We consider the problem of reconstructing the shape of an acoustic or electromagnetic scatterer from far eld measurements of the scattered wave corresponding to one incident time harmonic wave in the resonance region. This problem is di cult to solve since it is nonlinear and severely ill-posed. The characterization of the Fr echet derivatives, which has been accomplished some years ago by Kirs...

Geostatistics has become a preferred tool for the identification of lithofacies from sparse data, such as measurements of hydraulic conductivity and porosity. Recently we demonstrated that the support vector machine (SVM), a tool from machine learning, can be readily adapted for this task, and offers significant advantages. On the conceptual side, the SVM avoids the use of untestable assumption...

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We introduce PseudoNet, a new pseudolikelihood -based estimator of the inverse covariance matrix, that has a number of useful statistical and computational properties. We show, through detailed experiments with synthetic as well as real-world finance and wind power data, that PseudoNet outperforms related methods in terms of estimation error and support recovery, making it well-suited for use i...

We discuss methods for a priori selection of parameters to be estimated in inverse problem formulations (such as Maximum Likelihood, Ordinary and Generalized Least Squares) for dynamical systems with numerous state variables and an even larger number of parameters. We illustrate the ideas with an in-host model for HIV dynamics which has been successfully validated with clinical data and used fo...

Many inverse problems of practical interest are ill-posed in the sense that solutions do not depend continuously on data. To eeectively solve such problems, regularization methods are typically used. One problem associated with classical regularization methods is that the solution may be oversmoothed in the process. We present an alternative \local regularization" approach in which a decomposit...

Context dependency of knowledge models brings with it several problems: the unreliability of knowledge-based systems, maintenance costs, and limitations on sharing and reuse. Problem-solving methods are knowledge models of the reasoning process of knowledge-based systems. In this paper we present a method called inverse verification to deal with the context dependency of problem-solving methods...