Éditeur(s) : HAL CCSD
Résumé : International audience
Vulnerability–resilience indexes fail to grasp all dimensions of sustainability, whereas sustainable development has gained momentum. We fill this gap with a hierarchical multimetric composite index (Net Vulnerability Resilience Index: NVRI) whose robustness relies on a mathematical algorithm based on graph theory. A worldwide application shows that (i) both vulnerability and resilience are policy-responsive and that (ii) there is no determinism for a country to remain vulnerable or resilient. The NVRI enables us to identify a country’s strengths and weaknesses and determine the policy orientations that should be implemented to achieve sustainability.
World Development

hal-01291345
https://hal.archives-ouvertes.fr/hal-01291345
DOI : 10.1016/j.worlddev.2015.02.011

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : International audience
Establishing a direct link between climate change and fluctuations in animal populations through long-term monitoring is difficult given the paucity of baseline data. We hypothesized that social wasps are sensitive to climatic variations, and thus studied the impact of ENSO events on social wasp populations in French Guiana. We noted that during the 2000 La Niña year there was a 77.1% decrease in their nest abundance along ca. 5 km of forest edges, and that 70.5% of the species were no longer present. Two simultaneous 13-year surveys (1997-2009) confirmed the decrease in social wasps during La Niña years (2000 and 2006), while an increase occurred during the 2009 El Niño year. A 30-year weather survey showed that these phenomena corresponded to particularly high levels of rainfall, and that temperature, humidity and global solar radiation were correlated with rainfall. Using the Self-Organizing Map algorithm, we show that heavy rainfall during an entire rainy season has a negative impact on social wasps. Strong contrasts in rainfall between the dry season and the short rainy season exacerbate this effect. Social wasp populations never recovered to their pre-2000 levels. This is probably because these conditions occurred over four years; heavy rainfall during the major rainy seasons during four other years also had a detrimental effect. On the contrary, low levels of rainfall during the major rainy season in 2009 spurred an increase in social wasp populations. We conclude that recent climatic changes have likely resulted in fewer social wasp colonies because they have lowered the wasps' resistance to parasitoids and pathogens. These results imply that Neotropical social wasps can be regarded as bio-indicators because they highlight the impact of climatic changes not yet perceptible in plants and other animals.
ISSN: 1932-6203

hal-00825825
https://hal.archives-ouvertes.fr/hal-00825825
https://hal.archives-ouvertes.fr/hal-00825825/document
https://hal.archives-ouvertes.fr/hal-00825825/file/Dejean2011_Climate.pdf
DOI : 10.1371/journal.pone.0027004

Éditeur(s) : HAL CCSD springer
Résumé : International audience
This paper focuses on the study of the behavior of a genetic algorithm based classier system, the Adapted Pittsburgh Classier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning literature to assess the performances of learning methods when facing problems containing non markovian situations. Through this study, we discuss on the performance of the APCS upon two mazes (Woods 101 and Maze E2) and also on the eciency of an improvement of the APCS learning method inspired from the XCS: the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular sub-optimal solution to these maze problems. This statement is shown through a comparison between the results obtained by the XCS on two specic maze problems and those obtained by the APCS.
Lecture notes in computer science

hal-00542922
https://hal.archives-ouvertes.fr/hal-00542922
DOI : 10.1007/978-3-642-17508-4_8

Éditeur(s) : HAL CCSD
Résumé : International audience
In this work, we propose a novel approach for the discovery of frequent patterns in a social network on the basis of both vertex attributes and link frequency. With an analogy to the traditional task of mining frequent item sets, we show that the issue addressed can be formulated in terms of a conceptual analysis that elicits conceptual links. A social-based conceptual link is a synthetic representation of a set of links between groups of vertexes that share similar internal properties. We propose a first algorithm that optimizes the search into the concept lattice of conceptual links and extracts maximal frequent conceptual links. We study the performances of our solution and give experimental results obtained on a sample example. Finally we show that the set of conceptual links extracted provides a conceptual view of the social network.
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Istanbul, Turkey

hal-00786161
https://hal.archives-ouvertes.fr/hal-00786161
DOI : 10.1109/ASONAM.2012.15

