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ObjectiveTo investigate spatial heterogeneity of stunting prevalence among children in Côte d’Ivoire and examine changes in stunting between 1994 and 2011, to assess the impact of the 2002–2011 civil war that led to temporary partitioning of the country.DesignData from 1994, 1998 and 2011 Côte d’Ivoire Demographic and Health Surveys (DHS) were analysed using a geostatistical approach taking into account spatial autocorrelation. Stunting data were interpolated using ordinary kriging; spatial clusters with high and low stunting prevalence were identified using Kulldorff spatial scan statistics. Multilevel multivariable logistic regression was then carried out, with year of survey as the main independent variable and an interaction term for time by geographic zone (Abidjan, South, North).SettingCôte d’Ivoire, West Africa.SubjectsChildren aged 0–35 months included in three DHS (n6709).ResultsOverall stunting prevalence was 30·7, 28·7 and 27·8 % in 1994, 1998 and 2011, respectively (P=0·32). Clusters with high prevalence were found in 1994 (in the West region,PPP=0·01, respectively), but not in 2011. Abidjan was included in a cluster with low prevalence in all surveys (PConclusionsIn Côte d’Ivoire, significant changes in stunting prevalence were observed at the sub-national level between 1994 and 2011.

International audience; Cell biologists have long aimed at quantitatively modeling cell function. Recently, the outstanding progress of high-throughput measurement methods and data processing tools has made this a realistic goal. The aim of this paper is twofold: First, to suggest that, while much progress has been done in modeling cell states and transitions, current accounts of environmental cues driving these transitions remain insufficient. There is a need to provide an integrated view of the biochemical, topographical and mechanical information processed by cells to take decisions. It might be rewarding in the near future to try to connect cell environmental cues to physiologically relevant outcomes rather than modeling relationships between these cues and internal signaling networks. The second aim of this paper is to review exogenous signals that are sensed by living cells and significantly influence fate decisions. Indeed, in addition to the composition of the surrounding medium, cells are highly sensitive to the properties of neighboring surfaces, including the spatial organization of anchored molecules and substrate mechanical and topographical properties. These properties should thus be included in models of cell behavior. It is also suggested that attempts at cell modeling could strongly benefit from two research lines: (i) trying to decipher the way cells encode the information they retrieve from environment analysis, and (ii) developing more standardized means of assessing the quality of proposed models, as was done in other research domains such as protein structure prediction.

Trees are among the most studied data structures and several techniques have consequently been developed for comparing two trees belonging to the same category. Until the end of year 2020, there was a serious lack of suitable metrics for comparing two weighted trees or two trees from different categories. The problem of comparing two tree sets was not also specifically addressed. These limitations have been overcome in a paper published in 2021 where a customizable metric based on hidden Markov models have been proposed for comparing two tree sets, each containing a mixture of trees belonging to various categories. Unfortunately, that metric does not allow the use of non metric-dependent classifiers which take descriptor vectors as inputs. This paper addresses this drawback by deriving a descriptor vector for each tree set from the behavior of its associated models after a sufficiently long time. The comparison between two tree sets is then realized by comparing their associated descriptor vectors. Classification experiments carried out on the databases FirstLast-L (FL), FirstLast-LW (FLW) and Stanford Sentiment Treebank (SSTB) respectively showed best accuracies of 99.75%, 99.75% and 87.22%. These performances are respectively 40.75% and 20.52% better than the tree Edit distance respectively for FLW and SSTB. Additional clustering experiments exhibited 54.25%, 98.75% and 75.53% of correctly clustered instances respectively for FL, FLW and SSTB. No clustering was performed in existing work.; Les arbres font partie des structures de données les plus étudiées et plusieurs techniques ont par conséquent été développées pour comparer deux arbres appartenant à la même catégorie. Jusqu'à la fin de l'année 2020, il y avait un sérieux manque de métriques appropriées pour comparer deux arbres pondérés ou deux arbres de catégories différentes. Le problème de la comparaison de deux ensembles d'arbres n'a pas non plus été spécifiquement abordé. Ces limites ont été surmontées dans un article publié en 2021 dans lequel une métrique paramétrable basée sur des modèles de Markov cachés a été proposée pour comparer deux ensembles d'arbres, chacun contenant un mélange d'arbres appartenant à différentes catégories. Malheureusement, cette métrique ne permet pas l'utilisation de classificateurs ne dépendant pas d'une mesure de distance qui prennent généralement des vecteurs en entrées. Le présent article s'attaque à cette limite en proposant un descripteur vectoriel pour chaque ensemble d'arbres à partir du comportement sur une longue période des modèles qui lui sont associés. La comparaison entre deux ensembles d'arbres est alors réalisée en comparant leurs descripteurs vectoriels respectifs. Des expériences en classification menées sur les bases de données FirstLast-L (FL), FirstLast-LW (FLW) et Stanford Sentiment Treebank (SSTB) ont respectivement permis d'obtenir les meilleures performances de 99,75 %, 99,75 % et 87,22 %. Ces performances sont respectivement 40,75% et 20,52% meilleures que la 'Tree Edit distance' respectivement pour FLW et SSTB. Des expériences supplémentaires en clustering ont permis d'avoir respectivement 54,25 %, 98,75 % et 75,53 % instances correctement groupées pour FL, FLW et SSTB. Aucun clustering n'avait été effectué dans les travaux existants.

International audience; The paper presents the cartographic processing of the Landsat TM image by the two unsupervised classification methods of SAGA GIS: ISODATA and K-means clustering. The approaches were tested and compared for land cover type mapping. Vegetation areas were detected and separated from other land cover types in the study area of southwestern Iceland. The number of clusters was set to ten classes. The processing of the satellite image by SAGA GIS was achieved using Imagery Classification tools in the Geoprocessing menu of SAGA GIS. Unsupervised classification performed effectively in the unlabeled pixels for the land cover types using machine learning in GIS. Following an iterative approach of clustering, the pixels were grouped in each step of the algorithm and the clusters were reassigned as centroids. The paper contributes to the technical development of the application of machine learning in cartography by demonstrating the effectiveness of SAGA GIS in remote sensing data processing applied for vegetation and environmental mapping.

