it is known that a stochastic dierential equation (sde) induces two probabilisticobjects, namely a diusion process and a stochastic ow. while the diusion process isdetermined by the innitesimal mean and variance given by the coecients of the sde,this is not the case for the stochastic ow induced by the sde. in order to characterize thestochastic ow uniquely the innitesimal covariance give...

Abstract For a given matrix, we are interested in computing GR decompositions A = , where G is an isometry with respect to scalar products. The orthogonal QR decomposition the representative for Euclidian product. signature respective factorization as hyperbolic decomposition. Considering skew‐symmetric matrix leads symplectic standard approach based on successive elimination of subdiagonal ent...

An algorithm to generate samples with approximate first-, second-, and third-order moments is presented extending the Cholesky matrix decomposition to a Cholesky tensor decomposition of an arbitrary order. The tensor decomposition of the first-, second-, and third-order objective moments generates a non-linear system of equations. The algorithm solves these equations by numerical methods. The r...

The paper proposes a new covariance estimator for large covariance matrices when the variables have a natural ordering. Using the Cholesky decomposition of the inverse, we impose a banded structure on the Cholesky factor, and select the bandwidth adaptively for each row of the Cholesky factor, using a novel penalty we call nested Lasso. This structure has more flexibility than regular banding, ...

This paper discusses robust classification of hyperspectral images. Both methods for dimensionality reduction and robust estimation of classifier parameters in full dimension are presented. A new approach to dimensionality reduction that uses piecewise constant function approximation of the spectral curve is compared to conventional dimensionality reduction methods like principal components, fe...

Abstract: LDL (Learning Design Language) is an educational modelling language which was designed to model collaborative activities. This paper details the solution proposed to address, with LDL and the associated infrastructure LDI, the two steps presented in the workshop “Comparing Educational Modelling Languages on a case study”. The modelling of the case study is described in detail. Its “op...

We present a preconditioning technique, called support-graph preconditioning, and use it to analyze two classes of preconditioners. The technique was first described in a talk by Pravin Vaidya, who did not formally publish his results. Vaidya used the technique to devise and analyze a class of novel preconditioners. The technique was later extended by Gremban and Miller, who used it in the deve...

gates. We present an algorithm that is optimal up to a multiplicative constant, as well as Θ(log n) times faster than previous methods. While our results are primarily asymptotic, simulation results show that even for relatively small n our algorithm is faster and yields more efficient circuits than the standard method. Generically our algorithm can be interpreted as a matrix decomposition algo...

We introduce a weighted linear dynamic logic (weighted LDL for short) and show the expressive equivalence of its formulas to weighted rational expressions. This adds a new characterization for recognizable series to the fundamental Schützenberger theorem. Surprisingly, the equivalence does not require any restriction to our weighted LDL. Our results hold over arbitrary (resp. totally complete) ...