Éditeur(s) : HAL CCSD
Résumé : International audience
The paper proposes a new knowledge discovery method called MAX-FLMin for extracting frequent patterns in social networks. Unlike traditional approaches that mainly focus on the network topological structure, the originality of our solution is its ability to exploit information both on the network structure and the attributes of nodes in order to elicit specific regularities that we call "Frequent Links". This kind of patterns provides relevant knowledge about the groups of nodes most connected within the network. First, we detail the method proposed to extract maximal frequent links from social networks. Second, we show how the extracted patterns are used to generate aggregated networks that represent the initial social network with more semantics. Qualitative and quantitative studies are conducted to evaluate the performances of our algorithm in various configurations.
Database (Weston, Conn.) and Expert Systems Applications
Vienna, Austria

hal-00767052
https://hal.archives-ouvertes.fr/hal-00767052
DOI : 10.1007/978-3-642-32600-4_35

Éditeur(s) : HAL CCSD Springer Verlag
Résumé : under revision for IJCV
International audience
The k-nearest neighbors (k-NN) classification rule has proven extremely successful in countless many computer vision applications. For example, image categorization often relies on uniform voting among the nearest prototypes in the space of descriptors. In spite of its good properties, the classic k-NN rule suffers from high variance when dealing with sparse prototype datasets in high dimensions. A few techniques have been proposed to improve k-NN classification, which rely on either deforming the nearest neighborhood relationship or modifying the input space. In this paper, we propose a novel boosting algorithm, called UNN (Universal Nearest Neighbors), which induces leveraged k-NN, thus generalizing the classic k-NN rule. We redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. Weak classifiers are learned by UNN so as to minimize a surrogate risk. A major feature of UNN is the ability to learn which prototypes are the most relevant for a given class, thus allowing one for effective data reduction. Experimental results on the synthetic two-class dataset of Ripley show that such a filtering strategy is able to reject "noisy" prototypes. We carried out image categorization experiments on a database containing eight classes of natural scenes. We show that our method outperforms significantly the classic k-NN classification, while enabling significant reduction of the computational cost by means of data filtering.
ISSN: 0920-5691

hal-00481712
https://hal.archives-ouvertes.fr/hal-00481712
DOI : 10.1007/s11263-012-0539-2
ARXIV : 1001.1221

Éditeur(s) : HAL CCSD Academic Publishing Ltd
Résumé : International audience
An approximate procedure for solving the problem of nding a zero of a maximal monotone operator is proposed and its convergence is established under various conditions. More precisely, it is shown that this method weakly converges under natural assumptions and strongly converges provided that either the inverse of the involved operator is Lipschitz continuous around zero or the interior of the solution set is nonempty. A particular attention is given to the convex minimization case. AMS Subject Classication : Primary, 90C ; Secondary, 49M45, 65C.
ISSN: 1311-8080

hal-00779255
https://hal.univ-antilles.fr/hal-00779255
https://hal.univ-antilles.fr/hal-00779255/document
https://hal.univ-antilles.fr/hal-00779255/file/10.1.1.13.4261_3_.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
Geographic Information Systems (GIS) help addressing geographical and environmental issues by providing information about a region or a city as a set of maps (layers), each one displaying information about a given theme like roads, vegetation, tourist spots or museums for instance. By combining different layers on a region, one can associate a given area to characteristics from their related themes. Indeed, the information from two or more layers might be merged and then transformed into a new layer as defined in map "algebra". When a theme vocabulary is organized as a taxonomy with concepts linked by is-a relationships, there are different ways to annotate an area with a concept depending on the level selected into the layer taxonomy. In this paper, we present an heuristic- based approach for an optimal merging of such layers in a GIS. Our goal is to generate new layers which sum up information from several themes in a most useful way. Two optimization criteria are considered, the average size of resulting areas and the average informative value of their resulting annotation. We demonstrate the validity of the proposed solution, firstly, on a formal example, and then, on a real world application.
Proceedings of the 4th International Conference on Advanced Geographic Information Systems, Applications, and Services
Valencia, Spain

hal-00679555
https://hal.archives-ouvertes.fr/hal-00679555

Éditeur(s) : HAL CCSD IEEE
Résumé : Object classification is a challenging task in computer vision. Many approaches have been proposed to extract meaningful descriptors from images and classifying them in a supervised learning framework. In this paper, we revisit the classic k-nearest neighbors (k-NN) classification rule, which has shown to be very effective when dealing with local image descriptors. However, k-NN still features some major drawbacks, mainly due to the uniform voting among the nearest prototypes in the feature space. In this paper, we propose a generalization of the classic k-NN rule in a supervised learning (boosting) framework. Namely, we redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. To induce this classifier, we propose a novel learning algorithm, MLNN (Multiclass Leveraged Nearest Neighbors), which gives a simple procedure for performing prototype selection very efficiently. We tested our method on 12 categories of objects, and observed significant improvement over classic k-NN in terms of classification performances.
International Workshop on Image Analysis for Multimedia Interactive Services
Desenzano del Garda, Italy

