OpenRaiser/Intern-Atlas huggingface dataset:
Properties of the Polarization Transformations for the Likelihood Ratios of Symmetric B-DMCs
Venue: arXiv cs.IT 2013
Year: 2013

In this paper we investigate, starting with a symmetric B-DMC, the evolution of various probabilities of the likelihood ratios of the synthetic channels created by the recursive application of the basic polarization transformations. The analysis provides a new perspective into the theory of channel polarization initiated by Arıkan and helps us to address a problem related to approximating the computations of the likelihood ratios of the synthetic channels.
Consistency Checking and Querying in Probabilistic Databases under Integrity Constraints
Venue: arXiv cs.DB 2013
Year: 2013

We address the issue of incorporating a particular yet expressive form of integrity constraints (namely, denial constraints) into probabilistic databases. To this aim, we move away from the common way of giving semantics to probabilistic databases, which relies on considering a unique interpretation of the data, and address two fundamental problems: consistency checking and query evaluation. The former consists in verifying whether there is an interpretation which conforms to both the marginal probabilities of the tuples and the integrity constraints. The latter is the problem of answering queries under a "cautious" paradigm, taking into account all interpretations of the data in accordance with the constraints. In this setting, we investigate the complexity of the above-mentioned problems, and identify several tractable cases of practical relevance.
Reconstruction of Directed Networks from Consensus Dynamics
Venue: arXiv cs.SI 2013
Year: 2013

This paper addresses the problem of identifying the topology of an unknown, weighted, directed network running a consensus dynamics. We propose a methodology to reconstruct the network topology from the dynamic response when the system is stimulated by a wide-sense stationary noise of unknown power spectral density. The method is based on a node-knockout, or grounding, procedure wherein the grounded node broadcasts zero without being eliminated from the network. In this direction, we measure the empirical cross-power spectral densities of the outputs between every pair of nodes for both grounded and ungrounded consensus to reconstruct the unknown topology of the network. We also establish that in the special cases of undirected or purely unidirectional networks, the reconstruction does not need grounding. Finally, we extend our results to the case of a directed network assuming a general dynamics, and prove that the developed method can detect edges and their direction.
Information theoretically secure, enhanced Johnson noise based key distribution over the smart grid with switched filters
Venue: arXiv cs.CR 2013
Year: 2013

We introduce a protocol with a reconfigurable filter system to create non-overlapping single loops in the smart power grid for the realization of the Kirchhoff-Law-Johnson-(like)-Noise secure key distribution system. The protocol is valid for one-dimensional radial networks (chain-like power line) which are typical of the electricity distribution network between the utility and the customer. The speed of the protocol (the number of steps needed) versus grid size is analyzed. When properly generalized, such a system has the potential to achieve unconditionally secure key distribution over the smart power grid of arbitrary geometrical dimensions.
Performance Analysis of LMS Filter for SSPA Linearization in Different Modulation Conditions
Venue: arXiv cs.OH 2013
Year: 2013

The SSPA has wide application in Communication system, but its high output power varies due to its non linear gain. Pre-distortion method plays major role in power amplifier linearization. Polynomial is one of the methods used. The error estimation in Polynomial method is carried out by LMS Filter. Our main work is to analysis the error estimation performance of the LMS Filter for the Solid state power amplifiers (SSPA) in different modulation conditions. Here we are calculating the ACP and analyzing how effectively the memoryless non linearity has been reduced for all digital modulation techniques. All the analysis and results are taken using Matlab software.
A new type of judgement theorems for attribute characters in information system
Venue: arXiv cs.DS 2013
Year: 2013

