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Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of noisy linear measurements is an important problem in compressed sensing. In the high-dimensional setting, it is known that recovery with a vanishing fraction ...
Institute of Electrical and Electronics Engineers2012
TIBA is a tool to reconstruct phylogenetic trees from rearrangement data that consist of ordered lists of synteny blocks (or genes), where each synteny block is shared with all of its homologues in the input genomes. The evolution of these synteny blocks, ...
In this paper we focus on the application of global stochastic optimization methods to extremum estimators. We propose a general stochastic method the master method which includes several stochastic optimization algorithms as a particular case. The propose ...
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, ...
Loss tomography aims at inferring the loss rate of links in a network from end-to-end measurements. Previous work in [1] has developed optimal maximum likelihood estimators (MLEs) for link loss rates in a single-source multicast tree. However, only sub-opt ...
The goal of this thesis is to study an anisotropic adaptive algorithm for transonic compressible viscous flow around an airwing. A convection-diffusion model problem is considered, an anisotropic a posteriori error estimator for the H1 semi-norm of the err ...
We study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. To analyze its performance, we formulate the oversam ...
Institute of Electrical and Electronics Engineers2012
We study distributed least-mean square (LMS) estimation problems over adaptive networks, where nodes cooperatively work to estimate and track common parameters of an unknown system. We consider a scenario where the input and output response signals of the ...
We propose a novel statistical formulation of the image-reconstruction problem from noisy linear measurements. We derive an extended family of MAP estimators based on the theory of continuous-domain sparse stochastic processes. We highlight the crucial ro ...
A direct adaptive control methodology for the rejection of unmeasured non-vanishing disturbances is proposed. The approach uses the framework of polynomial RST controllers and relies on the internal model principle with additional degrees of freedom provid ...
