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cs/0601060
Robot Swarms in an Uncertain World: Controllable Adaptability
cs.RO
There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a method for the description of a goal-directed, or programmed, behaviour, interacting with uncertainty of environment, is described. We suggest reducing the structural (goals, intentions) and stochastic components (probability to realise the goal) of individual behaviour to random variables with nominal values to apply probabilistic approach. This allowed us to use a Normalized Entropy Index to detect the system state by estimating the contribution of each agent to the group behaviour. The number of possible group states is 27. We argue that adaptation has a limited number of possible paths between these 27 states. Paths and states can be programmed so that after adjustment to any particular case of task and conditions, adaptability will never involve chaos. We suggest the application of the model to operation of robots or other devices in remote and/or dangerous places.
cs/0601061
Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production
cs.RO
This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs). The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.
cs/0601062
Study of Self-Organization Model of Multiple Mobile Robot
cs.RO
A good organization model of multiple mobile robot should be able to improve the efficiency of the system, reduce the complication of robot interactions, and detract the difficulty of computation. From the sociology aspect of topology, structure and organization, this paper studies the multiple mobile robot organization formation and running mechanism in the dynamic, complicated and unknown environment. It presents and describes in detail a Hierarchical- Web Recursive Organization Model (HWROM) and forming algorithm. It defines the robot society leader; robotic team leader and individual robot as the same structure by the united framework and describes the organization model by the recursive structure. The model uses task-oriented and top-down method to dynamically build and maintain structures and organization. It uses market-based techniques to assign task, form teams and allocate resources in dynamic environment. The model holds several characteristics of self-organization, dynamic, conciseness, commonness and robustness.
cs/0601063
Optimal Point-to-Point Trajectory Tracking of Redundant Manipulators using Generalized Pattern Search
cs.RO
Optimal point-to-point trajectory planning for planar redundant manipulator is considered in this study. The main objective is to minimize the sum of the position error of the end-effector at each intermediate point along the trajectory so that the end-effector can track the prescribed trajectory accurately. An algorithm combining Genetic Algorithm and Pattern Search as a Generalized Pattern Search GPS is introduced to design the optimal trajectory. To verify the proposed algorithm, simulations for a 3-D-O-F planar manipulator with different end-effector trajectories have been carried out. A comparison between the Genetic Algorithm and the Generalized Pattern Search shows that The GPS gives excellent tracking performance.
cs/0601064
Robotics Vision-based Heuristic Reasoning for Underwater Target Tracking and Navigation
cs.RO
This paper presents a robotics vision-based heuristic reasoning system for underwater target tracking and navigation. This system is introduced to improve the level of automation of underwater Remote Operated Vehicles (ROVs) operations. A prototype which combines computer vision with an underwater robotics system is successfully designed and developed to perform target tracking and intelligent navigation. ...
cs/0601065
New Intelligent Transmission Concept for Hybrid Mobile Robot Speed Control
cs.RO
This paper presents a new concept of a mobile robot speed control by using two degree of freedom gear transmission. The developed intelligent speed controller utilizes a gear box which comprises of epicyclic gear train with two inputs, one coupled with the engine shaft and another with the shaft of a variable speed dc motor. The net output speed is a combination of the two input speeds and is governed by the transmission ratio of the planetary gear train. This new approach eliminates the use of a torque converter which is otherwise an indispensable part of all available automatic transmissions, thereby reducing the power loss that occurs in the box during the fluid coupling. By gradually varying the speed of the dc motor a stepless transmission has been achieved. The other advantages of the developed controller are pulling over and reversing the vehicle, implemented by intelligent mixing of the dc motor and engine speeds. This approach eliminates traditional braking system in entire vehicle design. The use of two power sources, IC engine and battery driven DC motor, utilizes the modern idea of hybrid vehicles. The new mobile robot speed controller is capable of driving the vehicle even in extreme case of IC engine failure, for example, due to gas depletion.
cs/0601066
On the Existence of Universally Decodable Matrices
cs.IT cs.DM math.IT
Universally decodable matrices (UDMs) can be used for coding purposes when transmitting over slow fading channels. These matrices are parameterized by positive integers $L$ and $N$ and a prime power $q$. The main result of this paper is that the simple condition $L \leq q+1$ is both necessary and sufficient for $(L,N,q)$-UDMs to exist. The existence proof is constructive and yields a coding scheme that is equivalent to a class of codes that was proposed by Rosenbloom and Tsfasman. Our work resolves an open problem posed recently in the literature.
cs/0601067
Design of Rate-Compatible Serially Concatenated Convolutional Codes
cs.IT math.IT
Recently a powerful class of rate-compatible serially concatenated convolutional codes (SCCCs) have been proposed based on minimizing analytical upper bounds on the error probability in the error floor region. Here this class of codes is further investigated by combining analytical upper bounds with extrinsic information transfer charts analysis. Following this approach, we construct a family of rate-compatible SCCCs with good performance in both the error floor and the waterfall regions over a broad range of code rates.
cs/0601070
Instanton analysis of Low-Density-Parity-Check codes in the error-floor regime
cs.IT cond-mat.dis-nn math.IT
In this paper we develop instanton method introduced in [1], [2], [3] to analyze quantitatively performance of Low-Density-Parity-Check (LDPC) codes decoded iteratively in the so-called error-floor regime. We discuss statistical properties of the numerical instanton-amoeba scheme focusing on detailed analysis and comparison of two regular LDPC codes: Tanner's (155, 64, 20) and Margulis' (672, 336, 16) codes. In the regime of moderate values of the signal-to-noise ratio we critically compare results of the instanton-amoeba evaluations against the standard Monte-Carlo calculations of the Frame-Error-Rate.
cs/0601072
Fast Frequent Querying with Lazy Control Flow Compilation
cs.PL cs.AI cs.SE
Control flow compilation is a hybrid between classical WAM compilation and meta-call, limited to the compilation of non-recursive clause bodies. This approach is used successfully for the execution of dynamically generated queries in an inductive logic programming setting (ILP). Control flow compilation reduces compilation times up to an order of magnitude, without slowing down execution. A lazy variant of control flow compilation is also presented. By compiling code by need, it removes the overhead of compiling unreached code (a frequent phenomenon in practical ILP settings), and thus reduces the size of the compiled code. Both dynamic compilation approaches have been implemented and were combined with query packs, an efficient ILP execution mechanism. It turns out that locality of data and code is important for performance. The experiments reported in the paper show that lazy control flow compilation is superior in both artificial and real life settings.
cs/0601073
A Theory of Routing for Large-Scale Wireless Ad-Hoc Networks
cs.IT cs.NI math.IT
In this work we develop a new theory to analyse the process of routing in large-scale ad-hoc wireless networks. We use a path integral formulation to examine the properties of the paths generated by different routing strategies in these kinds of networks. Using this theoretical framework, we calculate the statistical distribution of the distances between any source to any destination in the network, hence we are able to deduce a length parameter that is unique for each routing strategy. This parameter, defined as the {\it effective radius}, effectively encodes the routing information required by a node. Analysing the aforementioned statistical distribution for different routing strategies, we obtain a threefold result for practical Large-Scale Wireless Ad-Hoc Networks: 1) We obtain the distribution of the lengths of all the paths in a network for any given routing strategy, 2) We are able to identify "good" routing strategies depending on the evolution of its effective radius as the number of nodes, $N$, increases to infinity, 3) For any routing strategy with finite effective radius, we demonstrate that, in a large-scale network, is equivalent to a random routing strategy and that its transport capacity scales as $\Theta(\sqrt{N})$ bit-meters per second, thus retrieving the scaling law that Gupta and Kumar (2000) obtained as the limit for single-route large-scale wireless networks.
cs/0601074
Joint universal lossy coding and identification of i.i.d. vector sources
cs.IT cs.LG math.IT
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/0601075
On Universally Decodable Matrices for Space-Time Coding
cs.IT cs.DM math.IT
The notion of universally decodable matrices (UDMs) was recently introduced by Tavildar and Viswanath while studying slow fading channels. It turns out that the problem of constructing UDMs is tightly connected to the problem of constructing maximum distance separable (MDS) codes. In this paper, we first study the properties of UDMs in general and then we discuss an explicit construction of a class of UDMs, a construction which can be seen as an extension of Reed-Solomon codes. In fact, we show that this extension is, in a sense to be made more precise later on, unique. Moreover, the structure of this class of UDMs allows us to answer some open conjectures by Tavildar, Viswanath, and Doshi in the positive, and it also allows us to formulate an efficient decoding algorithm for this class of UDMs. It turns out that our construction yields a coding scheme that is essentially equivalent to a class of codes that was proposed by Rosenbloom and Tsfasman. Moreover, we point out connections to so-called repeated-root cyclic codes.
