Stochastic graphlet embedding

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordGraph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semistructured data as graphs where nodes correspond to primitives (parts, interest points, and segments) and edges characterize the relationships between these primitives. However, these nonvectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of - explicit/implicit - graph vectorization and embedding. This embedding process should be resilient to intraclass graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have a positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.Agence Nationale de la Recherche (ANR

UTILISATION D’UN MODELE HYBRIDE BASE SUR LA RLMS ET LES RNA-PMC POUR LA PREDICTION DES PARAMETRES INDICATEURS DE LA QUALITE DES EAUX SOUTERRAINES CAS DE LA NAPPE DE SOUSS-MASSA- MAROC

This work describes a new approach to the prediction of the parameters (microbiological, physical-chemical) groundwater quality indicators in the water table of Souss-Massa Morocco. The originality of this work lies in the application of a hybrid model based on the Stepwise Multiple Linear Regression and Neural Networks Multilayer Perceptron type. During the first stage, conventional statistical models namely the Stepwise Multiple Linear Regression was applied to a database that consists of eleven vectors as input vectors of the model and three vectors as the model output vectors in order to optimize the explanatory variables. In a second step, the optimized data base in the first step was used to construct a non recurring multi-layer network, the weights of the network connections are determined using the gradient back propagation algorithm. The data used as a database (learning, testing and validation) of the hybrid model are those relating to the analysis of 52 groundwater samples collected at several stations distributed in space and in time, of the groundwater Souss-Massa Morocco. The dependent variables (to explain or predict), which are three in number, are the Electrical Conductivity EC, the amount of Fecal Coliforms CF and Organic Matter MO

CONTRIBUTION A L’ETUDE HYDROGEOPHYSIQUE DES RIDES PRERIFAINES (MAROC) Apport du SIG et de la géophysique Appliquée

Le présent travail concerne l’étude hydrogéophysique par intégration de l’outil SIG des rides prérifaines. Ces dernières sont caractérisées par une série sédimentaire jurassique très variée et surmontée en légère discordance angulaire par les dépôts transgressifs du Miocène supérieur.L’étude hydrogéologique sur la base des campagnes piézométriques et de caractérisation physico-chimique a permis de mettre en évidence le sens des écoulements souterrains dans les différents aquifères qui présentent en général une bonne qualité des eaux, ainsi qu’une fracturation et perméabilité importante qui permet d’avoir des débits allant jusqu’au 40l/s.Les résultats des campagnes géophysiques par prospection sismique réalisée dans les rides prérifaines et plus précisément celles réalisées sur le secteur d’étude couvrant le synclinal de Ain Kerma et les rides ont permis l’élaboration de plusieurs cartes interprétatives qui ont permis de suivre les horizons aquifères potentiels avec la mise en évidence des failles qui ont structuré les rides.La tomographie électrique appliquée aux deux sites des sources thermales de Moulay Driss Zerhoun et celle de Moulay Yacoub Sghir a permis de contribuer à la compréhension hydrogéologique des rides a permis de mettre en évidence les failles d'alimentation de ces sources

UTILISATION D’UN MODELE HYBRIDE BASE SUR LA RLMS ET LES RNA-PMC POUR LA PREDICTION DES PARAMETRES INDICATEURS DE LA QUALITE DES EAUX SOUTERRAINES CAS DE LA NAPPE DE SOUSS-MASSA- MAROC

This work describes a new approach to the prediction of the parameters (microbiological, physical-chemical) groundwater quality indicators in the water table of Souss-Massa Morocco. The originality of this work lies in the application of a hybrid model based on the Stepwise Multiple Linear Regression and Neural Networks Multilayer Perceptron type. During the first stage, conventional statistical models namely the Stepwise Multiple Linear Regression was applied to a database that consists of eleven vectors as input vectors of the model and three vectors as the model output vectors in order to optimize the explanatory variables. In a second step, the optimized data base in the first step was used to construct a non recurring multi-layer network, the weights of the network connections are determined using the gradient back propagation algorithm. The data used as a database (learning, testing and validation) of the hybrid model are those relating to the analysis of 52 groundwater samples collected at several stations distributed in space and in time, of the groundwater Souss-Massa Morocco. The dependent variables (to explain or predict), which are three in number, are the Electrical Conductivity EC, the amount of Fecal Coliforms CF and Organic Matter MO

Knowledge transfer for scene-specific motion prediction

When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. In this work we exploit the interplay between these two key elements to predict scene-specific motion patterns. First, we extract patch descriptors encoding the probability of moving to the adjacent patches, and the probability of being in that particular patch or changing behavior. Then, we introduce a Dynamic Bayesian Network which exploits this scene specific knowledge for trajectory prediction. Experimental results demonstrate that our method is able to accurately predict trajectories and transfer predictions to a novel scene characterized by similar elements

Context Dependent SVMs for Interconnected Image Network Annotation

The exponential growth of interconnected networks, such as Flickr, currently makes them the standard way to share and explore data where users put contents and refer to others. These interconnections create valuable information in order to enhance the performance of many tasks in information retrieval including ranking and annotation. We introduce in this paper a novel image annotation framework based on support vector machines (SVMs) and a new class of kernels referred to as context-dependent. The method goes beyond the naive use of the intrinsic low level features (such as color, texture, shape, etc.) and context-free kernels, in order to design a kernel function applicable to interconnected databases such as social networks. The main contribution of our method includes a variational framework which helps designing this function using both intrinsic features and the underlying contextual information. This function also converges to a positive definite fixed-point, usable for SVM training and other kernel methods. When plugged in SVMs, our context-dependent kernel consistently improves the performance of image annotation, compared to context-free kernels, on hundreds of thousands of Flickr images.Hichem Sahbi, Xi Lihttp://www.acmmm10.org

Context-based support vector machines for interconnected image annotation

We introduce in this paper a novel image annotation approach based on support vector machines (SVMs) and a new class of kernels referred to as context-dependent. The method goes beyond the naive use of the intrinsic low level features (such as color, texture, shape, etc.) and context-free kernels, in order to design a kernel function applicable to interconnected databases such as social networks. The main contribution of our method includes (i) a variational approach which helps designing this function using both intrinsic features and the underlying contextual information resulting from different links and (ii) the proof of convergence of our kernel to a positive definite fixed-point, usable for SVM training and other kernel methods. When plugged in SVMs, our context-dependent kernel consistently improves the performance of image annotation, compared to context-free kernels, on hundreds of thousands of Flickr images.Hichem Sahbi and Xi L