OpenRaiser/Intern-Atlas huggingface dataset:
An Asymmetric Fingerprinting Scheme based on Tardos Codes
Venue: arXiv cs.CR 2010
Year: 2010

Tardos codes are currently the state-of-the-art in the design of practical collusion-resistant fingerprinting codes. Tardos codes rely on a secret vector drawn from a publicly known probability distribution in order to generate each Buyer's fingerprint. For security purposes, this secret vector must not be revealed to the Buyers. To prevent an untrustworthy Provider forging a copy of a Work with an innocent Buyer's fingerprint, previous asymmetric fingerprinting algorithms enforce the idea of the Buyers generating their own fingerprint. Applying this concept to Tardos codes is challenging since the fingerprint must be based on this vector secret. This paper provides the first solution for an asymmetric fingerprinting protocol dedicated to Tardos codes. The motivations come from a new attack, in which an untrustworthy Provider by modifying his secret vector frames an innocent Buyer.
How to Achieve the Optimal DMT of Selective Fading MIMO Channels?
Venue: arXiv cs.IT 2010
Year: 2010

In this paper, we consider a particular class of selective fading channel corresponding to a channel that is selective either in time or in frequency. For this class of channel, we propose a systematic way to achieve the optimal DMT derived in Coronel and Bölcskei, IEEE ISIT, 2007 by extending the non-vanishing determinant (NVD) criterion to the selective channel case. A new code construction based on split NVD parallel codes is then proposed to satisfy the NVD parallel criterion. This result is of significant interest not only in its own right, but also because it settles a long-standing debate in the literature related to the optimal DMT of selective fading channels.
MIMO Interference Alignment Over Correlated Channels with Imperfect CSI
Venue: arXiv cs.IT 2010
Year: 2010

Interference alignment (IA), given uncorrelated channel components and perfect channel state information, obtains the maximum degrees of freedom in an interference channel. Little is known, however, about how the sum rate of IA behaves at finite transmit power, with imperfect channel state information, or antenna correlation. This paper provides an approximate closed-form signal-to-interference-plus-noise-ratio (SINR) expression for IA over multiple-input-multiple-output (MIMO) channels with imperfect channel state information and transmit antenna correlation. Assuming linear processing at the transmitters and zero-forcing receivers, random matrix theory tools are utilized to derive an approximation for the post-processing SINR distribution of each stream for each user. Perfect channel knowledge and i.i.d. channel coefficients constitute special cases. This SINR distribution not only allows easy calculation of useful performance metrics like sum rate and symbol error rate, but also permits a realistic comparison of IA with other transmission techniques. More specifically, IA is compared with spatial multiplexing and beamforming and it is shown that IA may not be optimal for some performance criteria.
Last but not Least: Additional Positional Effects on Citation and Readership in arXiv
Venue: arXiv cs.DL 2010
Year: 2010

We continue investigation of the effect of position in announcements of newly received articles, a single day artifact, with citations received over the course of ensuing years. Earlier work [arXiv:0907.4740, arXiv:0805.0307] focused on the "visibility" effect for positions near the beginnings of announcements, and on the "self-promotion" effect associated to authors intentionally aiming for these positions, with both found correlated to a later enhanced citation rate. Here we consider a "reverse-visibility" effect for positions near the ends of announcements, and on a "procrastination" effect associated to submissions made within the 20 minute period just before the daily deadline. For two large subcommunities of theoretical high energy physics, we find a clear "reverse-visibility" effect, in which articles near the ends of the lists receive a boost in both short-term readership and long-term citations, almost comparable in size to the "visibility" effect documented earlier. For one of those subcommunities, we find an additional "procrastination" effect, in which last position articles submitted shortly before the deadline have an even higher citation rate than those that land more accidentally in that position. We consider and eliminate geographic effects as responsible for the above, and speculate on other possible causes, including "oblivious" and "nightowl" effects.
Multilevel Contracts for Trusted Components
Venue: arXiv cs.SE 2010
Year: 2010