cs/0601077
IDBE - An Intelligent Dictionary Based Encoding Algorithm for Text Data Compression for High Speed Data Transmission Over Internet
cs.IT math.IT
Compression algorithms reduce the redundancy in data representation to decrease the storage required for that data. Data compression offers an attractive approach to reducing communication costs by using available bandwidth effectively. Over the last decade there has been an unprecedented explosion in the amount of digital data transmitted via the Internet, representing text, images, video, sound, computer programs, etc. With this trend expected to continue, it makes sense to pursue research on developing algorithms that can most effectively use available network bandwidth by maximally compressing data. This research paper is focused on addressing this problem of lossless compression of text files. Lossless compression researchers have developed highly sophisticated approaches, such as Huffman encoding, arithmetic encoding, the Lempel-Ziv family, Dynamic Markov Compression (DMC), Prediction by Partial Matching (PPM), and Burrows-Wheeler Transform (BWT) based algorithms. However, none of these methods has been able to reach the theoretical best-case compression ratio consistently, which suggests that better algorithms may be possible. One approach for trying to attain better compression ratios is to develop new compression algorithms. An alternative approach, however, is to develop intelligent, reversible transformations that can be applied to a source text that improve an existing, or backend, algorithm's ability to compress. The latter strategy has been explored here.
cs/0601080
On Measure Theoretic definitions of Generalized Information Measures and Maximum Entropy Prescriptions
cs.IT math.IT
Though Shannon entropy of a probability measure $P$, defined as $- \int_{X} \frac{\ud P}{\ud \mu} \ln \frac{\ud P}{\ud\mu} \ud \mu$ on a measure space $(X, \mathfrak{M},\mu)$, does not qualify itself as an information measure (it is not a natural extension of the discrete case), maximum entropy (ME) prescriptions in the measure-theoretic case are consistent with that of discrete case. In this paper, we study the measure-theoretic definitions of generalized information measures and discuss the ME prescriptions. We present two results in this regard: (i) we prove that, as in the case of classical relative-entropy, the measure-theoretic definitions of generalized relative-entropies, R\'{e}nyi and Tsallis, are natural extensions of their respective discrete cases, (ii) we show that, ME prescriptions of measure-theoretic Tsallis entropy are consistent with the discrete case.
cs/0601081
An O(1) Solution to the Prefix Sum Problem on a Specialized Memory Architecture
cs.DS cs.CC cs.IR
In this paper we study the Prefix Sum problem introduced by Fredman. We show that it is possible to perform both update and retrieval in O(1) time simultaneously under a memory model in which individual bits may be shared by several words. We also show that two variants (generalizations) of the problem can be solved optimally in $\Theta(\lg N)$ time under the comparison based model of computation.
cs/0601083
Multilevel Coding for Channels with Non-uniform Inputs and Rateless Transmission over the BSC
cs.IT math.IT
We consider coding schemes for channels with non-uniform inputs (NUI), where standard linear block codes can not be applied directly. We show that multilevel coding (MLC) with a set of linear codes and a deterministic mapper can achieve the information rate of the channel with NUI. The mapper, however, does not have to be one-to-one. As an application of the proposed MLC scheme, we present a rateless transmission scheme over the binary symmetric channel (BSC).
cs/0601087
Processing of Test Matrices with Guessing Correction
cs.LG
It is suggested to insert into test matrix 1s for correct responses, 0s for response refusals, and negative corrective elements for incorrect responses. With the classical test theory approach test scores of examinees and items are calculated traditionally as sums of matrix elements, organized in rows and columns. Correlation coefficients are estimated using correction coefficients. In item response theory approach examinee and item logits are estimated using maximum likelihood method and probabilities of all matrix elements.
cs/0601088
An Algorithm for Constructing All Families of Codes of Arbitrary Requirement in an OCDMA System
cs.IT math.IT
A novel code construction algorithm is presented to find all the possible code families for code reconfiguration in an OCDMA system. The algorithm is developed through searching all the complete subgraphs of a constructed graph. The proposed algorithm is flexible and practical for constructing optical orthogonal codes (OOCs) of arbitrary requirement. Simulation results show that one should choose an appropriate code length in order to obtain sufficient number of code families for code reconfiguration with reasonable cost.
cs/0601089
Distributed Kernel Regression: An Algorithm for Training Collaboratively
cs.LG cs.AI cs.DC cs.IT math.IT
This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.
cs/0601090
Improved Nearly-MDS Expander Codes
cs.IT math.IT
A construction of expander codes is presented with the following three properties: (i) the codes lie close to the Singleton bound, (ii) they can be encoded in time complexity that is linear in their code length, and (iii) they have a linear-time bounded-distance decoder. By using a version of the decoder that corrects also erasures, the codes can replace MDS outer codes in concatenated constructions, thus resulting in linear-time encodable and decodable codes that approach the Zyablov bound or the capacity of memoryless channels. The presented construction improves on an earlier result by Guruswami and Indyk in that any rate and relative minimum distance that lies below the Singleton bound is attainable for a significantly smaller alphabet size.
cs/0601091
Communication Over MIMO Broadcast Channels Using Lattice-Basis Reduction
cs.IT math.IT
A simple scheme for communication over MIMO broadcast channels is introduced which adopts the lattice reduction technique to improve the naive channel inversion method. Lattice basis reduction helps us to reduce the average transmitted energy by modifying the region which includes the constellation points. Simulation results show that the proposed scheme performs well, and as compared to the more complex methods (such as the perturbation method) has a negligible loss. Moreover, the proposed method is extended to the case of different rates for different users. The asymptotic behavior of the symbol error rate of the proposed method and the perturbation technique, and also the outage probability for the case of fixed-rate users is analyzed. It is shown that the proposed method, based on LLL lattice reduction, achieves the optimum asymptotic slope of symbol-error-rate (called the precoding diversity). Also, the outage probability for the case of fixed sum-rate is analyzed.
cs/0601092
LLL Reduction Achieves the Receive Diversity in MIMO Decoding
cs.IT math.IT
Diversity order is an important measure for the performance of communication systems over MIMO fading channels. In this paper, we prove that in MIMO multiple access systems (or MIMO point-to-point systems with V-BLAST transmission), lattice-reduction-aided decoding achieves the maximum receive diversity (which is equal to the number of receive antennas). Also, we prove that the naive lattice decoding (which discards the out-of-region decoded points) achieves the maximum diversity.
cs/0601093
Stability of Scheduled Multi-access Communication over Quasi-static Flat Fading Channels with Random Coding and Joint Maximum Likelihood Decoding
cs.IT math.IT
We consider stability of scheduled multiaccess message communication with random coding and joint maximum-likehood decoding of messages. 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 number of messages that may be scheduled for simultaneous transmission is limited to a given maximum value, and the channels from transmitters to receiver are quasi-static, flat, and have independent fades. 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 rate vectors achievable by the class of stationary scheduling policies, (ii) we show for any message arrival rate 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) in the limit of large message lengths, we show that the stability region of message nat arrival rate vectors has information-theoretic capacity region interpretation.
cs/0601094
Stability of Scheduled Message Communication over Degraded Broadcast Channels
cs.IT math.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/0601095
On the Weight Enumerator and the Maximum Likelihood Performance of Linear Product Codes
cs.IT math.IT
Product codes are widely used in data-storage, optical and wireless applications. Their analytical performance evaluation usually relies on the truncated union bound, which provides a low error rate approximation based on the minimum distance term only. In fact, the complete weight enumerator of most product codes remains unknown. In this paper, concatenated representations are introduced and applied to compute the complete average enumerators of arbitrary product codes over a field Fq. The split weight enumerators of some important constituent codes (Hamming, Reed-Solomon) are studied and used in the analysis. The average binary weight enumerators of Reed Solomon product codes are also derived. Numerical results showing the enumerator behavior are presented. By using the complete enumerators, Poltyrev bounds on the maximum likelihood performance, holding at both high and low error rates, are finally shown and compared against truncated union bounds and simulation results.
cs/0601098
Energy Efficiency and Delay Quality-of-Service in Wireless Networks
cs.IT math.IT
The energy-delay tradeoffs in wireless networks are studied using a game-theoretic framework. A multi-class multiple-access network is considered in which users choose their transmit powers, and possibly transmission rates, in a distributed manner to maximize their own utilities while satisfying their delay quality-of-service (QoS) requirements. The utility function considered here measures the number of reliable bits transmitted per Joule of energy consumed and is particularly useful for energy-constrained networks. The Nash equilibrium solution for the proposed non-cooperative game is presented and closed-form expressions for the users' utilities at equilibrium are obtained. Based on this, the losses in energy efficiency and network capacity due to presence of delay-sensitive users are quantified. The analysis is extended to the scenario where the QoS requirements include both the average source rate and a bound on the average total delay (including queuing delay). It is shown that the incoming traffic rate and the delay constraint of a user translate into a "size" for the user, which is an indication of the amount of resources consumed by the user. Using this framework, the tradeoffs among throughput, delay, network capacity and energy efficiency are also quantified.
cs/0601099
Adaptive Linear Programming Decoding
cs.IT math.IT
Detectability of failures of linear programming (LP) decoding and its potential for improvement by adding new constraints motivate the use of an adaptive approach in selecting the constraints for the LP problem. In this paper, we make a first step in studying this method, and show that it can significantly reduce the complexity of the problem, which was originally exponential in the maximum check-node degree. We further show that adaptively adding new constraints, e.g. by combining parity checks, can provide large gains in the performance.
