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Showing posts from 2016

Signal and image classification with invariant descriptors (scattering transforms): Internship

[Internship position closed] [English] Signal and image classification with invariant descriptors (scattering transforms) [Français] Classification de signaux et d’images par descripteurs invariants (scattering transforms) Application and additional details http://www.laurent-duval.eu/lcd-2017-intern-invariant-descriptor.html Description The field of complex data analysis (data science) is interested in the extraction of suitable indicators used for dimension reduction, data comparison or classification. Initially based on application-dependent, physics-based descriptors or features, novel methods employ more generic and potentially multiscale descriptors, that can be used for machine learning or classification. Examples are to be found in SIFT-like (scale-invariant feature transform) techniques (ORB, SURF), in unsupervised or deep learning. The present internship focuses on the framework of scattering transform (S. Mallat et al.) and the associated classification tec

Kultur Pop 36 : Rebirth

Le volume 36 (Rebirth) de Kultur Pop , compilations de génériques de Radio France, vient de paraître. Au programme : Title Artist Track Zapateado Opus 23 (Pablo de Sarasate) [France Culture, Culture matin] Itzhak Perlman & Sam Sanders 01 Quatre danseries : L' échappée [France Culture, Etat d'alerte (fin)] Jean-Philippe Goude 02 Changanya [France Culture, La matinale du samedi] Lakuta 03 La lune rousse [France Culture, Backstage] Fakear 04 Satta [France Culture, Notre époque] Boozoo Bajou 05 Siegfried [France Culture, Interlude nuits] Erik Truffaz 06 El condor pasa [France Culture, Paso doble, début] Paul Desmond 07 Soleil Rouge [France Culture, Interlude nuits] Jean-Louis Matinier 08

Recherche scientifique utilitaire ? Jean Perrin, et la méthode scientifique

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Particulièrement ému de prononcer ces paroles dans un coin de ce jardin qu'aimait Marie Curie [...] je veux simplement tirer un enseignement, et vous monter par leur exemple comment toute nouveauté vraiment utile à l'homme ne peut être obtenue que par la découverte des choses inconnues poursuivies sans aucun préoccupation utilitaire. Ce n'est pas en désirant lutter contre le cancer que Marie Curie et Pierre Curie ont fait leurs immortelles découvertes [...]. Ainsi en tout domaine, pour acquérir de la puissance, pour diminuer ces corvées qu'il ne faut pas confondre avec un travail noble, pour faire reculer la vieillesse et la mort elle-même, pour briser enfin le cadre étroit où notre destin semblait à jamais enfermé, nous devons faciliter la recherche scientifique désintéressée. Vous tous qui allez m'écouter par dizaine de milliers, vous qui me voyez sans que je vous voie, entendez mon appel, et contribuez par toute votre influence à faciliter cette recherche conq

CHOPtrey: contextual online polynomial extrapolation for enhanced multi-core co-simulation of complex systems

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XKCD. My hobby: extrapolating CHOPtrey . A long, extrapolated and progressive scientific story. Ace, deuce, trey... ready? It all started with Cyril Faure , then a PhD student  with Nicolas Pernet  in real-time computing. He used to stop by my office. We had coffee, discussed books (from Descartes to Sci-Fi) and music (mostly Loudblast, Slayer, Pink Floyd, Manowar, Bolt Thrower). We exchanged ideas and concerns. One day, he told me about a worry in his thesis. Caveat : I am very bad at computer science, advanced programming, and had little hints about partitioned/slacked real-time co-simulation systems. So this was not about programming, but simulation and co-simulation. Big physical systems (engines, aircrafts, boats) are complicated to simulate. Protocols and methods include FMI standard (Functional Mockup Interface) and FMU (Functional Mockup Unit). Partitioning them into subsystems may help the simulation, but split discrete subsystems should communicate. Fast enough to

Pêcheurs de perles, une parabole (in French, BEADS and CHOPtrey)

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This post is about pearls found in apparently simple but yet complex industrial-type questions, and a handful of parabolas. Two practical applications are found in analytical chemistry with   BEADS: Baseline Estimation And Denoising w/ Sparsity  and in cyber-physical system co-simulation with   CHOPtrey: Contextual Polynomial extrapolation for real-time forecasting . The whole stuff  is just a  parabola , or a  parable .  I was not at my best level of confidence in this talk, even in French. I had to completely change the talk a couple of days before. Politics... The best dwells in the Dave Gilmour (or Pink Floyd) parts: David Gilmour , Echoes (the acoustic way) , live from Abbey Road (at the beginning) David Gilmour , Je crois entendre encore (Les pêcheurs de perles, Georges Bizet), at the  end David Gilmour , Rattle that lock , based on an  SNCF four-note jingle , and most notably in Milton's Paradise lost .  The talk, in French, parle de perles trouvées dans des ques

Trainlets: cropped wavelet decomposition for high-dimensional learning

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It's being a lonng time: element 120 from the aperiodic table of wavelets is the trainlet, from Jeremias Sulam, Student Member, Boaz Ophir, Michael Zibulevsky, and Michael Elad,  Trainlets: Dictionary Learning in High Dimensions : Abstract: Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the ability to learn a dictionary from signal examples, sparsity-inspired algorithms are often achieving state-of-the-art results in a wide variety of tasks. Yet, these methods have traditionally been restricted to small dimensions mainly due to the computational constraints that the dictionary learning problem entails. In the context of image processing, this implies handling small image patches. In this work we show how to efficiently handle bigger dimensions and go beyond the small patches in sparsity-based signal and image processing methods. We build our approach bas

M-band 2D dual-tree (Hilbert) wavelet multicomponent image denoising

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The toolbox implements a parametric nonlinear estimator that generalizes several wavelet shrinkage denoising methods. Dedicated to additive Gaussian noise, it adopts a multivariate statistical approach to take into account both the spatial and the inter-component correlations existing between the different wavelet subbands, using a Stein Unbiased Risk Estimator (SURE) principle, which derives optimal parameters. The wavelet choice is a slightly redundant multi-band geometrical dual-wavelet frame. Experiments on multispectral remote sensing images outperform conventional wavelet denoising techniques (including curvelets). Since they are based on MIMO filter banks (multi-input, multi-ooutput), in a mullti-band  fashion,, we can called they MIMOlets quite safely. The dual-tree wavelet consists in two directional wavelet trees, diisplayed below for a 4-band filter: 4-band directional dual-tree wavelets The set of wavelet functions implements: several dual-tree M-band wavelet tra