de \) is one of the most powerful analytical tools for low density parity check \( ldpc \) codes on memoryless binary input symmetric output channels the case of non symmetric channels is tackled either by the ldpc coset code ensemble \( a channel symmetrizing argument \) or by the generalized de for linear codes on non symmetric channels existing simulations show that the bit error rate performances of these two different approaches are nearly identical this paper explains this phenomenon by proving that as the minimum check node degree d c becomes sufficiently large , the performance discrepancy of the linear and the coset ldpc codes is theoretically indistinguishable this typicality of linear codes among the ldpc coset code ensemble provides insight into the concentration theorem of ldpc coset codes"
cs.DS|cs.SI,"in this paper , we study the problem of team member replacement given a team of people embedded in a social network working on the same task , find a good candidate who can fit in the team after one team member becomes unavailable we conjecture that a good team member replacement should have good skill matching as well as good structure matching we formulate this problem using the concept of graph kernel to tackle the computational challenges , we propose a family of fast algorithms by \( a \) designing effective pruning strategies , and \( b \) exploring the smoothness between the existing and the new team structures we conduct extensive experimental evaluations on real world datasets to demonstrate the effectiveness and efficiency our algorithms \( a \) perform significantly better than the alternative choices in terms of both precision and recall and \( b \) scale sub linearly"
cs.LG|cs.SI|stat.ML,"the stochastic block model \( sbm \) is a popular framework for studying community detection in networks this model is limited by the assumption that all nodes in the same community are statistically equivalent and have equal expected degrees the degree corrected stochastic block model \( dcsbm \) is a natural extension of sbm that allows for degree heterogeneity within communities this paper proposes a convexified modularity maximization approach for estimating the hidden communities under dcsbm this approach is based on a convex programming relaxation of the classical \( generalized \) modularity maximization formulation , followed by a novel doubly weighted ell 1 norm k median procedure we establish non asymptotic theoretical guarantees for both approximate clustering and perfect clustering in the special case of sbm , these theoretical results match the best known performance guarantees of computationally feasible algorithms numerically , we provide an efficient implementation of our algorithm , which is applied to both synthetic and real world networks experiment results show that our method enjoys competitive empirical performance compared to the state of the art tractable methods in the literature"
cs.IT,"we consider a system where an agent \( alice \) aims at transmitting a message to a second agent \( bob \) over a set of parallel channels , while keeping it secret from a third agent \( eve \) by using physical layer security techniques we assume that alice perfectly knows the set of channels with respect to bob , but she has only a statistical knowledge of the channels with respect to eve we derive bounds on the achievable outage secrecy rates , by considering coding either within each channel or across all parallel channels transmit power is adapted to the channel conditions , with a constraint on the average power over the whole transmission we also focus on the maximum cumulative outage secrecy rate that can be achieved moreover , in order to assess the performance in a real life scenario , we consider the use of practical error correcting codes we extend the definitions of security gap and equivocation rate , previously applied to the single additive white gaussian noise channel , to rayleigh distributed parallel channels , on the basis of the error rate targets and the outage probability bounds on these metrics are also derived , taking into account the statistics of the parallel channels numerical results are provided , that confirm the feasibility of the considered physical layer security techniques"
cs.IT,"spectrum management has been identified as a crucial step towards enabling the technology of a cognitive radio network \( crn \) most of the current works dealing with spectrum management in the crn focus on a single task of the problem , e g , spectrum sensing , spectrum decision , spectrum sharing or spectrum mobility in this two part paper , we argue that for certain network configurations , jointly performing several tasks of the spectrum management improves the spectrum efficiency specifically , our aim is to study the uplink resource management problem in a crn where there exist multiple cognitive users \( cus \) and access points \( aps \) the cus , in order to maximize their uplink transmission rates , have to associate to a suitable ap \( spectrum decision \) , and to share the channels used by this ap with other cus \( spectrum sharing \) these tasks are clearly interdependent , and the problem of how they should be carried out efficiently and in a distributed manner is still open in the literature"
cs.IT,"in this paper , we propose a new class of quantized message passing decoders for ldpc codes over the bsc the messages take values \( or levels \) from a finite set the update rules do not mimic belief propagation but instead are derived using the knowledge of trapping sets we show that the update rules can be derived to correct certain error patterns that are uncorrectable by algorithms such as bp and min sum in some cases even with a small message set , these decoders can guarantee correction of a higher number of errors than bp and min sum we provide particularly good 3 bit decoders for 3 left regular ldpc codes they significantly outperform the bp and min sum decoders , but more importantly , they achieve this at only a fraction of the complexity of the bp and min sum decoders"
cs.IT,"we consider the uplink of massive multipleinput multiple output systems in a multicell environment since the base station \( bs \) estimates the channel state information \( csi \) using the pilot signals transmitted from the users , each bs will have imperfect csi in practice assuming zero forcing method to eliminate the multi user interference , we derive the exact analytical expressions for the probability density function \( pdf \) of the signal to interference plus noise ratio \( sinr \) , the corresponding achievable rate , the outage probability , and the symbol error rate \( ser \) when the bs has imperfect csi an upper bound of the ser is also derived for an arbitrary number of antennas at the bs moreover , we derive the upper bound of the achievable rate for the case where the number of antennas at the bs goes to infinity , and the analysis is verified by presenting numerical results"
cs.SI,"social dynamics on a network may be accelerated or decelerated depending on which pairs of individuals in the network communicate early and which pairs do later the order with which the links in a given network are sequentially used , which we call the link order , may be a strong determinant of dynamical behaviour on networks , potentially adding a new dimension to effects of temporal networks relative to static networks here we study the effect of the link order on linear synchronisation dynamics we show that the synchronisation speed considerably depends on specific orders of links in addition , applying each single link for a long time to ensure strong pairwise synchronisation before moving to a next pair of individuals does not often enhance synchronisation of the entire network we also implement a simple greedy algorithm to optimise the link order in favour of fast synchronisation"
cs.AI|cs.LG|stat.ML,"recently dictionary screening has been proposed as an effective way to improve the computational efficiency of solving the lasso problem , which is one of the most commonly used method for learning sparse representations to address today 's ever increasing large dataset , effective screening relies on a tight region bound on the solution to the dual lasso typical region bounds are in the form of an intersection of a sphere and multiple half spaces one way to tighten the region bound is using more half spaces , which however , adds to the overhead of solving the high dimensional optimization problem in lasso screening this paper reveals the interesting property that the optimization problem only depends on the projection of features onto the subspace spanned by the normals of the half spaces this property converts an optimization problem in high dimension to much lower dimension , and thus sheds light on reducing the computation overhead of lasso screening based on tighter region bounds"
cs.DM,"we consider three important and well studied algorithmic problems in group theory the word , geodesic , and conjugacy problem we show transfer results from individual groups to graph products we concentrate on logspace complexity because the challenge is actually in small complexity classes , only the most difficult transfer result is for the conjugacy problem we have a general result for graph products , but even in the special case of a graph group the result is new graph groups are closely linked to the theory of mazurkiewicz traces which form an algebraic model for concurrent processes our proofs are combinatorial and based on well known concepts in trace theory we also use rewriting techniques over traces for the group theoretical part we apply bass serre theory but as we need explicit formulae and as we design concrete algorithms all our group theoretical calculations are completely explicit and accessible to non specialists"
cs.IT,"an algorithmic framework to compute sparse or minimal tv solutions of linear systems is proposed the framework includes both the kaczmarz method and the linearized bregman method as special cases and also several new methods such as a sparse kaczmarz solver the algorithmic framework has a variety of applications and is especially useful for problems in which the linear measurements are slow and expensive to obtain we present examples for online compressed sensing , tv tomographic reconstruction and radio interferometry"
cs.LG,"in order to classify the nonlinear feature with linear classifier and improve the classification accuracy , a deep learning network named kernel principal component analysis network \( kpcanet \) is proposed first , mapping the data into higher space with kernel principal component analysis to make the data linearly separable then building a two layer kpcanet to obtain the principal components of image finally , classifying the principal components with linearly classifier experimental results show that the proposed kpcanet is effective in face recognition , object recognition and hand writing digits recognition , it also outperforms principal component analysis network \( pcanet \) generally as well besides , kpcanet is invariant to illumination and stable to occlusion and slight deformation"
