Science & Mathematics

Ce travail de recherche se situe dans le cadre de la théorie de l’activité et dans celui de l’apprentissage social. Il porte sur les pratiques des internautes qui investissent du temps et du travail dans la mise en oeuvre et l’amélioration de la plate-forme d’enseignement en ligne « Moodle ». Même si leur objectif principal n’est pas l’apprentissage en tant que tel, leur activité nécessite de créer, de diffuser et d’acquérir certains types de savoirs et ainsi de se développer professionnellement. Alors que des informations circulent au sein des communautés virtuelles sur Internet, l’enquête ethnographique proposée considère l’activité « Moodle » comme une activité sociale située génératrice d’apprentissages. Elle repose d’une part sur un questionnaire sur les pratiques des intéressés (les Moodleurs) et d’autre part, s’appuie sur l’analyse des traces écrites qu’ils produisent sur le Web social. Les résultats permettent de mieux cerner qui sont ces « Moodleurs » et quelles sont leurs activités. Ils montrent aussi que si des apprentissages sont en jeu, ils concernent avant tout des savoir-faire. Il apparaît également qu’un développement professionnel est repérable dans cette dynamique collective interactionnelle qui ne s’effectue pas dans un cadre institutionnel classique.

We introduce a robust and high order strategy to perform the reinitialization in a level set framework. The reinitialization by closest points (RCP) method is based on geometric considerations. It relies on a gradient descent to find the closest points at the interface in order to solve the Eikonal equation and thus reinitializing the level set field. Furthermore, a new algorithm, also based on a similar geometric approach, is introduced to detect precisely all the ill-defined points of the level set. These points, also referred to as kinks, can mislead the gradient descent and more widely impact the accuracy of level set methods. This algorithm, coupled with the precise computation of the closest points of the interface, permits the novel method to be robust and accurate when performing the reinitialization every time step after solving the advection equation. Furthermore, they both require very few given parameters with the advantage of being based on a geometrical approach and independent of the application. The proposed method was tested on various benchmarks, and demonstrated equivalent or even better results compared to solving the Hamilton-Jacobi equation.

This work presents a Multi-Agent Aerial Transportation System (MAATS) with various aerial robots carrying a cable suspended load. First, the dynamical model of the coupled system is described using the Newton-Euler formalism, considering a mass connected to the aerial drones by rigid cables. The objective is to control the load's position to perform trajectory tracking using n drones. Hence, a hierarchical controller is designed using Lyapunov-like energy functions, where the resultant desired cable tension is employed as a virtual control input for the load control which in turn is distributed among the agents to obtain a suitable desired pose of each agent that ensures the trajectory tracking of the load. Then, position and attitude control of each drone is carried out using the desired pose. The stability analysis of the closed-loop system is provided, demonstrating the stability of the coupled system. Finally, the performance of the MAATS control strategy is verified in software-in-the-loop simulations, as well as in real-time experiments, considering 2 and 3 drones, and showing good behavior in spite of significant external perturbations acting on the cables, the load or the agents.

For three decades now, knowledge-based scoring functions that operate through the potential of mean force (PMF) approach have continuously proven useful for studying protein structures. Although these statistical potentials are not to be confused with their physics-based counterparts of the same name-i.e. PMFs obtained by molecular dynamics simulations-their particular success in assessing the native-like character of protein structure predictions has lead authors to consider the computed scores as approximations of the free energy. However, this physical justification is a matter of controversy since the beginning. Alternative interpretations based on Bayes' theorem have been proposed, but the misleading formalism that invokes the inverse Boltzmann law remains recurrent in the literature. In this article, we present a conceptually new method for ranking protein structure models by quality, which is (i) independent of any physics-based explanation and (ii) relevant to statistics and to a general definition of information gain. The theoretical development described in this study provides new insights into how statistical PMFs work, in comparison with our approach. To prove the concept, we have built interatomic distance-dependent scoring functions, based on the former and new equations, and compared their performance on an independent benchmark of 60,000 protein structures. The results demonstrate that our new formalism outperforms statistical PMFs in evaluating the quality of protein structural decoys. Therefore, this original type of score offers a possibility to improve the success of statistical PMFs in the various fields of structural biology where they are applied. The open-source code is available for download at https://gitlab.rpbs.univ-paris-diderot.fr/src/ig-score. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

Attribute-based access control (ABAC) provides a high level of flexibility that promotes security and information sharing. ABAC policy mining algorithms have potential to significantly reduce the cost of migration to ABAC, by partially automating the development of an ABAC policy from information about the existing access-control policy and attribute data. This paper presents an algorithm for mining ABAC policies from operation logs and attribute data. To the best of our knowledge, it is the first algorithm for this problem.