International audience; In this paper, we propose an efficient KNN service, called KPS (KNN-Peer-Sampling). The KPS service can be used in various contexts e.g. recommendation systems, information retrieval and data mining. KPS borrows concepts from P2P gossip-based clustering protocols to provide a localized and efficient KNN computation in large-scale systems. KPS is a sampling-based iterative approach, combining ran-domness, to provide serendipity and avoid local minimum, and clustering , to ensure fast convergence. We compare KPS against the state of the art KNN centralized computation algorithm NNDescent, on multiple datasets. The experiments confirm the efficiency of KPS over NNDescent: KPS improves significantly on the computational cost while converging quickly to a close to optimal KNN graph. For instance, the cost, expressed in number of pairwise similarity computations, is reduced by ≈ 23% and ≈ 49% to construct high quality KNN graphs for Jester and MovieLens datasets, respectively. In addition, the randomized nature of KPS ensures eventual convergence, not always achieved with NNDescent.

International audience; This paper provides a short overview of space time series clustering, which can be generally grouped into three main categories such as: hierarchical, partitioning-based, and overlapping clustering. The first hierarchical category is to identify hierarchies in space time series data. The second partitioning-based category focuses on determining disjoint partitions among the space time series data, whereas the third overlapping category explores fuzzy logic to determine the different correlations between the space time series clusters. We also further describe solutions for each category in this paper. Furthermore, we show the applications of these solutions in an urban traffic data captured on two urban smart cities (e.g., Odense in Denmark and Beijing in China).The perspectives on open questions and research challenges are also mentioned and discussed that allow to obtain a better understanding of the intuition, limitations, and benefits for the various space time series clustering methods. This work can thus provide the guidances to practitioners for selecting the most suitable methods for their used cases, domains, and applications.

International audience; Sargassum seaweed forms large floating mats drifting on the oceans. These mats are increasingly beaching on Caribbean islands, threatening the local wildlife and economies. This paper focuses on their tracking from space, in order to monitor these mats. More specifically, we focus on clustering sargassum mats on satellite images. This constitutes an important building block of the sargassum monitoring system, which then predicts the drift of those clusters to anticipate beachings and warn the local authorities. The difficulty of the clustering operation comes from the noisy nature of input data: image artefacts, partial cloud occlusion, mats discontinuities due to sea conditions, and the difficulty of acquiring ground truth data. This paper details our approach to overcome those challenges. We propose a method (hereafter named Sargassum Mats Detection Method-SMDM) that improves the mats identification by combining a first artefact detection step, a tailored clustering algorithm and a region growing algorithm.

International audience; Clustering is impacted by the regular increase of sample sizes which provides opportunity to reveal information previously out of scope. However, the volume of data leads to some issues related to the need of many computational resources and also to high energy consumption. Resorting to binned data depending on an adaptive grid is expected to give proper answer to such green computing issues while not harming the quality of the related estimation. After a brief review of existing methods, a first application in the context of univariate model-based clustering is provided, with a numerical illustration of its advantages. Finally, an initial formalization of the multivariate extension is done, highlighting both issues and possible strategies.

International audience; In this paper, we use tools of information geometry to compare, average and classify histograms. Beta distributions are fitted to the histograms and the corresponding Fisher information geometry is used for comparison. We show that this geometry is negatively curved, which guarantees uniqueness of the notion of mean, and makes it suitable to classify histograms through the popular K-means algorithm. We illustrate the use of these geometric tools in supervised and unsupervised classification procedures of two medical data-sets, cardiac shape deformations for the detection of pulmonary hypertension and brain cortical thickness for the diagnosis of Alzheimer's disease.

In this paper, we propose a multivariate Hawkes framework for modelling and predicting cyber attacks frequency. The inference is based on a public data set containing features of data breaches targeting the US industry. As a main output of this paper, we demonstrate the ability of Hawkes models to capture self-excitation and interactions of data breaches depending on their type and targets. In this setting, we detail prediction results providing the full joint distribution of future cyber attacks times of occurrence. In addition, we show that a non-instantaneous excitation in the multivariate Hawkes model, which is not the classical framework of the exponential kernel, better fits with our data. In an insurance framework, this study allows to determine quantiles for number of attacks, useful for an internal model, as well as the frequency component for a data breach guarantee.

This paper is related to a wide project aiming at discoving from different streams of information (i.e. daily publication from the Internet), weak signals possibly sent by whistleblowers. The current study presented in this paper tackles the particular problem of clustering topics at multi-levels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words. In this context, we present a novel idea combining LDA (in charge clustering) and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the "a priori" number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the optimal K for LDA; (2) gathering the optimal clusters from different levels of clustering., L'étude présentée s'inscrit dans le cadre du développement d'une plateforme d'analyse automatique de documents associée à un service sécurisé lanceurs d'alerte, de type GlobalLeaks, focalisé sur la révélation de faits/événements/actions en lien avec des problématiques environnementales. Le présent article aborde le problème particulier du regroupement de sujets à plusieurs niveaux à partir de documents multiples, puis de l'extraction de descripteurs significatifs, tels que des listes pondérées de mots. Dans ce contexte, nous présentons une nouvelle idée combinant LDA (en charge du clustering) et Word2vec (fournissant une métrique de cohérence concernant les sujets partitionnés) comme méthode potentielle pour limiter le nombre "a priori" de cluster K habituellement nécessaire dans les approches classiques du partitionnement. Nous avons proposé 2 mises en œuvre de cette idée, respectivement en mesure de : (1) trouver le K optimal pour LDA ; (2) rassembler les clusters optimaux de différents niveaux de clustering.

International audience; We develop a fast-deployed crop health monitoring system using state-of-the-art technologies to collect data from crop fields in order to take appropriate and timely actions. For the proposed resource optimized system, Saudi Arabian agriculture is taken as a case study. To achieve the desire goals, we harness IoT (Internet of Things) and drones in agriculture to establish rapid system deployment. This paper focuses on the data collection from crop field by organizing heterogeneous IoT devices in clusters and localise them for data harvesting. Clusters are formed by considering the path of UAVs, sensors heterogeneity, weather conditions, fluctuation of sensor nodes, and the communication cost of IoT devices. For localisation, carrying larger or heavier arrays of antennas and receivers with a small size UAV is also a major issue considered in this paper. Hence, we introduce a dynamic clustering and virtual antenna array to develop a complete data collection scheme supported by simulations and experimental tests with proof-of-concept devices. The results are analysed and found promising in terms of energy efficiency, throughput, ease of use, and deployment time. Whole the system is developed with the concept that it can install in rural and remote area with minimum deployment time and agile enough that can collect data in worst conditions (bad weather, hostile environment, fluctuating nodes, poor infrastructure, with or without an established network). In broader sense it can map easily in many similar applications where data is needed to be harvested from a wide range of heterogeneous sensors without existing any infrastructure and ground topology.