hal-00481725
https://hal.archives-ouvertes.fr/hal-00481725
https://hal.archives-ouvertes.fr/hal-00481725/document
https://hal.archives-ouvertes.fr/hal-00481725/file/pbnn_wiamis_10.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
The large majority of methods proposed in literature for handwriting recognition assume that words are produced drawing large parts of the ink without lifting the pen, other than horizontal bars and dots. This fundamental assumption, however, does not always hold: while some educational systems provide explicit training for producing continuous handwriting, minimizing the number of pen-up during the production of a word, others do not. As a consequence, whenever the handwriting presents pen-up within a word, the recognition performance can drop significantly. In a preliminary study, we presented an algorithm for discriminating among different types of ink appearing in handwriting, namely isolated characters, cursive, dots, horizontal and vertical bars, based on the use of a suitable set of features. In this paper, we have characterized the discriminative power of each considered feature according to different measures and we have proposed a method for combining the different feature rankings. We have also used the Fischer's Linear Discriminant Analysis (LDA) for exhaustively selecting the best feature subsets with increasing number of features. Finally, we have compared the results obtained by using the feature subsets provided by LDA with those obtained with the feature subsets selected according to our feature ranking. The experimental results, on different datasets of handwritten words, showed that our approach successfully achieves its aim allowing to reduce the computational cost without affecting the overall performance of the recognition process.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165877
https://hal.univ-antilles.fr/hal-01165877
https://hal.univ-antilles.fr/hal-01165877/document
https://hal.univ-antilles.fr/hal-01165877/file/IGS_2015_submission_13.pdf

Éditeur(s) : HAL CCSD Editions Dalloz
Résumé : International audience
For a given voting situation, the Strong Borda Paradox occurs when a Condorcet loser exists and is elected. A Condorcet loser is a candidate that loses all his pairwise comparisons. In three-candidate elections, we use an analytical approach to find out, the range of all the scoring rules that can exhibit the Strong Borda Paradox under some well-known preference restrictions and we describe all the scenarios with respect to the rank of the Condorcet loser in the collective rankings. Using the parameterized Barvinok’s algorithm, we provide a simplified representation of the likelihood of the Strong Borda Paradox for the Plurality rule and the Antiplurality rule (given the size of the electorate) with the impartial and anonymous culture condition for each type of restriction.
ISSN: 0373-2630

hal-01631180
https://hal.univ-antilles.fr/hal-01631180
DOI : 10.3917/redp.273.0375

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we use a multifractal approach based on the computation of two spectrums for image analysis and texture segmentation problems. The two spectrums are the Legendre Spectrum, determined by classical methods, and the Large Deviation Spectrum, determined by kernel density estimation. We propose a way for the fusion of these two spectrums to improve textured image segmentation results. An unsupervised k-means is used as clustering approach for the texture classification. The algorithm is applied on mosaic image built using IKONOS images and various natural textures from the Brodatz album. The segmentation obtained with our approach gives better results than the application of each spectrum separately.
CIARP Proceedings
Cancun, Mexico

hal-00602268
https://hal.archives-ouvertes.fr/hal-00602268

Éditeur(s) : HAL CCSD Wiley-Blackwell
Résumé : We use InSAR to measure deformation and kinematics of the Mw = 4.9 Borujerd (2005/05/03) and Mw = 6.1 Chalan-Chulan (2006/03/31) earthquakes that occurred in the Zagros fold-and-thrust belt. The focal mechanism of the 2006 event is consistent with right lateral strike-slip motion and the event ruptured the Dorud-Borujerd segment of the Main Recent Fault. An Envisat interferogram spanning the 2006 event shows peak ground deformation of 9 cm in the satellite line-of-sight along a 10 km long fault portion. The interferogram spanning the 2005 earthquake is rather related to atmospheric artefact than to ground deformation. Dislocation models of the 2006 Chalan-Chulan event indicate dextral slip amounting to a maximum of 90 cm at a depth of 4 km. The predicted vertical displacements are in good agreement with differential levelling data. The 2006 event filled only a small part of the seismic gap located between large M = 7 events that occurred in 1909 and 1957.
ISSN: 0954-4879

hal-00411313
https://hal.archives-ouvertes.fr/hal-00411313
DOI : 10.1111/j.1365-3121.2008.00797.x