cs/0601102
Geometric symmetry in the quadratic Fisher discriminant operating on image pixels
cs.IT cs.CV math.IT
This article examines the design of Quadratic Fisher Discriminants (QFDs) that operate directly on image pixels, when image ensembles are taken to comprise all rotated and reflected versions of distinct sample images. A procedure based on group theory is devised to identify and discard QFD coefficients made redundant by symmetry, for arbitrary sampling lattices. This procedure introduces the concept of a degeneracy matrix. Tensor representations are established for the square lattice point group (8-fold symmetry) and hexagonal lattice point group (12-fold symmetry). The analysis is largely applicable to the symmetrisation of any quadratic filter, and generalises to higher order polynomial (Volterra) filters. Experiments on square lattice sampled synthetic aperture radar (SAR) imagery verify that symmetrisation of QFDs can improve their generalisation and discrimination ability.
cs/0601103
Google Web APIs - an Instrument for Webometric Analyses?
cs.IR
This paper introduces Google Web APIs (Google APIs) as an instrument and playground for webometric studies. Several examples of Google APIs implementations are given. Our examples show that this Google Web Service can be used successfully for informetric Internet based studies albeit with some restrictions.
cs/0601105
The Perceptron Algorithm: Image and Signal Decomposition, Compression, and Analysis by Iterative Gaussian Blurring
cs.CV
A novel algorithm for tunable compression to within the precision of reproduction targets, or storage, is proposed. The new algorithm is termed the `Perceptron Algorithm', which utilises simple existing concepts in a novel way, has multiple immediate commercial application aspects as well as it opens up a multitude of fronts in computational science and technology. The aims of this paper are to present the concepts underlying the algorithm, observations by its application to some example cases, and the identification of a multitude of potential areas of applications such as: image compression by orders of magnitude, signal compression including sound as well, image analysis in a multilayered detailed analysis, pattern recognition and matching and rapid database searching (e.g. face recognition), motion analysis, biomedical applications e.g. in MRI and CAT scan image analysis and compression, as well as hints on the link of these ideas to the way how biological memory might work leading to new points of view in neural computation. Commercial applications of immediate interest are the compression of images at the source (e.g. photographic equipment, scanners, satellite imaging systems), DVD film compression, pay-per-view downloads acceleration and many others identified in the present paper at its conclusion and future work section.
cs/0601106
The `Face on Mars': a photographic approach for the search of signs of past civilizations from a macroscopic point of view, factoring long-term erosion in image reconstruction
cs.CV
This short article presents an alternative view of high resolution imaging from various sources with the aim of the discovery of potential sites of archaeological importance, or sites that exhibit `anomalies' such that they may merit closer inspection and analysis. It is conjectured, and to a certain extent demonstrated here, that it is possible for advanced civilizations to factor in erosion by natural processes into a large scale design so that main features be preserved even with the passage of millions of years. Alternatively viewed, even without such intent embedded in a design left for posterity, it is possible that a gigantic construction may naturally decay in such a way that even cataclysmic (massive) events may leave sufficient information intact with the passage of time, provided one changes the point of view from high resolution images to enhanced blurred renderings of the sites in question.
cs/0601107
Structure of Optimal Input Covariance Matrices for MIMO Systems with Covariance Feedback under General Correlated Fading
cs.IT math.IT
We describe the structure of optimal Input covariance matrices for single user multiple-input/multiple-output (MIMO) communication system with covariance feedback and for general correlated fading. Our approach is based on the novel concept of right commutant and recovers previously derived results for the Kronecker product models. Conditions are derived which allow a significant simplification of the optimization problem.
cs/0601108
Fast Lexically Constrained Viterbi Algorithm (FLCVA): Simultaneous Optimization of Speed and Memory
cs.CV cs.AI cs.DS
Lexical constraints on the input of speech and on-line handwriting systems improve the performance of such systems. A significant gain in speed can be achieved by integrating in a digraph structure the different Hidden Markov Models (HMM) corresponding to the words of the relevant lexicon. This integration avoids redundant computations by sharing intermediate results between HMM's corresponding to different words of the lexicon. In this paper, we introduce a token passing method to perform simultaneously the computation of the a posteriori probabilities of all the words of the lexicon. The coding scheme that we introduce for the tokens is optimal in the information theory sense. The tokens use the minimum possible number of bits. Overall, we optimize simultaneously the execution speed and the memory requirement of the recognition systems.
cs/0601109
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
cs.AI
Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due to incomplete and erroneous data, because they do not build reliable models and solutions guaranteed to address the user's genuine problem as she perceives it. Other fields such as reliable computation offer combinations of models and associated methods to handle these types of uncertain data, but lack an expressive framework characterising the resolution methodology independently of the model. We present a unifying framework that extends the CP formalism in both model and solutions, to tackle ill-defined combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modelling and solving methodologies from different fields into the CP paradigm to provide reliable and efficient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We define resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions.
cs/0601110
Mutual Information Games in Multi-user Channels with Correlated Jamming
cs.IT math.IT
We investigate the behavior of two users and one jammer in an AWGN channel with and without fading when they participate in a non-cooperative zero-sum game, with the channel's input/output mutual information as the objective function. We assume that the jammer can eavesdrop the channel and can use the information obtained to perform correlated jamming. Under various assumptions on the channel characteristics, and the extent of information available at the users and the jammer, we show the existence, or otherwise non-existence of a simultaneously optimal set of strategies for the users and the jammer. In all the cases where the channel is non-fading, we show that the game has a solution, and the optimal strategies are Gaussian signalling for the users and linear jamming for the jammer. In fading channels, we envision each player's strategy as a power allocation function over the channel states, together with the signalling strategies at each channel state. We reduce the game solution to a set of power allocation functions for the players and show that when the jammer is uncorrelated, the game has a solution, but when the jammer is correlated, a set of simultaneously optimal power allocation functions for the users and the jammer does not always exist. In this case, we characterize the max-min user power allocation strategies and the corresponding jammer power allocation strategy.
cs/0601113
An Efficient Pseudo-Codeword Search Algorithm for Linear Programming Decoding of LDPC Codes
cs.IT cond-mat.dis-nn math.IT
In Linear Programming (LP) decoding of a Low-Density-Parity-Check (LDPC) code one minimizes a linear functional, with coefficients related to log-likelihood ratios, over a relaxation of the polytope spanned by the codewords \cite{03FWK}. In order to quantify LP decoding, and thus to describe performance of the error-correction scheme at moderate and large Signal-to-Noise-Ratios (SNR), it is important to study the relaxed polytope to understand better its vertexes, so-called pseudo-codewords, especially those which are neighbors of the zero codeword. In this manuscript we propose a technique to heuristically create a list of these neighbors and their distances. Our pseudo-codeword-search algorithm starts by randomly choosing the initial configuration of the noise. The configuration is modified through a discrete number of steps. Each step consists of two sub-steps. Firstly, one applies an LP decoder to the noise-configuration deriving a pseudo-codeword. Secondly, one finds configuration of the noise equidistant from the pseudo codeword and the zero codeword. The resulting noise configuration is used as an entry for the next step. The iterations converge rapidly to a pseudo-codeword neighboring the zero codeword. Repeated many times, this procedure is characterized by the distribution function (frequency spectrum) of the pseudo-codeword effective distance. The effective distance of the coding scheme is approximated by the shortest distance pseudo-codeword in the spectrum. The efficiency of the procedure is demonstrated on examples of the Tanner $[155,64,20]$ code and Margulis $p=7$ and $p=11$ codes (672 and 2640 bits long respectively) operating over an Additive-White-Gaussian-Noise (AWGN) channel.
cs/0601114
Efficient Query Answering over Conceptual Schemas of Relational Databases : Technical Report
cs.DB cs.LO
We develop a query answering system, where at the core of the work there is an idea of query answering by rewriting. For this purpose we extend the DL DL-Lite with the ability to support n-ary relations, obtaining the DL DLR-Lite, which is still polynomial in the size of the data. We devise a flexible way of mapping the conceptual level to the relational level, which provides the users an SQL-like query language over the conceptual schema. The rewriting technique adds value to conventional query answering techniques, allowing to formulate simpler queries, with the ability to infer additional information that was not stated explicitly in the user query. The formalization of the conceptual schema and the developed reasoning technique allow checking for consistency between the database and the conceptual schema, thus improving the trustiness of the information system.
cs/0601115
Decision Making with Side Information and Unbounded Loss Functions
cs.LG cs.IT math.IT
We consider the problem of decision-making with side information and unbounded loss functions. Inspired by probably approximately correct learning model, we use a slightly different model that incorporates the notion of side information in a more generic form to make it applicable to a broader class of applications including parameter estimation and system identification. We address sufficient conditions for consistent decision-making with exponential convergence behavior. In this regard, besides a certain condition on the growth function of the class of loss functions, it suffices that the class of loss functions be dominated by a measurable function whose exponential Orlicz expectation is uniformly bounded over the probabilistic model. Decay exponent, decay constant, and sample complexity are discussed. Example applications to method of moments, maximum likelihood estimation, and system identification are illustrated, as well.