cs.AI,"we introduce game networks \( g nets \) , a novel representation for multi agent decision problems compared to other game theoretic representations , such as strategic or extensive forms , g nets are more structured and more compact more fundamentally , g nets constitute a computationally advantageous framework for strategic inference , as both probability and utility independencies are captured in the structure of the network and can be exploited in order to simplify the inference process an important aspect of multi agent reasoning is the identification of some or all of the strategic equilibria in a game we present original convergence methods for strategic equilibrium which can take advantage of strategic separabilities in the g net structure in order to simplify the computations specifically , we describe a method which identifies a unique equilibrium as a function of the game payoffs , and one which identifies all equilibria"
cs.IT,"applied impulse controls cutoff markov multidimensional diffusion process during transformation to brownian diffusion and back to markov process , concurrently produce feller kernel and generate quantum information dynamics that initiate schr odinger bridge and entanglement transition jumps describe mixture of alternating markov brownian processes which model interactive process cutting correlation of random process with interacting virtual events of real world reveals hidden classical and quantum information in each cutoff entropy integral functional measures hidden information covering the process correlations for kernel and bridge under this transformation the interactive information processes implies transformation of entropy portion from cutting internal boundary to the information dynamics with feedback to attractive external cutoff boundary of the interaction transforming and selecting the entropy portion through interactive impulse creates information observer which kills uncertainty \( entropy \) to get information in certain information dynamics under minimax law of optimal extraction and consumption of information for complex interactions the minimax law variation equations determine structure of the information dynamics arising at this transformation information path functional integrates multiple hidden information contributions of the cutting process correlations in information units , binds their information in doublets and triplets structures , and enfolds this sequence in the information network \( in \) that successively decreases the entropy and maximizes information of the micro macrodynamics the enclosed triplets , sequentially attaching to in , free the bound information , rise information forces , attract , order , structure information units hierarchy , encode doublet triplet logic , compose quantum , classical computation , and integrate in memory and coding"
cs.LG|stat.ML,"we provide the first algorithm for online bandit linear optimization whose regret after t rounds is of order sqrt td ln n on any finite class x of n actions in d dimensions , and of order d sqrt t \( up to log factors \) when x is infinite these bounds are not improvable in general the basic idea utilizes tools from convex geometry to construct what is essentially an optimal exploration basis we also present an application to a model of linear bandits with expert advice interestingly , these results show that bandit linear optimization with expert advice in d dimensions is no more difficult \( in terms of the achievable regret \) than the online d armed bandit problem with expert advice \( where exp4 is optimal \)"
cs.AI,"bound propagation is an important artificial intelligence technique used in constraint programming tools to deal with numerical constraints it is typically embedded within a search procedure \( branch and prune \) and used at every node of the search tree to narrow down the search space , so it is critical that it be fast the procedure invokes constraint propagators until a common fixpoint is reached , but the known algorithms for this have a pseudo polynomial worst case time complexity they are fast indeed when the variables have a small numerical range , but they have the well known problem of being prohibitively slow when these ranges are large an important question is therefore whether strongly polynomial algorithms exist that compute the common bound consistent fixpoint of a set of constraints this paper answers this question in particular we show that this fixpoint computation is in fact np complete , even when restricted to binary linear constraints"
cs.LG,"we propose a transition based dependency parser using recurrent neural networks with long short term memory \( lstm \) units this extends the feedforward neural network parser of chen and manning \( 2014 \) and enables modelling of entire sequences of shift reduce transition decisions on the google web treebank , our lstm parser is competitive with the best feedforward parser on overall accuracy and notably achieves more than 3 improvement for long range dependencies , which has proved difficult for previous transition based parsers due to error propagation and limited context information our findings additionally suggest that dropout regularisation on the embedding layer is crucial to improve the lstm 's generalisation"
cs.DM,"let g be any connected graph on n vertices , n ge 2 let k be any positive integer suppose that a fire breaks out on some vertex of g then in each turn k firefighters can protect vertices of g each can protect one vertex not yet on fire next a fire spreads to all unprotected neighbours the emph k surviving rate of g , denoted by rho k \( g \) , is the expected fraction of vertices that can be saved from the fire by k firefighters , provided that the starting vertex is chosen uniformly at random in this paper , it is shown that for any planar graph g we have rho 3 \( g \) ge frac 2 21 moreover , 3 firefighters are needed for the first step only after that it is enough to have 2 firefighters per each round this result significantly improves known solutions to a problem of cai and wang \( there was no positive bound known for surviving rate of general planar graph with only 3 firefighters \) the proof is done using the separator theorem for planar graphs"
cs.IT,"we present several novel identities and inequalities relating the mutual information and the directed information in systems with feedback the internal blocks within such systems are restricted only to be causal mappings , but are allowed to be non linear , stochastic and time varying moreover , the involved signals can be arbitrarily distributed we bound the directed information between signals inside the feedback loop by the mutual information between signals inside and outside the feedback loop this fundamental result has an interesting interpretation as a law of conservation of information flow building upon it , we derive several novel identities and inequalities , which allow us to prove some existing information inequalities under less restrictive assumptions finally , we establish new relationships between nested directed informations inside a feedback loop this yields a new and general data processing inequality for systems with feedback"
cs.LG|stat.ML,"nonnegative matrix factorization \( nmf \) under the separability assumption can provably be solved efficiently , even in the presence of noise , and has been shown to be a powerful technique in document classification and hyperspectral unmixing this problem is referred to as near separable nmf and requires that there exists a cone spanned by a small subset of the columns of the input nonnegative matrix approximately containing all columns in this paper , we propose a preconditioning based on semidefinite programming making the input matrix well conditioned this in turn can improve significantly the performance of near separable nmf algorithms which is illustrated on the popular successive projection algorithm \( spa \) the new preconditioned spa is provably more robust to noise , and outperforms spa on several synthetic data sets we also show how an active set method allow us to apply the preconditioning on large scale real world hyperspectral images"
cs.IT,"this paper presents polar coding schemes for the 2 user discrete memoryless broadcast channel \( dm bc \) which achieve marton 's region with both common and private messages this is the best achievable rate region known to date , and it is tight for all classes of 2 user dm bcs whose capacity regions are known to accomplish this task , we first construct polar codes for both the superposition as well as the binning strategy by combining these two schemes , we obtain marton 's region with private messages only finally , we show how to handle the case of common information the proposed coding schemes possess the usual advantages of polar codes , i e , they have low encoding and decoding complexity and a super polynomial decay rate of the error probability we follow the lead of goela , abbe , and gastpar , who recently introduced polar codes emulating the superposition and binning schemes in order to align the polar indices , for both schemes , their solution involves some degradedness constraints that are assumed to hold between the auxiliary random variables and the channel outputs to remove these constraints , we consider the transmission of k blocks and employ a chaining construction that guarantees the proper alignment of the polarized indices the techniques described in this work are quite general , and they can be adopted to many other multi terminal scenarios whenever there polar indices need to be aligned"
cs.DS|cs.LG,"in the past few years powerful generalizations to the euclidean k means problem have been made , such as bregman clustering 7 , co clustering \( i e , simultaneous clustering of rows and columns of an input matrix \) 9 , 18 , and tensor clustering 8 , 34 like k means , these more general problems also suffer from the np hardness of the associated optimization researchers have developed approximation algorithms of varying degrees of sophistication for k means , k medians , and more recently also for bregman clustering 2 however , there seem to be no approximation algorithms for bregman co and tensor clustering in this paper we derive the first \( to our knowledge \) guaranteed methods for these increasingly important clustering settings going beyond bregman divergences , we also prove an approximation factor for tensor clustering with arbitrary separable metrics through extensive experiments we evaluate the characteristics of our method , and show that it also has practical impact"
cs.LG|stat.ML,we use a semisupervised learning algorithm based on a topological data analysis approach to assign functional categories to yeast proteins using similarity graphs this new approach to analyzing biological networks yields results that are as good as or better than state of the art existing approaches
