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Polarization-based Tests of Gravity with the Stochastic Gravitational-Wave Background
No full textInternational audienceThe direct observation of gravitational waves with Advanced LIGO and Advanced Virgo offers novel opportunities to test general relativity in strong-field, highly dynamical regimes. One such opportunity is the measurement of gravitational-wave polarizations. While general relativity predicts only two tensor gravitational-wave polarizations, general metric theories of gravity allow for up to four additional vector and scalar modes. The detection of these alternative polarizations would represent a clear violation of general relativity. The LIGO-Virgo detection of the binary black hole merger GW170814 has recently offered the first direct constraints on the polarization of gravitational waves. The current generation of ground-based detectors, however, is limited in its ability to sensitively determine the polarization content of transient gravitational-wave signals. Observation of the stochastic gravitational-wave background, in contrast, offers a means of directly measuring generic gravitational-wave polarizations. The stochastic background, arising from the superposition of many individually unresolvable gravitational-wave signals, may be detectable by Advanced LIGO at design sensitivity. In this paper, we present a Bayesian method with which to detect and characterize the polarization of the stochastic background. We explore prospects for estimating parameters of the background and quantify the limits that Advanced LIGO can place on vector and scalar polarizations in the absence of a detection. Finally, we investigate how the introduction of new terrestrial detectors like Advanced Virgo aid in our ability to detect or constrain alternative polarizations in the stochastic background. We find that, although the addition of Advanced Virgo does not notably improve detection prospects, it may dramatically improve our ability to estimate the parameters of backgrounds of mixed polarization
Mise en place d’un système d’information géographique pour la détection précoce et la prédiction des épidémies de paludisme à Madagascar
No full textWe describe a Malaria Early Warning System (MEWS) using various epidemic thresholds and a forecasting component with the support of recent technologies to improve the performance of a sentinel MEWS. Malaria-related data from sentinel sites collected by Short Message Service are automatically stored in a database hosted on a server at Institut Pasteur de Madagascar. Concomitantly our system routinely and automatically acquires site specific satellite weather data related to changes in malaria prevalence such as temperature, rainfall and Normalized Difference Vegetation Index (NDVI). A Malaria Control Intervention data base has also been. This system has already demonstrated its ability to detect a malaria outbreak in southeastern part of Madagascar in 2014. In a second time, we conducted a study to assess the relationship between the effectiveness of mass campaign of long-lasting insecticidal nets (LLIN) over time and malaria outbreaks identified in Madagascar from 2009 to 2015 through the Sentinel surveillance system. This study showed that the difference between efficacy and effectiveness may result in gaps in service coverage during the subsequent years contributing to malaria rebound well before the replacement of the LLINs and highlights the need of continuous distribution mechanism of LLINs.This work aims to maximize the usefulness of a sentinel surveillance system to predict and detect epidemics in limited-resource environments, to guide any changes in the orientation of malaria control programs and to provide practical examples and suggestions for use in other systems or settings.Cette thèse a permis la mise en place d’un système d’alerte précoce des épidémies de paludisme basé sur le système de surveillance sentinelle proposant différents seuils épidémiques et un modèle de prédiction. Les données collectées quotidiennement par SMS sont automatiquement stockées sur un serveur dédié. Concomitamment, le système acquiert systématiquement et de manière automatique des données satellitaires météorologiques sur chaque site sentinelle en lien avec les changements de prévalence du paludisme, tels que la température, les précipitations et l'indice de végétation (eng. NDVI). Une base de données des interventions contre le paludisme a également été créée. Ce système a déjà démontré sa capacité à détecter une épidémie de paludisme dans le sud-est du pays en 2014. Deuxièmement, nous avons réalisé une étude pour évaluer la relation entre la durée de l'efficacité de la campagne de masse des moustiquaires imprégnées d'insecticide à effet longue durée (MILD) et les épidémies de paludisme identifiées à Madagascar de 2009 à 2015 par le système de surveillance sentinelle. Cette étude a montré que la différence entre l'efficacité théorique et l’efficacité réelle peut entraîner des lacunes dans la couverture des services pendant les années suivantes, contribuant au rebond du paludisme et souligne la nécessité de mise en place de mécanisme de distribution continue de moustiquaires. Ce travail vise à maximiser l'utilité d'un système de surveillance sentinelle dans des milieux à ressources limitées, guider les changements dans l'orientation des programmes de lutte et fournir des exemples pratiques pour son utilisation dans d'autres systèmes ou contextes
Surcharge de travail en fonction de l'environnement de conduite, de l'expérience des conducteurs et de leur état interne