The research of attribute characters in information system which contains core, necessary, unnecessary is a basic and important issue in attribute reduct. Many methods for the judgement of attribute characters are based on the relationship between the objects and attributes. In this paper, a new type of judgement theorems which are absolutely based on the relationship among attributes is proposed for the judgement of attribute characters. The method is through comparing the two new attribute sets $E(a)$ and $N(a)$ with respect to the designated attribute $a$ which is proposed in this paper. We conclude that which type of the attribute $a$ belongs to is determined by the relationship between $E(a)$ and $N(a)$ in essence. Secondly, more concise and clear results are given about the judgment of the attribute characters through analyzing the properties of refinement and precise-refinement between $E(a)$ and $N(a)$ in topology. In addition, the relationship among attributes are discussed which is useful for constructing a reduct in the last section of this paper. In the last, we propose a reduct algorithm based on $E(a)$, and this algorithm is an extended application of the analysis of attribute characters above.
Nonasymptotic Probability Bounds for Fading Channels Exploiting Dedekind Zeta Functions
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, new probability bounds are derived for algebraic lattice codes. This is done by using the Dedekind zeta functions of the algebraic number fields involved in the lattice constructions. In particular, it is shown how to upper bound the error performance of a finite constellation on a Rayleigh fading channel and the probability of an eavesdropper's correct decision in a wiretap channel. As a byproduct, an estimate of the number of elements with a certain algebraic norm within a finite hyper-cube is derived. While this type of estimates have been, to some extent, considered in algebraic number theory before, they are now brought into novel practice in the context of fading channel communications. Hence, the interest here is in small-dimensional lattices and finite constellations rather than in the asymptotic behavior.
Iterative MapReduce for Large Scale Machine Learning
Venue: arXiv cs.DC 2013
Year: 2013

Large datasets ("Big Data") are becoming ubiquitous because the potential value in deriving insights from data, across a wide range of business and scientific applications, is increasingly recognized. In particular, machine learning - one of the foundational disciplines for data analysis, summarization and inference - on Big Data has become routine at most organizations that operate large clouds, usually based on systems such as Hadoop that support the MapReduce programming paradigm. It is now widely recognized that while MapReduce is highly scalable, it suffers from a critical weakness for machine learning: it does not support iteration. Consequently, one has to program around this limitation, leading to fragile, inefficient code. Further, reliance on the programmer is inherently flawed in a multi-tenanted cloud environment, since the programmer does not have visibility into the state of the system when his or her program executes. Prior work has sought to address this problem by either developing specialized systems aimed at stylized applications, or by augmenting MapReduce with ad hoc support for saving state across iterations (driven by an external loop). In this paper, we advocate support for looping as a first-class construct, and propose an extension of the MapReduce programming paradigm called {\em Iterative MapReduce}. We then develop an optimizer for a class of Iterative MapReduce programs that cover most machine learning techniques, provide theoretical justifications for the key optimization steps, and empirically demonstrate that system-optimized programs for significant machine learning tasks are competitive with state-of-the-art specialized solutions.
Expressing Ethnicity through Behaviors of a Robot Character
Venue: arXiv cs.CL 2013
Year: 2013

Achieving homophily, or association based on similarity, between a human user and a robot holds a promise of improved perception and task performance. However, no previous studies that address homophily via ethnic similarity with robots exist. In this paper, we discuss the difficulties of evoking ethnic cues in a robot, as opposed to a virtual agent, and an approach to overcome those difficulties based on using ethnically salient behaviors. We outline our methodology for selecting and evaluating such behaviors, and culminate with a study that evaluates our hypotheses of the possibility of ethnic attribution of a robot character through verbal and nonverbal behaviors and of achieving the homophily effect.
Computing Traversal Times on Dynamic Markovian Paths
Venue: arXiv cs.NI 2013
Year: 2013

In source routing, a complete path is chosen for a packet to travel from source to destination. While computing the time to traverse such a path may be straightforward in a fixed, static graph, doing so becomes much more challenging in dynamic graphs, in which the state of an edge in one time slot (i.e., its presence or absence) is random, and may depend on its state in the previous time step. The traversal time is due to both time spent waiting for edges to appear and time spent crossing them once they become available. We compute the expected traversal time (ETT) for a dynamic path in a number of special cases of stochastic edge dynamics models, and for three edge failure models, culminating in a surprisingly challenging yet realistic setting in which the initial configuration of edge states for the entire path is known. We show that the ETT for this "initial configuration" setting can be computed in quadratic time, by an algorithm based on probability generating functions. We also give several linear-time upper and lower bounds on the ETT.
Permutation decoding of Z2Z4-linear codes
Venue: arXiv cs.IT 2013
Year: 2013