In this thesis, we propose a novel framework for knowledge-based segmentation using high-order Markov Random Fields (MRFs). We represent the shape model as a point distribution graphical model which encodes pose invariant shape priors through L1 sparse higher order cliques. Each triplet clique encodes the local shape variation statistics on the angle measurements which inherit invariance to global transformations (i.e. translation,rotation and scale). A sparse higher-order graph structure is learned through MRF training using dual decomposition, producing boosting efficiency while preserving its ability to represent the shape variation.We incorporate the prior knowledge in a novel framework for model-based segmentation.We address the segmentation problem as a maximum a posteriori (MAP) estimation in a probabilistic framework. A global MRF energy function is defined to jointly combine regional statistics, boundary support as well as shape prior knowledge for estimating the optimal model parameters (i.e. the positions of the control points). The pose-invariant priors are encoded in second-order MRF potentials, while regional statistics acting on a derived image feature space can be exactly factorized using Divergence theorem. Furthermore, we propose a novel framework for joint model-pixel segmentation towardsa more refined segmentation when exact boundary delineation is of interest. Aunified model-based and pixel-driven integrated graphical model is developed to combine both top-down and bottom-up modules simultaneously. The consistency between the model and the image space is introduced by a model decomposition which associates the model parts with pixels labeling. Both of the considered higher-order MRFs are optimized efficiently using state-of the-art MRF optimization algorithms. Promising results on computer vision and medical image applications demonstrate the potential of the proposed segmentation methods.

The aim of this study was to develop synthetic magnetite nanoparticles (nFe3O4) with preferential reactivity for trace elements (TE) for possible environmental applications as adsorbents. The synthetic magnetite materials obtained through the co-precipitation of both Fe 3+ and Fe 2+ ions (Fe 2+ / Fe 3+ = 0.5) were characterized by a set of complementary techniques such as X-ray diffraction, transmission and scanning electron microscopy, Fourier transform infrared and Raman spectroscopy, and BET adsorption method. The resulting nFe3O4 displayed a wide specific surface area (100 m 2 g-1) with particles reaching a size of about 10 nm, smaller than those of the well-crystallized commercial ones (cFe3O4) estimated at 80 nm while showing a BET surface area of 6.8 m² g-1. The adsorption properties of the synthetic nFe304 magnetite nanoparticles were characterized and compared to commercial analogues with the adsorption of both arsenic and copper. The equilibrium adsorption isotherms were properly fitted with Langmuir and Freundlich equation models. The maximum adsorption capacity for the solid phase, qm, obtained for the adsorption of arsenic onto nFe3O4 had an increase of 69.46% comparative to the value obtained for the adsorption of arsenic on cFe3O4. The results suggested that the iron oxide nanoparticles

Dans cet article nous analysons les échanges sur la plateforme StackOverlfow à l'aide de l'archive disponible en libre accès. Nous nous intéressons aux communautés R et Pandas, en focalisant sur les transferts d'utilisateurs d'un logiciel vers l'autre (Pandas étant de facture plus récente). Nos résultats montrent la rareté de ces transferts et le caractère très dual du support dans les groupes d'utilisateurs, dans lesquels des utilisateurs intéressés, souvent liés à des entreprises commerciales, ne font que répondre aux problèmes posés, tandis qu'une masse importante d'usagers ne fait qu'interroger à la cantonade, et reste dans des rôles passifs. Nous insistons sur le fait que ces réseaux numériques sont, en définitive, loin de se présenter comme des systèmes d'échanges généralisés dominés par la réciprocité et l'absence de hiérarchie.