cs.IT,"in this paper we consider the generalization of binary spatially coupled low density parity check \( sc ldpc \) codes to finite fields gf \( q \) , q geq 2 , and develop design rules for q ary sc ldpc code ensembles based on their iterative belief propagation \( bp \) decoding thresholds , with particular emphasis on low latency windowed decoding \( wd \) we consider transmission over both the binary erasure channel \( bec \) and the binary input additive white gaussian noise channel \( biawgnc \) and present results for a variety of \( j , k \) regular sc ldpc code ensembles constructed over gf \( q \) using protographs thresholds are calculated using protograph versions of q ary density evolution \( for the bec \) and q ary extrinsic information transfer analysis \( for the biawgnc \) we show that wd of q ary sc ldpc codes provides significant threshold gains compared to corresponding \( uncoupled \) q ary ldpc block code \( ldpc bc \) ensembles when the window size w is large enough and that these gains increase as the finite field size q 2 m increases moreover , we demonstrate that the new design rules provide wd thresholds that are close to capacity , even when both m and w are relatively small \( thereby reducing decoding complexity and latency \) the analysis further shows that , compared to standard flooding schedule decoding , wd of q ary sc ldpc code ensembles results in significant reductions in both decoding complexity and decoding latency , and that these reductions increase as m increases for applications with a near threshold performance requirement and a constraint on decoding latency , we show that using q ary sc ldpc code ensembles , with moderate q 2 , instead of their binary counterparts results in reduced decoding complexity"
cs.IT,"we present a comparative study of the performance of various polar code constructions in an additive white gaussian noise \( awgn \) channel a polar code construction is any algorithm that selects k best among n possible polar bit channels at the design signal to noise ratio \( design snr \) in terms of bit error rate \( ber \) optimal polar code construction is hard and therefore many suboptimal polar code constructions have been proposed at different computational complexities polar codes are also non universal meaning the code changes significantly with the design snr however , it is not known which construction algorithm at what design snr constructs the best polar codes we first present a comprehensive survey of all the well known polar code constructions along with their full implementations we then propose a heuristic algorithm to find the best design snr for constructing best possible polar codes from a given construction algorithm the proposed algorithm involves a search among several possible design snrs we finally use our algorithm to perform a comparison of different construction algorithms using extensive simulations we find that all polar code construction algorithms generate equally good polar codes in an awgn channel , if the design snr is optimized"
cs.DM|cs.DS,"in the recent research of data mining , frequent structures in a sequence of graphs have been studied intensively , and one of the main concern is changing structures along a sequence of graphs that can capture dynamic properties of data on the contrary , we newly focus on preserving structures in a graph sequence that satisfy a given property for a certain period , and mining such structures is studied as for an onset , we bring up two structures , a connected vertex subset and a clique that exist for a certain period we consider the problem of enumerating these structures and present polynomial delay algorithms for the problems their running time may depend on the size of the representation , however , if each edge has at most one time interval in the representation , the running time is o \( v e 3 \) for connected vertex subsets and o \( min delta 5 , e 2 delta \) for cliques , where the input graph is g \( v , e \) with maximum degree delta to the best of our knowledge , this is the first approach to the treatment of this notion , namely , preserving structures"
cs.IT,recently we extended approximate message passing \( amp \) algorithm to be able to handle general invariant matrix ensembles in this contribution we extend our s amp approach to non linear observation models we obtain generalized amp \( gamp \) algorithm as the special case when the measurement matrix has zero mean iid gaussian entries our derivation is based upon 1 \) deriving expectation propagation \( ep \) like algorithms from the stationary points equations of the gibbs free energy under first and second moment constraints and 2 \) applying additive free convolution in free probability theory to get low complexity updates for the second moment quantities
cs.IT,"in this work we study the reliability and secrecy performance achievable by practical ldpc codes over the gaussian wiretap channel while several works have already addressed this problem in asymptotic conditions , i e , under the hypothesis of codewords of infinite length , only a few approaches exist for the finite length regime we propose an approach to measure the performance of practical codes and compare it with that achievable in asymptotic conditions moreover , based on the secrecy metrics we adopt to achieve this target , we propose a code optimization algorithm which allows to design irregular ldpc codes able to approach the ultimate performance limits even at moderately small codeword lengths \( in the order of 10000 bits \)"
cs.IT,"this paper presents a significant modification to the random demodulator \( rd \) of tropp et al for sub nyquist sampling of frequency sparse signals the modification , termed constrained random demodulator , involves replacing the random waveform , essential to the operation of the rd , with a constrained random waveform that has limits on its switching rate because fast switching waveforms may be hard to generate cleanly the result is a relaxation on the hardware requirements with a slight , but manageable , decrease in the recovery guarantees the paper also establishes the importance of properly choosing the statistics of the constrained random waveform if the power spectrum of the random waveform matches the distribution on the tones of the input signal \( i e , the distribution is proportional to the power spectrum \) , then recovery of the input signal tones is improved the theoretical guarantees provided in the paper are validated through extensive numerical simulations and phase transition plots"
cs.DM|cs.DS,"it is well known as an existence result that every 3 connected graph g \( v , e \) on more than 4 vertices admits a sequence of contractions and a sequence of removal operations to k 4 such that every intermediate graph is 3 connected we show that both sequences can be computed in optimal time , improving the previously best known running times of o \( v 2 \) to o \( v e \) this settles also the open question of finding a linear time 3 connectivity test that is certifying and extends to a certifying 3 edge connectivity test in the same time the certificates used are easy to verify in time o \( e \)"
cs.SI,"in the last decades , the study of models for large real world networks has been a very popular and active area of research a reasonable model should not only replicate all the structural properties that are observed in real world networks \( for example , heavy tailed degree distributions , high clustering and small diameter \) , but it should also be amenable to mathematical analysis there are plenty of models that succeed in the first task but are hard to analyze rigorously on the other hand , a multitude of proposed models , like classical random graphs , can be studied mathematically , but fail in creating certain aspects that are observed in real world networks recently , papadopoulos , krioukov , boguna and vahdat infocom'10 introduced a random geometric graph model that is based on hyperbolic geometry the authors argued empirically and by some preliminary mathematical analysis that the resulting graphs have many of the desired properties moreover , by computing explicitly a maximum likelihood fit of the internet graph , they demonstrated impressively that this model is adequate for reproducing the structure of real graphs with high accuracy in this work we initiate the rigorous study of random hyperbolic graphs we compute exact asymptotic expressions for the expected number of vertices of degree k for all k up to the maximum degree and provide small probabilities for large deviations we also prove a constant lower bound for the clustering coefficient in particular , our findings confirm rigorously that the degree sequence follows a power law distribution with controllable exponent and that the clustering is nonvanishing"
cs.AI,"the adaptation to situations of sequential choice under uncertainty of decision criteria which deviate from \( subjective \) expected utility raises the problem of ensuring the selection of a nondominated strategy in particular , when following the suggestion of machina and mcclennen of giving up separability \( also known as consequentialism \) , which requires the choice of a substrategy in a subtree to depend only on data relevant to that subtree , one must renounce to the use of dynamic programming , since bellman 's principle is no longer valid an interpretation of mcclennen 's resolute choice , based on cooperation between the successive selves of the decision maker , is proposed implementations of resolute choice which prevent money pumps negative prices of information or , more generally , choices of dominated strategies , while remaining computationally tractable , are proposed"
cs.IT,"in this paper , we present a construction of linear codes over f 2 t from boolean functions , which is a generalization of ding 's method cite theorem 9 ding15 based on this construction , we give two classes of linear codes tilde c f and c f \( see theorem ref thm maincode1 and theorem ref thm maincodenew \) over f 2 t from a boolean function f f q rightarrow f 2 , where q 2 n and f 2 t is some subfield of f q the complete weight enumerator of tilde c f can be easily determined from the walsh spectrum of f , while the weight distribution of the code c f can also be easily settled particularly , the number of nonzero weights of tilde c f and c f is the same as the number of distinct walsh values of f as applications of this construction , we show several series of linear codes over f 2 t with two or three weights by using bent , semibent , monomial and quadratic boolean function f"
cs.AI|cs.LG|stat.ML,"in this work , we propose a generalized product of experts \( gpoe \) framework for combining the predictions of multiple probabilistic models we identify four desirable properties that are important for scalability , expressiveness and robustness , when learning and inferring with a combination of multiple models through analysis and experiments , we show that gpoe of gaussian processes \( gp \) have these qualities , while no other existing combination schemes satisfy all of them at the same time the resulting gp gpoe is highly scalable as individual gp experts can be independently learned in parallel very expressive as the way experts are combined depends on the input rather than fixed the combined prediction is still a valid probabilistic model with natural interpretation and finally robust to unreliable predictions from individual experts"
