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Éditorial

Fabio Rocca, Laurent Duval
2014 Oil & Gas Science and Technology  
Duval Advances in Signal Processing and Image Analysis for Physico-Chemical, Analytical Chemistry and Chemical Sensing Progrès en traitement des signaux et analyse des images pour les analyses physico-chimiques  ...  Fabio Rocca Politecnico di Milano, Membre du Comite´e´ditorial d'OGST Laurent Duval IFP Energies nouvelles Editorial ADVANCES IN SIGNAL PROCESSING AND IMAGE ANALYSIS FOR PHYSICO-CHEMICAL, ANALYTICAL CHEMISTRY  ...  AND CHEMICAL SENSING REFERENCES1 Duval L., Duarte L.T., Jutten C. (2013) An overview of signal processing issues in chemical sensing, in 38th International Conference on Acoustics, Speech, and Signal  ... 
doi:10.2516/ogst/2014004 fatcat:2k2uv7no6fduldt6penrooai5u

Hilbert Pairs Of $M$-Band Orthonormal Wavelet Bases

Caroline Chaux, Laurent Duval, J.C. Pesquet
2004 Zenodo  
Publication in the conference proceedings of EUSIPCO, Viena, Austria, 2004
doi:10.5281/zenodo.38616 fatcat:3ekuhbfta5ffjc55kjstijs7m4

Two Denoising Surelet Methods For Complex Oversampled Subband Decompositions

Laurent Duval, Jérôme Gauthier, J.C. Pesquet
2008 Zenodo  
Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 2008
doi:10.5281/zenodo.41193 fatcat:ywn6itl5j5bjxfrvlaa65un6xq

BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement [article]

Aurelie Pirayre, Camille Couprie, Laurent Duval, Jean-Christophe Pesquet
2017 bioRxiv   pre-print
Duval are with IFP Energies nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France. E-mail: aurelie.pirayre@ifpen.fr • C. Couprie is with Facebook AI Research, Paris, France. • J.-C.  ... 
doi:10.1101/114769 fatcat:j7mz6u44jvgdtbgym2i6ebegny

A Convex Variational Approach For Multiple Removal In Seismic Data

Caroline Chaux, Laurent Duval, Diego Gragnaniello, J.C. Pesquet
2012 Zenodo  
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 2012
doi:10.5281/zenodo.52090 fatcat:v3dnjqbtzbhllfze7u7dq5yct4

Coherent Noise Removal In Seismic Data With Redundant Multiscale Directional Filters

Laurent Duval, Hérald Rabeson, Sergi Ventosa
2011 Zenodo  
Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011
doi:10.5281/zenodo.42654 fatcat:wt3rqnbkqzbnhg33ezxfz7dkju

PLOURDE, Michel, dir., avec la collaboration d'Hélène DUVAL et de Pierre GEORGEAULT, Le français au Québec. 400 ans d'histoire et de vie (Saint-Laurent/Québec, Fides/Les Publications du Québec, 2000), 516 p

Jacques Leclerc
2001 Revue d'histoire de l'Amérique française  
doi:10.7202/010382ar fatcat:xgvutcrt4jevnfptld2z4tkq5q

Shallow-ice microstructure at Dome Concordia, Antarctica

Laurent Arnaud, Jérôme Weiss, Michel Gay, Paul Duval
2000 Annals of Glaciology  
Between 430 m and 500 m, a marked decrease of crystal size is observed and compared with a similar trend obtained in the "old" Dome C ice core formerly associated with the Holocene/Last Glacial transition (Duval  ...  Therefore, as previously described by Duval and Lorius (1980) , the linear increase of the mean crystal area with depth characterizes a normal grain-growth process during Holocene.  ...  This correlation between crystal size and climate is well illustrated by a strong decrease of the mean crystal size at a climatic transition, such as the Holocene/Last Glacial Maximum (LGM) transition (Duval  ... 
doi:10.3189/172756400781820813 fatcat:htc2wd5m4rg5pp5lbzr3xlbxkq

A constrained-based optimization approach for seismic data recovery problems [article]

Mai Quyen Pham and Caroline Chaux and Laurent Duval and Jean-Christophe Pesquet
2014 arXiv   pre-print
Random and structured noise both affect seismic data, hiding the reflections of interest (primaries) that carry meaningful geophysical interpretation. When the structured noise is composed of multiple reflections, its adaptive cancellation is obtained through time-varying filtering, compensating inaccuracies in given approximate templates. The under-determined problem can then be formulated as a convex optimization one, providing estimates of both filters and primaries. Within this framework,
more » ... e criterion to be minimized mainly consists of two parts: a data fidelity term and hard constraints modeling a priori information. This formulation may avoid, or at least facilitate, some parameter determination tasks, usually difficult to perform in inverse problems. Not only classical constraints, such as sparsity, are considered here, but also constraints expressed through hyperplanes, onto which the projection is easy to compute. The latter constraints lead to improved performance by further constraining the space of geophysically sound solutions.
arXiv:1406.4687v1 fatcat:gmsnhbdsx5h2tna2qudk5dlkca