cs/0601120
On The Minimum Mean-Square Estimation Error of the Normalized Sum of Independent Narrowband Waves in the Gaussian Channel
cs.IT math.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/0601121
A Multi-Relational Network to Support the Scholarly Communication Process
cs.DL cs.AI cs.IR
The general pupose of the scholarly communication process is to support the creation and dissemination of ideas within the scientific community. At a finer granularity, there exists multiple stages which, when confronted by a member of the community, have different requirements and therefore different solutions. In order to take a researcher's idea from an initial inspiration to a community resource, the scholarly communication infrastructure may be required to 1) provide a scientist initial seed ideas; 2) form a team of well suited collaborators; 3) located the most appropriate venue to publish the formalized idea; 4) determine the most appropriate peers to review the manuscript; and 5) disseminate the end product to the most interested members of the community. Through the various delinieations of this process, the requirements of each stage are tied soley to the multi-functional resources of the community: its researchers, its journals, and its manuscritps. It is within the collection of these resources and their inherent relationships that the solutions to scholarly communication are to be found. This paper describes an associative network composed of multiple scholarly artifacts that can be used as a medium for supporting the scholarly communication process.
cs/0601123
Low density codes achieve the rate-distortion bound
cs.IT math.IT
We propose a new construction for low-density source codes with multiple parameters that can be tuned to optimize the performance of the code. In addition, we introduce a set of analysis techniques for deriving upper bounds for the expected distortion of our construction, as well as more general low-density constructions. We show that (with an optimal encoding algorithm) our codes achieve the rate-distortion bound for a binary symmetric source and Hamming distortion. Our methods also provide rigorous upper bounds on the minimum distortion achievable by previously proposed low-density constructions.
cs/0601124
Power Control for User Cooperation
cs.IT math.IT
For a fading Gaussian multiple access channel with user cooperation, we obtain the optimal power allocation policies that maximize the rates achievable by block Markov superposition coding. The optimal policies result in a coding scheme that is simpler than the one for a general multiple access channel with generalized feedback. This simpler coding scheme also leads to the possibility of formulating an otherwise non-concave optimization problem as a concave one. Using the channel state information at the transmitters to adapt the powers, we demonstrate significant gains over the achievable rates for existing cooperative systems.
cs/0601126
Approximate Linear Time ML Decoding on Tail-Biting Trellises in Two Rounds
cs.IT math.IT
A linear time approximate maximum likelihood decoding algorithm on tail-biting trellises is prsented, that requires exactly two rounds on the trellis. This is an adaptation of an algorithm proposed earlier with the advantage that it reduces the time complexity from O(mlogm) to O(m) where m is the number of nodes in the tail-biting trellis. A necessary condition for the output of the algorithm to differ from the output of the ideal ML decoder is reduced and simulation results on an AWGN channel using tail-biting rrellises for two rate 1/2 convoluational codes with memory 4 and 6 respectively are reported
cs/0601129
Instantaneously Trained Neural Networks
cs.NE cs.AI
This paper presents a review of instantaneously trained neural networks (ITNNs). These networks trade learning time for size and, in the basic model, a new hidden node is created for each training sample. Various versions of the corner-classification family of ITNNs, which have found applications in artificial intelligence (AI), are described. Implementation issues are also considered.
cs/0601130
From Dumb Wireless Sensors to Smart Networks using Network Coding
cs.IT cs.NI math.IT
The vision of wireless sensor networks is one of a smart collection of tiny, dumb devices. These motes may be individually cheap, unintelligent, imprecise, and unreliable. Yet they are able to derive strength from numbers, rendering the whole to be strong, reliable and robust. Our approach is to adopt a distributed and randomized mindset and rely on in network processing and network coding. Our general abstraction is that nodes should act only locally and independently, and the desired global behavior should arise as a collective property of the network. We summarize our work and present how these ideas can be applied for communication and storage in sensor networks.
cs/0601131
Scalable Algorithms for Aggregating Disparate Forecasts of Probability
cs.AI cs.DC cs.IT math.IT
In this paper, computational aspects of the panel aggregation problem are addressed. Motivated primarily by applications of risk assessment, an algorithm is developed for aggregating large corpora of internally incoherent probability assessments. The algorithm is characterized by a provable performance guarantee, and is demonstrated to be orders of magnitude faster than existing tools when tested on several real-world data-sets. In addition, unexpected connections between research in risk assessment and wireless sensor networks are exposed, as several key ideas are illustrated to be useful in both fields.
cs/0601132
A Study on the Global Convergence Time Complexity of Estimation of Distribution Algorithms
cs.AI cs.NE
The Estimation of Distribution Algorithm is a new class of population based search methods in that a probabilistic model of individuals is estimated based on the high quality individuals and used to generate the new individuals. In this paper we compute 1) some upper bounds on the number of iterations required for global convergence of EDA 2) the exact number of iterations needed for EDA to converge to global optima.
cs/0602004
Conjunctive Queries over Trees
cs.DB cs.AI cs.CC cs.LO
We study the complexity and expressive power of conjunctive queries over unranked labeled trees represented using a variety of structure relations such as ``child'', ``descendant'', and ``following'' as well as unary relations for node labels. We establish a framework for characterizing structures representing trees for which conjunctive queries can be evaluated efficiently. Then we completely chart the tractability frontier of the problem and establish a dichotomy theorem for our axis relations, i.e., we find all subset-maximal sets of axes for which query evaluation is in polynomial time and show that for all other cases, query evaluation is NP-complete. All polynomial-time results are obtained immediately using the proof techniques from our framework. Finally, we study the expressiveness of conjunctive queries over trees and show that for each conjunctive query, there is an equivalent acyclic positive query (i.e., a set of acyclic conjunctive queries), but that in general this query is not of polynomial size.
cs/0602006
A Visual Query Language for Complex-Value Databases
cs.DB cs.HC
In this paper, a visual language, VCP, for queries on complex-value databases is proposed. The main strength of the new language is that it is purely visual: (i) It has no notion of variable, quantification, partiality, join, pattern matching, regular expression, recursion, or any other construct proper to logical, functional, or other database query languages and (ii) has a very natural, strong, and intuitive design metaphor. The main operation is that of copying and pasting in a schema tree. We show that despite its simplicity, VCP precisely captures complex-value algebra without powerset, or equivalently, monad algebra with union and difference. Thus, its expressive power is precisely that of the language that is usually considered to play the role of relational algebra for complex-value databases.
cs/0602007
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data
cs.CR cs.IT math.IT
We provide formal definitions and efficient secure techniques for - turning noisy information into keys usable for any cryptographic application, and, in particular, - reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying material that, unlike traditional cryptographic keys, is (1) not reproducible precisely and (2) not distributed uniformly. We propose two primitives: a "fuzzy extractor" reliably extracts nearly uniform randomness R from its input; the extraction is error-tolerant in the sense that R will be the same even if the input changes, as long as it remains reasonably close to the original. Thus, R can be used as a key in a cryptographic application. A "secure sketch" produces public information about its input w that does not reveal w, and yet allows exact recovery of w given another value that is close to w. Thus, it can be used to reliably reproduce error-prone biometric inputs without incurring the security risk inherent in storing them. We define the primitives to be both formally secure and versatile, generalizing much prior work. In addition, we provide nearly optimal constructions of both primitives for various measures of ``closeness'' of input data, such as Hamming distance, edit distance, and set difference.
cs/0602011
The intuitionistic fragment of computability logic at the propositional level
cs.LO cs.AI math.LO
This paper presents a soundness and completeness proof for propositional intuitionistic calculus with respect to the semantics of computability logic. The latter interprets formulas as interactive computational problems, formalized as games between a machine and its environment. Intuitionistic implication is understood as algorithmic reduction in the weakest possible -- and hence most natural -- sense, disjunction and conjunction as deterministic-choice combinations of problems (disjunction = machine's choice, conjunction = environment's choice), and "absurd" as a computational problem of universal strength. See http://www.cis.upenn.edu/~giorgi/cl.html for a comprehensive online source on computability logic.
cs/0602014
Game theoretic aspects of distributed spectral coordination with application to DSL networks
cs.IT math.IT
In this paper we use game theoretic techniques to study the value of cooperation in distributed spectrum management problems. We show that the celebrated iterative water-filling algorithm is subject to the prisoner's dilemma and therefore can lead to severe degradation of the achievable rate region in an interference channel environment. We also provide thorough analysis of a simple two bands near-far situation where we are able to provide closed form tight bounds on the rate region of both fixed margin iterative water filling (FM-IWF) and dynamic frequency division multiplexing (DFDM) methods. This is the only case where such analytic expressions are known and all previous studies included only simulated results of the rate region. We then propose an alternative algorithm that alleviates some of the drawbacks of the IWF algorithm in near-far scenarios relevant to DSL access networks. We also provide experimental analysis based on measured DSL channels of both algorithms as well as the centralized optimum spectrum management.
cs/0602015
On the Asymptotic Performance of Multiple Antenna Channels with Fast Channel Feedback
cs.IT math.IT
In this paper, we analyze the asymptotic performance of multiple antenna channels where the transmitter has either perfect or finite bit channel state information. Using the diversity-multiplexing tradeoff to characterize the system performance, we demonstrate that channel feedback can fundamentally change the system behavior. Even one-bit of information can increase the diversity order of the system compared to the system with no transmitter information. In addition, as the amount of channel information at the transmitter increases, the diversity order for each multiplexing gain increases and goes to infinity for perfect transmitter information. The major reason for diversity order gain is a "location-dependent" temporal power control, which adapts the power control strategy based on the average channel conditions of the channel.
