Résumé : (Funding) Digitized with funding from the Digital Library of the Caribbean grant awarded by TICFIA.
(Creation/Production Credits) Digitization performed at the U.S. Embassy in Haiti from material held at other institutions
(Dates or Sequential Designation) juin 1956. numero 28.
Haiti

Droits : All rights reserved by the source institution.

Éditeur(s) : HAL CCSD Society for Industrial and Applied Mathematics
Résumé : International audience
We discuss the control of distributed systems with incomplete data following the notion of no-regret control (or, equivalently, Pareto control) used by Lions in [C. R. Acad. Sci. Paris Ser. I Math., 302 (1986), pp. 223-227] and [C. R. Acad. Sci. Paris Ser. I Math., 302 (1992), pp. 1253-1257]. We associate with the no-regret control a sequence of low-regret controls defined by a quadratic perturbation previously used by Nakoulima, Omrane, and Velin in [C. R. Acad. Sci. Paris Ser. I Math., 330 (2000), pp. 801-806]. In the first part, we prove that the perturbed system corresponds to a sequence of standard control problems and converges to the no-regret (or Pareto) control for which we obtain a singular optimality system. We give also some applications. In the second part, we show how the method can be extended to the evolution case. Equations of parabolic type, Petrowsky type, or hyperbolic type are considered.
ISSN: 0363-0129

hal-00764259
https://hal.univ-antilles.fr/hal-00764259
https://hal.univ-antilles.fr/hal-00764259/document
https://hal.univ-antilles.fr/hal-00764259/file/38018.pdf
DOI : 10.1137/S0363012900380188

Éditeur(s) : HAL CCSD
Résumé : International audience
This work proposes an original approach using mutual information and Pareto curve jointly for feature selection. Mutual information is used to estimate dependency criterion between features and classes and redundancy criterion between features taken two by two. Unlike some studies, these criteria are used simultaneously to compute Pareto curve and determine the optimal feature sets. This approach is tested on more reference data. Several clustering algorithms are used to compute classification accuracy. The obtained results show the importance of our tools and its ability to select the best feature sets that give the better description.
GRETSI Proceedings
Bordeaux, France

hal-00602260
https://hal.archives-ouvertes.fr/hal-00602260

Éditeur(s) : HAL CCSD
Résumé : French Guiana is a region located on the Guiana Shield in South America. Is presents at the same time a high originality in biodiversity, a good state of conservation and a strong correlation to Amazonian carbon stocks. Knowledge on evolution of the forest structure of French Guiana is however still insufficient to bring an optimal management at territory scale. Availability as never seen before of remote sensing data at high spatial resolution such as LiDAR, on large coverage, offers the opportunity to assess the forest resource in French Guiana. The work done during this thesis aim mostly to study the structure and the variations of the canopy height, the factors determining its variability and the consequences of forest logging activities. The data used during this thesis come from an airborne LiDAR and field data from Office National des Forêts (ONF). This thesis manuscript is divided in three parts. The first part introduces the problem and sets the objectives of the thesis. The second part takes the so-called "article" form, that is, the different chapters that compose it are scientific articles published or in the course of publication, it is composed of three studies (I) A study on the gaps detection and environmental determinants on the gap sizes distribution in natural forest: How to characterize the gap size distribution and are there environmental determinants that influence the statistical distribution of gap size? This article has been submitted in Biogeoscience. (ii) A study of the control of the environment on the canopy height structure and variations at different spatial resolutions. Which spatial scales are relevant for analyzing the environmental determinants of canopy height changes in tropical forests from remote sensing data? How can the environment help or not map the height of the canopy in the rainforest? This article was accepted for publication in Remote Sensing. (iii) a third study where we quantified the effects of Reduced Impact Logging (RIL) on the structure and variation of canopy height. What are the effects of logging on canopy height variations and gap size distribution? This article will be submitted for publication in a Forest Ecology and Management journal. In the last part, after a synthesis of the main results, we propose a conclusion.
La Guyane française est une région située sur le Plateau des Guyanes en Amérique du sud. Elle présente à la fois une grande originalité en termes de biodiversité, un bon état de conservation et une forte contribution au stock de carbone Amazonien. Les connaissances sur l’évolution de la structure forestière de la Guyane française restent cependant insuffisantes pour offrir une gestion optimale à l’échelle du territoire. La disponibilité sans précédent de données de télédétection à très haute résolution spatiale telles que le LiDAR , sur de larges couvertures nous offre l'opportunité d’évaluer la ressource forestière de la Guyane Française. Les travaux réalisés dans le cadre de cette thèse visent prioritairement à étudier la structure et les variations de la hauteur de canopée, les facteurs déterminant sa variabilité ainsi que les conséquences des activités d’exploitation forestière. Les données utilisées au cours de la thèse proviennent d’un LiDAR aéroporté et des données de terrains de l'Office National des Forêts (ONF). Ce manuscrit de thèse est divisé en trois grandes parties. La première partie introduit la problématique et fixe les objectifs de la thèse. La seconde partie prend la forme dite « sur article », c'est-à-dire que les différents chapitres qui la compose sont des articles scientifiques publiés ou en cours de publication, elle est composée de trois études. (i) Une étude concernant la détection des trouées et des déterminants environnementaux sur la distribution de tailles des trouées en forêt non exploitée : Comment caractériser la distribution de taille des trouées et est-ce qu’il y a des déterminants environnementaux qui influencent la distribution statistique de taille de trouées? Cet article à été soumis dans Biogeoscience. (ii) Une étude du contrôle de l'environnement sur la structure et les variations de la hauteur de canopée à différentes résolutions spatiales. Quelles échelles spatiales sont pertinentes pour analyser les déterminants environnementaux des variations de hauteur de la canopée en forêt tropicale à partir des données de télédétection ? Comment l’environnement peut aider, ou pas, à cartographier la hauteur de la canopée en forêt tropicale ? Cet article à été accepté pour publication dans Remote Sensing. (iii) Une troisième étude où nous avons quantifié les effets de l’exploitation à faible impact (EFI) sur la structure et la variation de la hauteur de canopée. Quelles sont les effets de l’exploitation forestière sur les variations de hauteur de canopée et sur la distribution de taille des trouées ? Cet article sera soumis pour publication dans une revue scientifique (Forest Ecology and Management). Dans la dernière partie, après une synthèse des principaux résultats, nous proposons des pistes de développement.
https://tel.archives-ouvertes.fr/tel-01527553

