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Generic Uniqueness of the Bias Vector of Mean-Payoff Zero-Sum Games
No full textInternational audienceUnder some ergodicity conditions, finite state space mean payoff zero-sum games can be solved using a nonlinear fixed point problem, involving a vector (bias or potential), which determines the optimal strategies. A basic issue is to check when the bias is unique. We show that this is always the case for generic values of the payments of the game. We also discuss the application of this result to the perturbation analysis of policy iteration
An a posteriori error estimator for shape optimization: application to EIT
No full textInternational audienc
MatVPC: A User-Friendly MATLAB-Based Tool for the Simulation and Evaluation of Systems Pharmacology Models
No full textInternational audienceQuantitative systems pharmacology (QSP) models are progressively entering the arena of contemporary pharmacology. The efficient implementation and evaluation of complex QSP models necessitates the development of flexible computational tools that are built into QSP mainstream software. To this end, we present MatVPC, a versatile MATLAB-based tool that accommodates QSP models of any complexity level. MatVPC executes Monte Carlo simulations as well as automatic construction of visual predictive checks (VPCs) and quantified VPCs (QVPCs). VPC is a model diagnostic tool that facilitates the evaluation of both the structural and the stochastic part of a model. It is constructed by superimposing the observations over the model simulations while accounting for both the interindivid-ual variability as well as the residual variability. 1 Once underutilized, 2 the VPC now is recognized as one of the most valuable model diagnostics in pharmacological model evaluation. 3–5 Its superiority over comparable diagnostic tools has been established 6 and reflected by the fact that regulatory agencies recommend it as one of the central model diagnostics.
Climatology of mid-latitude MSTID events observed by the DEMETER satellite in the period 2005-2010
No full textInternational audienceUsing plasma measurements from the CNES DEMETER micro-satellite, we have performed a global survey of ionospheric disturbances observed at middle and low latitudes on the nightime part of the DEMETER orbit in the local time sector 21.30-22.30 LT. This study encompasses the 6 years of the satellite operations, from 2005 to 2010, including years of moderate magnetic activity of solar cycles 23 and 24 and the deep solar minimum in 2009-2010. We report in this poster a statistical analysis of MSTID events characterized by quasi-periodic variations of the O+ density observed below ~ 40° geomagnetic latitudes with wavelengths ranging from 350 to 700 km. Although detected in both hemispheres they occur predominantly at southern latitudes with a rather strong peak over the Pacific ocean. A detailed analysis has shown that these events may be sorted in 4 categories according to their latitudinal extent. Most of them are restricted to a latitude band between ~ 15° and 40° geomagnetic in North or South but some of them extend from mid latitudes in one hemisphere to low latitude in the other hemisphere, thus spanning equatorial regions up to 5-10°. The apparent negative correlation with magnetic activity seems to indicate that most of these events are driven by AGW originating from low altitude atmospheric levels and not triggered by auroral phenomena. We shall present the seasonal and inter-annual variations showing significant changes associated with solar activity. Our results will be compared to other ground-based or satellite observations and our investigation pointed out a strong effect of these MSTID and their parent AGW on the electrodynamics of the low latitude ionosphere
Learning spatiotemporal trajectories from manifold-valued longitudinal data
No full textInternational audienceWe propose a Bayesian mixed-effects model to learn typical scenarios of changes from longitudinal manifold-valued data, namely repeated measurements of the same objects or individuals at several points in time. The model allows to estimate a group-average trajectory in the space of measurements. Random variations of this trajectory result from spatiotemporal transformations, which allow changes in the direction of the trajectory and in the pace at which trajectories are followed. The use of the tools of Riemannian geometry allows to derive a generic algorithm for any kind of data with smooth constraints, which lie therefore on a Riemannian manifold. Stochastic approximations of the Expectation-Maximization algorithm is used to estimate the model parameters in this highly non-linear setting.The method is used to estimate a data-driven model of the progressive impairments of cognitive functions during the onset of Alzheimer's disease. Experimental results show that the model correctly put into correspondence the age at which each individual was diagnosed with the disease, thus validating the fact that it effectively estimated a normative scenario of disease progression. Random effects provide unique insights into the variations in the ordering and timing of the succession of cognitive impairments across different individuals
Fouille de Données dans les Réseaux Sociaux et d’Information : Dynamiques et Applications
No full textNetworks (or graphs) have become ubiquitous as data from diverse disciplines can naturally be mapped to graph structures. The problem of extracting meaningful information from large scale graph data in an efficient and effective way has become crucial and challenging with several important applications and towards this end, graph mining and analysis methods constitute prominent tools. This dissertation contributes models, tools and observations to problems that arise in the area of mining social and information networks. We built upon computationally efficient graph mining methods in order to: (i) design models for analyzing the structure and dynamics of real-world networks towards unraveling properties that can further be used in practical applications; (ii) develop algorithmic tools for large-scale analytics on data with inherent (e.g., social networks) or without inherent (e.g., text) graph structure. In particular, for the former point we show how to