A Probabilistic Semantics for Cognitive Maps [chapter]

Aymeric Le Dorze, Béatrice Duval, Laurent Garcia, David Genest, Philippe Leray, Stéphane Loiseau
2015 Lecture Notes in Computer Science  
Mots-clés Bayesian network [7], Causality [8], Cognitive map [9], Probabilities [10] Résumé en anglais Cognitive maps are a graphical knowledge representation model that describes influences between concepts, each influence being quantified by a value. Most cognitive map models use values the semantics of which is not formally defined. This paper introduces the probabilistic cognitive maps, a new cognitive map model where the influence values are assumed to be probabilities. We formally define
more » ... his model and redefine the propagated influence, an operation that computes the global influence between two concepts in the map, to be in accordance with this semantics. To prove the soundness of our model, we propose a method to represent any probabilistic cognitive map as a Bayesian network. URL de la notice
doi:10.1007/978-3-319-25210-0_10 fatcat:x2tofd46hvgrdfa27la7ivh4qq

Learning physical properties of anomalous random walks using graph neural networks [article]

Hippolyte Verdier, Maxime Duval, François Laurent, Alhassan Cassé, Christian Vestergaard, Jean-Baptiste Masson
2021 arXiv   pre-print
Single particle tracking allows probing how biomolecules interact physically with their natural environments. A fundamental challenge when analysing recorded single particle trajectories is the inverse problem of inferring the physical model or class of models of the underlying random walks. Reliable inference is made difficult by the inherent stochastic nature of single particle motion, by experimental noise, and by the short duration of most experimental trajectories. Model identification is
more » ... urther complicated by the fact that main physical properties of random walk models are only defined asymptotically, and are thus degenerate for short trajectories. Here, we introduce a new, fast approach to inferring random walk properties based on graph neural networks (GNNs). Our approach consists in associating a vector of features with each observed position, and a sparse graph structure with each observed trajectory. By performing simulation-based supervised learning on this construct [1], we show that we can reliably learn models of random walks and their anomalous exponents. The method can naturally be applied to trajectories of any length. We show its efficiency in analysing various anomalous random walks of biological relevance that were proposed in the AnDi challenge [2]. We explore how information is encoded in the GNN, and we show that it learns relevant physical features of the random walks. We furthermore evaluate its ability to generalize to types of trajectories not seen during training, and we show that the GNN retains high accuracy even with few parameters. We finally discuss the possibility to leverage these networks to analyse experimental data.
arXiv:2103.11738v1 fatcat:ypbfhnxryja7raeqchhw4jdaiq

Characterizing the spatial pattern of solar supergranulation using the bispectrum [article]

Vincent G. A. Böning, Aaron C. Birch, Laurent Gizon, Thomas L. Duvall Jr., Jesper Schou
2020 arXiv   pre-print
Both LCT and TD histograms are asymmetric about zero and have a nonzero skewness, which is well known (e.g., Duvall & Gizon 2000) .  ...  ., Duvall & Gizon 2000) . The origin of supergranulation as a dominant scale of convection remains unclear (see Rincon & Rieutord 2018 , for a review).  ... 
arXiv:2002.08262v1 fatcat:4tzjdsgjhzgt5kuzbcgz3zufyq

Structure and Evolution of Supergranulation from Local Helioseismology

Johann Hirzberger, Laurent Gizon, Sami K. Solanki, Thomas L. Duvall
2008 Solar Physics  
The lifetime of the pattern was estimated by Gizon, Duvall, and Schou (2003) to be about two days.  ...  We consider about one order of magnitude more supergranules than Duvall and Gizon (2000) and del .  ... 
doi:10.1007/s11207-008-9206-8 fatcat:bdhypncmgzcybc4ltoxzz7u7fu

A Non-Separable 2D Complex Modulated Lapped Transform And Its Applications To Seismic Data Filtering

Jérôme Gauthier, Laurent Duval, J.C. Pesquet
2005 Zenodo  
Publication in the conference proceedings of EUSIPCO, Antalya, Turkey, 2005
doi:10.5281/zenodo.39194 fatcat:ixwb7sgbvjbczoiasapoof6wda

Chromatogram baseline estimation and denoising using sparsity (BEADS)

Xiaoran Ning, Ivan W. Selesnick, Laurent Duval
2014 Chemometrics and Intelligent Laboratory Systems  
This paper jointly addresses the problems of chromatogram baseline correction and noise reduction. The proposed approach is based on modeling the series of chromatogram peaks as sparse with sparse derivatives, and on modeling the baseline as a low-pass signal. A convex optimization problem is formulated so as to encapsulate these non-parametric models. To account for the positivity of chromatogram peaks, an asymmetric penalty function is utilized. A robust, computationally efficient, iterative
more » ... lgorithm is developed that is guaranteed to converge to the unique optimal solution. The approach, termed Baseline Estimation And Denoising with Sparsity (BEADS), is evaluated and compared with two state-of-the-art methods using both simulated and real chromatogram data.
doi:10.1016/j.chemolab.2014.09.014 fatcat:w56m5znbm5clljvcv6c5in4nqq
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