OpenRaiser/Intern-Atlas huggingface dataset:
Distributed Spectrum Sensing with Sequential Ordered Transmissions to a Cognitive Fusion Center
Venue: arXiv cs.IT 2011
Year: 2011

Cooperative spectrum sensing is a robust strategy that enhances the detection probability of primary licensed users. However, a large number of detectors reporting to a fusion center for a final decision causes significant delay and also presumes the availability of unreasonable communication resources at the disposal of a network searching for spectral opportunities. In this work, we employ the idea of sequential detection to obtain a quick, yet reliable, decision regarding primary activity. Local detectors take measurements, and only a few of them transmit the log likelihood ratios (LLR) to a fusion center in descending order of LLR magnitude. The fusion center runs a sequential test with a maximum imposed on the number of sensors that can report their LLR measurements. We calculate the detection thresholds using two methods. The first achieves the same probability of error as the optimal block detector. In the second, an objective function is constructed and decision thresholds are obtained via backward induction to optimize this function. The objective function is related directly to the primary and secondary throughputs with inbuilt privilege for primary operation. Simulation results demonstrate the enhanced performance of the approaches proposed in this paper. We also investigate the case of fading channels between the local sensors and the fusion center, and the situation in which the sensing cost is negligible.
A statistical learning algorithm for word segmentation
Venue: arXiv cs.CL 2011
Year: 2011

In natural speech, the speaker does not pause between words, yet a human listener somehow perceives this continuous stream of phonemes as a series of distinct words. The detection of boundaries between spoken words is an instance of a general capability of the human neocortex to remember and to recognize recurring sequences. This paper describes a computer algorithm that is designed to solve the problem of locating word boundaries in blocks of English text from which the spaces have been removed. This problem avoids the complexities of speech processing but requires similar capabilities for detecting recurring sequences. The algorithm relies entirely on statistical relationships between letters in the input stream to infer the locations of word boundaries. A Viterbi trellis is used to simultaneously evaluate a set of hypothetical segmentations of a block of adjacent words. This technique improves accuracy but incurs a small latency between the arrival of letters in the input stream and the sending of words to the output stream. The source code for a C++ version of this algorithm is presented in an appendix.
Energy-Delay Considerations in Coded Packet Flows
Venue: arXiv cs.IT 2011
Year: 2011

We consider a line of terminals which is connected by packet erasure channels and where random linear network coding is carried out at each node prior to transmission. In particular, we address an online approach in which each terminal has local information to be conveyed to the base station at the end of the line and provide a queueing theoretic analysis of this scenario. First, a genie-aided scenario is considered and the average delay and average transmission energy depending on the link erasure probabilities and the Poisson arrival rates at each node are analyzed. We then assume that all nodes cannot send and receive at the same time. The transmitting nodes in the network send coded data packets before stopping to wait for the receiving nodes to acknowledge the number of degrees of freedom, if any, that are required to decode correctly the information. We analyze this problem for an infinite queue size at the terminals and show that there is an optimal number of coded data packets at each node, in terms of average completion time or transmission energy, to be sent before stopping to listen.
Transmission Schemes based on Sum Rate Analysis in Distributed Antenna Systems
Venue: arXiv cs.IT 2011
Year: 2011

In this paper, we study single cell multi-user downlink distributed antenna systems (DAS) where antenna ports are geographically separated in a cell. First, we derive an expression of the ergodic sum rate for the DAS in the presence of pathloss. Then, we propose a transmission selection scheme based on the derived expressions which does not require channel state information at the transmitter. Utilizing the knowledge of distance information from a user to each distributed antenna (DA) port, we consider the optimization of pairings of DA ports and users to maximize the system performance. Based on the ergodic sum rate expressions, the proposed scheme chooses the best mode maximizing the ergodic sum rate among mode candidates. In our proposed scheme, the number of mode candidates are greatly reduced compared to that of ideal mode selection. In addition, we analyze the signal to noise ratio cross-over point for different modes using the sum rate expressions. Through Monte Carlo simulations, we show the accuracy of our derivations for the ergodic sum rate. Moreover, simulation results with the pathloss modeling confirm that the proposed scheme produces the average sum rate identical to the ideal mode selection with significantly reduced candidates.
Incremental Top-k List Comparison Approach to Robust Multi-Structure Model Fitting
Venue: arXiv cs.CV 2011
Year: 2011

Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-$k$ lists) derived from residual sorting information. Our method simultaneously (1) guides the sampling such that hypotheses corresponding to all true structures can be quickly retrieved and (2) filters the hypotheses such that only a small but very promising subset remain. This permits the usage of simple agglomerative clustering on the surviving hypotheses for accurate model selection. The outcome is a highly efficient multi-structure robust estimation technique. Experiments on synthetic and real data show the superior performance of our approach over previous methods.
Interleaved computation for persistent homology
Venue: arXiv cs.CG 2011
Year: 2011

We describe an approach to bounded-memory computation of persistent homology and betti barcodes, in which a computational state is maintained with updates introducing new edges to the underlying neighbourhood graph and percolating the resulting changes into the simplex stream feeding the persistence algorithm. We further discuss the memory consumption and resulting speed and complexity behaviours of the resulting algorithm.
Activity-Based Search for Black-Box Contraint-Programming Solvers
Venue: arXiv cs.AI 2011
Year: 2011

Robust search procedures are a central component in the design of black-box constraint-programming solvers. This paper proposes activity-based search, the idea of using the activity of variables during propagation to guide the search. Activity-based search was compared experimentally to impact-based search and the WDEG heuristics. Experimental results on a variety of benchmarks show that activity-based search is more robust than other heuristics and may produce significant improvements in performance.
Transformation from Identity Stone Age to Digital Identity
Venue: arXiv cs.CR 2011
Year: 2011

Technological conversion, political interests and Business drivers has triggered a means, to establish individual characterization and personalization. People started raising concerns on multiple identities managed across various zones and hence various solutions were designed. Technological advancement has brought various issues and concerns around Identity assurance, privacy and policy enabled common Authentication framework. A compressive framework is needed to established common identity model to address national needs like standards, regulation and laws, minimum risk, interoperability and to provide user with a consistent context or user experience. This document focuses on Transformation path of identity stone age to Identity as in state. It defines a digital identity zone model (DIZM) to showcase the Global Identity defined across the ecosystem. Also, provide insight of emerging Technology trend to enable Identity assurance, privacy and policy enabled common Authentication framework.
Re-thinking Enrolment in Identity Card Schemes
Venue: arXiv cs.OH 2011
Year: 2011

Many countries around the world have initiated national ID card programs in the last decade. These programs are considered of strategic value to governments due to its contribution in enhancing existing identity management systems. Considering the total cost of such programs which goes up to billions of dollars, the success in attaining their objectives is a crucial element in the agendas of political systems in countries worldwide. Our experience in the field shows that many of such projects have been challenged to deliver their primary objectives of population enrolment, and therefore resulted in failing to meet deadlines and keeping up with budgetary constraints. The purpose of this paper is to explain the finding of a case study action research aimed to introduce a new approach to how population are enrolled in national ID programs. This is achieved through presenting a case study of a business process reengineering initiative undertaken in the UAE national ID program. The scope of this research is limited to the enrolment process within the program. This article also intends to explore the possibilities of significant results with the new proposed enrolment approach with the application of BPR. An overview of the ROI study has been developed to illustrate such efficiencies.
Universality for the Noisy Slepian-Wolf Problem Via Spatial Coupling
Venue: arXiv cs.IT 2011
Year: 2011

We consider a noisy Slepian-Wolf problem where two correlated sources are separately encoded and transmitted over two independent binary memoryless symmetric channels. Each channel capacity is assumed to be characterized by a single parameter which is not known at the transmitter. The receiver has knowledge of both the source correlation and the channel parameters. We call a system universal if it retains near-capacity performance without channel knowledge at the transmitter. Kudekar et al. recently showed that terminated low-density parity-check (LDPC) convolutional codes (a.k.a. spatially-coupled LDPC ensembles) can have belief-propagation thresholds that approach their maximum a-posteriori thresholds. This was proven for binary erasure channels and shown empirically for binary memoryless symmetric channels. They also conjectured that the principle of spatial coupling is very general and the phenomenon of threshold saturation applies to a very broad class of graphical models. In this work, we derive an area theorem for the joint decoder and empirically show that threshold saturation occurs for this problem. As a result, we demonstrate near-universal performance for this problem using the proposed spatially-coupled coding system. A similar result is also discussed briefly for the 2-user multiple-access channel.
On the geometry of wireless network multicast in 2-D
Venue: arXiv cs.IT 2011
Year: 2011

