On the automorphisms group of the asymptotic pants complex of an infinite surface of genus zero

The braided Thompson group $\mathcal B$ is an asymptotic mapping class group of a sphere punctured along the standard Cantor set, endowed with a rigid structure. Inspired from the case of finite type surfaces we consider a Hatcher-Thurston cell complex whose vertices are asymptotically trivial pants decompositions. We prove that the automorphism group $\hat{\mathcal B^{\frac{1}{2}}}$ of this complex is also an asymptotic mapping class group in a weaker sense. Moreover $\hat{\mathcal B^{\frac{1}{2}}}$ is obtained by $\mathcal B$ by first adding new elements called half-twists and further completing it.Comment: revised version,17p., 13 figure

Méthodes numériques et statistiques pour l'analyse de trajectoire dans un cadre de geométrie Riemannienne.

This PhD proposes new Riemannian geometry tools for the analysis of longitudinal observations of neuro-degenerative subjects. First, we propose a numerical scheme to compute the parallel transport along geodesics. This scheme is efficient as long as the co-metric can be computed efficiently. Then, we tackle the issue of Riemannian manifold learning. We provide some minimal theoretical sanity checks to illustrate that the procedure of Riemannian metric estimation can be relevant. Then, we propose to learn a Riemannian manifold so as to model subject's progressions as geodesics on this manifold. This allows fast inference, extrapolation and classification of the subjects.Cette thèse porte sur l'élaboration d'outils de géométrie riemannienne et de leur application en vue de la modélisation longitudinale de sujets atteints de maladies neuro-dégénératives. Dans une première partie, nous prouvons la convergence d'un schéma numérique pour le transport parallèle. Ce schéma reste efficace tant que l'inverse de la métrique peut être calculé rapidement. Dans une deuxième partie, nous proposons l'apprentissage une variété et une métrique riemannienne. Après quelques résultats théoriques encourageants, nous proposons d'optimiser la modélisation de progression de sujets comme des géodésiques sur cette variété

Non-injective representations of a closed surface group into PSL(2,R)PSL(2,\mathbb R)

Let $e$ denote the Euler class on the space $Hom(\Gamma_g, PSL(2,\mathbb R))$ of representations of the fundamental group $\Gamma_g$ of the closed surface $\Sigma_g$ of genus $g$. Goldman showed that the connected components of $Hom(\Gamma_g, PSL(2,\mathbb R))$ are precisely the inverse images $e^{-1}(k)$, for $2-2g\leq k\leq 2g-2$, and that the components of Euler class $2-2g$ and $2g-2$ consist of the injective representations whose image is a discrete subgroup of $PSL(2,\mathbb R)$. We prove that non-faithful representations are dense in all the other components. We show that the image of a discrete representation essentially determines its Euler class. Moreover, we show that for every genus and possible corresponding Euler class, there exist discrete representations.Comment: 15 pages, 2 figure

Comfort and energy consumption of hydronic heating radiant ceilings and walls based on CFD analysis

This article presents the methodology and results of a hybrid numerical optimization study of a heating ceiling and wall hydronic radiant panel system in a typical residential building located in Quebec City, Canada. The comfort and energy consumption of the system are the two figures of merit that are considered in the multiobjective optimization analysis. The main design variables are the position and dimension of the panels, and the fluid inlet temperature. The hybrid numerical method features a 2D CFD model of a typical empty room, coupled with a semi-analytic radiant panel model specially developed for coupling with CFD. This strategy allows considering the real room geometry, while providing at the same time accurate temperature profiles of the radiant panels and detailed temperature and comfort data field in the room. The results show that there is no unique optimal solution but rather a family of optimal designs (Pareto fronts) for which the solutions are trade-offs between the two objectives. When adjusting correctly the fluid inlet temperature, it is also possible to achieve nearly Pareto optimal solutions, even when reducing the total panel surface by 66%. This means that the temperature control of the fluid is the most important parameter for maximizing comfort and minimizing energy consumption of hydronic heating radiant panels

An Investigation of Response and Stimulus Modality Transfer Effects after Dual-Task Training in Younger and Older

