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2014
Oil & Gas Science and Technology
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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
2004
Zenodo
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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
2008
Zenodo
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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]
2017
bioRxiv
pre-print
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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
2012
Zenodo
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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
2011
Zenodo
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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
2001
Revue d'histoire de l'Amérique française
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Shallow-ice microstructure at Dome Concordia, Antarctica
2000
Annals of Glaciology
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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]
2014
arXiv
pre-print
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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,
arXiv:1406.4687v1
fatcat:gmsnhbdsx5h2tna2qudk5dlkca
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... 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.
A Probabilistic Semantics for Cognitive Maps
[chapter]
2015
Lecture Notes in Computer Science
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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
doi:10.1007/978-3-319-25210-0_10
fatcat:x2tofd46hvgrdfa27la7ivh4qq
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... 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
Learning physical properties of anomalous random walks using graph neural networks
[article]
2021
arXiv
pre-print
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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
arXiv:2103.11738v1
fatcat:ypbfhnxryja7raeqchhw4jdaiq
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... 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.
Characterizing the spatial pattern of solar supergranulation using the bispectrum
[article]
2020
arXiv
pre-print
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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
2008
Solar Physics
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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
2005
Zenodo
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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)
2014
Chemometrics and Intelligent Laboratory Systems
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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
doi:10.1016/j.chemolab.2014.09.014
fatcat:w56m5znbm5clljvcv6c5in4nqq
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... 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.
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