International audience; This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers in a platform as GlobalLeaks. The study presented in this paper tackles the particular problem of clustering topics at multi-levels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multi-dimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2Vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the "a priori" number of cluster k usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best k for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a non-traditional visualization approach based on a multi-agents system which combines both dimension reduction and interactivity.; L'étude présentée s'inscrit dans le cadre du développement d'une plateforme d'analyse automatique de documents associée à un service sécurisé lanceurs d'alerte, de type GlobalLeaks. Cet article se focalise principalement sur la recherche de signaux faibles présents dans les documents. Il s'agit d'une problématique investiguée dans un grand nombre de champs disciplinaires et de cadres applicatifs. Nous supposons que chaque document est un mélange d'un petit nombre de thèmes ou catégories, et que la création de chaque mot est attribuable en termes de probabilités à l'un des thèmes du document. Les catégories des documents transmis ne sont pas connues"a priori". Les mots-clés présents dans les documents représentatifs de ces catégories sont également inconnus. L'analyse des documents reçus doit simultanément permettre de découvrir les thèmes, classer les documents relativement à ces thèmes, détecter les mots-clés pertinents relatifs aux thèmes et enfin découvrir les mots-clés relevant d'un thème "signal faible" éventuel. Pour atteindre cet objectif, nous proposons une définition du signal faible qui conditionne l'approche conjointe modèle thématique / plongement lexical, et contraint le choix des méthodes LDA et Word2Vec. Nous proposons d'évaluer les partitions obtenues grâce à un indice de cohérence sur la collection de mots représentative de chaque thème obtenu. Les clusters obtenus sont ainsi plus cohérents au sens contextuel. La détection du cluster associé au signal faible est alors plus aisée et plus pertinente.

International audience; Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems.Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs.Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated.Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.

Working paper Il s'agit d'un preprint qui a été soumis dans une revue à comité de lecture; This paper presents a novel algorithm for performing inference and/or clustering in semiparametric copula-based mixture models. The algorithm replaces the standard kernel density estimator by a weighted version that permits to take into account the constraints put on the underlying marginal densities. Lower misclassification error rates and better estimates are obtained on simulations. The pointwise consistency of the weighted kernel density estimator is established under an assumption on the rate of convergence of the sample maximum.

International audience; This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers in a platform as GlobalLeaks. The study presented in this paper tackles the particular problem of clustering topics at multi-levels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multi-dimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2Vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the "a priori" number of cluster k usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best k for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a non-traditional visualization approach based on a multi-agents system which combines both dimension reduction and interactivity.; L'étude présentée s'inscrit dans le cadre du développement d'une plateforme d'analyse automatique de documents associée à un service sécurisé lanceurs d'alerte, de type GlobalLeaks. Cet article se focalise principalement sur la recherche de signaux faibles présents dans les documents. Il s'agit d'une problématique investiguée dans un grand nombre de champs disciplinaires et de cadres applicatifs. Nous supposons que chaque document est un mélange d'un petit nombre de thèmes ou catégories, et que la création de chaque mot est attribuable en termes de probabilités à l'un des thèmes du document. Les catégories des documents transmis ne sont pas connues"a priori". Les mots-clés présents dans les documents représentatifs de ces catégories sont également inconnus. L'analyse des documents reçus doit simultanément permettre de découvrir les thèmes, classer les documents relativement à ces thèmes, détecter les mots-clés pertinents relatifs aux thèmes et enfin découvrir les mots-clés relevant d'un thème "signal faible" éventuel. Pour atteindre cet objectif, nous proposons une définition du signal faible qui conditionne l'approche conjointe modèle thématique / plongement lexical, et contraint le choix des méthodes LDA et Word2Vec. Nous proposons d'évaluer les partitions obtenues grâce à un indice de cohérence sur la collection de mots représentative de chaque thème obtenu. Les clusters obtenus sont ainsi plus cohérents au sens contextuel. La détection du cluster associé au signal faible est alors plus aisée et plus pertinente.

National audience; A new robust lane marking extraction algorithm for monocular vision is proposed based on Two-Dimension Declivity. It is designed for the urban roads with difficult conditions (shadow, high brightness, etc.). In this paper, we propose a locating system which, from an embedded camera, allows lateral positioning of a vehicle by detecting road markings. The primary contribution of the paper is that it supplies a robust method made up of six steps: (i) Image Pre-processing, (ii) Enhanced Declivity Operator (DE), (iii) Mathematical Morphology, (iv) Labeling, (v) Hough Transform and (vi) Line Segment Clustering. The experimental results have shown the high performance of our algorithm in various road scenes. This validation stage has been done with a sequence of simulated images. Results are very promising: more than 90% of marking lines are extracted for less than 12% of false alarm.

International audience; In this paper, we define a cluster based scheduling algorithm for Cognitive Radio Sensor Networks (CRSNs). To avoid inter-clusters collision, we assign fixed channels only to nodes having one-hop neighbors out of their clusters. We denote these nodes as specific nodes. Previous studies assign distinct channels to whole neighbor clusters to avoid inter-clusters collision. Our objective is to optimize the spatial reuse and to increase the network throughput while saving sensors energy. We start by assigning channels only to the specific nodes. Once the problem of inter-clusters collision is solved, each cluster head (CH) schedules the transmissions in its cluster independently. For the cluster members that are specific nodes, the CH assigns only time slots because the channel assignment is already done. For other cluster members (CMs) (not specific nodes), the CH assigns the pair (channel, slot). Two solutions are proposed in this paper to schedule the CMs: The Frame Intra Cluster Multichannel Scheduling algorithm denoted Frame-ICMS and the Slot Intra Cluster Multichannel Scheduling algorithm denoted Slot-ICMS. We evaluate the performance of these algorithms in case of accurate PUs activity detection and in case of bad PUs activity estimation. We prove that our proposals outperform an existing one especially in terms of energy saving.