cs.IT,"we propose a blind interference alignment scheme for the vector broadcast channel where the transmitter is equipped with m antennas and there are k receivers , each equipped with a reconfigurable antenna capable of switching among m preset modes without any knowledge of the channel coefficient values at the transmitters and with only mild assumptions on the channel coherence structure we show that mk m k 1 degrees of freedom are achievable the key to the blind interference alignment scheme is the ability of the receivers to switch between reconfigurable antenna modes to create short term channel fluctuation patterns that are exploited by the transmitter the achievable scheme does not require cooperation between transmit antennas and is therefore applicable to the mxk x network as well only finite symbol extensions are used , and no channel knowledge at the receivers is required to null the interference"
cs.IT,"in this paper , locally repairable codes with all symbol locality are studied methods to modify already existing codes are presented also , it is shown that with high probability , a random matrix with a few extra columns guaranteeing the locality property , is a generator matrix for a locally repairable code with a good minimum distance the proof of this also gives a constructive method to find locally repairable codes constructions are given of three infinite classes of optimal vector linear locally repairable codes over an alphabet of small size , not depending on the size of the code length"
cs.IT,"we study the quadratic residue weight enumerators of the dual projective reed solomon codes of dimensions 5 and q 4 over the finite field mathbb f q our main results are formulas for the coefficients of the the quadratic residue weight enumerators for such codes if q p v and we fix v and vary p then our formulas for the coefficients of the dimension q 4 code involve only polynomials in p and the trace of the q th and \( q p 2 \) th hecke operators acting on spaces of cusp forms for the congruence groups operatorname sl 2 \( mathbb z \) , gamma 0 \( 2 \) , and gamma 0 \( 4 \) the main tool we use is the eichler selberg trace formula , which gives along the way a variation of a theorem of birch on the distribution of rational point counts for elliptic curves with prescribed 2 torsion over a fixed finite field"
cs.IT,it is demonstrated that appropriately chosen computable metrics based on self correlation properties provide a degree of determinism sufficient to segregate binary strings by level of information content
cs.IT,heisenberg like and fisher information based uncertainty relations which extend and generalize previous similar expressions are obtained for n fermion d dimensional systems the contributions of both spatial and spin degrees of freedom are taken into account the accuracy of some of these generalized spinned uncertainty like relations is numerically examined for a large number of atomic and molecular systems
cs.DM|cs.DS,"we study the approximation hardness of the shortest superstring , the maximal compression and the maximum asymmetric traveling salesperson \( max atsp \) problem we introduce a new reduction method that produces strongly restricted instances of the shortest superstring problem , in which the maximal orbit size is eight \( with no character appearing more than eight times \) and all given strings having length four based on this reduction method , we are able to improve the best up to now known approximation lower bound for the shortest superstring problem and the maximal compression problem by an order of magnitude the results imply also an improved approximation lower bound for the max atsp problem"
cs.LG|stat.ML,"latent variable models are one popular approach to modeling sequences one problem with sequence models , including latent variable models , is that their exact learning algorithms are usually intractable in t , the length of the sequence , necessitating the use of approximation algorithms though these algorithms are faster than their exact counterparts , they are iterative and computationally expensive however , models with fast algorithms can be designed for commonly occurring subsets of the set of sequences we propose a new statistical model for a subset the set of sequences with inertia our learning algorithms , at time complexity o \( t log t \) , are significantly faster than those of general purpose latent variable sequence models"
cs.LG|stat.ML,"discussion of latent variable graphical model selection via convex optimization by venkat chandrasekaran , pablo a parrilo and alan s willsky arxiv 1008 1290"
cs.LG|stat.ML,structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning in this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems our extensive numerical experiments demonstrate that the presented technique outperforms its state of the art competitors and has several advantages over approaches that do not put structured constraints on the dictionary elements
cs.DS|cs.LG,"online optimization problems arise in many resource allocation tasks , where the future demands for each resource and the associated utility functions change over time and are not known apriori , yet resources need to be allocated at every point in time despite the future uncertainty in this paper , we consider online optimization problems with general concave utilities we modify and extend an online optimization algorithm proposed by devanur et al for linear programming to this general setting the model we use for the arrival of the utilities and demands is known as the random permutation model , where a fixed collection of utilities and demands are presented to the algorithm in random order we prove that under this model the algorithm achieves a competitive ratio of 1 o \( epsilon \) under a near optimal assumption that the bid to budget ratio is o left \( frac epsilon 2 log left \( m epsilon right \) right \) , where m is the number of resources , while enjoying a significantly lower computational cost than the only algorithm known to achieve optimal competitive ratio proposed by kesselheim et al we draw a connection between the proposed algorithm and subgradient methods used in convex optimization in addition , we present numerical experiments that demonstrate the performance and speed of this algorithm in comparison to existing algorithms"
cs.AI,"in this paper with the aid of genetic algorithm and fuzzy theory , we present a hybrid job scheduling approach , which considers the load balancing of the system and reduces total execution time and execution cost we try to modify the standard genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory the main goal of this research is to assign the jobs to the resources with considering the vm mips and length of jobs the new algorithm assigns the jobs to the resources with considering the job length and resources capacities we evaluate the performance of our approach with some famous cloud scheduling models the results of the experiments show the efficiency of the proposed approach in term of execution time , execution cost and average degree of imbalance \( di \)"
cs.IT,"in this paper we present an fpga based implementation of linear programming \( lp \) decoding lp decoding frames error correction as an optimization problem this is in contrast to variants of belief propagation \( bp \) decoding that view error correction as a problem of graphical inference there are many advantages to taking the optimization perspective convergence guarantees , improved performance in certain regimes , and a methodology for incorporating the latest developments in optimization techniques however , lp decoding , when implemented with standard lp solvers , does not easily scale to the blocklengths of modern error correction codes in earlier work , we showed that by drawing on decomposition methods from optimization theory , specifically the alternating direction method of multipliers \( admm \) , we could build an lp decoding solver that was competitive with bp , both in terms of performance and speed we also observed empirically that lp decoders have much better high snr performance in the error floor regime , a trait of particular relevance to optical transport and storage applications while our previous implementation was in floating point , in this paper we report initial results of a fixed point , hardware based realization of our admm lp decoder"
cs.IT,"in this paper , a novel approach for optimizing the joint deployment of small cell base stations and wireless backhaul links is proposed this joint deployment scenario is cast as a multi objective optimization problem under the constraints of limited backhaul capacity and outage probability to address the problem , a novel adaptive algorithm that integrates epsilon method , lagrangian relaxation and tabu search is proposed to obtain the pareto optimal solution set simulation results show that the proposed algorithm is quite effective in finding the optimal solutions the proposed joint deployment model can be used for planning small cell networks"
cs.DM,a polynomial algorithm is obtained for the np complete linear ordering problem
cs.DM,"it is a fundamental property of non letter lyndon words that they can be expressed as a concatenation of two shorter lyndon words this leads to a naive lower bound log 2 \( n \) 1 for the number of distinct lyndon factors that a lyndon word of length n must have , but this bound is not optimal in this paper we show that a much more accurate lower bound is log phi \( n \) 1 , where phi denotes the golden ratio \( 1 sqrt 5 \) 2 we show that this bound is optimal in that it is attained by the fibonacci lyndon words we then introduce a mapping l x that counts the number of lyndon factors of length at most n in an infinite word x we show that a recurrent infinite word x is aperiodic if and only if l x l f , where f is the fibonacci infinite word , with equality if and only if f is in the shift orbit closure of f"
cs.DM,"we investigate the problem of construction of small size universal petri nets with inhibitor arcs we consider four descriptional complexity parameters the number of places , transitions , inhibitor arcs , and the maximal degree of a transition , each of which we try to minimize we give six constructions having the following values of parameters \( listed in the above order \) \( 30 , 34 , 13 , 3 \) , \( 14 , 31 , 51 , 8 \) , \( 11 , 31 , 79 , 11 \) , \( 21 , 25 , 13 , 5 \) , \( 67 , 64 , 8 , 3 \) , \( 58 , 55 , 8 , 5 \) that improve the few known results on this topic our investigation also highlights several interesting trade offs"
cs.SI,detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences social networks also exhibit dynamic nature as these networks change continuously with the passage of time social networks might also present a hierarchical structure led by individuals that play important roles in a society such as managers and decision makers detection and visualization of these networks changing over time is a challenging problem where communities change as a function of events taking place in the society and the role people play in it in this paper we address these issues by presenting a system to analyze dynamic social networks the proposed system is based on dynamic graph discretization and graph clustering the system allows detection of major structural changes taking place in social communities over time and reveals hierarchies by identifying influential people in a social networks we use two different data sets for the empirical evaluation and observe that our system helps to discover interesting facts about the social and hierarchical structures present in these social networks