cs/0602018
Improving the CSIEC Project and Adapting It to the English Teaching and Learning in China
cs.CY cs.AI cs.CL cs.HC cs.MA
In this paper after short review of the CSIEC project initialized by us in 2003 we present the continuing development and improvement of the CSIEC project in details, including the design of five new Microsoft agent characters representing different virtual chatting partners and the limitation of simulated dialogs in specific practical scenarios like graduate job application interview, then briefly analyze the actual conditions and features of its application field: web-based English education in China. Finally we introduce our efforts to adapt this system to the requirements of English teaching and learning in China and point out the work next to do.
cs/0602020
Inter-Block Permuted Turbo Codes
cs.IT math.IT
The structure and size of the interleaver used in a turbo code critically affect the distance spectrum and the covariance property of a component decoder's information input and soft output. This paper introduces a new class of interleavers, the inter-block permutation (IBP) interleavers, that can be build on any existing "good" block-wise interleaver by simply adding an IBP stage. The IBP interleavers reduce the above-mentioned correlation and increase the effective interleaving size. The increased effective interleaving size improves the distance spectrum while the reduced covariance enhances the iterative decoder's performance. Moreover, the structure of the IBP(-interleaved) turbo codes (IBPTC) is naturally fit for high rate applications that necessitate parallel decoding. We present some useful bounds and constraints associated with the IBPTC that can be used as design guidelines. The corresponding codeword weight upper bounds for weight-2 and weight-4 input sequences are derived. Based on some of the design guidelines, we propose a simple IBP algorithm and show that the associated IBPTC yields 0.3 to 1.2 dB performance gain, or equivalently, an IBPTC renders the same performance with a much reduced interleaving delay. The EXIT and covariance behaviors provide another numerical proof of the superiority of the proposed IBPTC.
cs/0602021
Using Domain Knowledge in Evolutionary System Identification
cs.AI math.AP
Two example of Evolutionary System Identification are presented to highlight the importance of incorporating Domain Knowledge: the discovery of an analytical indentation law in Structural Mechanics using constrained Genetic Programming, and the identification of the repartition of underground velocities in Seismic Prospection. Critical issues for sucessful ESI are discussed in the light of these results.
cs/0602022
Avoiding the Bloat with Stochastic Grammar-based Genetic Programming
cs.AI
The application of Genetic Programming to the discovery of empirical laws is often impaired by the huge size of the search space, and consequently by the computer resources needed. In many cases, the extreme demand for memory and CPU is due to the massive growth of non-coding segments, the introns. The paper presents a new program evolution framework which combines distribution-based evolution in the PBIL spirit, with grammar-based genetic programming; the information is stored as a probability distribution on the gra mmar rules, rather than in a population. Experiments on a real-world like problem show that this approach gives a practical solution to the problem of intron growth.
cs/0602023
Information theory and Thermodynamics
cs.IT math.IT
A communication theory for a transmitter broadcasting to many receivers is presented. In this case energetic considerations cannot be neglected as in Shannon theory. It is shown that, when energy is assigned to the information bit, information theory complies with classical thermodynamic and is part of it. To provide a thermodynamic theory of communication it is necessary to define equilibrium for informatics systems that are not in thermal equilibrium and to calculate temperature, heat, and entropy with accordance to Clausius inequality. It is shown that for a binary file the temperature is proportional to the bit energy and that information is thermodynamic entropy. Equilibrium exists in random files that cannot be compressed. Thermodynamic bounds on the computing power of a physical device, and the maximum information that an antenna can broadcast are calculated.
cs/0602027
Explaining Constraint Programming
cs.PL cs.AI
We discuss here constraint programming (CP) by using a proof-theoretic perspective. To this end we identify three levels of abstraction. Each level sheds light on the essence of CP. In particular, the highest level allows us to bring CP closer to the computation as deduction paradigm. At the middle level we can explain various constraint propagation algorithms. Finally, at the lowest level we can address the issue of automatic generation and optimization of the constraint propagation algorithms.
cs/0602028
Analysis of Belief Propagation for Non-Linear Problems: The Example of CDMA (or: How to Prove Tanaka's Formula)
cs.IT math.IT
We consider the CDMA (code-division multiple-access) multi-user detection problem for binary signals and additive white gaussian noise. We propose a spreading sequences scheme based on random sparse signatures, and a detection algorithm based on belief propagation (BP) with linear time complexity. In the new scheme, each user conveys its power onto a finite number of chips l, in the large system limit. We analyze the performances of BP detection and prove that they coincide with the ones of optimal (symbol MAP) detection in the l->\infty limit. In the same limit, we prove that the information capacity of the system converges to Tanaka's formula for random `dense' signatures, thus providing the first rigorous justification of this formula. Apart from being computationally convenient, the new scheme allows for optimization in close analogy with irregular low density parity check code ensembles.
cs/0602030
Single-Symbol Maximum Likelihood Decodable Linear STBCs
cs.IT math.IT
Space-Time block codes (STBC) from Orthogonal Designs (OD) and Co-ordinate Interleaved Orthogonal Designs (CIOD) have been attracting wider attention due to their amenability for fast (single-symbol) ML decoding, and full-rate with full-rank over quasi-static fading channels. However, these codes are instances of single-symbol decodable codes and it is natural to ask, if there exist codes other than STBCs form ODs and CIODs that allow single-symbol coding? In this paper, the above question is answered in the affirmative by characterizing all linear STBCs, that allow single-symbol ML decoding (not necessarily full-diversity) over quasi-static fading channels-calling them single-symbol decodable designs (SDD). The class SDD includes ODs and CIODs as proper subclasses. Further, among the SDD, a class of those that offer full-diversity, called Full-rank SDD (FSDD) are characterized and classified.
cs/0602031
Classifying Signals with Local Classifiers
cs.AI
This paper deals with the problem of classifying signals. The new method for building so called local classifiers and local features is presented. The method is a combination of the lifting scheme and the support vector machines. Its main aim is to produce effective and yet comprehensible classifiers that would help in understanding processes hidden behind classified signals. To illustrate the method we present the results obtained on an artificial and a real dataset.
cs/0602032
Finite-State Dimension and Real Arithmetic
cs.CC cs.IT math.IT
We use entropy rates and Schur concavity to prove that, for every integer k >= 2, every nonzero rational number q, and every real number alpha, the base-k expansions of alpha, q+alpha, and q*alpha all have the same finite-state dimension and the same finite-state strong dimension. This extends, and gives a new proof of, Wall's 1949 theorem stating that the sum or product of a nonzero rational number and a Borel normal number is always Borel normal.
cs/0602035
n-Channel Entropy-Constrained Multiple-Description Lattice Vector Quantization
cs.IT math.IT
In this paper we derive analytical expressions for the central and side quantizers which, under high-resolutions assumptions, minimize the expected distortion of a symmetric multiple-description lattice vector quantization (MD-LVQ) system subject to entropy constraints on the side descriptions for given packet-loss probabilities. We consider a special case of the general n-channel symmetric multiple-description problem where only a single parameter controls the redundancy tradeoffs between the central and the side distortions. Previous work on two-channel MD-LVQ showed that the distortions of the side quantizers can be expressed through the normalized second moment of a sphere. We show here that this is also the case for three-channel MD-LVQ. Furthermore, we conjecture that this is true for the general n-channel MD-LVQ. For given source, target rate and packet-loss probabilities we find the optimal number of descriptions and construct the MD-LVQ system that minimizes the expected distortion. We verify theoretical expressions by numerical simulations and show in a practical setup that significant performance improvements can be achieved over state-of-the-art two-channel MD-LVQ by using three-channel MD-LVQ.
cs/0602036
R\'{e}seaux d'Automates de Caianiello Revisit\'{e}
cs.NE
We exhibit a family of neural networks of McCulloch and Pitts of size $2nk+2$ which can be simulated by a neural networks of Caianiello of size $2n+2$ and memory length $k$. This simulation allows us to find again one of the result of the following article: [Cycles exponentiels des r\'{e}seaux de Caianiello et compteurs en arithm\'{e}tique redondante, Technique et Science Informatiques Vol. 19, pages 985-1008] on the existence of neural networks of Caianiello of size $2n+2$ and memory length $k$ which describes a cycle of length $k \times 2^{nk}$.
cs/0602038
Minimum Cost Homomorphisms to Proper Interval Graphs and Bigraphs
cs.DM cs.AI
For graphs $G$ and $H$, a mapping $f: V(G)\dom V(H)$ is a homomorphism of $G$ to $H$ if $uv\in E(G)$ implies $f(u)f(v)\in E(H).$ If, moreover, each vertex $u \in V(G)$ is associated with costs $c_i(u), i \in V(H)$, then the cost of the homomorphism $f$ is $\sum_{u\in V(G)}c_{f(u)}(u)$. For each fixed graph $H$, we have the {\em minimum cost homomorphism problem}, written as MinHOM($H)$. The problem is to decide, for an input graph $G$ with costs $c_i(u),$ $u \in V(G), i\in V(H)$, whether there exists a homomorphism of $G$ to $H$ and, if one exists, to find one of minimum cost. Minimum cost homomorphism problems encompass (or are related to) many well studied optimization problems. We describe a dichotomy of the minimum cost homomorphism problems for graphs $H$, with loops allowed. When each connected component of $H$ is either a reflexive proper interval graph or an irreflexive proper interval bigraph, the problem MinHOM($H)$ is polynomial time solvable. In all other cases the problem MinHOM($H)$ is NP-hard. This solves an open problem from an earlier paper. Along the way, we prove a new characterization of the class of proper interval bigraphs.
