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International audience; Graph clustering is one of the key techniques to understand structures that are presented in networks. In addition to clusters, bridges and outliers detection is also a critical task as it plays an important role in the analysis of networks. Recently, several graph clustering methods are developed and used in multiple application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on the structural clustering algorithm. Yet, this kind of algorithm is based on the structural similarity. This latter requires to parse all graph’ edges in order to compute the structural similarity. However, the height needs of similarity computing make this algorithm more adequate for small graphs, without significant support to deal with large-scale networks. In this paper, we propose a novel distributed graph clustering algorithm based on structural graph clustering. The experimental results show the efficiency in terms of running time of the proposed algorithm in large networks compared to existing structural graph clustering methods.

International audience; Decision trees are efficient means for building classification models due to the compressibility, simplicity and ease of interpretation of their results. However, during the construction phase of decision trees, the outputs are often large trees that are affected by many uncertainties in the data (particularity, noise and residual variation). Combining attribute selection and data sampling presents one of the most promising research directions to overcome decision tree construction problems. However, the search space composed of all possible combinations of subsets of training samples and attributes is extremely large. In this paper, a novel approach is presented that allows generating an optimized decision tree by selecting an optimal couple of training samples and attributes subsets for training. As the search space of candidate couples of training samples and attributes subsets is extremely large, we use particle swarm optimization to make the search of an “optimal” solution tractable. The selected optimized solution helps in avoiding over-fitting and complexity problems suffered in the construction phase of decision trees. We conducted an extensive experimental evaluation on 22 datasets from the UCI Machine Learning Repository. The obtained results show that the proposed approach outperforms state-of-the-art classical as well as evolutionary decision tree construction methods in terms of simplicity, accuracy, and F-measure. We further evaluate our approach on a real-world engineering application for condition monitoring of rotating machinery under severe non-stationary conditions. The obtained results showed that the proposed approach allowed to optimize the use of instantaneous angular speed to diagnose gears defects.

Ionizing-radiation-resistant bacteria (IRRB) could be used for bioremediation of radioactive wastes and in the therapeutic industry. Limited computational works are available for the prediction of bacterial ionizing radiation resistance (IRR). In this work, we present ABClass, an in silico approach that predicts if an unknown bacterium belongs to IRRB or ionizing-radiation-sensitive bacteria (IRSB). This approach is based on a multiple instance learning (MIL) formulation of the IRR prediction problem. It takes into account the relation between semantically related instances across bags. In ABClass, a preprocessing step is performed in order to extract substructures/motifs from each set of related sequences. These motifs are then used as attributes to construct a vector representation for each set of sequences. In order to compute partial prediction results, a discriminative classifier is applied to each sequence of the unknown bag and its correspondent related sequences in the learning dataset. Finally, an aggregation method is applied to generate the final result. The algorithm provides good overall accuracy rates. ABClass can be downloaded at the following link: http://homepages.loria.fr/SAridhi/software/MIL/.

International audience

International audience; Automatic protein function annotation is a challenging task that is fundamental in many medical applications. Indeed, the capability to predict whether a protein has a given function is a key step for disease understanding and drug design. For such reasons, many authors have proposed computational methods for protein function prediction. One key element that is present in many proposals is similarity functions. Such functions are often used to compute the pairwise similarity between two proteins. It is commonly accepted that proteins with similar structures share the same function. Nevertheless, no previous works have focused on proposing a similarity function that is specifically designed for protein function annotation. In this work, we analyze the best similarity functions for the protein function annotation task and propose a new one. We performed experiments in a simple pairwise similarity scenario and also using our proposal as part of a more complex protein function annotation method. Based on the results, we can state that our proposal is a valid alternative as a building block of many protein function annotation methods.