We provide a geometric solution to the problem of optimal relay positioning to maximize the multicast rate for low-SNR networks. The networks we consider, consist of a single source, multiple receivers and the only intermediate and locatable node as the relay. We construct network the hypergraph of the system nodes from the underlying information theoretic model of low-SNR regime that operates using superposition coding and FDMA in conjunction (which we call the "achievable hypergraph model"). We make the following contributions. 1) We show that the problem of optimal relay positioning maximizing the multicast rate can be completely decoupled from the flow optimization by noticing and exploiting geometric properties of multicast flow. 2) All the flow maximizing the multicast rate is sent over at most two paths, in succession. The relay position is dependent only on one path (out of the two), irrespective of the number of receiver nodes in the system. Subsequently, we propose simple and efficient geometric algorithms to compute the optimal relay position. 3) Finally, we show that in our model at the optimal relay position, the difference between the maximized multicast rate and the cut-set bound is minimum. We solve the problem for all (Ps,Pr) pairs of source and relay transmit powers and the path loss exponent αgreater than 2.
Error Probability Bounds for Binary Relay Trees with Crummy Sensors
Venue: arXiv cs.IT 2011
Year: 2011

We study the detection error probability associated with balanced binary relay trees, in which sensor nodes fail with some probability. We consider N identical and independent crummy sensors, represented by leaf nodes of the tree. The root of the tree represents the fusion center, which makes the final decision between two hypotheses. Every other node is a relay node, which fuses at most two binary messages into one binary message and forwards the new message to its parent node. We derive tight upper and lower bounds for the total error probability at the fusion center as functions of N and characterize how fast the total error probability converges to 0 with respect to N. We show that the convergence of the total error probability is sub-linear, with the same decay exponent as that in a balanced binary relay tree without sensor failures. We also show that the total error probability converges to 0, even if the individual sensors have total error probabilities that converge to 1/2 and the failure probabilities that converge to 1, provided that the convergence rates are sufficiently slow.
A Nonlinear Approach to Interference Alignment
Venue: arXiv cs.IT 2011
Year: 2011

Cadambe and Jafar (CJ) alignment strategy for the K-user scalar frequency-selective fading Gaussian channel, with encoding over blocks of 2n+1 random channel coefficients (subcarriers) is considered. The linear zero-forcing (LZF) strategy is compared with a novel approach based on lattice alignment and lattice decoding (LD). Despite both LZF and LD achieve the same degrees of freedom, it is shown that LD can achieve very significant improvements in terms of error rates at practical SNRs with respect to the conventional LZF proposed in the literature. We also show that these gains are realized provided that channel gains are controlled to be near constant, for example, by means of power control and opportunistic carrier and user selection strategies. In presence of relatively-small variations in the normalized channel coefficient amplitudes, CJ alignment strategy yields very disappointing results at finite SNRs, and the gain of LD over ZLF significantly reduces. In light of these results, the practical applicability of CJ alignment scheme remains questionable, in particular for Rayleigh fading channels, where channel inversion power control yields to unbounded average transmit power.
Reasoning about Minimal Belief and Negation as Failure
Venue: arXiv cs.AI 2011
Year: 2011

We investigate the problem of reasoning in the propositional fragment of MBNF, the logic of minimal belief and negation as failure introduced by Lifschitz, which can be considered as a unifying framework for several nonmonotonic formalisms, including default logic, autoepistemic logic, circumscription, epistemic queries, and logic programming. We characterize the complexity and provide algorithms for reasoning in propositional MBNF. In particular, we show that entailment in propositional MBNF lies at the third level of the polynomial hierarchy, hence it is harder than reasoning in all the above mentioned propositional formalisms for nonmonotonic reasoning. We also prove the exact correspondence between negation as failure in MBNF and negative introspection in Moore's autoepistemic logic.
Robust Agent Teams via Socially-Attentive Monitoring
Venue: arXiv cs.MA 2011
Year: 2011

Agents in dynamic multi-agent environments must monitor their peers to execute individual and group plans. A key open question is how much monitoring of other agents' states is required to be effective: The Monitoring Selectivity Problem. We investigate this question in the context of detecting failures in teams of cooperating agents, via Socially-Attentive Monitoring, which focuses on monitoring for failures in the social relationships between the agents. We empirically and analytically explore a family of socially-attentive teamwork monitoring algorithms in two dynamic, complex, multi-agent domains, under varying conditions of task distribution and uncertainty. We show that a centralized scheme using a complex algorithm trades correctness for completeness and requires monitoring all teammates. In contrast, a simple distributed teamwork monitoring algorithm results in correct and complete detection of teamwork failures, despite relying on limited, uncertain knowledge, and monitoring only key agents in a team. In addition, we report on the design of a socially-attentive monitoring system and demonstrate its generality in monitoring several coordination relationships, diagnosing detected failures, and both on-line and off-line applications.
AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks
Venue: arXiv cs.AI 2011
Year: 2011

