Scaling in a continuous time model for biological aging

In this paper we consider a generalization to the asexual version of the Penna model for biological aging, where we take a continuous time limit. The genotype associated to each individual is an interval of real numbers over which Dirac $\delta$--functions are defined, representing genetically programmed diseases to be switched on at defined ages of the individual life. We discuss two different continuous limits for the evolution equation and two different mutation protocols, to be implemented during reproduction. Exact stationary solutions are obtained and scaling properties are discussed.Comment: 10 pages, 6 figure

Efficient methods of non-myopic sensor management for multitarget tracking

Abstract. This paper develops two efficient methods of non-myopic (long-term) sensor management and investigates the benefit in the set-ting of multitarget tracking. The underlying tracking methodology is based on recursive estimation of a Joint Multitarget Probability Density (JMPD), which is implemented using particle filtering methods. The my-opic sensor management scheme is predicated on maximizing the expected Rényi Information Divergence between the current JMPD and the JMPD after a measurement has been made. A full non-myopic strategy based on this information theoretic method is calculated using Monte Carlo methods for a model problem. Since this is computationally intractable when looking more than a small number of time steps ahead, two alterna-tive strategies are investigated. First, we develop an information-directed search algorithm which focusses the Monte Carlo evaluations on action sequences that are most informative. Second, we give two approximate methods which replace the value-to-go with an easily computed function which captures the long term value of the current action. The perfor-mance of these methods is compared to the myopic scheme in terms of tracking performance and computational requirements.

On classes of non-Gaussian asymptotic minimizers in entropic uncertainty principles

In this paper we revisit the Bialynicki-Birula & Mycielski uncertainty principle and its cases of equality. This Shannon entropic version of the well-known Heisenberg uncertainty principle can be used when dealing with variables that admit no variance. In this paper, we extend this uncertainty principle to Renyi entropies. We recall that in both Shannon and Renyi cases, and for a given dimension n, the only case of equality occurs for Gaussian random vectors. We show that as n grows, however, the bound is also asymptotically attained in the cases of n-dimensional Student-t and Student-r distributions. A complete analytical study is performed in a special case of a Student-t distribution. We also show numerically that this effect exists for the particular case of a n-dimensional Cauchy variable, whatever the Renyi entropy considered, extending the results of Abe and illustrating the analytical asymptotic study of the student-t case. In the Student-r case, we show numerically that the same behavior occurs for uniformly distributed vectors. These particular cases and other ones investigated in this paper are interesting since they show that this asymptotic behavior cannot be considered as a "Gaussianization" of the vector when the dimension increases

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced. © 2013 Elsevier Inc. All rights reserved

Multistatic radar change detection using a sparse imaging approach

Abstract—This paper describes a sparse imaging approach for estimating change images from a constellation of multistatic radar. In our setup, radar antennas are arranged around the perimeter of a surveillance region. This provides large angular diversity but a very small angular sampling. Conventional backprojection imaging techniques applied to this data produce sidelobes which severely limit the utility of the imagery. We describe an innovative change imaging method which enforces sparseness on the estimated image. The method is illustrated with collected multistatic radar data, showing that the sparseness model produces excellent images with very limited sampling of the aperture. I