This paper proposes a semi-formal methodology for modeling and verification of analog circuits behavioral properties using multivariate optimization techniques. Analog circuit differential models are automatically extracted and their qualitative behavior is computed for interval-valued parameters, inputs and initial conditions. The method has the advantage of guaranteeing the rough enclosure of any possible dynamical behavior of analog circuits. The circuit behavioral properties are then verified on the generated transient response bounds. Experimental results show that the resulting state variable envelopes can be effectively employed for a sound verification of analog circuit properties, in an acceptable run-time.

Tectonic inversion in the Bouhedma-Boudouaou Mountains was investigated through recent field work and seismic lines interpretation calibrated with petroleum well data. Located to the Central-Southern Atlas of Tunisia, this area signed shortened intra-continental fold-and-thrust belts. Two dissymmetric anticlines characterize Bouhedma – Boudouaou major fold. These structures show a strong virgation respectively from E-W to NNE-SSW as a response to the interference between both tectonic inversion and tectonic inheritance. This complex geometry is driven by Mesozoic rifting, which marked an extensional inherited regime. A set of late Triassic-Early Jurassic E-W and NW-SE normal faults dipping respectively to the North and to the East seems to widely affect the overall geodynamic evolution of this domain. They result in major thickness changes across the hanging wall and the footwall blocks in response with the rifting activity. Tectonic inversion is inferred from convergence between African and European plates since late Cretaceous. During Serravalian - Tortonian event, NW-SE trending paroxysm led to: 1) folding of pre-inversion and syn-inversion strata, 2) reactivation of pre-existing normal faults to reverse ones and 3) orogeny of the main structures with NE-SW and E-W trending. The compressional feature still remains active during Quaternary event (Post-Villafranchian) with N-S trending compression. Contraction during inversion generates folding and internal deformation as well as Fault-Propagation-Fold and folding related strike.

Recently, graph mining approaches have become very popular, especially in certain domains such as bioinformatics, chemoinformatics and social networks. One of the most challenging tasks is frequent subgraph discovery. This task has been highly motivated by the tremendously increasing size of existing graph databases. Due to this fact, there is an urgent need of efficient and scaling approaches for frequent subgraph discovery. In this paper, we propose a novel approach for large-scale subgraph mining by means of a density-based partitioning technique, using the MapReduce framework. Our partitioning aims to balance computational load on a collection of machines. We experimentally show that our approach decreases significantly the execution time and scales the subgraph discovery process to large graph databases.

In the North African Atlas, Triassic sedimentary rocks exhibit a variety of deformation styles that are caused by the interaction between halokinesis, tectonics and sedimentation. This paper aims to present an integrated study documenting the occurrence of salt-tectonic processes in the Maknassy-Mezzouna orogenic system. The interpretation of both, field mapping and seismic data suggests the existence of a complex tectonic evolution of these Triassic evaporites , with the occurrence of Mesozoic extensional/transtensional and Tertiary compressional/transpressional tectonic regime accompanied by sedimentary loading. This evolution took place along three major events of either lateral or vertical migration of Triassic evaporites. The first one is tectonic-driven and is related to the extensional/transtensional tectonic regime, which occurred during the break-up of Pangea and rifting of Neotethyan Ocean in the Early Jurassic . The second remobilization of salt Triassic rock developed during the Cretaceous. The initial mobilization of the Triassic salt was probably induced by sedimentary loading with a limited contribution of tectonic forces. During its third stage of evolution, salt Triassic rocks evolve into salt sheets due to the shortening deformation occurred during Tertiary and Quaternary in relationship with the closure of the Neotethyan realm. Salt migration controls subsidence , generates depocenter shifts and causes thickness variation of the entire post-Triassic sedimentary sequence . The signature of salt Triassic syn-sedimentary control is confirmed by thickness reduction and abrupt variation of structural dip that affects all the sedimentary pile in the flanks of salt sheets. In seismic sections the syn-sedimentary salt tectonic control is observed by geometries like pinching out, thickness reduction, onlap termination geometry as well as unconformities identified along the flanks of the salt sheets.