International audience; This paper deals with the problem of improving data-based diagnosis of continuous systems taking advantage of the system control information represented as discrete event dynamics. The approach starts from dynamic clustering results and, combining the information about operational modes, automatically generates a discrete event system that improves clustering results interpretability for decision-making purposes and enhances fault detection capabilities by the inclusion of event related dynamics. The generated timed discrete event system is adaptive thanks to the dynamic nature of the clusterer from which it was learned, namely DyClee. The timed discrete event system brings valuable temporal information to distinguish behaviors that are non-diagnosable based solely on the clustering itself.

International audience; This paper presents an original approach combining Artificial Neural Networks (ANNs) and clustering in order to detect pollutant peaks. We developed air quality forecasting models using machine learning methods applied to hourly concentrations of ozone (O 3), nitrogen dioxide (NO 2) and particulate matter (PM 10) 24 hours ahead. MultiLayer Perceptron (MLP) was used alone, then hybridized successively with hierarchical clustering and with a combination of self-organizing map and k-means clustering. Clustering methods were used to subdivide the dataset, and then an MLP was trained on each subset. Two urban sites of Corsica Island in the western Mediterranean Sea were investigated. These models showed a good global precision (Index of Agreement reaching 0.87 for O 3 , 0.80 for NO 2 and 0.74 for PM 10). Considering it is particularly important than forecasting model used on an operational basis correctly predict pollution peaks, a sensitivity analysis was performed using Receiver Operating Characteristic curves (ROC curves). It allowed to evaluate the behaviour and the robustness of the models for high concentration situations. The results show that for PM 10 and O 3 , hybrid models made of a combination of clustering and MLP outperform classical MLP most of the time for high concentration prediction. An operational tool has been built with the models presented in this paper, and is used for air quality forecasting in Corsica.

International audience; In this paper, we use nonparametric runs-based tests to analyze the randomness and the persistence of relative returns of hedge funds. Runs tests are implemented on a universe of hedge funds extracted from HFR database over the period spanning January 2000 to December 2012. Our findings suggest that i) slightly less than 80% of the studied universe has returns at random, ii) a similar figure is found out when focusing on relative returns, iii) hedge funds that do present clustering in their relative returns are mainly found within Event Driven and Relative Value strategies, iv) and for relative returns, results vary with the type of the benchmark nature (peer group average or traditional). This paper also emphasizes that runs tests may be a useful tool for investors in their fund's selection process.

Energy optimization has become a crucial issue in the realm of ICT. This paper addresses the problem of energy consumption in a Metro Ethernet network. Ethernet technology deployments have been increasing tremendously because of their simplicity and low cost. However, much research remains to be conducted to address energy efficiency in Ethernet networks. In this paper, we propose a novel Energy Aware Forwarding Strategy for Metro Ethernet networks based on a modification of the Internet Energy Aware Routing (EAR) algorithm. Our contribution identifies the set of links to turn off and maintain links with minimum energy impact on the active state. Our proposed algorithm could be a superior choice for use in networks with low saturation, as it involves a tradeoff between maintaining good network performance and minimizing the active links in the network. Performance evaluation shows that, at medium load traffic, energy savings of 60% can be achieved. At high loads, energy savings of 40% can be achieved without affecting the network performance.

6 pages; International audience; Traditional Network Intrusion Detection Systems (NIDSs) rely on either specialized signatures of previously seen attacks, or on expensive and difficult to produce labeled traffic datasets for profiling and training. Both approaches share a common downside: they require the knowledge provided by an external agent, either in terms of signatures or as normal-operation profiles. In this paper we describe UNIDS, an Unsupervised NIDS capable of detecting 0-day attacks, i.e., network attacks for which no signature is yet available, without using any kind of signatures, labeled traffic, or training. UNIDS uses a novel unsupervised outliers detection approach based on Sub-Space Clustering and Multiple Evidence Accumulation techniques to pin-point different kinds of network intrusions and attacks such as DoS/DDoS, probing attacks, propagation of worms, buffer overflows, illegal access to network resources, etc. In this paper we make the strong point that the de-facto approach for NIDS, namely the application of rule-based detection techniques, can be highly harmful for the protected network in case of 0-day attacks. In contrast, we show how UNIDS can work as a complementary system to current NIDS to detect the occurrence of previously unseen attacks. For doing so, we compare the performance of a standard rule-based NIDS against UNIDS to detect 0-day attacks in the well-known KDD99 dataset. In addition, we also compare the performance of UNIDS against other popular unsupervised detection techniques to detect attacks in traces collected at two operation networks.

International audience; Pervasive Information System (PIS) represents a new generation of Information Systems (IS) available anytime, anywhere in a pervasive environment. In this paper, we propose to enhance PIS transparency and efficiency through a context-aware intentional service prediction approach. This approach allows anticipating user's future needs, offering and recommending him the most suitable service in a transparent and discrete way. We detail in this paper our service prediction mechanism and present encouraging experimental results demonstrating our proposition.

International audience; Wireless sensors networks (WSNs) are traditionally composed of large number of low-cost and tiny homogenous sensors nodes connected through a wireless network that gather data to be treated locally or relayed to the sink node through multi-hop wireless transmission. Moreover, such issues are very critical due servers resources constraints like efficient energy, stock limitation and lifetime of network. Several solutions were proposed to minimize the traffic into network. Clustering algorithms have been widely used to reduce energy consumption. In this context, the key point in such topology is to select a cluster. One of solutions is to select a cluster alternately. However, this choice does not consider the energy as important criteria in actual papers. In order to limit energy consumption, our new method is proposed in this paper to optimization Low Energy Adaptive Clustering Hierarchy (O- LEACH) to improve existing LEACH and LEACH-C by selecting cluster according to the residual energy of nodes dynamically. The simulation results show that proposed algorithm achieve longer stability by comparison to original LEACH and LEACH-C.

