Distributed Maximum Likelihood for Simultaneous Self-localization and Tracking in Sensor Networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters.Comment: shorter version is about to appear in IEEE Transactions of Signal Processing; 22 pages, 15 figure

Выделение оползневых границ по динамическим параметрам продольных преломленных волн

Дана стаття присвячена темі виділення кордонів обвального тіла за допомогою динамічних параметрів заломлених хвиль. Проведено визначення кордону двома способами: за допомогою параметра Спаського, а так само за допомогою аналізу спектрів сейсмограм. Здійснена оцінка коефіцієнта загасання сейсмічної хвилі на обвалі небезпечній ділянці.This article is dedicated to the topic selection boundaries of the landslide body by means of dynamic parameters of the refracted waves. Boundary definition is produced in two ways: by Spassky parameter, as well as by analysis of the spectra of seismograms. An assessment of the attenuation coefficient of seismic waves at the landslide site was performed

Forest resampling for distributed sequential Monte Carlo

This paper brings explicit considerations of distributed computing architectures and data structures into the rigorous design of Sequential Monte Carlo (SMC) methods. A theoretical result established recently by the authors shows that adapting interaction between particles to suitably control the Effective Sample Size (ESS) is sufficient to guarantee stability of SMC algorithms. Our objective is to leverage this result and devise algorithms which are thus guaranteed to work well in a distributed setting. We make three main contributions to achieve this. Firstly, we study mathematical properties of the ESS as a function of matrices and graphs that parameterize the interaction amongst particles. Secondly, we show how these graphs can be induced by tree data structures which model the logical network topology of an abstract distributed computing environment. Thirdly, we present efficient distributed algorithms that achieve the desired ESS control, perform resampling and operate on forests associated with these trees

Nonequilibrium Reweighting on the Driven Diffusive Lattice Gas

The nonequilibrium reweighting technique, which was recently developed by the present authors, is used for the study of the nonequilibrium steady states. The renewed formulation of the nonequlibrium reweighting enables us to use the very efficient multi-spin coding. We apply the nonequilibrium reweighting to the driven diffusive lattice gas model. Combining with the dynamical finite-size scaling theory, we estimate the critical temperature Tc and the dynamical exponent z. We also argue that this technique has an interesting feature that enables explicit calculation of derivatives of thermodynamic quantities without resorting to numerical differences.Comment: Accepted for publication in J. Phys. A (Lett.

Nucleotide substrate binding characterization in human pancreatic-type ribonucleases.

International audienceHuman genome contains a group of more than a dozen similar genes with diverse biological functions including antiviral, antibacterial and angiogenesis activities. The characterized gene products of this group show significant sequence similarity and a common structural fold associated with binding and cleavage of ribonucleic acid (RNA) substrates. Therefore, these proteins have been categorized as members of human pancreatic-type ribonucleases (hRNases). hRNases differ in cell/tissue localization and display distinct substrate binding preferences and a wide range of ribonucleolytic catalytic efficiencies. Limited information is available about structural and dynamical properties that influence this diversity among these homologous RNases. Here, we use computer simulations to characterize substrate interactions, electrostatics and dynamical properties of hRNases 1-7 associated with binding to two nucleotide substrates (ACAC and AUAU). Results indicate that even with complete conservation of active-site catalytic triad associated with ribonucleolytic activity, these enzymes show significant differences in substrate interactions. Detailed characterization suggests that in addition to binding site electrostatic and van der Waals interactions, dynamics of distal regions may also play a role in binding. Another key insight is that a small difference in temperature of 300 K (used in experimental studies) and 310 K (physiological temperature) shows significant changes in enzyme-substrate interactions

26Al-26Mg and stable isotopes investigated in ureilites

第6回極域科学シンポジウム[OA] 南極隕石11月17日（火）　国立国語研究所 2階 講

Some discussions of D. Fearnhead and D. Prangle's Read Paper "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation"

This report is a collection of comments on the Read Paper of Fearnhead and Prangle (2011), to appear in the Journal of the Royal Statistical Society Series B, along with a reply from the authors.Comment: 10 page

A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models

This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (PMC), which is based on an iterative importance sampling approach. An important drawback of this methodology is the degeneracy of the importance weights when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a novel method that performs a nonlinear transformation on the importance weights. This operation reduces the weight variation, hence it avoids their degeneracy and increases the efficiency of the importance sampling scheme, specially when drawing from a proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a very simple problem that enables us to clearly show and discuss the main features of the proposed technique. As a practical application, we have also considered the popular (and challenging) problem of estimating the rate parameters of stochastic kinetic models (SKM). SKMs are highly multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.Comment: 35 pages, 8 figure