Université libre de Bruxelles

About: Université libre de Bruxelles is a education organization based out in Brussels, Belgium. It is known for research contribution in the topics: Population & Breast cancer. The organization has 24974 authors who have published 56969 publications receiving 2084303 citations. The organization is also known as: ULB.

Entropy production as correlation between system and reservoir

Abstract: We derive an exact (classical and quantum) expression for the entropy production of a finite system placed in contact with one or several finite reservoirs, each of which is initially described by a canonical equilibrium distribution. Although the total entropy of system plus reservoirs is conserved, we show that system entropy production is always positive and is a direct measure of system–reservoir correlations and/or entanglements. Using an exactly solvable quantum model, we illustrate our novel interpretation of the Second Law in a microscopically reversible finite-size setting, with strong coupling between the system and the reservoirs. With this model, we also explicitly show the approach of our exact formulation to the standard description of irreversibility in the limit of a large reservoir.

Formulaic sequences and perceived oral proficiency: putting a Lexical Approach to the test

Abstract: This study reports a small-scale experiment that was set up to estimate the extent to which (i) the use of formulaic sequences (standardized phrases such as collocations and idiomatic expressions) ...

An aggregation-based algebraic multigrid method

Abstract: An algebraic multigrid method is presented to solve large systems of linear equations. The coarsen- ing is obtained by aggregation of the unknowns. The aggregation scheme uses two passes of a pairwise matching algorithm applied to the matrix graph, resulting in most cases in a decrease of the number of variables by a factor slightly less than four. The matching algorithm favors the strongest negative coupling(s), inducing a problem depen- dant coarsening. This aggregation is combined with piecewise constant (unsmoothed) prolongation, ensuring low setup cost and memory requirements. Compared with previous aggregation-based multigrid methods, the scalability is enhanced by using a so-called K-cycle multigrid scheme, providing Krylov subspace acceleration at each level. This paper is the logical continuation of (SIAM J. Sci. Comput., 30 (2008), pp. 1082-1103), where the analysis of a model anisotropic problem shows that aggregation-based two-grid methods may have optimal order convergence, and of (Numer. Lin. Alg. Appl., 15 (2008), pp. 473-487), where it is shown that K-cycle multigrid may provide optimal or near optimal convergence under mild assumptions on the two-grid scheme. Whereas in these papers only model problems with geometric aggregation were considered, here a truly algebraic method is presented and tested on a wide range of discrete second order scalar elliptic PDEs, including nonsymmetric and unstructured problems. Numerical results indicate that the proposed method may be significantly more robust as black box solver than the classical AMG method as implemented in the code AMG1R5 by K. Stuben. The parallel implementation is also discussed. Satisfactory speedups are obtained on a medium size multi-processor cluster that is typical of today com- puter market. A code implemanting the method is freely available for download both as a Fortran program and a Matlab function.

Information-theoretic inference of large transcriptional regulatory networks

Abstract: The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.