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.

Evaluation of hyperfine parameter distributions from overlapped Mossbauer spectra of amorphous alloys

Abstract: An improved version of the Hesse-Rubartsch method is described. This method is applied, together with an adapted peak shape, to the evaluation of the hyperfine field distribution in an amorphous Fe79.5Si1.5B19 alloy at room temperature. The detailed structure of this distribution is discussed.

Clustered Multi-Task Learning: A Convex Formulation

Abstract: In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the others. In the context of learning linear functions for supervised classification or regression, this can be achieved by including a priori information about the weight vectors associated with the tasks, and how they are expected to be related to each other. In this paper, we assume that tasks are clustered into groups, which are unknown beforehand, and that tasks within a group have similar weight vectors. We design a new spectral norm that encodes this a priori assumption, without the prior knowledge of the partition of tasks into groups, resulting in a new convex optimization formulation for multi-task learning. We show in simulations on synthetic examples and on the IEDB MHC-I binding dataset, that our approach outperforms well-known convex methods for multi-task learning, as well as related non-convex methods dedicated to the same problem.

Vibrational Properties of CuO and Cu4O3 from First-Principles Calculations, and Raman and Infrared Spectroscopy

Abstract: A combined experimental and theoretical study is reported on the vibrational properties of tenorite CuO and paramelaconite Cu4O3. The optically active modes have been measured by Raman scattering and infrared absorption spectroscopy. First-principles calculations have been carried out with the LDA+U approach to account for strong electron correlation in the copper oxides. The vibrational properties have been computed ab initio using the so-called direct method. Excellent agreement is found between the measured Raman and infrared peak positions and the calculated phonon frequencies at the Brillouin zone center, which allows the assignment of all prominent peaks of the Cu4O3 spectra. Through a detailed analysis of the displacement eigenvectors, it is shown that a close relationship exists between the Raman modes of CuO and Cu4O3.

Protein homology detection using string alignment kernels

Abstract: Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences. Results: We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection. Availability: Software and data available upon request.