NNT : 2016YANE0006
tel-01527553
https://tel.archives-ouvertes.fr/tel-01527553
https://tel.archives-ouvertes.fr/tel-01527553/document
https://tel.archives-ouvertes.fr/tel-01527553/file/these-youven-goulamoussene2.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper we propose a hybrid approach using mutual information and multi-objective optimization for feature subset selection problem. The hybrid aspect is due to the sequence of a filter method and a wrapper method in order to take advantages of both. The filter method reduces the exploration space by keeping subsets having good internal properties and the wrapper method chooses among the remaining subsets with a classification performances criterion. In the filter step, the subsets are evaluated in a multi-objective way to ensure diversity within the subsets. The evaluation is based on the mutual information to estimate the dependency between features and classes and the redundancy between features within the same subset. We kept the non-dominated (Pareto optimal) subsets for the second step. In the wrapper step, the selection is made according to the stability of the subsets regarding classification performances during learning stage on a set of classifiers to avoid the specialization of the selected subsets for a given classifiers. The proposed hybrid approach is experimented on a variety of reference data sets and compared to the classical feature selection methods FSDD and mRMR. The resulting algorithm outperforms these algorithms and the computation complexity remains acceptable even if it increases with regards to these two fast selection methods.
Journal of Informatics and Mathematical Sciences

hal-01375982
https://hal.archives-ouvertes.fr/hal-01375982

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : International audience
BACKGROUND: The use of pesticides and the related environmental contaminations can lead to human exposure to various molecules. In early-life, such exposures could be responsible for adverse developmental effects. However, human health risks associated with exposure to complex mixtures are currently under-explored. OBJECTIVE: THIS PROJECT AIMS AT ANSWERING THE FOLLOWING QUESTIONS: What is the influence of exposures to multiple pesticides on the metabolome? What mechanistic pathways could be involved in the metabolic changes observed? METHODS: Based on the PELAGIE cohort (Brittany, France), 83 pregnant women who provided a urine sample in early pregnancy, were classified in 3 groups according to the surface of land dedicated to agricultural cereal activities in their town of residence. Nuclear magnetic resonance-based metabolomics analyses were performed on urine samples. Partial Least Squares Regression-Discriminant Analysis (PLS-DA) and polytomous regressions were used to separate the urinary metabolic profiles from the 3 exposure groups after adjusting for potential confounders. RESULTS: The 3 groups of exposure were correctly separated with a PLS-DA model after implementing an orthogonal signal correction with pareto standardizations (R2 = 90.7% and Q2 = 0.53). After adjusting for maternal age, parity, body mass index and smoking habits, the most statistically significant changes were observed for glycine, threonine, lactate and glycerophosphocholine (upward trend), and for citrate (downward trend). CONCLUSION: This work suggests that an exposure to complex pesticide mixtures induces modifications of metabolic fingerprints. It can be hypothesized from identified discriminating metabolites that the pesticide mixtures could increase oxidative stress and disturb energy metabolism.
ISSN: 1932-6203

inserm-00863407
http://www.hal.inserm.fr/inserm-00863407
http://www.hal.inserm.fr/inserm-00863407/document
http://www.hal.inserm.fr/inserm-00863407/file/journal.pone.0064433.pdf
PUBMED : 23704985