The problem of adaptive noisy clustering is investigated. Given a set of noisy observations $Z_i=X_i+\epsilon_i$, $i=1,...,n$, the goal is to design clusters associated with the law of $X_i$'s, with unknown density $f$ with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach as the popular {\it $k$-means} is not suitable in this case. In this paper, we propose a noisy $k$-means minimization, which is based on the $k$-means loss function and a deconvolution estimator of the density $f$. In particular, this approach suffers from the dependence on a bandwidth involved in the deconvolution kernel. Fast rates of convergence for the excess risk are proposed for a particular choice of the bandwidth, which depends on the smoothness of the density $f$. Then, we turn out into the main issue of the paper: the data-driven choice of the bandwidth. We state an adaptive upper bound for a new selection rule, called ERC (Empirical Risk Comparison). This selection rule is based on the Lepski's principle, where empirical risks associated with different bandwidths are compared. Finally, we illustrate that this adaptive rule can be used in many statistical problems of $M$-estimation where the empirical risk depends on a nuisance parameter., Comment: 22 pages

International audience; The disambiguation of named entities is a challenge in many fields such as scientometrics, social networks, record linkage, citation analysis, semantic web...etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions... Therefore, the search of names of persons or of organizations is difficult as soon as a single name might appear in many different forms. This paper proposes two approaches to disambiguate on the affiliations of authors of scientific papers in bibliographic databases: the first way considers that a training dataset is available, and uses a Naive Bayes model. The second way assumes that there is no learning resource, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and the approach is already partially applied in a scientific survey department. However, our experiments also highlight that our approach has some limitations: it cannot process efficiently highly unbalanced data. Alternatives solutions are possible for future developments, particularly with the use of a recent clustering algorithm relying on feature maximization.

International audience; The disambiguation of named entities is a challenge in many elds such as sciento- metrics, social networks, record linkage, citation analysis, semantic web...etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions... So the search of names of persons or of organization is di cult, a single name can appear in di erent forms. This paper proposes two approaches to disambiguate on the a liations of authors of sci- enti c papers in bibliographic databases: the rst way, considers that we have a training corpus, and uses a Naive Bayesian model. The second way assumes that we have not re- source learning, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and are already partially applied in a scienti c survey department. However, we aware that our approach may have limitations: we can't process e ciently highly unbalanced data but solutions are possible for future developments.

International audience; Collaborative innovation is an unavoidable need for the small and medium enterprises (SME) both in terms of economic scale and technological knowledge. Risks and the innovation power are analyzed for the wealth of collaboration. This paper aims to present the synergy index as a multiplier of the innovation power of research partners to construct a successful collaboration. The proposed index can be used with different number of companies in collaboration cluster and the synergy maximization is guaranteed by using a new particle swarm algorithm, Foraging Search. This paper will give the formulation and criteria of the synergy index in detail. A sample synergy index application for the Turkish SMEs will clarify the steps to follow.

This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat à l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU-time consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centers found by the clustering method based on projections, compared to the “true” ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem.

ISSN: 1828-6003, CD ISSN: 1828-6011; International audience; The topic of research exposed in this paper concerns Content Based image retrieval in a heterogeneous high database. The increase of storage capacities and the evolution of compression techniques have generated an explosion of the digital information quantity. Their computerization opens a vast field of applications. In this setting we are interested more especially in the problem of the dimensionality reduction and spectral clustering of a heterogeneous database of images in order to image retrieval by the content. Our new gait described in this paper consists to: in first phase the description of the database images by a hybrid descriptor which are Interest SIFT points combined with texture descriptor given by the application of Wavelet transform. The descriptor is multi-dimensional, robust and invariant to changes and scales. In second phase the representation of the database images as a convex graph. In third phase the reduction of the space of representation by the application of an unsupervised spectral classification (The Spectral training uses information contained in the eigenvectors of the normalized matrix of transition to detect structures in data.) That will provide us classes of images that has shortcoming the Eigen-values calculated on the matrix of symmetry. As last phase, we use the Nyström theory that will permit us, not to recalculate the all Eigen-values, but only the lasts one.

International audience; We propose, in this paper, a new biometric identification approach which aims to improve recognition performances in identification systems. We aim to split the identity database into well separated partitions in order to simplify the identification task. In this paper we develop a face identification system and we use the reference algorithms of eigenfaces and fisherfaces in order to extract different features describing each identity. These features, which describe faces, are generally optimized to establish the required identity in a classical identification process. In this work, we develop a novel criterion to extract features used to partition the identity database. We develop database partitioning with clustering methods which split the gallery by bringing together identities which have similar features and separating dissimilar features in different bins. Pruning the most dissimilar bins from the query identity features allows us to improve the identification performances. We report results from the XM2VTS database.

Peer-to-peer protocols play an increasingly instrumental role in Internet content distribution. It is therefore important to gain a complete understanding of how these protocols behave in practice and how their operating parameters affect overall system performance. This paper presents the first detailed experimental investigation of the peer selection strategy in the popular BitTorrent protocol. By observing more than 40 nodes in instrumented private torrents, we validate three protocol properties that, though believed to hold, have not been previously demonstrated experimentally: the clustering of similar-bandwidth peers, the effectiveness of BitTorrent's sharing incentives, and the peers' high uplink utilization. In addition, we observe that BitTorrent's modified choking algorithm in seed state provides uniform service to all peers, and that an underprovisioned initial seed leads to absence of peer clustering and less effective sharing incentives. Based on our results, we provide guidelines for seed provisioning by content providers, and discuss a tracker protocol extension that addresses an identified limitation of the protocol., Comment: This article is the author version of a paper accepted at ACM SIGMETRICS'2007. In particular, this paper is different from the technical report inria-00112066, version 1 - 21 November 2006. The technical report inria-00112066, version 1 - 21 November 2006 is a extended version of this paper (same title and authors, but different content) Version 2 is the same content as version 1, but with a different class in order to match the camera ready format for SIGMETRICS

International audience; This paper focuses on reduced-order modeling for contact mechanics problems treated by Lagrange multipliers. The high nonlinearity of the dual solutions lead to poor classical data compression. A hyper-reduction approach based on a reduced integration domain (RID) is considered. The dual reduced basis is the restriction to the RID of the full-order dual basis, which ensures the hyper-reduced model to respect the non-linearity constraints. However, the verification of the solvability condition, associated with the well-posedness of the solution, may induce an extension of the primal reduced basis without guaranteeing accurate dual forces. We highlight the strong link between the condition number of the projected contact rigidity matrix and the precision of the dual reduced solutions. Two efficient strategies of enrichment of the primal POD reduced basis are then introduced. However, for large parametric variation of the contact zone, the reachable dual precision may remain limited. A clustering strategy on the parametric space is then proposed in order to deal with piece-wise low-rank approximations. On each cluster, a local accurate hyper-reduced model is built thanks to the enrichment strategies. The overall solution is then deeply improved while preserving an interesting compression of both primal and dual bases.