cs/0602039
Path Summaries and Path Partitioning in Modern XML Databases
cs.DB
We study the applicability of XML path summaries in the context of current-day XML databases. We find that summaries provide an excellent basis for optimizing data access methods, which furthermore mixes very well with path-partitioned stores. We provide practical algorithms for building and exploiting summaries, and prove its benefits through extensive experiments.
cs/0602044
Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm
cs.CV
A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved. The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance. The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization. Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time.
cs/0602045
Emergence Explained
cs.MA cs.DC cs.GL
Emergence (macro-level effects from micro-level causes) is at the heart of the conflict between reductionism and functionalism. How can there be autonomous higher level laws of nature (the functionalist claim) if everything can be reduced to the fundamental forces of physics (the reductionist position)? We cut through this debate by applying a computer science lens to the way we view nature. We conclude (a) that what functionalism calls the special sciences (sciences other than physics) do indeed study autonomous laws and furthermore that those laws pertain to real higher level entities but (b) that interactions among such higher-level entities is epiphenomenal in that they can always be reduced to primitive physical forces. In other words, epiphenomena, which we will identify with emergent phenomena, do real higher-level work. The proposed perspective provides a framework for understanding many thorny issues including the nature of entities, stigmergy, the evolution of complexity, phase transitions, supervenience, and downward entailment. We also discuss some practical considerations pertaining to systems of systems and the limitations of modeling.
cs/0602046
Analysis of LDGM and compound codes for lossy compression and binning
cs.IT math.IT
Recent work has suggested that low-density generator matrix (LDGM) codes are likely to be effective for lossy source coding problems. We derive rigorous upper bounds on the effective rate-distortion function of LDGM codes for the binary symmetric source, showing that they quickly approach the rate-distortion function as the degree increases. We also compare and contrast the standard LDGM construction with a compound LDPC/LDGM construction introduced in our previous work, which provably saturates the rate-distortion bound with finite degrees. Moreover, this compound construction can be used to generate nested codes that are simultaneously good as source and channel codes, and are hence well-suited to source/channel coding with side information. The sparse and high-girth graphical structure of our constructions render them well-suited to message-passing encoding.
cs/0602048
On the Optimality of the ARQ-DDF Protocol
cs.IT math.IT
The performance of the automatic repeat request-dynamic decode and forward (ARQ-DDF) cooperation protocol is analyzed in two distinct scenarios. The first scenario is the multiple access relay (MAR) channel where a single relay is dedicated to simultaneously help several multiple access users. For this setup, it is shown that the ARQ-DDF protocol achieves the optimal diversity multiplexing tradeoff (DMT) of the channel. The second scenario is the cooperative vector multiple access (CVMA) channel where the users cooperate in delivering their messages to a destination equipped with multiple receiving antennas. For this setup, we develop a new variant of the ARQ-DDF protocol where the users are purposefully instructed not to cooperate in the first round of transmission. Lower and upper bounds on the achievable DMT are then derived. These bounds are shown to converge to the optimal tradeoff as the number of transmission rounds increases.
cs/0602049
Cooperative Lattice Coding and Decoding
cs.IT math.IT
A novel lattice coding framework is proposed for outage-limited cooperative channels. This framework provides practical implementations for the optimal cooperation protocols proposed by Azarian et al. In particular, for the relay channel we implement a variant of the dynamic decode and forward protocol, which uses orthogonal constellations to reduce the channel seen by the destination to a single-input single-output time-selective one, while inheriting the same diversity-multiplexing tradeoff. This simplification allows for building the receiver using traditional belief propagation or tree search architectures. Our framework also generalizes the coding scheme of Yang and Belfiore in the context of amplify and forward cooperation. For the cooperative multiple access channel, a tree coding approach, matched to the optimal linear cooperation protocol of Azarain et al, is developed. For this scenario, the MMSE-DFE Fano decoder is shown to enjoy an excellent tradeoff between performance and complexity. Finally, the utility of the proposed schemes is established via a comprehensive simulation study.
cs/0602050
Outage Capacity of the Fading Relay Channel in the Low SNR Regime
cs.IT math.IT
In slow fading scenarios, cooperation between nodes can increase the amount of diversity for communication. We study the performance limit in such scenarios by analyzing the outage capacity of slow fading relay channels. Our focus is on the low SNR and low outage probability regime, where the adverse impact of fading is greatest but so are the potential gains from cooperation. We showed that while the standard Amplify-Forward protocol performs very poorly in this regime, a modified version we called the Bursty Amplify-Forward protocol is optimal and achieves the outage capacity of the network. Moreover, this performance can be achieved without a priori channel knowledge at the receivers. In contrast, the Decode-Forward protocol is strictly sub-optimal in this regime. Our results directly yield the outage capacity per unit energy of fading relay channels.
cs/0602051
On the utility of the multimodal problem generator for assessing the performance of Evolutionary Algorithms
cs.NE
This paper looks in detail at how an evolutionary algorithm attempts to solve instances from the multimodal problem generator. The paper shows that in order to consistently reach the global optimum, an evolutionary algorithm requires a population size that should grow at least linearly with the number of peaks. It is also shown a close relationship between the supply and decision making issues that have been identified previously in the context of population sizing models for additively decomposable problems. The most important result of the paper, however, is that solving an instance of the multimodal problem generator is like solving a peak-in-a-haystack, and it is argued that evolutionary algorithms are not the best algorithms for such a task. Finally, and as opposed to what several researchers have been doing, it is our strong belief that the multimodal problem generator is not adequate for assessing the performance of evolutionary algorithms.
cs/0602052
The OverRelational Manifesto
cs.DB cs.DS
The OverRelational Manifesto (below ORM) proposes a possible approach to creation of data storage systems of the next generation. ORM starts from the requirement that information in a relational database is represented by a set of relation values. Accordingly, it is assumed that the information about any entity of an enterprise must also be represented as a set of relation values (the ORM main requirement). A system of types is introduced, which allows one to fulfill the main requirement. The data are represented in the form of complex objects, and the state of any object is described as a set of relation values. Emphasize that the types describing the objects are encapsulated, inherited, and polymorphic. Then, it is shown that the data represented as a set of such objects may also be represented as a set of relational values defined on the set of scalar domains (dual data representation). In the general case, any class is associated with a set of relation variables (R-variables) each one containing some data about all objects of this class existing in the system. One of the key points is the fact that the usage of complex (from the user's viewpoint) refined names of R-variables and their attributes makes it possible to preserve the semantics of complex data structures represented in the form of a set of relation values. The most important part of the data storage system created on the approach proposed is an object-oriented translator operating over a relational DBMS. The expressiveness of such a system is comparable with that of OO programming languages.
cs/0602053
How to Beat the Adaptive Multi-Armed Bandit
cs.DS cs.LG
The multi-armed bandit is a concise model for the problem of iterated decision-making under uncertainty. In each round, a gambler must pull one of $K$ arms of a slot machine, without any foreknowledge of their payouts, except that they are uniformly bounded. A standard objective is to minimize the gambler's regret, defined as the gambler's total payout minus the largest payout which would have been achieved by any fixed arm, in hindsight. Note that the gambler is only told the payout for the arm actually chosen, not for the unchosen arms. Almost all previous work on this problem assumed the payouts to be non-adaptive, in the sense that the distribution of the payout of arm $j$ in round $i$ is completely independent of the choices made by the gambler on rounds $1, \dots, i-1$. In the more general model of adaptive payouts, the payouts in round $i$ may depend arbitrarily on the history of past choices made by the algorithm. We present a new algorithm for this problem, and prove nearly optimal guarantees for the regret against both non-adaptive and adaptive adversaries. After $T$ rounds, our algorithm has regret $O(\sqrt{T})$ with high probability (the tail probability decays exponentially). This dependence on $T$ is best possible, and matches that of the full-information version of the problem, in which the gambler is told the payouts for all $K$ arms after each round. Previously, even for non-adaptive payouts, the best high-probability bounds known were $O(T^{2/3})$, due to Auer, Cesa-Bianchi, Freund and Schapire. The expected regret of their algorithm is $O(T^{1/2}) for non-adaptive payouts, but as we show, $\Omega(T^{2/3})$ for adaptive payouts.
cs/0602054
Explicit Space-Time Codes Achieving The Diversity-Multiplexing Gain Tradeoff
cs.IT math.IT
A recent result of Zheng and Tse states that over a quasi-static channel, there exists a fundamental tradeoff, referred to as the diversity-multiplexing gain (D-MG) tradeoff, between the spatial multiplexing gain and the diversity gain that can be simultaneously achieved by a space-time (ST) block code. This tradeoff is precisely known in the case of i.i.d. Rayleigh-fading, for T>= n_t+n_r-1 where T is the number of time slots over which coding takes place and n_t,n_r are the number of transmit and receive antennas respectively. For T < n_t+n_r-1, only upper and lower bounds on the D-MG tradeoff are available. In this paper, we present a complete solution to the problem of explicitly constructing D-MG optimal ST codes, i.e., codes that achieve the D-MG tradeoff for any number of receive antennas. We do this by showing that for the square minimum-delay case when T=n_t=n, cyclic-division-algebra (CDA) based ST codes having the non-vanishing determinant property are D-MG optimal. While constructions of such codes were previously known for restricted values of n, we provide here a construction for such codes that is valid for all n. For the rectangular, T > n_t case, we present two general techniques for building D-MG-optimal rectangular ST codes from their square counterparts. A byproduct of our results establishes that the D-MG tradeoff for all T>= n_t is the same as that previously known to hold for T >= n_t + n_r -1.