This article contributes to the design and the verification of trusted components and services. The contracts are declined at several levels to cover then different facets, such as component consistency, compatibility or correctness. The article introduces multilevel contracts and a design+verification process for handling and analysing these contracts in component models. The approach is implemented with the COSTO platform that supports the Kmelia component model. A case study illustrates the overall approach.
On the Rate Achievable for Gaussian Relay Channels Using Superposition Forwarding
Venue: arXiv cs.IT 2010
Year: 2010

We analyze the achievable rate of the superposition of block Markov encoding (decode-and-forward) and side information encoding (compress-and-forward) for the three-node Gaussian relay channel. It is generally believed that the superposition can out perform decode-and-forward or compress-and-forward due to its generality. We prove that within the class of Gaussian distributions, this is not the case: the superposition scheme only achieves a rate that is equal to the maximum of the rates achieved by decode-and-forward or compress-and-forward individually. We also present a superposition scheme that combines broadcast with decode-and-forward, which even though does not achieve a higher rate than decode-and-forward, provides us the insight to the main result mentioned above.
Application of DAC Codeword Spectrum: Expansion Factor
Venue: arXiv cs.IT 2010
Year: 2010

Distributed Arithmetic Coding (DAC) proves to be an effective implementation of Slepian-Wolf Coding (SWC), especially for short data blocks. To study the property of DAC codewords, the author has proposed the concept of DAC codeword spectrum. For equiprobable binary sources, the problem was formatted as solving a system of functional equations. Then, to calculate DAC codeword spectrum in general cases, three approximation methods have been proposed. In this paper, the author makes use of DAC codeword spectrum as a tool to answer an important question: how many (including proper and wrong) paths will be created during the DAC decoding, if no path is pruned? The author introduces the concept of another kind of DAC codeword spectrum, i.e. time spectrum, while the originally-proposed DAC codeword spectrum is called path spectrum from now on. To measure how fast the number of decoding paths increases, the author introduces the concept of expansion factor which is defined as the ratio of path numbers between two consecutive decoding stages. The author reveals the relation between expansion factor and path/time spectrum, and proves that the number of decoding paths of any DAC codeword increases exponentially as the decoding proceeds. Specifically, when symbols `0' and `1' are mapped onto intervals [0, q) and [(1-q), 1), where 0.5<q<1, the author proves that expansion factor converges to 2q as the decoding proceeds.
Kolmogorov Complexity in perspective. Part I: Information Theory and Randomnes
Venue: arXiv cs.LO 2010
Year: 2010

We survey diverse approaches to the notion of information: from Shannon entropy to Kolmogorov complexity. Two of the main applications of Kolmogorov complexity are presented: randomness and classification. The survey is divided in two parts in the same volume. Part I is dedicated to information theory and the mathematical formalization of randomness based on Kolmogorov complexity. This last application goes back to the 60's and 70's with the work of Martin-Löf, Schnorr, Chaitin, Levin, and has gained new impetus in the last years.
Diffusion of a fluid through a viscoelastic solid
Venue: arXiv cs.CE 2010
Year: 2010

This paper is concerned with the diffusion of a fluid through a viscoelastic solid undergoing large deformations. Using ideas from the classical theory of mixtures and a thermodynamic framework based on the notion of maximization of the rate of entropy production, the constitutive relations for a mixture of a viscoelastic solid and a fluid (specifically Newtonian fluid) are derived. By prescribing forms for the specific Helmholtz potential and the rate of dissipation, we derive the relations for the partial stress in the solid, the partial stress in the fluid, the interaction force between the solid and the fluid, and the evolution equation of the natural configuration of the solid. We also use the assumption that the volume of the mixture is equal to the sum of the volumes of the two constituents in their natural state as a constraint. Results from the developed model are shown to be in good agreement with the experimental data for the diffusion of various solvents through high temperature polyimides that are used in the aircraft industry. The swelling of a viscoelastic solid under the application of an external force is also studied.
Cascade, Triangular and Two Way Source Coding with degraded side information at the second user
Venue: arXiv cs.IT 2010
Year: 2010