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : International audience
The outer membrane proteins (OMPs) of Gram-negative bacteria play a crucial role in virulence and pathogenesis. Identification of these proteins represents an important goal for bacterial proteomics, because it aids in vaccine development. Here, we have developed such an approach for Ehrlichia ruminantium, the obligate intracellular bacterium that causes heartwater. A preliminary whole proteome analysis of elementary bodies, the extracellular infectious form of the bacterium, had been performed previously, but information is limited about OMPs in this organism and about their role in the protective immune response. Identification of OMPs is also essential for understanding Ehrlichia’s OM architecture, and how the bacterium interacts with the host cell environment. First, we developed an OMP extraction method using the ionic detergent sarkosyl, which enriched the OM fraction. Second, proteins were separated via one-dimensional electrophoresis, and digested peptides were analyzed via nano-liquid chromatographic separation coupled with mass spectrometry (LCMALDI-TOF/TOF). Of 46 unique proteins identified in the OM fraction, 18 (39%) were OMPs, including 8 proteins involved in cell structure and biogenesis, 4 in transport/virulence, 1 porin, and 5 proteins of unknown function. These experimental data were compared to the predicted subcellular localization of the entire E. ruminantium proteome, using three different algorithms. This work represents the most complete proteome characterization of the OM fraction in Ehrlichia spp. The study indicates that suitable subcellular fractionation experiments combined with straightforward computational analysis approaches are powerful for determining the predominant subcellular localization of the experimentally observed proteins.We identified proteins potentially involved in E. ruminantium athogenesis, which are good novel targets for candidate vaccines. Thus, combining bioinformatics and proteomics, we discovered new OMPs for E. ruminantium that are valuable data for those investigating new vaccines against this organism. In summary, we provide both pioneering data and novel insights into the pathogenesis of this obligate intracellular bacterium.
ISSN: 1932-6203

hal-01203402
https://hal.univ-antilles.fr/hal-01203402
https://hal.univ-antilles.fr/hal-01203402/document
https://hal.univ-antilles.fr/hal-01203402/file/2015_Moumene_PLOSONE-1.pdf
DOI : 10.1371/journal.pone.0116758

Éditeur(s) : HAL CCSD
Résumé : International audience
The fusion of regions from segmentation is often a required step in order to analyze objects in the scene. It allows to group under a same closure property the partitions of an image, which denote the same object. In this way, several studies have been made. Most of the algorithms are based on global information to evaluate the relevance of region combinations and fusion. In this paper, we propose a merging process based on Gestalt vision's theory. The particularity of our method is to preserve photographer's region of interest thanks to the definition of a new criterion based on Di Zenzo gradient. Thus, region fusion preserves photographer's relevant objects of the scene. The performance of the proposed method is evaluated using various natural images, including Berkeley image database and its accuracy is shown.
Color and Imaging Conference
San Jose, CA, United States

hal-00637144
https://hal.archives-ouvertes.fr/hal-00637144

Résumé : Les graphes définis à partir de structures algébriques possèdent d’excellentes propriétés de symétries particulièrement intéressantes. L’exemple le plus flagrant est la notion de graphe de Cayley qui s’est révélée très riche non seulement du point de vue théorique mais aussi pratique par ses applications à de nombreux domaines incluant l’architecture des réseaux ou les machines parallèles. Néanmoins, la régularité des graphes de Cayley se révèle parfois être une limite étant donné qu’ils sont toujours sommet-transitifs et donc en particulier non pertinents pour générer des réseaux semiréguliers.Cette observation a motivé, en 2005, la définition d’une nouvelle classe de graphes définis à partir d’un groupe, appelés G-graphes. Ils possèdent aussi de nombreuses propriétés de régularité mais de manière moins restrictive.Cette thèse propose un nouveau regard sur cette classe de graphes par une approche plutôt orientée recherche opérationnelle alors que la grande majorité des études précédentes est dominée par des approches essentiellement algébriques. Nous-nous sommes alors intéressés à plusieurs questions :— La caractérisation des G-graphes : nous proposons des améliorations par rapport aux précédents résultats.— Identifier des classes de graphes comme des G-graphes grâce à des isomorphismes ou en utilisant le théorème de caractérisation.— Etudier la structure et les propriétés de ces graphes, en particulier pour de possibles applications aux réseaux : colorations semi-régulières, symétries et robustesse.— Une approche algorithmique pour la reconnaissance de cette classe avec notamment un premier exemple de cas polynomial lorsque le groupe est abélien.
Interactions between graph theory and group theory have already led to interesting results for both domains. Graphs defined from algebraic groups have highly symmetrical structure giving birth to interesting properties. The most famous example is Cayley graphs, which revealed to be particularly interesting both from a theoretical and a practical point of view due to their applications in several domains including network architecture or parallel machines. Nevertheless, the regularity of Cayley graphs is also a limit as they are always vertex-transitive and therefore not relevant to generate semi-regular networks. This observation motivated the definition, in 2005, of a new family of graphs defined from a group, called G-graphs. They also have many regular properties but are less restrictive. These graphs are in particular semi-regular k-partite, with a chromatic number k directly given in the group representation and they can be either transitive or not.This thesis proposes a new insight into this class of graphs using an approach based on operational research while most of previous studies have been so far dominated by algebraic approaches. Then, the thesis addresses different kind of questions:— Characterizing G-graphs: we propose improvements of previous results.— Identifying some classes of graphs as G-graphs through isomorphism or using the characterization theorem.— Studying the structure and properties of these graphs, in particular for possible applications to networks: semi-regular coloring, symmetries and robustness.— Algorithmic approach for recognizing this class with a first example of polynomial case when the group is abelian.