cs.IT,"polar codes are constructed for m user multiple access channels \( mac \) whose input alphabet size is a prime number the block error probability under successive cancelation decoding decays exponentially with the square root of the block length although the sum capacity is achieved by this coding scheme , some points in the symmetric capacity region may not be achieved in the case where the channel is a combination of linear channels , we provide a necessary and sufficient condition characterizing the channels whose symmetric capacity region is preserved upon the polarization process we also provide a sufficient condition for having a total loss in the dominant face"
cs.DM|cs.DS,"we introduce two methods for speeding up adiabatic quantum computations by increasing the energy between the ground and first excited states our methods are even more general they can be used to shift a hamiltonian 's density of states away from the ground state , so that fewer states occupy the low lying energies near the minimum , hence allowing for faster adiabatic passages to find the ground state with less risk of getting caught in an undesired low lying excited state during the passage even more generally , our methods can be used to transform a discrete optimization problem into a new one whose unique minimum still encodes the desired answer , but with the objective function 's values forming a different landscape aspects of the landscape such as the objective function 's range , or the values of certain coefficients , or how many different inputs lead to a given output value , can be decreased or increased one of the many examples for which these methods are useful is in finding the ground state of a hamiltonian using nmr if it is difficult to find a molecule such that the distances between the spins match the interactions in the hamiltonian , the interactions in the hamiltonian can be changed without at all changing the ground state we apply our methods to an aqc algorithm for integer factorization , and the first method reduces the maximum runtime in our example by up to 754 , and the second method reduces the maximum runtime of another example by up to 250 these two methods may also be combined"
cs.IT,"multitrack detection with array head reading is a promising technique proposed for next generation magnetic storage systems the multihead multitrack \( mhmt \) system is characterized by intersymbol interference \( isi \) in the downtrack direction and intertrack interference \( iti \) in the crosstrack direction constructing the trellis of a mhmt maximum likelihood \( ml \) detector requires knowledge of the iti , which is generally unknown at the receiver in addition , to retain efficiency , the ml detector requires a static estimate of the iti , whose true value may in reality vary in this paper we propose a modified ml detector on the n head , n track \( n h n t \) channel which could efficiently track the change of iti , and adapt to new estimates the trellis used in the proposed detector is shown to be independent of the iti level a gain loop structure is used to estimate the iti simulation results show that the proposed detector offers a performance advantage in settings where complexity constraints limit the traditional ml detector to use a static iti estimate"
cs.IT,"because of the emerging field of internet of things \( iot \) , future backscatter rfid is required to be more reliable and data intensive motivated by this , orthogonal space time block code \( ostbc \) , which is very successful in mobile communications for its low complexity and high performance , has already been investigated for backscatter rfid on the other hand , a recently proposed scheme called unitary query was shown to be able to considerably improve the reliability of backscatter radio by exploiting query diversity therefore incorporating the classical ostbc \( at the tag end \) with the recently proposed unitary query \( at the query end \) seems to be promising however , in this paper , we show that simple , direct employment of ostbc together with unitary query incurs a linear decoding problem and eventually leads to a severe performance degradation as a re design of the recently proposed unitary query and the classical ostbc specifically for mimo backscatter rfid , we present a butq mostbc design pair idea by proposing the block level unitary query \( butq \) at the query end and the corresponding modified ostbc \( mostbc \) at the tag end the proposed butq mostbc can resolve the linear decoding problem , keep the simplicity and high performance properties of the classical ostbc , and achieve the query diversity for the m times l times n mimo backscatter rfid channel"
cs.SI|stat.ML,"online discussion forums are complex webs of overlapping subcommunities \( macrolevel structure , across threads \) in which users enact different roles depending on which subcommunity they are participating in within a particular time point \( microlevel structure , within threads \) this sub network structure is implicit in massive collections of threads to uncover this structure , we develop a scalable algorithm based on stochastic variational inference and leverage topic models \( lda \) along with mixed membership stochastic block \( mmsb \) models we evaluate our model on three large scale datasets , cancer threadstarter \( 22k users and 14 4k threads \) , cancer namemention \( 15 1k users and 12 4k threads \) and stackoverflow \( 1 19 million users and 4 55 million threads \) qualitatively , we demonstrate that our model can provide useful explanations of microlevel and macrolevel user presentation characteristics in different communities using the topics discovered from posts quantitatively , we show that our model does better than mmsb and lda in predicting user reply structure within threads in addition , we demonstrate via synthetic data experiments that the proposed active sub network discovery model is stable and recovers the original parameters of the experimental setup with high probability"
cs.DS|cs.IT,"random linear network coding \( rlnc \) has emerged as a powerful tool for robust high throughput multicast projection analysis a recently introduced technique shows that the distributed packetized rlnc protocol achieves \( order \) optimal and perfectly pipelined information dissemination in many settings in the original approach to rnlc intermediate nodes code together all available information this requires intermediate nodes to keep considerable data available for coding moreover , it results in a coding complexity that grows linearly with the size of this data while this has been identified as a problem , approaches that combine queuing theory and network coding have heretofore not provided a succinct representation of the memory needs of network coding at intermediates nodes this paper shows the surprising result that , in all settings with a continuous stream of data , network coding continues to perform optimally even if only one packet per node is kept in active memory and used for computations this leads to an extremely simple rlnc protocol variant with drastically reduced requirements on computational and memory resources by extending the projection analysis , we show that in all settings in which the rlnc protocol was proven to be optimal its finite memory variant performs equally well in the same way as the original projection analysis , our technique applies in a wide variety of network models , including highly dynamic topologies that can change completely at any time in an adversarial fashion"
cs.IT,"we consider the problem of secret key extraction when n honest parties and an eavesdropper share correlated information we present a family of probability distributions and give the full characterization of its distillation properties this formalism allows us to design a rich variety of cryptographic scenarios in particular , we provide examples of multipartite probability distributions containing non distillable secret correlations , also known as bound information"
cs.IT,"it has previously been shown that ensembles of terminated protograph based low density parity check \( ldpc \) convolutional codes have a typical minimum distance that grows linearly with block length and that they are capable of achieving capacity approaching iterative decoding thresholds on the binary erasure channel \( bec \) in this paper , we review a recent result that the dramatic threshold improvement obtained by terminating ldpc convolutional codes extends to the additive white gaussian noise \( awgn \) channel also , using a \( 3 , 6 \) regular protograph based ldpc convolutional code ensemble as an example , we perform an asymptotic trapping set analysis of terminated ldpc convolutional code ensembles in addition to capacity approaching iterative decoding thresholds and linearly growing minimum distance , we find that the smallest non empty trapping set of a terminated ensemble grows linearly with block length"
cs.IT,"we consider an energy minimization problem for cooperative lte networks to reduce energy consumption , we investigate how to jointly optimize the transmit power and the association between cells and user equipments \( ues \) , by taking into consideration joint transmission \( jt \) , one of the coordinated multipoint \( comp \) techniques we formulate the optimization problem mathematically for solving the problem , a dynamic power allocation algorithm that adjusts the transmit power of all cells , and an algorithm for optimizing the cell ue association , are proposed the two algorithms are iteratively used in an algorithmic framework to enhance the energy performance numerically , the proposed algorithms can lead to lower energy consumption than the optimal energy setting in the non jt case in comparison to fixed power allocation in jt , the proposed dynamic power allocation algorithm is able to significantly reduce the energy consumption"
cs.DS,"we consider the problem of recovering items matching a partially specified pattern in multidimensional trees \( quadtrees and k d trees \) we assume the traditional model where the data consist of independent and uniform points in the unit square for this model , in a structure on n points , it is known that the number of nodes c n \( xi \) to visit in order to report the items matching a random query xi , independent and uniformly distributed on 0 , 1 , satisfies mathbf e c n \( xi \) sim kappa n beta , where kappa and beta are explicit constants we develop an approach based on the analysis of the cost c n \( s \) of any fixed query s in 0 , 1 , and give precise estimates for the variance and limit distribution of the cost c n \( x \) our results permit us to describe a limit process for the costs c n \( x \) as x varies in 0 , 1 one of the consequences is that mathbf e max x in 0 , 1 c n \( x \) sim gamma n beta this settles a question of devroye pers comm , 2000"