We consider the Cascade and Triangular rate-distortion problems where the same side information is available at the source node and User 1, and the side information available at User 2 is a degraded version of the side information at the source node and User 1. We characterize the rate-distortion region for these problems. For the Cascade setup, we showed that, at User 1, decoding and re-binning the codeword sent by the source node for User 2 is optimum. We then extend our results to the Two way Cascade and Triangular setting, where the source node is interested in lossy reconstruction of the side information at User 2 via a rate limited link from User 2 to the source node. We characterize the rate distortion regions for these settings. Complete explicit characterizations for all settings are also given in the Quadratic Gaussian case. We conclude with two further extensions: A triangular source coding problem with a helper, and an extension of our Two Way Cascade setting in the Quadratic Gaussian case.
Unifying the Landscape of Cell-Probe Lower Bounds
Venue: arXiv cs.DS 2010
Year: 2010

We show that a large fraction of the data-structure lower bounds known today in fact follow by reduction from the communication complexity of lopsided (asymmetric) set disjointness. This includes lower bounds for: * high-dimensional problems, where the goal is to show large space lower bounds. * constant-dimensional geometric problems, where the goal is to bound the query time for space O(n polylog n). * dynamic problems, where we are looking for a trade-off between query and update time. (In this case, our bounds are slightly weaker than the originals, losing a lglg n factor.) Our reductions also imply the following new results: * an Omega(lg n / lglg n) bound for 4-dimensional range reporting, given space O(n polylog n). This is quite timely, since a recent result solved 3D reporting in O(lglg n) time, raising the prospect that higher dimensions could also be easy. * a tight space lower bound for the partial match problem, for constant query time. * the first lower bound for reachability oracles. In the process, we prove optimal randomized lower bounds for lopsided set disjointness.
3-D Rigid Models from Partial Views - Global Factorization
Venue: arXiv cs.CV 2010
Year: 2010

The so-called factorization methods recover 3-D rigid structure from motion by factorizing an observation matrix that collects 2-D projections of features. These methods became popular due to their robustness - they use a large number of views, which constrains adequately the solution - and computational simplicity - the large number of unknowns is computed through an SVD, avoiding non-linear optimization. However, they require that all the entries of the observation matrix are known. This is unlikely to happen in practice, due to self-occlusion and limited field of view. Also, when processing long videos, regions that become occluded often appear again later. Current factorization methods process these as new regions, leading to less accurate estimates of 3-D structure. In this paper, we propose a global factorization method that infers complete 3-D models directly from the 2-D projections in the entire set of available video frames. Our method decides whether a region that has become visible is a region that was seen before, or a previously unseen region, in a global way, i.e., by seeking the simplest rigid object that describes well the entire set of observations. This global approach increases significantly the accuracy of the estimates of the 3-D shape of the scene and the 3-D motion of the camera. Experiments with artificial and real videos illustrate the good performance of our method.
Exact Analysis of Rate Adaptation Algorithms in Wireless LANs
Venue: arXiv cs.NI 2010
Year: 2010

Rate adaptation plays a key role in determining the performance of wireless LANs. In this paper, we introduce a semi-Markovian framework to analyze the performance of two of the most popular rate adaptation algorithms used in wireless LANs, namely Automatic Rate Fallback (ARF) and Adaptive Automatic Rate Fallback (AARF). Given our modeling assumptions, the analysis is exact and provides closed form expressions for the achievable throughput of ARF and AARF. We illustrate the benefit of our analysis by numerically comparing the throughput performance of ARF and AARF in two different channel regimes. The results show that neither of these algorithms consistently outperforms the other. We thus propose and analyze a new variant to AARF, called Persistent AARF (or PAARF), and show that it achieves a good compromise between the two algorithms, often performing close to the best algorithm in each of the studied regimes. The numerical results also shed light into the impact of MAC overhead on the performance of the three algorithms. In particular, they show that the more conservative strategies AARF and PAARF scale better as the bit rate increases.
Alternatives to speech in low bit rate communication systems
Venue: arXiv cs.MM 2010
Year: 2010