model the engagement dynamics of large social networks and how to assess their vulnerability with respect to user departures from the network. In both cases, by unraveling the dynamics of real social networks, regularities and patterns about their structure and formation can be identified; such knowledge can further be used in various applications including churn prediction, anomaly detection and building robust social networking systems. For the latter, we examine how to identify influential users in complex networks, having direct applications to epidemic control and viral marketing and how to utilize graph mining techniques in order to enhance text analytics tasks and in particular the one of text categorization.Les réseaux (ou graphes) sont devenus omniprésents en raison de leur capacité à représenter naturellement les données trouvées dans de nombreuses et diverses disciplines. Extraire efficacement des informations pertinentes de graphes à grande échelle est un problème crucial et difficile ayant de nombreuses applications. À cette fin, les méthodes de fouille de données graphiques constituent des outils importants. Par l’introduction de nouveaux modèles et outils, et par la réalisation d’observations, cette thèse contribue à la résolution de problèmes qui se posent dans le domaine de la fouille de données provenant des réseaux sociaux et d'information. Nous utilisons des méthodes efficaces de fouille de données graphiques afin de: (i) concevoir des modèles pour l'analyse structurelle et dynamique des réseaux réels dans le but d’extraire des connaissances pouvant être utilisées en pratique, (ii) développer des outils algorithmiques pour l'analyse à grande échelle de données intrinsèquement graphiques (par exemple, réseaux sociaux) ou non intrinsèquement graphiques (par exemple, le texte). En particulier, pour le premier point, nous montrons comment modéliser la dynamique d'engagement au sein de grands réseaux sociaux et comment évaluer leur vulnérabilité par rapport aux départs des utilisateurs. Dans les deux cas, en mettant à jour la dynamique de réseaux sociaux réels, nous pouvons identifier des régularités et des motifs dans leur structure et leur formation. De telles connaissances trouvent de nombreuses applications comme la prévision du churn, la détection des anomalies et la construction de systèmes de réseautage robustes. Dans le deuxième point, nous nous concentrons sur l’identification des utilisateurs influents dans les réseaux complexes, avec des applications directes sur le contrôle des épidémies et le marketing viral, et sur l’utilisation de techniques de fouille de données graphiques pour améliorer les tâches d'analyse de texte et en particulier la classification du texte
A Strong Distillery
No full textInternational audienceAbstract machines for the strong evaluation of λ-terms (that is, under abstractions) are a mostly neglected topic, despite their use in the implementation of proof assistants and higher-order logic programming languages. This paper introduces a machine for the simplest form of strong evaluation, leftmost-outermost (call-by-name) evaluation to normal form, proving it correct, complete, and bounding its overhead. Such a machine, deemed Strong Milner Abstract Machine, is a variant of the KAM computing normal forms and using just one global environment. Its properties are studied via a special form of decoding, called a distillation, into the Linear Substitution Calculus, neatly reformulating the machine as a standard micro-step strategy for explicit substitutions, namely linear leftmost-outermost reduction, i.e. the extension to normal form of linear head reduction. Additionally, the overhead of the machine is shown to be linear both in the number of steps and in the size of the initial term, validating its design. The study highlights two distinguished features of strong machines, namely backtracking phases and their interactions with abstractions and environments
The role of convective overshooting clouds in tropical stratosphere-troposphere dynamical coupling
No full textInternational audienceThis paper investigates the role of deep convection and overshooting convective clouds in stratosphere–troposphere dynamical coupling in the tropics during two large major stratospheric sudden warming events in January 2009 and January 2010. During both events, convective activity and precipitation increased in the equatorial Southern Hemisphere as a result of a strengthening of the Brewer–Dobson circulation induced by enhanced stratospheric planetary wave activity. Correlation coefficients between variables related to the convective activity and the vertical velocity were calculated to identify the processes connecting stratospheric variability to the troposphere. Convective overshooting clouds showed a direct relationship to lower stratospheric upwelling at around 70–50 hPa. As the tropospheric circulation change lags behind that of the stratosphere, outgoing longwave radiation shows almost no simultaneous correlation with the stratospheric upwelling. This result suggests that the stratospheric circulation change first penetrates into the troposphere through the modulation of deep convective activity
Ammonia emissions from biomass burning: comparison between satellite-derived emissions and bottom-up inventories
No full textInternational audienc
S-wave Superconductivity in Optimally Doped SrTi 1−x Nb x O 3 Unveiled by Electron Irradiation
No full textInternational audienceWe report on a study of electric resistivity and magnetic susceptibility measurements in electron irradiated SrTi0.987Nb0.013O3 single crystals. Point-like defects, induced by electron irradiation, lead to an almost threefold enhancement of the residual resistivity, but barely affect the superconducting critical temperature (Tc). The pertinence of Anderson's theorem provides strong evidence for a s-wave superconducting order parameter. Stronger scattering leads to a reduction of the effective coherence length (ξ) and lifts the upper critical field (Hc2), with a characteristic length scale five times larger than electronic mean-free-path. Combined with thermal conductivity data pointing to multiple nodeless gaps, the current results identify optimally doped SrTi1−xNbxO3 as a multi-band s-wave superconductor with unusually long-range electrodynamics