An alternative permutation decoding method is described which can be used for any binary systematic encoding scheme, regardless whether the code is linear or not. Thus, the method can be applied to some important codes such as Z2Z4-linear codes, which are binary and, in general, nonlinear codes in the usual sense. For this, it is proved that these codes allow a systematic encoding scheme. As a particular example, this permutation decoding method is applied to some Hadamard Z2Z4-linear codes.
Update report: LEO-II version 1.5
Venue: arXiv cs.LO 2013
Year: 2013

Recent improvements of the LEO-II theorem prover are presented. These improvements include a revised ATP interface, new translations into first-order logic, rule support for the axiom of choice, detection of defined equality, and more flexible strategy scheduling.
An Efficient Hybrid Localization Technique in Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

Sensor nodes are low cost, low power devices that are used to collect physical data and monitor environmental conditions from remote locations. Wireless Sensor Networks(WSN) are collection of sensor nodes, coordinating among themselves to perform a particular task. Localization is defined as the deployment of the sensor nodes at known locations in the network. Localization techniques are classified as Centralized and Distributed. MDS-Map and SDP are some of the centralized algorithms while Diffusion, Gradient,APIT, Bounding Box, Relaxation-Based and Coordinate System Stitching come under Distributed algorithms. In this paper, we propose a new hybrid localization technique, which combines the advantages of the centralized and distributed algorithms and overcomes some of the drawbacks of the existing techniques. Simulations done with J-Sim prove advantage of the proposed scheme in terms of localization error calculated by varying the sink nodes, increasing node density and increasing communication range.
Towards a serious games evacuation simulator
Venue: arXiv cs.MA 2013
Year: 2013

The evacuation of complex buildings is a challenge under any circumstances. Fire drills are a way of training and validating evacuation plans. However, sometimes these plans are not taken seriously by their participants. It is also difficult to have the financial and time resources required. In this scenario, serious games can be used as a tool for training, planning and evaluating emergency plans. In this paper a prototype of a serious games evacuation simulator is presented. To make the environment as realistic as possible, 3D models were made using Blender and loaded onto Unity3D, a popular game engine. This framework provided us with the appropriate simulation environment. Some experiences were made and results show that this tool has potential for practitioners and planners to use it for training building occupants.
Computing convolution on grammar-compressed text
Venue: arXiv cs.DS 2013
Year: 2013

The convolution between a text string $S$ of length $N$ and a pattern string $P$ of length $m$ can be computed in $O(N \log m)$ time by FFT. It is known that various types of approximate string matching problems are reducible to convolution. In this paper, we assume that the input text string is given in a compressed form, as a \emph{straight-line program (SLP)}, which is a context free grammar in the Chomsky normal form that derives a single string. Given an SLP $\mathcal{S}$ of size $n$ describing a text $S$ of length $N$, and an uncompressed pattern $P$ of length $m$, we present a simple $O(nm \log m)$-time algorithm to compute the convolution between $S$ and $P$. We then show that this can be improved to $O(\min\{nm, N-α\} \log m)$ time, where $α\geq 0$ is a value that represents the amount of redundancy that the SLP captures with respect to the length-$m$ substrings. The key of the improvement is our new algorithm that computes the convolution between a trie of size $r$ and a pattern string $P$ of length $m$ in $O(r \log m)$ time.
Bit Level Soft Decision Decoding of Triple Parity Reed Solomon Codes through Automorphism Groups
Venue: arXiv cs.IT 2013
Year: 2013