cs/0602055
Revisiting Evolutionary Algorithms with On-the-Fly Population Size Adjustment
cs.NE
In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies have been done in the past to investigate the role of population sizing in evolutionary algorithms. In addition to those studies, several self-adjusting population sizing mechanisms have been proposed in the literature. This paper revisits the latter topic and pays special attention to the genetic algorithm with adaptive population size (APGA), for which several researchers have claimed to be very effective at autonomously (re)sizing the population. As opposed to those previous claims, this paper suggests a complete opposite view. Specifically, it shows that APGA is not capable of adapting the population size at all. This claim is supported on theoretical grounds and confirmed by computer simulations.
cs/0602056
Building Scenarios for Environmental Management and Planning: An IT-Based Approach
cs.MA
Oftentimes, the need to build multidiscipline knowledge bases, oriented to policy scenarios, entails the involvement of stakeholders in manifold domains, with a juxtaposition of different languages whose semantics can hardly allow inter-domain transfers. A useful support for planning is the building up of durable IT based interactive platforms, where it is possible to modify initial positions toward a semantic convergence. The present paper shows an area-based application of these tools, for the integrated distance-management of different forms of knowledge expressed by selected stakeholders about environmental planning issues, in order to build alternative development scenarios. Keywords: Environmental planning, Scenario building, Multi-source knowledge, IT-based
cs/0602058
Incremental Redundancy Cooperative Coding for Wireless Networks: Cooperative Diversity, Coding, and Transmission Energy Gain
cs.IT math.IT
We study an incremental redundancy (IR) cooperative coding scheme for wireless networks. To exploit the spatial diversity benefit we propose a cluster-based collaborating strategy for a quasi-static Rayleigh fading channel model and based on a network geometric distance profile. Our scheme enhances the network performance by embedding an IR cooperative coding scheme into an existing noncooperative route. More precisely, for each hop, we form a collaborating cluster of M-1 nodes between the (hop) sender and the (hop) destination. The transmitted message is encoded using a mother code and partitioned into M blocks corresponding to the each of M slots. In the first slot, the (hop) sender broadcasts its information by transmitting the first block, and its helpers attempt to relay this message. In the remaining slots, the each of left-over M-1 blocks is sent either through a helper which has successfully decoded the message or directly by the (hop) sender where a dynamic schedule is based on the ACK-based feedback from the cluster. By employing powerful good codes (e.g., turbo codes, LDPC codes, and raptor codes) whose performance is characterized by a threshold behavior, our approach improves the reliability of a multi-hop routing through not only cooperation diversity benefit but also a coding advantage. The study of the diversity and the coding gain of the proposed scheme is based on a new simple threshold bound on the frame-error rate (FER) of maximum likelihood decoding. A average FER upper bound and its asymptotic (in large SNR) version are derived as a function of the average fading channel SNRs and the code threshold.
cs/0602060
eJournal interface can influence usage statistics: implications for libraries, publishers, and Project COUNTER
cs.IR cs.DL
The design of a publisher's electronic interface can have a measurable effect on electronic journal usage statistics. A study of journal usage from six COUNTER-compliant publishers at thirty-two research institutions in the United States, the United Kingdom and Sweden indicates that the ratio of PDF to HTML views is not consistent across publisher interfaces, even after controlling for differences in publisher content. The number of fulltext downloads may be artificially inflated when publishers require users to view HTML versions before accessing PDF versions or when linking mechanisms, such as CrossRef, direct users to the full text, rather than the abstract, of each article. These results suggest that usage reports from COUNTER-compliant publishers are not directly comparable in their current form. One solution may be to modify publisher numbers with adjustment factors deemed to be representative of the benefit or disadvantage due to its interface. Standardization of some interface and linking protocols may obviate these differences and allow for more accurate cross-publisher comparisons.
cs/0602062
Learning rational stochastic languages
cs.LG
Given a finite set of words w1,...,wn independently drawn according to a fixed unknown distribution law P called a stochastic language, an usual goal in Grammatical Inference is to infer an estimate of P in some class of probabilistic models, such as Probabilistic Automata (PA). Here, we study the class of rational stochastic languages, which consists in stochastic languages that can be generated by Multiplicity Automata (MA) and which strictly includes the class of stochastic languages generated by PA. Rational stochastic languages have minimal normal representation which may be very concise, and whose parameters can be efficiently estimated from stochastic samples. We design an efficient inference algorithm DEES which aims at building a minimal normal representation of the target. Despite the fact that no recursively enumerable class of MA computes exactly the set of rational stochastic languages over Q, we show that DEES strongly identifies tis set in the limit. We study the intermediary MA output by DEES and show that they compute rational series which converge absolutely to one and which can be used to provide stochastic languages which closely estimate the target.
cs/0602065
Similarity of Objects and the Meaning of Words
cs.CV cs.IR
We survey the emerging area of compression-based, parameter-free, similarity distance measures useful in data-mining, pattern recognition, learning and automatic semantics extraction. Given a family of distances on a set of objects, a distance is universal up to a certain precision for that family if it minorizes every distance in the family between every two objects in the set, up to the stated precision (we do not require the universal distance to be an element of the family). We consider similarity distances for two types of objects: literal objects that as such contain all of their meaning, like genomes or books, and names for objects. The latter may have literal embodyments like the first type, but may also be abstract like ``red'' or ``christianity.'' For the first type we consider a family of computable distance measures corresponding to parameters expressing similarity according to particular featuresdistances generated by web users corresponding to particular semantic relations between the (names for) the designated objects. For both families we give universal similarity distance measures, incorporating all particular distance measures in the family. In the first case the universal distance is based on compression and in the second case it is based on Google page counts related to search terms. In both cases experiments on a massive scale give evidence of the viability of the approaches. between pairs of literal objects. For the second type we consider similarity
cs/0602067
Renyi to Renyi -- Source Coding under Siege
cs.IT cs.DS math.IT
A novel lossless source coding paradigm applies to problems of unreliable lossless channels with low bit rates, in which a vital message needs to be transmitted prior to termination of communications. This paradigm can be applied to Alfred Renyi's secondhand account of an ancient siege in which a spy was sent to scout the enemy but was captured. After escaping, the spy returned to his base in no condition to speak and unable to write. His commander asked him questions that he could answer by nodding or shaking his head, and the fortress was defended with this information. Renyi told this story with reference to prefix coding, but maximizing probability of survival in the siege scenario is distinct from yet related to the traditional source coding objective of minimizing expected codeword length. Rather than finding a code minimizing expected codeword length $\sum_{i=1}^n p(i) l(i)$, the siege problem involves maximizing $\sum_{i=1}^n p(i) \theta^{l(i)}$ for a known $\theta \in (0,1)$. When there are no restrictions on codewords, this problem can be solve using a known generalization of Huffman coding. The optimal solution has coding bounds which are functions of Renyi entropy; in addition to known bounds, new bounds are derived here. The alphabetically constrained version of this problem has applications in search trees and diagnostic testing. A novel dynamic programming algorithm -- based upon the oldest known algorithm for the traditional alphabetic problem -- optimizes this problem in $O(n^3)$ time and $O(n^2)$ space, whereas two novel approximation algorithms can find a suboptimal solution faster: one in linear time, the other in $O(n \log n)$. Coding bounds for the alphabetic version of this problem are also presented.
cs/0602071
Geographic Gossip: Efficient Aggregation for Sensor Networks
cs.IT math.IT
Gossip algorithms for aggregation have recently received significant attention for sensor network applications because of their simplicity and robustness in noisy and uncertain environments. However, gossip algorithms can waste significant energy by essentially passing around redundant information multiple times. For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is caused by slow mixing times of random walks on those graphs. We propose and analyze an alternative gossiping scheme that exploits geographic information. By utilizing a simple resampling method, we can demonstrate substantial gains over previously proposed gossip protocols. In particular, for random geometric graphs, our algorithm computes the true average to accuracy $1/n^a$ using $O(n^{1.5}\sqrt{\log n})$ radio transmissions, which reduces the energy consumption by a $\sqrt{\frac{n}{\log n}}$ factor over standard gossip algorithms.