This paper describes a framework and a method with which speech communication can be analyzed. The framework consists of a set of low bit rate, short-range acoustic communication systems, such as speech, but that are quite different from speech. The method is to systematically compare these systems according to different objective functions such as data rate, computational overhead, psychoacoustic effects and semantics. One goal of this study is to better understand the nature of human communication. Another goal is to identify acoustic communication systems that are more efficient than human speech for some specific purposes.
Sparse and silent coding in neural circuits
Venue: arXiv cs.NE 2010
Year: 2010

Sparse coding algorithms are about finding a linear basis in which signals can be represented by a small number of active (non-zero) coefficients. Such coding has many applications in science and engineering and is believed to play an important role in neural information processing. However, due to the computational complexity of the task, only approximate solutions provide the required efficiency (in terms of time). As new results show, under particular conditions there exist efficient solutions by minimizing the magnitude of the coefficients (`$l_1$-norm') instead of minimizing the size of the active subset of features (`$l_0$-norm'). Straightforward neural implementation of these solutions is not likely, as they require \emph{a priori} knowledge of the number of active features. Furthermore, these methods utilize iterative re-evaluation of the reconstruction error, which in turn implies that final sparse forms (featuring `population sparseness') can only be reached through the formation of a series of non-sparse representations, which is in contrast with the overall sparse functioning of the neural systems (`lifetime sparseness'). In this article we present a novel algorithm which integrates our previous `$l_0$-norm' model on spike based probabilistic optimization for sparse coding with ideas coming from novel `$l_1$-norm' solutions. The resulting algorithm allows neurally plausible implementation and does not require an exactly defined sparseness level thus it is suitable for representing natural stimuli with a varying number of features. We also demonstrate that the combined method significantly extends the domain where optimal solutions can be found by `$l_1$-norm' based algorithms.
A Parameterized Centrality Metric for Network Analysis
Venue: arXiv cs.SI 2010
Year: 2010

A variety of metrics have been proposed to measure the relative importance of nodes in a network. One of these, alpha-centrality [Bonacich, 2001], measures the number of attenuated paths that exist between nodes. We introduce a normalized version of this metric and use it to study network structure, specifically, to rank nodes and find community structure of the network. Specifically, we extend the modularity-maximization method [Newman and Girvan, 2004] for community detection to use this metric as the measure of node connectivity. Normalized alpha-centrality is a powerful tool for network analysis, since it contains a tunable parameter that sets the length scale of interactions. By studying how rankings and discovered communities change when this parameter is varied allows us to identify locally and globally important nodes and structures. We apply the proposed method to several benchmark networks and show that it leads to better insight into network structure than alternative methods.
Sublinear Optimization for Machine Learning
Venue: arXiv cs.LG 2010
Year: 2010

We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for which sublinear-time algorithms were not known before. These new algorithms use a combination of a novel sampling techniques and a new multiplicative update algorithm. We give lower bounds which show the running times of many of our algorithms to be nearly best possible in the unit-cost RAM model. We also give implementations of our algorithms in the semi-streaming setting, obtaining the first low pass polylogarithmic space and sublinear time algorithms achieving arbitrary approximation factor.
Revisiting deadlock prevention: a probabilistic approach
Venue: arXiv cs.DC 2010
Year: 2010

