Contrasting high-resolution characteristic shock-capturing methods in aeroacoustic test problems

Two shock-capturing methods are considered. One is based on a standard conservative Roe scheme with van Leer's MUSCL variable extrapolation method applied to characteristic variables and a Runge-Kutta time stepping scheme. The other is based on the novel CABARET space-time scheme, which uses two sets of staggered variables, one for the conservation step and the other for characteristic splitting into local Riemann invariants. The methods are compared in a range of 2-D inviscid compressible flow test cases. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Geometric optimization methods for the analysis of gene expression data

DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce the dimension of the data set and to extract meaningful biological information. This work shows that Independent Component Analysis (ICA) is a promising approach for the analysis of genome-wide transcriptomic data. The paper first presents an overview of the most popular algorithms to perform ICA. These algorithms are then applied on a microarray breast-cancer data set. Some issues about the application of ICA and the evaluation of biological relevance of the results are discussed. This study indicates that ICA significantly outperforms Principal Component Analysis (PCA).