cs.IT,"schein and gallager introduced the gaussian parallel relay channel in 2000 they proposed the amplify and forward \( af \) and the decode and forward \( df \) strategies for this channel for a long time , the best known achievable rate for this channel was based on the af and df with time sharing \( af df \) recently , a rematch and forward \( rf \) scheme for the scenario in which different amounts of bandwidth can be assigned to the first and second hops were proposed in this paper , we propose a emph combined amplify and decode forward \( cadf \) scheme for the gaussian parallel relay channel we prove that the cadf scheme always gives a better achievable rate compared to the rf scheme , when there is a bandwidth mismatch between the first hop and the second hop furthermore , for the equal bandwidth case \( schein 's setup \) , we show that the time sharing between the cadf and the df schemes \( cadf df \) leads to a better achievable rate compared to the time sharing between the rf and the df schemes \( rf df \) as well as the af df"
cs.IT,"there is a growing interest in energy efficient or so called green wireless communication to reduce the energy consumption in cellular networks since today 's wireless terminals are typically equipped with multiple network access interfaces such as bluetooth , wi fi , and cellular networks , this paper investigates user terminals cooperating with each other in transmitting their data packets to a base station \( bs \) by exploiting the multiple network access interfaces , referred to as inter network cooperation , to improve the energy efficiency in cellular uplink transmission given target outage probability and data rate requirements , we develop a closed form expression of energy efficiency in bits per joule for the inter network cooperation by taking into account the path loss , fading , and thermal noise effects numerical results show that when the cooperating users move towards to each other , the proposed inter network cooperation significantly improves the energy efficiency as compared with the traditional non cooperation and intra network cooperation this implies that given a certain amount of bits to be transmitted , the inter network cooperation requires less energy than the traditional non cooperation and intra network cooperation , showing the energy saving benefit of inter network cooperation"
cs.DM,"the maximum genus gamma m \( g \) of a graph g is the largest genus of an orientable surface into which g has a cellular embedding combinatorially , it coincides with the maximum number of disjoint pairs of adjacent edges of g whose removal results in a connected spanning subgraph of g in this paper we prove that removing pairs of adjacent edges from g arbitrarily while retaining connectedness leads to at least gamma m \( g \) 2 pairs of edges removed this allows us to describe a greedy algorithm for the maximum genus of a graph our algorithm returns an integer k such that gamma m \( g \) 2 le k le gamma m \( g \) , providing a simple method to efficiently approximate maximum genus as a consequence of our approach we obtain a 2 approximate counterpart of xuong 's combinatorial characterisation of maximum genus"
cs.DM,"we define the geometric thickness of a graph to be the smallest number of layers such that we can draw the graph in the plane with straight line edges and assign each edge to a layer so that no two edges on the same layer cross the geometric thickness lies between two previously studied quantities , the \( graph theoretical \) thickness and the book thickness we investigate the geometric thickness of the family of complete graphs , k n we show that the geometric thickness of k n lies between ceiling \( \( n 5 646 \) 0 342 \) and ceiling \( n 4 \) , and we give exact values of the geometric thickness of k n for n 12 and n in 15 , 16 we also consider the geometric thickness of the family of complete bipartite graphs in particular , we show that , unlike the case of complete graphs , there are complete bipartite graphs with arbitrarily large numbers of vertices for which the geometric thickness coincides with the standard graph theoretical thickness"
cs.IT,"in this paper , we consider the joint design of the transceivers for a multiple access multiple input and multiple output \( mimo \) system having inter symbol interference \( isi \) channels the system we consider is equipped with the minimum mean square error \( mmse \) decision feedback \( df \) detector traditionally , transmitter designs for this system have been based on constraints of either the transmission power or the signal to interference and noise ratio \( sinr \) for each user here , we explore a novel perspective and examine a transceiver design which is based on a fixed sum mutual information constraint and minimizes the arithmetic average of mean square error of mmse decision feedback detection for this optimization problem , a closed form solution is obtained and is achieved if and only if the averaged sum mutual information is uniformly distributed over each active subchannel meanwhile , the mutual information of the currently detected user is uniformly distributed over each individual symbol within the block signal of the user , assuming all the previous user signals have been perfectly detected"
cs.SI,"large software projects are among most sophisticated human made systems consisting of a network of interdependent parts past studies of software systems from the perspective of complex networks have already led to notable discoveries with different applications nevertheless , our comprehension of the structure of software networks remains to be only partial we here investigate correlations or mixing between linked nodes and show that software networks reveal dichotomous node degree mixing similar to that recently observed in biological networks we further show that software networks also reveal characteristic clustering profiles and mixing hence , node mixing in software networks significantly differs from that in , e g , the internet or social networks we explain the observed mixing through the presence of groups of nodes with common linking pattern more precisely , besides densely linked groups known as communities , software networks also consist of disconnected groups denoted modules , core periphery structures and other moreover , groups coincide with the intrinsic properties of the underlying software projects , which promotes practical applications in software engineering"
cs.IT,"random network coding recently attracts attention as a technique to disseminate information in a network this paper considers a non coherent multi shot network , where the unknown and time variant network is used several times in order to create dependencies between the different shots , particular convolutional codes in rank metric are used these codes are so called \( partial \) unit memory \( \( p \) um \) codes , i e , convolutional codes with memory one first , distance measures for convolutional codes in rank metric are shown and two constructions of \( p \) um codes in rank metric based on the generator matrices of maximum rank distance codes are presented second , an efficient error erasure decoding algorithm for these codes is presented its guaranteed decoding radius is derived and its complexity is bounded finally , it is shown how to apply these codes for error correction in random linear and affine network coding"
cs.AI|cs.DS,"we study the performance of stochastic local search algorithms for random instances of the k satisfiability \( k sat \) problem we introduce a new stochastic local search algorithm , chainsat , which moves in the energy landscape of a problem instance by em never going upwards in energy chainsat is a emph focused algorithm in the sense that it considers only variables occurring in unsatisfied clauses we show by extensive numerical investigations that chainsat and other focused algorithms solve large k sat instances almost surely in linear time , up to high clause to variable ratios alpha for example , for k 4 we observe linear time performance well beyond the recently postulated clustering and condensation transitions in the solution space the performance of chainsat is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters , yet no such minima are encountered we also study the geometry of the solution space as accessed by stochastic local search algorithms"
cs.AI,"conventional face to face classrooms are still the main learning system applied in indonesia in assisting such conventional learning towards an optimal learning , formative evaluations are needed to monitor the progress of the class this task can be very hard when the size of the class is large hence , this research attempted to create a classroom monitoring system based on student data of department of electrical engineering and information technology in order to achieve the goal , a student modeling using case based reasoning was proposed a generic student model based on a framework was developed the model represented student knowledge of a subject the result showed that the system was able to store and retrieve student data for suggestion of the current situation and formative evaluation for one of the subject in the department"
cs.IT,"in this paper we propose a new soft input soft output equalization algorithm , offering very good performance complexity tradeoffs it follows the structure of the bcjr algorithm , but dynamically constructs a simplified trellis during the forward recursion in each trellis section , only the m states with the strongest forward metric are preserved , similar to the m bcjr algorithm unlike the m bcjr , however , the remaining states are not deleted , but rather merged into the surviving states the new algorithm compares favorably with the reduced state bcjr algorithm , offering better performance and more flexibility , particularly for systems with higher order modulations"
cs.LG|stat.ML,"in ranking problems , the goal is to learn a ranking function from labeled pairs of input points in this paper , we consider the related comparison problem , where the label indicates which element of the pair is better , or if there is no significant difference we cast the learning problem as a margin maximization , and show that it can be solved by converting it to a standard svm we use simulated nonlinear patterns and a real learning to rank sushi data set to show that our proposed svmcompare algorithm outperforms svmrank when there are equality pairs"
cs.DM,"we study multidimensional configurations \( infinite words \) and subshifts of low pattern complexity using tools of algebraic geometry we express the configuration as a multivariate formal power series over integers and investigate the setup when there is a non trivial annihilating polynomial a non zero polynomial whose formal product with the power series is zero such annihilator exists , for example , if the number of distinct patterns of some finite shape d in the configuration is at most the size d of the shape this is our low pattern complexity assumption we prove that the configuration must be a sum of periodic configurations over integers , possibly with unbounded values as a specific application of the method we obtain an asymptotic version of the well known nivat 's conjecture we prove that any two dimensional , non periodic configuration can satisfy the low pattern complexity assumption with respect to only finitely many distinct rectangular shapes d"