In this work, a solution strategy based on the use of Padé approximants is investigated for efficient solution of parametric finite element problems such as, for example, frequency sweep analyses. An improvement to the Padé-based expansion of the solution vector components is proposed, suggesting the advantageous a priori estimate of the poles of the solution. This allows for the intervals of approximation to be chosen a priori in connection with the Padé approximants to be used. The choice of these approximants is supported by the Montessus de Ballore theorem, proving the convergenceof a series of approximants with fixed denominator degrees. An acoustic case study is presented in order to illustrate the potential of the approach proposed by the authors.

This paper studies the problem of global stabilization of discrete-time linear systems subject to input saturation and time delay. The considered time-delay systems are first transformed into delay-free systems based on prediction technique. Then, by utilizing saturation functions technique, the corresponding global stabilizing controllers are proposed for two special discrete-time linear systems-a chain of integrators and oscillators, and explicit conditions guaranteeing stability are also given. Both current and delayed feedback information are utilized in the controller design, and some free parameters are also introduced into these controllers. These advantages can help improve the control performance significantly. Subsequently, a systematic control design procedure for globally stabilizing general discrete-time linear systems subject to multiple inputs and/or multiple inputs delays is proposed. The design procedure is in an explicit and recursive way with explicit conditions guaranteeing stability being given, and thus is easier to use than the existing one. Finally, the effectiveness of the proposed approaches are illustrated by three numerical examples.

Combustion instabilities can develop in modern gas-turbines as large amplitude pressure oscillations coupled with heat release fluctuations. In extreme cases, they lead to irreversible damage which can destroy the combustor. Prediction and control of all acoustic modes of the configuration at the design stage are therefore required to avoid these instabilities. This is a challenging task because of the large number of parameters involved. This situation becomes even more complex when considering uncertainties of the underlying models and input parameters. The forward uncertainty quantification problem is addressed in the case of a single swirled burner combustor. First, a Helmholtz solver is used to analyze the thermoacoustic modes of the combustion chamber. The Flame Transfer Function measured experimentally is used as a flame model for the Helmholtz solver. Then, the frequency of oscillation and the growth rate of the first thermoacoustic mode are computed in 24 different operating points. Comparisons between experimental and numerical results show good agreements except for modes which are marginally stable/unstable. The main reason is that the uncertainties can arbitrary change the nature of these modes (stable vs unstable); in other words, the usual mode classification stable/unstable must be replaced by a more continuous description such as the risk factor, i.e. the probability for a mode to be unstable given the uncertainties on the input parameters. To do so, a Monte Carlo analysis is performed using 4000 Helmholtz simulations of a single experimental operating point but with random perturbations on the FTF parameters. This allows the computation of the risk factor associated to this acoustic mode. Finally, the analysis of the Monte Carlo database suggests that a reduced two-step UQ strategy may be efficient to deal with thermoacoustics in such a system. First, two bilinear surrogate models are tuned from a moderate number of Helmholtz solutions (a few tens). Then, these algebraic models are used to perform a Monte Carlo analysis at reduced cost and approximate the risk factor of the mode. The accuracy and efficiency of this reduced UQ strategy are assessed by comparing the reference risk factor given by the full Monte Carlo database and the approximate risk factor obtained by the surrogate models. It shows a good agreement which proves that reduced efficient methods can be used to predict unstable modes.

Personal Health Records (PHR) are user-friendly, online solutions that give patients a way of managing their own health information. Many of the current PHR systems allow storage providers to access patients’ data. Recently, architectures of storing PHRs in cloud have been proposed. However, privacy remains a major issue for patients. Consequently, it is a promising method to encrypt PHRs before outsourcing. Encrypting PHRs prevents health organizations from analyzing medical data. In this paper, we propose a protocol that would allow health organizations to produce statistical information about encrypted PHRs stored in the cloud. The protocol depends on two threshold homomorphic cryptosystems: Goldwasser-Micali (GM) and Paillier. It executes queries on Kd-trees that are constructed from encrypted health records. It also prevents patients from inferring what health organizations are concerned about. We experimentally evaluate the performance of the proposed protocol and report on the results of implementation.