Rémi Flamary
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Publications / Livres et chapitres de livre
Toutes Revues Conférences Livres et chapitres Autres
Travaux soumis ou sous presse |
2016 |
| D. Mary, R. Flamary, C. Theys, C. Aime, Mathematical Tools for Instrumentation and Signal Processing in Astronomy, 2016. |
| Abstract: This book is a collection of 13 articles corresponding to lectures and research works exposed at the Summer school of the CNRS titled « Bases mathématiques pour l’instrumentation et le traitement du signal en astronomie ». The school took place in Nice and Porquerolles, France, from June 1 to 5, 2015. This book contains three parts: I. Astronomy in the coming decade and beyond The three chapters of this part emphasize the strong interdisciplinary nature of Astrophysics, both at theoretical and observational levels, and the increasingly larger sizes of data sets produced by increasingly more complex instruments and infrastructures. These remarkable features call in the same time for more mathematical tools in signal processing and instrumentation, in particular in statistical modeling, large scale inference, data mining, machine learning, and for efficient processing solutions allowing their implementation. II. Mathematical concepts, methods and tools The first chapter of this part starts with an example of how pure mathematics can lead to new instrumental concepts, in this case for exoplanet detection. The four other chapters of this part provide a detailed introduction to four main topics: Orthogonal functions as a powerful tool for modeling signals and images, covering Fourier, Fourier-Legendre, Fourier-Bessel series for 1D signals and Spherical Harmonic series for 2D signals; Optimization and machine learning methods with application to inverse problems, denoising and classication, with on-line numerical experiments; Large scale statistical inference with adaptive procedures allowing to control the False Discovery Rate, like the Benjamini-Hochberg procedure, its Bayesian interpretation and some variations; Processing solutions for large data sets, covering the Hadoop framework and YARN, the main tools for the management of both the storage and computing capacities of a cluster of machines and also recent solutions like Spark. III. Application: tools in action This parts collects a number of current research works where some tools above are presented in action: optimization for deconvolution, statistical modeling, multiple testing, optical and instrumental models. The applications of this part include astronomical imaging, detection and estimation of circumgalactic structures, and detection of exoplanets. |
BibTeX:
@book{mary2016mathematical,
author = {Mary, David and Flamary, Remi and Theys, Celine and Aime, Claude},
title = {Mathematical Tools for Instrumentation and Signal Processing in Astronomy},
publisher = {EDP Sciences},
year = {2016}
} |
| S. Canu, R. Flamary, D. Mary, Introduction to optimization with applications in astronomy and astrophysics, Mathematical tools for instrumentation and signal processing in astronomy, 2016. |
| Abstract: This chapter aims at providing an introduction to numerical optimization with some applications in astronomy and astrophysics. We provide important preliminary definitions that will guide the reader towards different optimization procedures. We discuss three families of optimization problems and describe numerical algorithms allowing, when this is possible, to solve these problems. For each family, we present in detail simple examples and more involved advanced examples. As a final illustration, we focus on two worked-out examples of optimization applied to astronomical data. The first application is a supervised classification of RR-Lyrae stars. The second one is the denoising of galactic spectra formulated by means of sparsity inducing models in a redundant dictionary. |
BibTeX:
@incollection{canu2016introduction,
author = { Canu, Stephane, and Flamary, Remi and Mary, David},
title = {Introduction to optimization with applications in astronomy and astrophysics},
booktitle = { Mathematical tools for instrumentation and signal processing in astronomy},
editor = { {Mary, David and Flamary, Remi, and Theys, Celine, and Aime, Claude}},
year = {2016}
} |
| R. Flamary, A. Rakotomamonjy, M. Sebag, Apprentissage statistique pour les BCI, Les interfaces cerveau-ordinateur 1, fondements et méthodes, pp 197-215, 2016. |
| Abstract: Ce chapitre introduit l'apprentissage statistique et son application aux interfaces cerveau-machine. Dans un premier temps, le principe général de l'apprentissage supervisé est présenté et les difficultés de mise en oeuvre sont discutées, en particulier les aspects relatifs a la sélection de capteurs et l'apprentissage multi- sujets. Ce chapitre détaille également la validation d'une approche d'apprentissage, incluant les différentes mesures de performance et l’optimisation des hyper-paramètres de l'algorithme considéré. Le lecteur est invité à expérimenter les algorithmes décrits : une boite a outils Matlab/Octave 1 permet de reproduire les expériences illustrant le chapitre et contient les détails d'implémentation des différentes méthodes. |
BibTeX:
@incollection{flamary2016apprentissage,
author = { Flamary, Remi and Rakotomamonjy, Alain, and Sebag, Michele},
title = {Apprentissage statistique pour les BCI},
pages = { 197-215},
booktitle = { Les interfaces cerveau-ordinateur 1, fondements et méthodes},
editor = { {Clerc, Maureen and Bougrain, Laurent and Lotte, Fabien}},
publisher = { ISTE Editions},
year = {2016}
} |
| R. Flamary, A. Rakotomamonjy, M. Sebag, Statistical learning for BCIs, Brain Computer Interfaces 1: Fundamentals and Methods, pp 185-206, 2016. |
| Abstract: This chapter introduces statistical learning and its applications to brain–computer interfaces. We begin by presenting the general principles of supervised learning and discussing the difficulties raised by its implementation, with a particular focus on aspects related to selecting sensors and multisubject learning. This chapter also describes in detail how a learning approach may be validated, including various metrics of performance and optimization of the hyperparameters of the considered algorithms. We invite the reader to experiment with the algorithms described here: the illustrative experiments included in this chapter may be reproduced using a Matlab/Octave toolbox, which contains the implementation details of the various different methods. |
BibTeX:
@incollection{flamary2016statistical,
author = { Flamary, Remi and Rakotomamonjy, Alain, and Sebag, Michele},
title = {Statistical learning for BCIs},
pages = { 185-206},
booktitle = { Brain Computer Interfaces 1: Fundamentals and Methods},
editor = { {Clerc, Maureen and Bougrain, Laurent and Lotte, Fabien}},
publisher = { ISTE Ltd and John Wiley and Sons Inc },
year = {2016}
} |