Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

Reversible Computation: Extending Horizons of Computing

This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first

Reversible Computation: Extending Horizons of Computing

This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first

Empirical evidence for nonlinearity and irreversibility of commodity futures prices

Theory suggests that commodity futures price levels and returns data may exhibit both nonlinear and nonreversible features. This paper attempts to provide a thorough empiri- cally investigation of these claims. The data set is composed of 25 individual continuous contract commodity futures series which fall within a number of industry sectors including softs, precious metals, energy, and livestock. Employing both time-domain and frequency- domain tests examining the higher order cumulant properties of these series, it is shown that they exhibit both nonlinearities and irreversibility differing across industry sector. Furthermore, in modeling these series I estimate a number of parametric models able to capture irreversibility such as the linear mixed causal/noncausal autoregressive model and various purely causal nonlinear models, since there is a close connection between these two classes of models. It is shown that the linear causal ARMA model is unable to adequately account for the features of the data and while the mixed causal/noncausal model improves model fit significantly by capturing latent irreversibility, the vast majority of the nonlinearity these series exhibit is of the “nonlinear in variance” type. Finally, out of sample forecasts and an evaluation of the estimated unconditional distribution of the mixed causal/noncausal models suggest that there may still exist model misspecification

Empirical evidence for nonlinearity and irreversibility of commodity futures prices

Theory suggests that commodity futures price levels and returns data may exhibit both nonlinear and nonreversible features. This paper attempts to provide a thorough empiri- cally investigation of these claims. The data set is composed of 25 individual continuous contract commodity futures series which fall within a number of industry sectors including softs, precious metals, energy, and livestock. Employing both time-domain and frequency- domain tests examining the higher order cumulant properties of these series, it is shown that they exhibit both nonlinearities and irreversibility differing across industry sector. Furthermore, in modeling these series I estimate a number of parametric models able to capture irreversibility such as the linear mixed causal/noncausal autoregressive model and various purely causal nonlinear models, since there is a close connection between these two classes of models. It is shown that the linear causal ARMA model is unable to adequately account for the features of the data and while the mixed causal/noncausal model improves model fit significantly by capturing latent irreversibility, the vast majority of the nonlinearity these series exhibit is of the “nonlinear in variance” type. Finally, out of sample forecasts and an evaluation of the estimated unconditional distribution of the mixed causal/noncausal models suggest that there may still exist model misspecification

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Evolutionary Genomics

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Reliability Abstracts and Technical Reviews January-December 1968

No abstract availabl