http://www.theses.fr/2014AGUY0787/document

Éditeur(s) : HAL CCSD Taylor & Francis
Résumé : International audience
We study the convergence of a variable version of the Krasnoselski-Mann algorithm applied to a primal dual fixed point problem. The link with Spingarn's partial inverse method is made, and an application to feasibility problems and mathematical programming is also proposed.
ISSN: 0163-0563

hal-00779984
https://hal.univ-antilles.fr/hal-00779984
DOI : 10.1080/01630560902987436

Éditeur(s) : HAL CCSD Hindawi Publishing Corporation
Résumé : International audience
This paper is concerned with the study of a penalization-gradient algorithmfor solving variational inequalities, namely, find x ∈ C such that Ax, y − x ≥ 0 for all y ∈ C, where A : H → H is a single-valued operator, C is a closed convex set of a real Hilbert space H. Given Ψ : H → ∪ { ∞} which acts as a penalization function with respect to the constraint x ∈ C, and a penalization parameter βk, we consider an algorithm which alternates a proximal step with respect to ∂Ψ and a gradient step with respect to A and reads as xk I λkβk∂Ψ −1 xk−1 − λkAxk−1 . Under mild hypotheses, we obtain weak convergence for an inverse strongly monotone operator and strong convergence for a Lipschitz continuous and strongly monotone operator. Applications to hierarchical minimization and fixed-point problems are also given and the multivalued case is reached by replacing themultivalued operator by its Yosida approximatewhich is always Lipschitz continuous.
ISSN: 0161-1712

hal-00776654
https://hal.univ-antilles.fr/hal-00776654
https://hal.univ-antilles.fr/hal-00776654/document
https://hal.univ-antilles.fr/hal-00776654/file/305856.pdf
DOI : 10.1155/2011/305856

Éditeur(s) : HAL CCSD IOS Press
Résumé : International audience
Recent work has used graphs to modelize expression data from microarray experiments, in view of partitioning the genes into clusters. In this paper, we introduce the use of a decomposition by clique separators. Our aim is to improve the classical clustering methods in two ways: first we want to allow an overlap between clusters, as this seems biologically sound, and second we want to be guided by the structure of the graph to define the number of clusters. We test this approach with a well-known yeast database (Saccharomyces cerevisiae). Our results are good, as the expression profiles of the clusters we find are very coherent. Moreover, we are able to organize into another graph the clusters we find, and order them in a fashion which turns out to respect the chronological order defined by the the sporulation process.
ISSN: 1386-6338

hal-00521936
https://hal.archives-ouvertes.fr/hal-00521936

Éditeur(s) : HAL CCSD
Résumé : International audience
We instrumented a realistic car simulator to extract low level data related to the driver's use of the vehicle controls. After proceeding these data, we generated features that were fed to a Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Our goal was determine if the driver's Blood Alcohol Content (BAC) was over $0.4g.l^{-1}$ or not, and even estimate the BAC value. Our device process the vehicle's controls data and then outputs the user BAC. We discuss the results of the prototype using the MLP and SVM algorithms in both single-user and multi-user context for detection of drunk drivers and estimation of the BAC value. The prototype performed better with single user base than with multi-user, and provided comparable results with MLP and SVM.
ESANN 2013 proccedings,European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Bruges, Belgium

hal-00850138
https://hal.archives-ouvertes.fr/hal-00850138