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung and Chang, 1989; Shachter and Peot, 1989) and self-importance sampling (Shachter and Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, and Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, and Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.
Conflict-Directed Backjumping Revisited
Venue: arXiv cs.AI 2011
Year: 2011

In recent years, many improvements to backtracking algorithms for solving constraint satisfaction problems have been proposed. The techniques for improving backtracking algorithms can be conveniently classified as look-ahead schemes and look-back schemes. Unfortunately, look-ahead and look-back schemes are not entirely orthogonal as it has been observed empirically that the enhancement of look-ahead techniques is sometimes counterproductive to the effects of look-back techniques. In this paper, we focus on the relationship between the two most important look-ahead techniques---using a variable ordering heuristic and maintaining a level of local consistency during the backtracking search---and the look-back technique of conflict-directed backjumping (CBJ). We show that there exists a "perfect" dynamic variable ordering such that CBJ becomes redundant. We also show theoretically that as the level of local consistency that is maintained in the backtracking search is increased, the less that backjumping will be an improvement. Our theoretical results partially explain why a backtracking algorithm doing more in the look-ahead phase cannot benefit more from the backjumping look-back scheme. Finally, we show empirically that adding CBJ to a backtracking algorithm that maintains generalized arc consistency (GAC), an algorithm that we refer to as GAC-CBJ, can still provide orders of magnitude speedups. Our empirical results contrast with Bessiere and Regin's conclusion (1996) that CBJ is useless to an algorithm that maintains arc consistency.
Arenas of Finite State Machines
Venue: arXiv cs.FL 2011
Year: 2011

Finite state machines are widely used as a sound mathematical formalism that appropriately describes large scale, distributed and complex systems. Multiple interactions of finite state machines in complex systems are well captured by the notion of non-flat systems. Non--flat systems are "finite state machines" where each "state" can be either a basic state or an aggregate of finite state machines. By expanding a non-flat system, a flat system is obtained which is an ordinary finite state machine. In this paper we introduce a novel class of non--flat systems called Arena of Finite State Machines (AFSM). AFSMs are collections of finite state machines that interact concurrently through a communication network. We propose a notion of compositional bisimulation that allows checking bisimulation equivalence of AFSMs by directly exploiting their communication networks and hence, without the need of expanding the AFSMs to finite state machines. Compositional bisimulation allows a computational complexity reduction when checking bisimulation equivalence of AFSMs, as formally quantified in the paper. An application of the proposed framework to the regulation of gene expression in the bacterium Escherichia coli is also presented.
Lower Bounds for Sparse Recovery
Venue: arXiv cs.DS 2011
Year: 2011

We consider the following k-sparse recovery problem: design an m x n matrix A, such that for any signal x, given Ax we can efficiently recover x' satisfying ||x-x'||_1 <= C min_{k-sparse} x"} ||x-x"||_1. It is known that there exist matrices A with this property that have only O(k log (n/k)) rows. In this paper we show that this bound is tight. Our bound holds even for the more general /randomized/ version of the problem, where A is a random variable and the recovery algorithm is required to work for any fixed x with constant probability (over A).
A Consistent Semantics of Self-Adjusting Computation
Venue: arXiv cs.PL 2011
Year: 2011

This paper presents a semantics of self-adjusting computation and proves that the semantics are correct and consistent. The semantics integrate change propagation with the classic idea of memoization to enable reuse of computations under mutation to memory. During evaluation, reuse of a computation via memoization triggers a change propagation that adjusts the reused computation to reflect the mutated memory. Since the semantics integrate memoization and change-propagation, it involves both non-determinism (due to memoization) and mutation (due to change propagation). Our consistency theorem states that the non-determinism is not harmful: any two evaluations of the same program starting at the same state yield the same result. Our correctness theorem states that mutation is not harmful: self-adjusting programs are consistent with purely functional programming. We formalize the semantics and their meta-theory in the LF logical framework and machine check our proofs using Twelf.