International audience; Vehicular ad hoc networks (VANETs) provide an effective technology for vehicles communicating while on the road. Vehicles are organized in an ad hoc networks offering support for both safety-critical messaging, multi-hop entertainment applications, and connection to internet services through Road Side Units (RSUs). Sharing data between connected vehicles can help commuting people exchange travel information, and allows content delivery and entertainment applications using Peer-to-Peer (P2P) techniques. Despite the widely documented benefits of P2P techniques, there are a number of challenges faced when deployed in such a highly dynamic environments as VANETs. Such difficulties include the broadcast storm problem, network partition, and the temporal network fragmentation to name few. These issues are principally due to the high mobility of vehicles and thus to the constantly changing topology of the network. In this paper, we present a survey of the existing P2P approaches in the context of VANETs including a comprehensive survey of the various problems related to data sharing in VANET using P2P techniques, along with their proposed solutions from the perspective of data access, information lookup, routing, and message management. In this study, we provide a classification of the studied approaches in terms of network overlays, information structures, and network coding. To differentiate between the discussed approaches, we include a comparative study based on the crucial parameters and identify and discuss the open research challenges in P2P techniques. To the best of our knowledge, this survey constitutes the first attempt at a comprehensive analysis of P2P approaches in VANETs, which describes more appropriately the state-of-art of this specific topic of research.

The noncausal/causal alternation is the pairing of two verb forms that refer to the same core event but differ in the absence vs. presence of a causer for this event (e.g. rise vs. raise, open (intr.) vs. open (tr.), die vs. kill). Languages differ in their overall preferences among the possible strategies for coding this alternation. This study uses machine-learning methods (clustering and tree-based computational classifiers) to investigate the predictive power of the noncausal/causal alternation for the genealogical affiliation of 38 languages belonging to the Atlantic, Mande and Mel families. The languages studied here belong to different contact areas in Senegal and its surroundings. The three families are all affiliated to the Niger-Congo phylum but display quite different typological profiles. The present paper elaborates on an earlier study that used a standard list of 18 verb pairs to establish the coding strategies in these languages. Apart from highlighting which coding strategies are favored in each family, our quantitative analyses show that the family affiliation of the 38 languages can be predicted with an accuracy above the majority baseline based on the information of the noncausal/causal alternation in the 18 verb pairs, but that the predictive power of verb pairs 1-9 is generally lower than of verb pairs 10-18. Our results confirm the hypothesis that the first group of verb pairs shows universal rather than lineage-specific tendencies concerning the noncausal/causal alternation. Furthermore, our analyses identify which of the 18 verb pairs (and their correlated coding strategies) have the highest predictive power. This study opens new avenues for identifying the relevant synchronic data for genealogical classification in historical linguistics. Future studies could replicate the same analysis in different language families to assess if our results are universal or specific to some language families., L’alternance noncausal/causal désigne l’appariement de deux formes verbales référant à un même événement mais se distinguant par l'absence vs. présence d’un causateur de l’événement (e.g. rise vs. raise, open (intr.) vs. open (tr.) ou die vs. kill). Les langues emploient diverses stratégies pour encoder cette opposition. Cette étude utilise des méthodes d’apprentissage-machine (clustering et classificateurs computationnels fondés sur des arbres de décision) afin d’analyser le pouvoir prédictif des stratégies d’encodage sur l'affiliation généalogique de 38 langues appartenant aux familles Atlantique, Mandé et Mel. Les langues de l’enquête sont essentiellement localisées au Sénégal et dans ses environs où existent des zones de contact. Ces trois familles relèvent du même phylum Niger-Congo mais présentent des profils typologiques assez différents. Le traitement de l’alternance causale comme marqueur généalogique est traité ici à partir d’une étude antérieure utilisant une liste standardisée de 18 paires de verbes pour établir les stratégies employées dans ces langues. En plus d'indiquer quelles stratégies sont favorisées dans chaque famille, nos analyses quantitatives montrent que l'affiliation généalogique des 38 langues peut être prédite avec une grande précision à partir des stratégies de codage de l’alternance causale utilisées pour les 18 paires de verbes, mais que le pouvoir prédictif des paires de verbes 1-9 est généralement inférieur à celui des paires de verbes 10-18. Ces résultats confirment l'hypothèse selon laquelle le premier groupe de verbes montre des tendances universelles plutôt qu’une variation interlinguistique dans le marquage de l’alternance noncausal/causal. De plus, notre analyse identifie pour ces familles de langues quelles paires de verbes spécifiques parmi les 18 sélectionnées ont le pouvoir prédictif le plus élevé. Cette approche ouvre de nouvelles voies pour résoudre un problème fondamental de la linguistique historique, celui des filtres nécessaires pour trier les données synchroniques pertinentes pour la classification généalogique. Des études futures pourraient reproduire la même analyse sur différentes familles de langues pour évaluer si ces résultats sont universels ou spécifiques à certaines familles de langues.

International audience; Neurodevelopmental disorders (NDDs) constitute a major health issue with >10% of the general worldwide population affected by at least one of these conditions-such as Autism Spectrum Disorders (ASD) and Attention Deficit Hyperactivity Disorders (ADHD). Each NDD is particularly complex to dissect for several reasons, including a high prevalence of comorbidities and a substantial heterogeneity of the clinical presentation. At the genetic level, several thousands of genes have been identified (polygenicity), while a part of them was already involved in other psychiatric conditions (pleiotropy). Given these multiple sources of variance, gathering sufficient data for the proper application and evaluation of machine learning (ML) techniques is essential but challenging. In this chapter, we offer an overview of the ML methods most widely used to tackle NDDs complexity-from stratification techniques to diagnosis prediction. We point out challenges specific to NDDs such as early diagnosis, that can benefit from the recent advances in the ML field. These techniques also have the potential to delineate homogeneous subgroups of patients that would enable a refined understanding of underlying physiopathology. We finally survey a selection of recent papers that we consider as particularly representative of the opportunities offered by contemporary ML techniques applied to large open datasets, or that illustrate the challenges faced by current approaches to be addressed in the near future.