cs.LG,"learning a good distance metric in feature space potentially improves the performance of the knn classifier and is useful in many real world applications many metric learning algorithms are however based on the point estimation of a quadratic optimization problem , which is time consuming , susceptible to overfitting , and lack a natural mechanism to reason with parameter uncertainty , an important property useful especially when the training set is small and or noisy to deal with these issues , we present a novel bayesian metric learning method , called bayesian nca , based on the well known neighbourhood component analysis method , in which the metric posterior is characterized by the local label consistency constraints of observations , encoded with a similarity graph instead of independent pairwise constraints for efficient bayesian optimization , we explore the variational lower bound over the log likelihood of the original nca objective experiments on several publicly available datasets demonstrate that the proposed method is able to learn robust metric measures from small size dataset and or from challenging training set with labels contaminated by errors the proposed method is also shown to outperform a previous pairwise constrained bayesian metric learning method"
cs.IT,"puncturing is a well known coding technique widely used for constructing rate compatible codes in this paper , we consider the problem of puncturing low density parity check codes and propose a new algorithm for intentional puncturing the algorithm is based on the puncturing of untainted symbols , i e nodes with no punctured symbols within their neighboring set it is shown that the algorithm proposed here performs better than previous proposals for a range of coding rates and short proportions of punctured symbols"
cs.DM|cs.DS,"a circle graph is the intersection graph of a set of chords in a circle keil discrete applied mathematics , 42 \( 1 \) 51 63 , 1993 proved that dominating set , connected dominating set , and total dominating set are np complete in circle graphs to the best of our knowledge , nothing was known about the parameterized complexity of these problems in circle graphs in this paper we prove the following results , which contribute in this direction dominating set , independent dominating set , connected dominating set , total dominating set , and acyclic dominating set are w 1 hard in circle graphs , parameterized by the size of the solution whereas both connected dominating set and acyclic dominating set are w 1 hard in circle graphs , it turns out that connected acyclic dominating set is polynomial time solvable in circle graphs if t is a given tree , deciding whether a circle graph has a dominating set isomorphic to t is np complete when t is in the input , and fpt when parameterized by v \( t \) we prove that the fpt algorithm is subexponential"
cs.AI,"in this paper , we explore the interplay between network topology and time criticality in a military logistics system a general goal of this work \( and previous work \) is to evaluate land transportation requirements or , more specifically , how to design appropriate fleets of military general service vehicles that are tasked with the supply and re supply of military units dispersed in an area of operation the particular focus of this paper is to gain a better understanding of how the logistics environment changes when current army vehicles with fixed transport characteristics are replaced by a new generation of modularised vehicles that can be configured task specifically the experimental work is conducted within a well developed strategic planning simulation environment which includes a scenario generation engine for automatically sampling supply and re supply missions and a multi objective meta heuristic search algorithm \( i e evolutionary algorithm \) for solving the particular scheduling and routing problems the results presented in this paper allow for a better understanding of how \( and under what conditions \) a modularised vehicle fleet can provide advantages over the currently implemented system"
cs.SI,"a practical approach to protecting networks against epidemic processes such as spreading of infectious diseases , malware , and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components because determining the optimal order to remove nodes is a computationally hard problem , various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal morone and makse proposed an algorithm employing the non backtracking matrix of given networks , which outperforms various existing algorithms with a reasonable computational cost in fact , many empirical networks have community structure , compromising the assumption of local tree like structure on which the original algorithm is based we develop an immunization algorithm by synergistically combining the morone makse algorithm and coarse graining of the network in which we regard a community as a supernode the proposed algorithm works more efficiently than the morone makse and other algorithms on networks with community structure"
cs.DS,"performing random walks in networks is a fundamental primitive that has found numerous applications in communication networks such as token management , load balancing , network topology discovery and construction , search , and peer to peer membership management while several such algorithms are ubiquitous , and use numerous random walk samples , the walks themselves have always been performed naively in this paper , we focus on the problem of performing random walk sampling efficiently in a distributed network given bandwidth constraints , the goal is to minimize the number of rounds and messages required to obtain several random walk samples in a continuous online fashion we present the first round and message optimal distributed algorithms that present a significant improvement on all previous approaches the theoretical analysis and comprehensive experimental evaluation of our algorithms show that they perform very well in different types of networks of differing topologies in particular , our results show how several random walks can be performed continuously \( when source nodes are provided only at runtime , i e , online \) , such that each walk of length ell can be performed exactly in just tilde o \( sqrt ell d \) rounds , \( where d is the diameter of the network \) , and o \( ell \) messages this significantly improves upon both , the naive technique that requires o \( ell \) rounds and o \( ell \) messages , and the sophisticated algorithm of dassarma et al podc 2010 that has the same round complexity as this paper but requires omega \( m sqrt ell \) messages \( where m is the number of edges in the network \) our theoretical results are corroborated through extensive experiments on various topological data sets our algorithms are fully decentralized , lightweight , and easily implementable , and can serve as building blocks in the design of topologically aware networks"
cs.DM,"in 2012 b 'ona showed the rather surprising fact that the cumulative number of occurrences of the classical patterns 231 and 213 are the same on the set of permutations avoiding 132 , beside the pattern based statistics 231 and 213 do not have the same distribution on this set here we show that if it is required for the symbols playing the role of 1 and 3 in the occurrences of 231 and 213 to be adjacent , then the obtained statistics are equidistributed on the set of 132 avoiding permutations actually , expressed in terms of vincular patterns , we prove the following more general results the statistics based on the patterns b ca , b ac and ba c , together with other statistics , have the same joint distribution on s n \( 132 \) , and so do the patterns bc a and c ab and up to trivial transformations , these statistics are the only based on length three proper \( not classical nor adjacent \) vincular patterns which are equidistributed on a set of permutations avoiding a classical length three pattern"
cs.AI|cs.IT|cs.LG,"general purpose intelligent learning agents cycle through \( complex , non mdp \) sequences of observations , actions , and rewards on the other hand , reinforcement learning is well developed for small finite state markov decision processes \( mdps \) so far it is an art performed by human designers to extract the right state representation out of the bare observations , i e to reduce the agent setup to the mdp framework before we can think of mechanizing this search for suitable mdps , we need a formal objective criterion the main contribution of this article is to develop such a criterion i also integrate the various parts into one learning algorithm extensions to more realistic dynamic bayesian networks are developed in a companion article"
cs.DS|cs.IT,"let p p \( i \) be a measure of strictly positive probabilities on the set of nonnegative integers although the countable number of inputs prevents usage of the huffman algorithm , there are nontrivial p for which known methods find a source code that is optimal in the sense of minimizing expected codeword length for some applications , however , a source code should instead minimize one of a family of nonlinear objective functions , beta exponential means , those of the form log a sum i p \( i \) a n \( i \) , where n \( i \) is the length of the i th codeword and a is a positive constant applications of such minimizations include a novel problem of maximizing the chance of message receipt in single shot communications \( a 1 \) and a previously known problem of minimizing the chance of buffer overflow in a queueing system \( a 1 \) this paper introduces methods for finding codes optimal for such exponential means one method applies to geometric distributions , while another applies to distributions with lighter tails the latter algorithm is applied to poisson distributions and both are extended to alphabetic codes , as well as to minimizing maximum pointwise redundancy the aforementioned application of minimizing the chance of buffer overflow is also considered"
cs.LG|stat.ML,"auc \( area under roc curve \) is an important evaluation criterion , which has been popularly used in many learning tasks such as class imbalance learning , cost sensitive learning , learning to rank , etc many learning approaches try to optimize auc , while owing to the non convexity and discontinuousness of auc , almost all approaches work with surrogate loss functions thus , the consistency of auc is crucial however , it has been almost untouched before in this paper , we provide a sufficient condition for the asymptotic consistency of learning approaches based on surrogate loss functions based on this result , we prove that exponential loss and logistic loss are consistent with auc , but hinge loss is inconsistent then , we derive the q norm hinge loss and general hinge loss that are consistent with auc we also derive the consistent bounds for exponential loss and logistic loss , and obtain the consistent bounds for many surrogate loss functions under the non noise setting further , we disclose an equivalence between the exponential surrogate loss of auc and exponential surrogate loss of accuracy , and one straightforward consequence of such finding is that adaboost and rankboost are equivalent"