We revisit the deadlock-prevention problem by focusing on priority digraphs instead of the traditional wait-for digraphs. This has allowed us to formulate deadlock prevention in terms of prohibiting the occurrence of directed cycles even in the most general of wait models (the so-called AND-OR model, in which prohibiting wait-for directed cycles is generally overly restrictive). For a particular case in which the priority digraphs are somewhat simplified, we introduce a Las Vegas probabilistic mechanism for resource granting and analyze its key aspects in detail.
State Dependent Attempt Rate Modeling of Single Cell IEEE~802.11 WLANs with Homogeneous Nodes and Poisson Packet Arrivals
Venue: arXiv cs.NI 2010
Year: 2010

Analytical models for IEEE 802.11-based WLANs are invariably based on approximations, such as the well-known \textit{decoupling approximation} proposed by Bianchi for modeling single cell WLANs consisting of saturated nodes. In this paper, we provide a new approach to model the situation when the nodes are not saturated. We study a State Dependent Attempt Rate (SDAR) approximation to model $M$ queues (one queue per node) served by the CSMA/CA protocol as standardized in the IEEE 802.11 DCF MAC protocol. The approximation is that, when $n$ of the $M$ queues are non-empty, the transmission attempt probability of the $n$ non-empty nodes is given by the long-term transmission attempt probability of $n$ "saturated" nodes as provided by Bianchi's model. The SDAR approximation reduces a single cell WLAN with non-saturated nodes to a "coupled queue system". When packets arrive to the $M$ queues according to independent Poisson processes, we provide a Markov model for the coupled queue system with SDAR service. \textit{The main contribution of this paper is to provide an analysis of the coupled queue process by studying a lower dimensional process, and by introducing a certain conditional independence approximation}. We show that the SDAR model of contention provides an accurate model for the DCF MAC protocol in single cells, and report the simulation speed-ups thus obtained by our \textit{model-based simulation}.
Online Square Packing
Venue: arXiv cs.DS 2010
Year: 2010

We analyze the problem of packing squares in an online fashion: Given a semi-infinite strip of width 1 and an unknown sequence of squares of side length in [0,1] that arrive from above, one at a time. The objective is to pack these items as they arrive, minimizing the resulting height. Just like in the classical game of Tetris, each square must be moved along a collision-free path to its final destination. In addition, we account for gravity in both motion (squares must never move up) and position (any final destination must be supported from below). A similar problem has been considered before; the best previous result is by Azar and Epstein, who gave a 4-competitive algorithm in a setting without gravity (i.e., with the possibility of letting squares "hang in the air") based on ideas of shelf-packing: Squares are assigned to different horizontal levels, allowing an analysis that is reminiscent of some bin-packing arguments. We apply a geometric analysis to establish a competitive factor of 3.5 for the bottom-left heuristic and present a 34/13=2.615...-competitive algorithm.
Certificate size reduction in Abstraction-Carrying Code
Venue: arXiv cs.PL 2010
Year: 2010

Carrying Code (ACC) has recently been proposed as a framework for mobile code safety in which the code supplier provides a program together with an abstraction (or abstract model of the program) whose validity entails compliance with a predefined safety policy. The advantage of providing a (fixpoint) abstraction to the code consumer is that its validity is checked in a single pass (i.e., one iteration) of an abstract interpretation-based checker. A main challenge to make ACC useful in practice is to reduce the size of certificates as much as possible while at the same time not increasing checking time. The intuitive idea is to only include in the certificate information that the checker is unable to reproduce without iterating. We introduce the notion of reduced certificate which characterizes the subset of the abstraction which a checker needs in order to validate (and re-construct) the full certificate in a single pass. Based on this notion, we instrument a generic analysis algorithm with the necessary extensions in order to identify the information relevant to the checker. Interestingly, the fact that the reduced certificate omits (parts of) the abstraction has implications in the design of the checker. We provide the sufficient conditions which allow us to ensure that 1) if the checker succeeds in validating the certificate, then the certificate is valid for the program (correctness) and 2) the checker will succeed for any reduced certificate which is valid (completeness). Our approach has been implemented and benchmarked within the ciaopp system. The experimental results show that our proposal is able to greatly reduce the size of certificates in practice.To appear in Theory and Practice of Logic Programming (TPLP).