cs/0602072
Turbo Decoding on the Binary Erasure Channel: Finite-Length Analysis and Turbo Stopping Sets
cs.IT math.IT
This paper is devoted to the finite-length analysis of turbo decoding over the binary erasure channel (BEC). The performance of iterative belief-propagation (BP) decoding of low-density parity-check (LDPC) codes over the BEC can be characterized in terms of stopping sets. We describe turbo decoding on the BEC which is simpler than turbo decoding on other channels. We then adapt the concept of stopping sets to turbo decoding and state an exact condition for decoding failure. Apply turbo decoding until the transmitted codeword has been recovered, or the decoder fails to progress further. Then the set of erased positions that will remain when the decoder stops is equal to the unique maximum-size turbo stopping set which is also a subset of the set of erased positions. Furthermore, we present some improvements of the basic turbo decoding algorithm on the BEC. The proposed improved turbo decoding algorithm has substantially better error performance as illustrated by the given simulation results. Finally, we give an expression for the turbo stopping set size enumerating function under the uniform interleaver assumption, and an efficient enumeration algorithm of small-size turbo stopping sets for a particular interleaver. The solution is based on the algorithm proposed by Garello et al. in 2001 to compute an exhaustive list of all low-weight codewords in a turbo code.
cs/0602074
The entropy rate of the binary symmetric channel in the rare transitions regime
cs.IT math.IT
This note has been withdrawn by the author as the more complete result was recently proved by A.Quas and Y.Peres
cs/0602076
Exploring term-document matrices from matrix models in text mining
cs.IR cs.DB cs.DL
We explore a matrix-space model, that is a natural extension to the vector space model for Information Retrieval. Each document can be represented by a matrix that is based on document extracts (e.g. sentences, paragraphs, sections). We focus on the performance of this model for the specific case in which documents are originally represented as term-by-sentence matrices. We use the singular value decomposition to approximate the term-by-sentence matrices and assemble these results to form the pseudo-``term-document'' matrix that forms the basis of a text mining method alternative to traditional VSM and LSI. We investigate the singular values of this matrix and provide experimental evidence suggesting that the method can be particularly effective in terms of accuracy for text collections with multi-topic documents, such as web pages with news.
cs/0602079
SISO APP Searches in Lattices with Tanner Graphs
cs.IT cs.DS math.IT
An efficient, low-complexity, soft-output detector for general lattices is presented, based on their Tanner graph (TG) representations. Closest-point searches in lattices can be performed as non-binary belief propagation on associated TGs; soft-information output is naturally generated in the process; the algorithm requires no backtrack (cf. classic sphere decoding), and extracts extrinsic information. A lattice's coding gain enables equivalence relations between lattice points, which can be thereby partitioned in cosets. Total and extrinsic a posteriori probabilities at the detector's output further enable the use of soft detection information in iterative schemes. The algorithm is illustrated via two scenarios that transmit a 32-point, uncoded super-orthogonal (SO) constellation for multiple-input multiple-output (MIMO) channels, carved from an 8-dimensional non-orthogonal lattice (a direct sum of two 4-dimensional checkerboard lattice): it achieves maximum likelihood performance in quasistatic fading; and, performs close to interference-free transmission, and identically to list sphere decoding, in independent fading with coordinate interleaving and iterative equalization and detection. Latter scenario outperforms former despite the absence of forward error correction coding---because the inherent lattice coding gain allows for the refining of extrinsic information. The lattice constellation is the same as the one employed in the SO space-time trellis codes first introduced for 2-by-2 MIMO by Ionescu et al., then independently by Jafarkhani and Seshadri. Complexity is log-linear in lattice dimensionality, vs. cubic in sphere decoders.
cs/0602081
Low-Density Parity-Check Code with Fast Decoding Speed
cs.IT math.IT
Low-Density Parity-Check (LDPC) codes received much attention recently due to their capacity-approaching performance. The iterative message-passing algorithm is a widely adopted decoding algorithm for LDPC codes \cite{Kschischang01}. An important design issue for LDPC codes is designing codes with fast decoding speed while maintaining capacity-approaching performance. In another words, it is desirable that the code can be successfully decoded in few number of decoding iterations, at the same time, achieves a significant portion of the channel capacity. Despite of its importance, this design issue received little attention so far. In this paper, we address this design issue for the case of binary erasure channel. We prove that density-efficient capacity-approaching LDPC codes satisfy a so called "flatness condition". We show an asymptotic approximation to the number of decoding iterations. Based on these facts, we propose an approximated optimization approach to finding the codes with good decoding speed. We further show that the optimal codes in the sense of decoding speed are "right-concentrated". That is, the degrees of check nodes concentrate around the average right degree.
cs/0602083
A third level trigger programmable on FPGA for the gamma/hadron separation in a Cherenkov telescope using pseudo-Zernike moments and the SVM classifier
cs.CV cs.AI
We studied the application of the Pseudo-Zernike features as image parameters (instead of the Hillas parameters) for the discrimination between the images produced by atmospheric electromagnetic showers caused by gamma-rays and the ones produced by atmospheric electromagnetic showers caused by hadrons in the MAGIC Experiment. We used a Support Vector Machine as classification algorithm with the computed Pseudo-Zernike features as classification parameters. We implemented on a FPGA board a kernel function of the SVM and the Pseudo-Zernike features to build a third level trigger for the gamma-hadron separation task of the MAGIC Experiment.
cs/0602084
Universal Codes as a Basis for Time Series Testing
cs.IT math.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.
cs/0602085
Twenty (or so) Questions: $D$-ary Length-Bounded Prefix Coding
cs.IT cs.DS math.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 $O(\log n)$.
cs/0602086
On the Block Error Probability of LP Decoding of LDPC Codes
cs.IT math.IT
In his thesis, Wiberg showed the existence of thresholds for families of regular low-density parity-check codes under min-sum algorithm decoding. He also derived analytic bounds on these thresholds. In this paper, we formulate similar results for linear programming decoding of regular low-density parity-check codes.
cs/0602087
Bounds on the Threshold of Linear Programming Decoding
cs.IT math.IT
Whereas many results are known about thresholds for ensembles of low-density parity-check codes under message-passing iterative decoding, this is not the case for linear programming decoding. Towards closing this knowledge gap, this paper presents some bounds on the thresholds of low-density parity-check code ensembles under linear programming decoding.
cs/0602088
Towards Low-Complexity Linear-Programming Decoding
cs.IT math.IT
We consider linear-programming (LP) decoding of low-density parity-check (LDPC) codes. While it is clear that one can use any general-purpose LP solver to solve the LP that appears in the decoding problem, we argue in this paper that the LP at hand is equipped with a lot of structure that one should take advantage of. Towards this goal, we study the dual LP and show how coordinate-ascent methods lead to very simple update rules that are tightly connected to the min-sum algorithm. Moreover, replacing minima in the formula of the dual LP with soft-minima one obtains update rules that are tightly connected to the sum-product algorithm. This shows that LP solvers with complexity similar to the min-sum algorithm and the sum-product algorithm are feasible. Finally, we also discuss some sub-gradient-based methods.
cs/0602089
Pseudo-Codeword Analysis of Tanner Graphs from Projective and Euclidean Planes
cs.IT cs.DM math.IT
In order to understand the performance of a code under maximum-likelihood (ML) decoding, one studies the codewords, in particular the minimal codewords, and their Hamming weights. In the context of linear programming (LP) decoding, one's attention needs to be shifted to the pseudo-codewords, in particular to the minimal pseudo-codewords, and their pseudo-weights. In this paper we investigate some families of codes that have good properties under LP decoding, namely certain families of low-density parity-check (LDPC) codes that are derived from projective and Euclidean planes: we study the structure of their minimal pseudo-codewords and give lower bounds on their pseudo-weight.
cs/0602091
Feedback Capacity of Stationary Gaussian Channels
cs.IT math.IT
The feedback capacity of additive stationary Gaussian noise channels is characterized as the solution to a variational problem. Toward this end, it is proved that the optimal feedback coding scheme is stationary. When specialized to the first-order autoregressive moving average noise spectrum, this variational characterization yields a closed-form expression for the feedback capacity. In particular, this result shows that the celebrated Schalkwijk-Kailath coding scheme achieves the feedback capacity for the first-order autoregressive moving average Gaussian channel, positively answering a long-standing open problem studied by Butman, Schalkwijk-Tiernan, Wolfowitz, Ozarow, Ordentlich, Yang-Kavcic-Tatikonda, and others. More generally, it is shown that a k-dimensional generalization of the Schalkwijk-Kailath coding scheme achieves the feedback capacity for any autoregressive moving average noise spectrum of order k. Simply put, the optimal transmitter iteratively refines the receiver's knowledge of the intended message.
cs/0602092
Inconsistent parameter estimation in Markov random fields: Benefits in the computation-limited setting
cs.LG cs.IT math.IT math.ST stat.TH
Consider the problem of joint parameter estimation and prediction in a Markov random field: i.e., the model parameters are estimated on the basis of an initial set of data, and then the fitted model is used to perform prediction (e.g., smoothing, denoising, interpolation) on a new noisy observation. Working under the restriction of limited computation, we analyze a joint method in which the \emph{same convex variational relaxation} is used to construct an M-estimator for fitting parameters, and to perform approximate marginalization for the prediction step. The key result of this paper is that in the computation-limited setting, using an inconsistent parameter estimator (i.e., an estimator that returns the ``wrong'' model even in the infinite data limit) can be provably beneficial, since the resulting errors can partially compensate for errors made by using an approximate prediction technique. En route to this result, we analyze the asymptotic properties of M-estimators based on convex variational relaxations, and establish a Lipschitz stability property that holds for a broad class of variational methods. We show that joint estimation/prediction based on the reweighted sum-product algorithm substantially outperforms a commonly used heuristic based on ordinary sum-product.