cs.IT,"low complexity joint estimation of synchronization impairments and channel in a single user mimo ofdm system is presented in this letter based on a system model that takes into account the effects of synchronization impairments such as carrier frequency offset , sampling frequency offset , and symbol timing error , and channel , a maximum likelihood \( ml \) algorithm for the joint estimation is proposed to reduce the complexity of ml grid search , the number of received signal samples used for estimation need to be reduced the conventional channel estimation methods using least squares \( ls \) fail for the reduced sample under determined system , which results in poor performance of the joint estimator the proposed ml algorithm uses compressed sensing \( cs \) based channel estimation method in a sparse fading scenario , where the received samples used for estimation are less than that required for an ls based estimation the performance of the estimation method is studied through numerical simulations , and it is observed that cs based joint estimator performs better than ls based joint estimator"
cs.DS,number decision diagrams \( ndd \) provide a natural finite symbolic representation for regular set of integer vectors encoded as strings of digit vectors \( least or most significant digit first \) the convex hull of the set of vectors represented by a ndd is proved to be an effectively computable convex polyhedron
cs.AI,"description logic programs \( dl programs \) proposed by eiter et al constitute an elegant yet powerful formalism for the integration of answer set programming with description logics , for the semantic web in this paper , we generalize the notions of completion and loop formulas of logic programs to description logic programs and show that the answer sets of a dl program can be precisely captured by the models of its completion and loop formulas furthermore , we propose a new , alternative semantics for dl programs , called the em canonical answer set semantics , which is defined by the models of completion that satisfy what are called canonical loop formulas a desirable property of canonical answer sets is that they are free of circular justifications some properties of canonical answer sets are also explored"
cs.AI|cs.LG,"rules complement and extend ontologies on the semantic web we refer to these rules as onto relational since they combine dl based ontology languages and knowledge representation formalisms supporting the relational data model within the tradition of logic programming and deductive databases rule authoring is a very demanding knowledge engineering task which can be automated though partially by applying machine learning algorithms in this chapter we show how inductive logic programming \( ilp \) , born at the intersection of machine learning and logic programming and considered as a major approach to relational learning , can be adapted to onto relational learning for the sake of illustration , we provide details of a specific onto relational learning solution to the problem of learning rule based definitions of dl concepts and roles with ilp"
cs.DS,"we give an exact algorithm for the 0 1 integer linear programming problem with a linear number of constraints that improves over exhaustive search by an exponential factor specifically , our algorithm runs in time 2 \( 1 text poly \( 1 c \) \) n where n is the number of variables and cn is the number of constraints the key idea for the algorithm is a reduction to the vector domination problem and a new algorithm for that subproblem"
cs.AI,"improving the throughput of molecular docking , a computationally intensive phase of the virtual screening process , is a highly sought area of research since it has a significant weight in the drug designing process with such improvements , the world might find cures for incurable diseases like hiv disease and cancer sooner our approach presented in this paper is to utilize a multi core environment to introduce data level parallelism \( dlp \) to the autodock vina software , which is a widely used for molecular docking software autodock vina already exploits instruction level parallelism \( ilp \) in multi core environments and therefore optimized for such environments however , with the results we have obtained , it can be clearly seen that our approach has enhanced the throughput of the already optimized software by more than six times this will dramatically reduce the time consumed for the lead identification phase in drug designing along with the shift in the processor technology from multi core to many core of the current era therefore , we believe that the contribution of this project will effectively make it possible to expand the number of small molecules docked against a drug target and improving the chances to design drugs for incurable diseases"
cs.IT,"we consider transmission over a binary input additive white gaussian noise channel using low density parity check codes one of the most popular techniques for decoding low density parity check codes is the linear programming decoder in general , the linear programming decoder is suboptimal i e , the word error rate is higher than the optimal , maximum a posteriori decoder in this paper we present a systematic approach to enhance the linear program decoder more precisely , in the cases where the linear program outputs a fractional solution , we give a simple algorithm to identify frustrated cycles which cause the output of the linear program to be fractional then adding these cycles , adaptively to the basic linear program , we show improved word error rate performance"
cs.DM|cs.DS,"in this paper , we consider the online version of the machine minimization problem \( introduced by chuzhoy et al , focs 2004 \) , where the goal is to schedule a set of jobs with release times , deadlines , and processing lengths on a minimum number of identical machines since the online problem has strong lower bounds if all the job parameters are arbitrary , we focus on jobs with uniform length our main result is a complete resolution of the deterministic complexity of this problem by showing that a competitive ratio of e is achievable and optimal , thereby improving upon existing lower and upper bounds of 2 09 and 5 2 respectively we also give a constant competitive online algorithm for the case of uniform deadlines \( but arbitrary job lengths \) to the best of our knowledge , no such algorithm was known previously finally , we consider the complimentary problem of throughput maximization where the goal is to maximize the sum of weights of scheduled jobs on a fixed set of identical machines \( introduced by bar noy et al stoc 1999 \) we give a randomized online algorithm for this problem with a competitive ratio of e e 1 previous results achieved this bound only for the case of a single machine or in the limit of an infinite number of machines"
cs.AI|cs.LG|stat.ML,"for most problems in science and engineering we can obtain data that describe the system from various perspectives and record the behaviour of its individual components heterogeneous data sources can be collectively mined by data fusion fusion can focus on a specific target relation and exploit directly associated data together with data on the context or additional constraints in the paper we describe a data fusion approach with penalized matrix tri factorization that simultaneously factorizes data matrices to reveal hidden associations the approach can directly consider any data sets that can be expressed in a matrix , including those from attribute based representations , ontologies , associations and networks we demonstrate its utility on a gene function prediction problem in a case study with eleven different data sources our fusion algorithm compares favourably to state of the art multiple kernel learning and achieves higher accuracy than can be obtained from any single data source alone"
cs.IT,"we consider the problem of communicating over a multiple input multiple output \( mimo \) real valued channel for which no mathematical model is specified , and achievable rates are given as a function of the channel input and output sequences known a posteriori this paper extends previous results regarding individual channels by presenting a rate function for the mimo individual channel , and showing its achievability in a fixed transmission rate communication scenario"
cs.IT,"the layered interference network is investigated with delayed channel state information \( csi \) at all nodes it is demonstrated how multi hopping can be utilized to increase the achievable degrees of freedom \( dof \) in particular , a multi phase transmission scheme is proposed for the k user 2k hop interference network in order to systematically exploit the layered structure of the network and delayed csi to achieve dof values that scale with k this result provides the first example of a network with distributed transmitters and delayed csi whose dof scales with the number of users"
cs.DS,"peptide sequencing from mass spectrometry data is a key step in proteome research especially de novo sequencing , the identification of a peptide from its spectrum alone , is still a challenge even for state of the art algorithmic approaches in this paper we present antilope , a new fast and flexible approach based on mathematical programming it builds on the spectrum graph model and works with a variety of scoring schemes antilope combines lagrangian relaxation for solving an integer linear programming formulation with an adaptation of yen 's k shortest paths algorithm it shows a significant improvement in running time compared to mixed integer optimization and performs at the same speed like other state of the art tools we also implemented a generic probabilistic scoring scheme that can be trained automatically for a dataset of annotated spectra and is independent of the mass spectrometer type evaluations on benchmark data show that antilope is competitive to the popular state of the art programs pepnovo and novohmm both in terms of run time and accuracy furthermore , it offers increased flexibility in the number of considered ion types antilope will be freely available as part of the open source proteomics library openms"
cs.IT,"a unified approach to energy efficient power control , applicable to a large family of receivers including the matched filter , the decorrelator , the \( linear \) minimum mean square error detector \( mmse \) , and the individually and jointly optimal multiuser detectors , has recently been proposed for code division multiple access \( cdma \) networks this unified power control \( upc \) algorithm exploits the linear relationship that has been shown to exist between the transmit power and the output signal to interference plus noise ratio \( sir \) in large systems based on this principle and by computing the multiuser efficiency , the upc algorithm updates the users' transmit powers in an iterative way to achieve the desired target sir in this paper , the convergence of the upc algorithm is proved for the matched filter , the decorrelator , and the mmse detector in addition , the performance of the algorithm in finite size systems is studied and compared with that of existing power control schemes the upc algorithm is particularly suitable for systems with randomly generated long spreading sequences \( i e , sequences whose period is longer than one symbol duration \)"