In recent years, Natural Language Processing (NLP) has become very popular withthe scientific community and also with industry because of the numerous opportunitiesit offers. Indeed, nowadays we are submerged by information and this raises the questionof its analysis. This is the direction we have taken in our thesis, namely how to makethe result of a classification easily analysable and understandable.Chapter 1 provides a contextualisation of the thesis and its interest. In addition, wepresent its structure and a synthetic summary of the thesis’ work.Chapter 2 is here to recall the state of the art of text representation methods, someadapted modelling methods, as well as models for directional data.Then comes Chapter 3, which presents the data that has followed us throughout thethesis. This dataset, which we built, is freely available, contains the 8-K reports of theS&P 500 companies for 2015 to 2019.In Chapter 4, inspired by some papers that worked on 8-K, we try to predict themovement of a financial asset according to the publication of an 8-K report. Differenttechniques are tried, both for text representation and classification, and we show in ourcase that increasing the complexity of the models does not necessarily improve the results.Chapter 5 follows on from the previous chapter, in which we focus on the compari-son of text representations for exploratory analysis using a classification algorithm. Ouraim here is to analyse several text representations and to propose new ways of visualisation for an easier understanding of classification.To continue in this direction, in Chapter 6, we propose a mixture model of vonMises-Fisher distribution penalized by the l 1 norm. This leads to sparse prototypes thatimprove clustering interpretability. We derive an EM algorithm for this model and illustrate the interest of our approach on a real data set. Moreover, we propose a path-following method that allows, in adequacy with model selection criteria, to automatically select the penalization parameter., Le traitement automatique des langues (NLP) a connu ces dernières années un grandengouement auprès de la communauté scientifique mais aussi des industriels pour lesnombreuses opportunités offertes. En effet, nous sommes, de nos jours, submergés parles informations et par les différentes façons de les analyser. C’est dans cette voie quenous avons orienté notre thèse, à savoir comment rendre le résultat d’une classificationfacilement analysable et compréhensible.Le Chapitre 1 permettra une contextualisation de la thèse et de son intérêt tout eny présentant sa structure. De plus, un résumé synthétique des travaux de la thèse y estprésenté.Le Chapitre 2 présentera l’état de l’art des modèles de représentation de textes uti-lisés, quelques méthodes de modélisations adaptées ainsi que des modèles pour les don-nées directionnelles.Puis, le Chapitre 3 présentera les données qui nous ont accompagné tout au longde la thèse. Ce jeu de données, que nous avons constitué et qui est disponible librement,contient les rapports 8-K des entreprises du S&P 500 pour les années 2015 à 2019.Dans le Chapitre 4, en nous inspirant de certains articles traitant des 8-K, nousessayerons de prédire le mouvement d’un actif financier selon la publication d’un rapport8-K. Différentes techniques sont testées, tant pour la représentation de textes que pourla classification, et nous démontrerons que la complexification des modèles n’amélioreque très légèrement les résultats de ladite classification.Ensuite, dans le Chapitre 5, nous nous concentrerons sur la comparaison de représentations de textes en vue d’une analyse exploratoire à l’aide d’un algorithme declassification. Notre but est d’analyser plusieurs représentations de textes et de proposerde nouvelles manières de visualiser pour mieux appréhender la classification.De même, dans le Chapitre 6, nous proposerons un modèle de mélange de distribution de von Mises-Fisher pénalisé par la norme l 1 . Ceci permet d’améliorer l’interprétabilité des clusters obtenus grâce notamment à la parcimonie des moyennes directionnelles.Nous dériverons un algorithme EM pour ce modèle et nous illustrerons l’intérêt de notreapproche sur plusieurs jeux de données réelles. De plus, nous proposerons une méthodede suivi de chemin qui permet en adéquation de critères de sélection de modèles, desélectionner automatiquement le paramètre de pénalisation.

One of the most challenging facets of dynamic ride-sharing problems is the automated matching process, especially in large-scale networks where the number of received requests is huge. Matching users to trips is very challenging in real-time since it must happen very quickly while it must ensure high quality solutions. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new space-time shareability function to put the most shareable trips inside separate clusters. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems.

The goal of this paper is to show the interest of combining various text analysis techniques (shallow parsing, semantic analysis, etc.) and some information access techniques (indexing, classification, clustering, mapping)) to developp an information analysis system to be used and customized by non-specialists of documentary languages. The paper shows how these techniques can be integrated to for a process chain including : XML reformating, information extraction, clustering, mapping., L'objectif de cet article est de montrer l'intérêt de l'emploi combiné de techniques d'analyse du texte (segmentation, lexicale, syntaxique, sémantique) et de diverses techniques d'accès à l'information (index, classification, catégorisation, cartographie) pour le développement d'un système de recherche et d'analyse d'information qui soit adapté à des non-spécialistes des langages documentaires et qui s'intègre dans un processus de veille. L'article montre comment ces techniques interviennent dans les fonctions d'un système d'analyse de l'information. L'originalité se situe dans l'approche (intégration de composants de text mining) qui est détaillée : reformatage XML des documents, visualisation des résultats, en passant par l'extraction des caractéristiques des documents et la classification.

In this paper, we are interested in discovering genetic and environmental factors that are involved in multifactorial diseases. Experiments have been achieved by the Biological Institute of Lille and many data has been generated. To exploit these data, data mining tools are required and we propose a two-phase optimisation approach using a specific genetic algorithm. During the first step, we select significant features with a specific genetic algorithm. Then, during the second step, we cluster affected individuals according to the features selected by the first phase. The paper describes the specificities of the genetic problem that we are studying, and presents in detail the genetic algorithm that we have developed to deal with this very large size feature selection problem. Results on both artificial and real data are presented.

We discuss and extend a powerful, geometric framework to represent the set of portfolios, which identifies the space of asset allocations with the points lying in a convex polytope. Based on this viewpoint, we survey certain state-of-the-art tools from geometric and statistical computing to handle important and difficult problems in digital finance. Although our tools are quite general, in this paper, we focus on two specific questions. The first concerns crisis detection, which is of prime interest for the public in general and for policy makers in particular because of the significant impact that crises have on the economy. Certain features in stock markets lead to this type of anomaly detection: Given the assets' returns, we describe the relationship between portfolios' return and volatility by means of a copula, without making any assumption on investors' strategies. We examine a recent method relying on copulae to construct an appropriate indicator that allows us to automate crisis detection. On real data the indicator detects all past crashes in the cryptocurrency market and from the DJ600-Europe index, from 1990 to 2008, the indicator identifies correctly 4 crises and issues one false positive for which we offer an explanation. Our second contribution is to introduce an original computational framework to model asset allocation strategies, which is of independent interest for digital finance and its applications. Our approach addresses the crucial question of evaluating portfolio management, and is relevant the individual managers as well as financial institutions. To evaluate portfolio performance, we provide a new portfolio score, based on the aforementioned framework and concepts. In particular, it relies on statistical properties of portfolios, and we show how they can be computed efficiently.