This paper discusses bit-level soft decoding of triple-parity Reed-Solomon (RS) codes through automorphism permutation. A new method for identifying the automorphism groups of RS binary images is first developed. The new algorithm runs effectively, and can handle more RS codes and capture more automorphism groups than the existing ones. Utilizing the automorphism results, a new bit-level soft-decision decoding algorithm is subsequently developed for general $(n,n-3,4)$ RS codes. Simulation on $(31,28,4)$ RS codes demonstrates an impressive gain of more than 1 dB at the bit error rate of $10^{-5}$ over the existing algorithms.
The failure risk analysis of digital circuits
Venue: arXiv cs.DM 2013
Year: 2013

To analyze the failure risk of asynchronous digital circuits the time-parameter is introduced into the Boolean algebra replacing the arithmetic operations by logical operations. There considered an example of construction of signals passing through the logical elements, using the described below mathematical apparatus.
Cliquewidth and Knowledge Compilation
Venue: arXiv cs.LO 2013
Year: 2013

In this paper we study the role of cliquewidth in succinct representation of Boolean functions. Our main statement is the following: Let $Z$ be a Boolean circuit having cliquewidth $k$. Then there is another circuit $Z^*$ computing the same function as $Z$ having treewidth at most $18k+2$ and which has at most $4|Z|$ gates where $|Z|$ is the number of gates of $Z$. In this sense, cliquewidth is not more `powerful' than treewidth for the purpose of representation of Boolean functions. We believe this is quite a surprising fact because it contrasts the situation with graphs where an upper bound on the treewidth implies an upper bound on the cliquewidth but not vice versa. We demonstrate the usefulness of the new theorem for knowledge compilation. In particular, we show that a circuit $Z$ of cliquewidth $k$ can be compiled into a Decomposable Negation Normal Form ({\sc dnnf}) of size $O(9^{18k}k^2|Z|)$ and the same runtime. To the best of our knowledge, this is the first result on efficient knowledge compilation parameterized by cliquewidth of a Boolean circuit.
Adaptive Randomized Distributed Space-Time Coding in Cooperative MIMO Relay Systems
Venue: arXiv cs.IT 2013
Year: 2013

An adaptive randomized distributed space-time coding (DSTC) scheme and algorithms are proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receivers and an amplify-and-forward (AF) cooperation strategy are considered. In the proposed DSTC scheme, a randomized matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. Linear MMSE expressions are devised to compute the parameters of the adaptive randomized matrix and the linear receive filter. A stochastic gradient algorithm is also developed to compute the parameters of the adaptive randomized matrix with reduced computational complexity. We also derive the upper bound of the error probability of a cooperative MIMO system employing the randomized space-time coding scheme first. The simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.
Statistical Mechanics Approach to Sparse Noise Denoising
Venue: arXiv cs.IT 2013
Year: 2013

Reconstruction fidelity of sparse signals contaminated by sparse noise is considered. Statistical mechanics inspired tools are used to show that the l1-norm based convex optimization algorithm exhibits a phase transition between the possibility of perfect and imperfect reconstruction. Conditions characterizing this threshold are derived and the mean square error of the estimate is obtained for the case when perfect reconstruction is not possible. Detailed calculations are provided to expose the mathematical tools to a wide audience.
On the computational complexity of Data Flow Analysis
Venue: arXiv cs.CC 2013
Year: 2013

We consider the problem of Data Flow Analysis over monotone data flow frameworks with a finite lattice. The problem of computing the Maximum Fixed Point (MFP) solution is shown to be P-complete even when the lattice has just four elements. This shows that the problem is unlikely to be efficiently parallelizable. It is also shown that the problem of computing the Meet Over all Paths (MOP) solution is NL-complete (and hence efficiently parallelizable) when the lattice is finite even for non-monotone data flow frameworks. These results appear in contrast with the fact that when the lattice is not finite, solving the MOP problem is undecidable and hence significantly harder than the MFP problem which is polynomial time computable for lattices of finite height.
About Inverse 3-SAT
Venue: arXiv cs.DS 2013
Year: 2013