It has been shown that dual-task training leads to significant improvement in dual-task performance in younger and older adults. However, the extent to which training benefits to untrained tasks requires further investigation. The present study assessed (a) whether dual-task training leads to cross-modality transfer in untrained tasks using new stimuli and/or motor responses modalities, (b) whether transfer effects are related to improved ability to prepare and maintain multiple task-set and/or enhanced response coordination, (c) whether there are age-related differences in transfer effects. Twenty-three younger and 23 older adults were randomly assigned to dual-task training or control conditions. All participants were assessed before and after training on three dual-task transfer conditions; (1) stimulus modality transfer (2) response modality transfer (3) stimulus and response modalities transfer task. Training group showed larger improvement than the control group in the three transfer dual-task conditions, which suggests that training leads to more than specific learning of stimuli/response associations. Attentional costs analyses showed that training led to improved dual-task cost, only in conditions that involved new stimuli or response modalities, but not both. Moreover, training did not lead to a reduced task-set cost in the transfer conditions, which suggests some limitations in transfer effects that can be expected. Overall, the present study supports the notion that cognitive plasticity for attentional control is preserved in late adulthood

Longitudinal autoencoder for multi-modal disease progression modelling

Imaging modalities and clinical measurement, as well as their time progression can be seen as heterogeneous observations of the same underlying disease process. The analysis of sequences of multi-modal observations, where not all modalities are present at each visit, is a challenging task. In this paper, we propose a multi-modal autoencoder for longitudinal data. The sequences of observations for each modality are encoded using a recurrent network into a latent variable. The variables for the different modalities are then fused into a common variable which describes a linear trajectory in a low-dimensional latent space. This latent space is mapped into the multi-modal observation space using separate decoders for each modality. We first illustrate the stability of the proposed model through simple scalar experiments. Then, we illustrate how information can be conveyed from one modality to refine predictions about the future using the learned autoencoder. Finally, we apply this approach to the prediction of future MRI for Alzheimer's patients

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results show high pixel-wise accuracy across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed and morphological metrics are extracted and compared against the literature. AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepsegComment: 14 pages, 7 figure

Determining the Complex Refractive Index of Materials in the Far-Infrared from Terahertz Time-Domain Data

Terahertz time‐domain spectroscopy is a well‐established technique to study the far‐infrared electromagnetic response of materials. Measurements are broadband, fast, and performed at room temperature. Moreover, compact systems are nowadays commercially available, which can be operated by nonspecialist staff. Thanks to the determination of the amplitude and phase of the recorded signals, both refractive index and absorption coefficient of the sample material can be obtained. However, determining these electromagnetic parameters should be performed cautiously when samples are more or less transparent. In this chapter, we explain how to extract the material parameters from terahertz time‐domain data. We list the main sources of error, and their contribution to uncertainties. We give rules to select the most adapted technique for an optimized characterization, depending on the transparency of the samples, and address the case of samples with strong absorption peaks or exhibiting scattering

Génération systématique de scénarios d'attaques contre des systèmes industriels

National audienceLes systèmes industriels (SCADA) sont la cible d'attaques informatiques depuis Stuxnet [4] en 2010. De part leur interaction avec le mode physique, leur protection est devenue une priorité pour les agences gouvernementales. Dans cet article, nous proposons une approche de modélisation d'attaquants dans un système industriel incluant la production automatique de scénarios d'attaques. Cette approche se focalise sur les capacités de l'attaquant et ses objectifs en fonc-tion des protocoles de communication auxquels il fait face. La description de l'approche est illustrée à l'aide d'un exemple

Filtrage et vérification de flux métiers dans les systèmes industriels

National audienceDe plus en plus d'attaques informatiques contre les systèmes indus-triels sont présentées par les médias. Ces systèmes tendent à devenir géo-graphiquement distribués et à communiquer via des réseaux vulnérables tels qu'Internet. Régissant de nos jours des domaines tels que la production et la distribution d'énergie, l'assainissement des eaux ou le nucléaire, la sécurité des systèmes industriels devient une priorité pour les gouver-nements. L'une des difficultés de la sécurisation des infrastructures in-dustrielles est la conciliation des propriétés de sécurité avec les attendus métiers en terme de flux. Pour ce faire, nous regardons comment filtrer les messages en tenant compte des aspects métiers. Ensuite, nous nous intéressons à la vérification formelle des propriétés des protocoles de communication industriels. Enfin nous proposons une approche Model-Based Testing permettant de générer des attaques informatiques contre des sys-tèmes industriels