Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Additive noise effect in digital phase detection

Abstract: The characteristic polynomials associated with the algorithms used in digital phase detection are used to investigate the effects of additive noise on phase measurements. First, it is shown that a loss factor η can be associated with any algorithm. This parameter describes the influence of the algorithm on the global signal-to-noise ratio (SNR). Second, the variance of the phase error is shown to depend mainly on the global SNR. The amplitude of a modulation of this variance at twice the signal frequency depends on a single parameter β. The material presented here extends previously published results, and as many as 19 algorithms are analyzed.

ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

Abstract: Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases. Here we propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases.

Numerical simulation of weakly compressible Bingham flows: The restart of pipeline flows of waxy crude oils

Abstract: In this paper we examine the numerical simulation of isothermal transient flows for a weakly compressible viscoplastic fluid in an axisymmetric pipe geometry. We use the Bingham model to describe the viscoplastic feature of the fluid and the compressibility is introduced in the continuity equation using the isothermal compressibility coefficient. Particular attention is devoted to the velocity-pressure problem in which the "true" (without regularization procedure) viscoplastic model is accounted for by using Lagrange multipliers techniques and augmented Lagrangian/Uzawa methods. The mass, momentum and constitutive equations are discretized using a finite volume method on a staggered grid with a TVD (Total Variation Diminishing) scheme for the convective terms. The resulting numerical method highlights strong and robust convergence properties. Obtained results regarding the transient solution underline the influence of compressibility on the flow pattern, especially in terms of yielded/unyielded regions, pressure and time to restart the flow.