The Inverse 3-SAT problem is known to be coNP Complete. This article shows a new interesting way to solve directly the problem by using closure under resolution and partial assignment properties. An algorithm is proposed which lets solve the (co)Inverse 3-SAT problem.
Streaming-Codes for Multicast over Burst Erasure Channels
Venue: arXiv cs.IT 2013
Year: 2013

We study the capacity limits of real-time streaming over burst-erasure channels. A stream of source packets must be sequentially encoded and the resulting channel packets must be transmitted over a two-receiver burst-erasure broadcast channel. The source packets must be sequentially reconstructed at each receiver with a possibly different reconstruction deadline. We study the associated capacity as a function of burst-lengths and delays at the two receivers. We establish that the operation of the system can be divided into two main regimes: a low-delay regime and a large-delay regime. We fully characterize the capacity in the large delay regime. The key to this characterization is an inherent slackness in the delay of one of the receivers. At every point in this regime we can reduce the delay of at-least one of the users until a certain critical value and thus it suffices to obtain code constructions for certain critical delays. We partially characterize the capacity in the low-delay regime. Our capacity results involve code constructions and converse techniques that appear to be novel. We also provide a rigorous information theoretic converse theorem in the point-to-point setting which was studied by Martinian in an earlier work.
2-stack pushall sortable permutations
Venue: arXiv cs.DM 2013
Year: 2013

In the 60's, Knuth introduced stack-sorting and serial compositions of stacks. In particular, one significant question arise out of the work of Knuth: how to decide efficiently if a given permutation is sortable with 2 stacks in series? Whether this problem is polynomial or NP-complete is still unanswered yet. In this article we introduce 2-stack pushall permutations which form a subclass of 2-stack sortable permutations and show that these two classes are closely related. Moreover, we give an optimal O(n^2) algorithm to decide if a given permutation of size n is 2-stack pushall sortable and describe all its sortings. This result is a step to the solve the general 2-stack sorting problem in polynomial time.
Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference
Venue: arXiv cs.AI 2013
Year: 2013

Recently, it has been shown how sampling actions from the predictive distribution over the optimal action-sometimes called Thompson sampling-can be applied to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution can then be constructed by a Bayesian superposition of the optimal policies weighted by their posterior probability that is updated by Bayesian inference and causal calculus. Here we discuss three important features of this approach. First, we discuss in how far such Thompson sampling can be regarded as a natural consequence of the Bayesian modeling of policy uncertainty. Second, we show how Thompson sampling can be used to study interactions between multiple adaptive agents, thus, opening up an avenue of game-theoretic analysis. Third, we show how Thompson sampling can be applied to infer causal relationships when interacting with an environment in a sequential fashion. In summary, our results suggest that Thompson sampling might not merely be a useful heuristic, but a principled method to address problems of adaptive sequential decision-making and causal inference.
Localized Dimension Growth: A Convolutional Random Network Coding Approach to Managing Memory and Decoding Delay
Venue: arXiv cs.IT 2013
Year: 2013

We consider an \textit{Adaptive Random Convolutional Network Coding} (ARCNC) algorithm to address the issue of field size in random network coding for multicast, and study its memory and decoding delay performances through both analysis and numerical simulations. ARCNC operates as a convolutional code, with the coefficients of local encoding kernels chosen randomly over a small finite field. The cardinality of local encoding kernels increases with time until the global encoding kernel matrices at related sink nodes have full rank.ARCNC adapts to unknown network topologies without prior knowledge, by locally incrementing the dimensionality of the convolutional code. Because convolutional codes of different constraint lengths can coexist in different portions of the network, reductions in decoding delay and memory overheads can be achieved. We show that this method performs no worse than random linear network codes in terms of decodability, and can provide significant gains in terms of average decoding delay or memory in combination, shuttle and random geometric networks.