International audience; This paper studies a statistical model for heteroscedastic (i.e., power fluctuating) signals embedded in white Gaussian noise. Using the Riemannian geometry theory, we propose an unified approach to tackle several problems related to this model. The first axis of contribution concerns parameters (signal subspace and power factors) estimation, for which we derive intrinsic Cramér-Rao bounds and propose a flexible Riemannian optimization algorithmic framework in order to compute the maximum likelihood estimator (as well as other cost functions involving the parameters). Interestingly, the obtained bounds are in closed forms and interpretable in terms of problem's dimensions and SNR. The second axis of contribution concerns the problem of clustering data assuming a mixture of heteroscedastic signals model, for which we generalize the Euclidean K-means++ to the considered Riemannian parameter space. We propose an application of the resulting clustering algorithm on the Indian Pines segmentation problem benchmark.

International audience; In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

International audience; Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional centerbased unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitative metric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clustering methods and a referential gene signature used in the literature, confirm state of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times better mathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

International audience; In this paper, we consider the problem of learning a graph structure from multivariate signals, known as graph signals. Such signals are multivariate observations carrying measurements corresponding to the nodes of an unknown graph, which we desire to infer. They are assumed to enjoy a sparse representation in the graph spectral domain, a feature which is known to carry information related to the cluster structure of a graph. The signals are also assumed to behave smoothly with respect to the underlying graph structure. For the graph learning problem, we propose a new optimization program to learn the Laplacian of this graph and provide two algorithms to solve it, called IGL-3SR and FGL-3SR. Based on a 3-step alternating procedure, both algorithms rely on standard minimization methods-such as manifold gradient descent or linear programming-and have lower complexity compared to state-of-the-art algorithms. While IGL-3SR ensures convergence, FGL-3SR acts as a relaxation and is significantly faster since its alternating process relies on multiple closed-form solutions. Both algorithms are evaluated on synthetic and real data. They are shown to perform as good or better than their competitors in terms of both numerical performance and scalability. Finally, we present a probabilistic interpretation of the proposed optimization program as a Factor Analysis Model.

International audience; Automated brain tumor segmentation is challenging given the tumor's variability in size, shape, and image intensity. This paper focuses on the fusion of multimodal information coming from different Magnetic Resonance (MR) imaging sequences. We argue it is important to exploit all the modality complementarity to better segment and later determine the aggressiveness of tumors. However, simply concatenating the multimodal data as channels of a single image generates a high volume of redundant information. Therefore, we propose a supervoxel-based approach that regroups pixels sharing perceptually similar information across the different modalities to produce a single coherent oversegmentation. To further reduce redundant information while keeping meaningful borders, we include a variance constraint and a supervoxel merging step. Our experimental validation shows that the proposed merging strategy produces high-quality clustering results useful for brain tumor segmentation. Indeed, our method reaches an ASA score of 0.712 compared to 0.316 for the monomodal approach, indicating that the supervoxels accommodate well tumor boundaries. Our approach also improves by 11.5% the Global Score (GS), showing clusters effectively group pixels similar in intensity and texture.

International audience; Community detection has become an important research direction for data mining in complex networks. It aims to identify topological structures and discover patterns in complex networks, which presents an important problem of great significance. Prediction of communities from Protein-Protein Interaction (PPI) networks is important problem in system biology as they control different cellular functions. These networks represent a set of proteins that collaborate at the same cellular function. With the increment of genome-scale protein-protein interaction data for different species, various computational methods focus on identifying protein community from PPI networks. In this paper, we are interested in evaluating the proposed genetic algorithm GA-PPI-Net and three clustering methods for community detection. In the computational tests carried out in this work, the proposed genetic algorithm achieved excellent results to detect existing or even new communities from PPI networks.

BackgroundIntegrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are generally heterogeneous: it is a common place to gather data in the form of trees, networks or factorial maps, as these representations all have an appealing visual interpretation that helps to study grouping patterns and interactions between entities. The question we aim to answer in this paper is that of the integration of such representations.ResultsTo this end, we provide a simple procedure to compare data with various types, in particular trees or networks, that relies essentially on two steps: the first step projects the representations into a common coordinate system; the second step then uses a multi-table integration approach to compare the projected data. We rely on efficient and well-known methodologies for each step: the projection step is achieved by retrieving a distance matrix for each representation form and then applying multidimensional scaling to provide a new set of coordinates from all the pairwise distances. The integration step is then achieved by applying a multiple factor analysis to the multiple tables of the new coordinates. This procedure provides tools to integrate and compare data available, for instance, as tree or network structures. Our approach is complementary to kernel methods, traditionally used to answer the same question.ConclusionOur approach is evaluated on simulation and used to analyze two real-world data sets: first, we compare several clusterings for different cell-types obtained from a transcriptomics single-cell data set in mouse embryos; second, we use our procedure to aggregate a multi-table data set from the TCGA breast cancer database, in order to compare several protein networks inferred for different breast cancer subtypes.

Soundscapes are increasingly used as innovative entry doors in environmental studies. Facing huge libraries of sound files which cannot be processed manually, acoustic indices provide an overview to the information contained in them, as well as to allow for automatic processing. Studies dealing with such indices have, however, focused more on specific topics or indices than on the overall characteristics of the soundscapes they were analyzing. The aim of this paper is to propose a holistic approach to soundscapes. Our hypothesis is that sufficient number and variety of indices can help frame the characteristics of sound environments and that the use of clustering algorithms allows us to group them in families and study the distribution of those across space and time, revealing a geography that will not necessarily coincide with the obvious landscape/visual geography. To demonstrate this point, we have run indices analysis and classification on a soundscapes database recorded in the Sonoran Desert region (Southeastern Arizona, USA). The results show that sound indices reveal temporal variations and patterns of soundscapes and point out to sometimes surprising similarities between otherwise different environments. As sound indices capture a wealth of information which characterizes the environment at a given place and time, they could be used as proxies to continuous monitoring without having to store extreme amounts of data.