No full textThe factors that lead to young drivers having accidents have been the subject of much research. In this article, we review the current literature concerning the links between the complexity of the driving environment, driving experience, workload, anxiety, vigilance, and driving performance. First, we outline the features that characterize the environment complexity and the drivers' experience. Automatic and controlled processing is typically found in simple and complex environments, respectively. It varies as a function of driving experience automation that increases with experience. The lowest levels of cognitive load and best performance are found among more experienced drivers compared to novices. Secondly, overload can be explained as a function of the environment complexity and driving experience. Several studies confirm theories stating that simple and very complex environments are particularly prone to overload; at the same time, workload is lower, and performance is better for experienced drivers compared to novices. Finally, we examine the literature in order to understand how levels of anxiety and vigilance modulate the effects of environmental complexity and a driver's experience on overload. According to the literature, costly environments (i.e. simple and very complex), combined with a lack of experience can result in high energy expenditure. A high level of anxiety and a lack of vigilance can thus impair performance through an increase in workload. Moreover, the increased workload may be directly due to the costly environment and lack of experience, which can increase energy expenditure, raise anxiety, and lower vigilance. Drawing up on various theories and empirical studies that find a link between these factors, the aim is to establish a model of the links between the characteristics of the driving environment and the individual characteristics that lead to road accidents. That will make it possible to develop specific ways to raise awareness, and new driving training modules.Les facteurs qui conduisent à l'accidentologie des jeunes conducteurs ont fait l'objet de nombreuses recherches. Dans cet article, nous passons en revue la littérature actuelle concernant les liens entre la complexité de l'environnement de conduite, l'expérience de conduite, la charge de travail, l'anxiété, la vigilance et la performance de conduite. Tout d'abord, nous décrivons les caractéristiques qui caractérisent la complexité de l'environnement et l'expérience des conducteurs. Le traitement automatique et contrôlé est généralement trouvé dans des environnements simples et complexes, respectivement. Il varie en fonction de l'automatisation de l'expérience de conduite qui augmente avec l'expérience. Les niveaux les plus bas de charge cognitive et les meilleures performances se trouvent chez les conducteurs plus expérimentés par rapport aux novices. Deuxièmement, la surcharge peut s'expliquer en fonction de la complexité de l'environnement et de l'expérience de conduite. Plusieurs études confirment les théories selon lesquelles les environnements simples et très complexes sont particulièrement sujets à la surcharge; dans le même temps, la charge de travail est plus faible et la performance est meilleure pour les conducteurs expérimentés que pour les novices. Enfin, nous examinons la littérature afin de comprendre comment les niveaux d'anxiété et de vigilance modulent les effets de la complexité environnementale et de l'expérience du conducteur en surcharge. Selon la littérature, des environnements coûteux (c'est-à-dire simples et très complexes), combinés à un manque d'expérience peuvent entraîner une dépense énergétique élevée. Un haut niveau d'anxiété et un manque de vigilance peuvent donc nuire à la performance en augmentant la charge de travail. De plus, l'augmentation de la charge de travail peut être directement attribuable à l'environnement coûteux et au manque d'expérience, ce qui peut augmenter les dépenses énergétiques, accroître l'anxiété et diminuer la vigilance. En s'appuyant sur diverses théories et études empiriques qui établissent un lien entre ces facteurs, il s'agit d'établir un modèle des liens entre les caractéristiques de l'environnement de conduite et les caractéristiques individuelles conduisant aux accidents de la route. Cela permettra de développer des moyens spécifiques de sensibilisation, et de nouveaux modules de formation à la conduite
Le défi des 1001 graphes
No full textNational audienceDans un graphe, existe-t-il un circuit visitant chaque sommet une fois et une seule ? Une question difficile pour certains graphes...Cet article explique comment les auteurs ont abordé et remporté la compétition internationale organisée par la Flinders University d'Adelaïde (Australie), intitulée FHCP Challenge, sur le problème du cycle hamiltonien dans les graphes
The VIMOS Public Extragalactic Redshift Survey (VIPERS). The distinct build-up of dense and normal massive passive galaxies
No full text14 pages, 13 figures, submitted to A&AInternational audienceWe use the final data from the VIPERS redshift survey to extract an unparalleled sample of more than 2000 massive M > 10^11 M_sun passive galaxies (MPGs) at redshift 0.5 < z < 1.0, based on their NUVrK colours. This enables us to investigate how the population of these objects was built up over cosmic time. We find that the evolution of the number density depends on the galaxy mean surface stellar mass density, Sigma. In particular, dense (Sigma > 2000 M_sun pc^-2) MPGs show a constant comoving number density over this redshift range, whilst this increases by a factor ~ 4 for the least dense objects, defined as having Sigma < 1000 M_sun pc^-2. We estimate stellar ages for the MPG population both fitting the Spectral Energy Distribution (SED) and through the D4000_n index, obtaining results in good agreement. Our findings are consistent with passive ageing of the stellar content of dense MPGs. We show that at any redshift the less dense MPGs are younger than dense ones and that their stellar populations evolve at a slower rate than predicted by passive evolution. This points to a scenario in which the overall population of MPGs was built up over the cosmic time by continuous addition of less dense galaxies: on top of an initial population of dense objects that passively evolves, new, larger, and younger MPGs continuously join the population at later epochs. Finally, we demonstrate that the observed increase in the number density of MPGs is totally accounted for by the observed decrease in the number density of correspondingly massive star forming galaxies (i.e. all the non-passive M > 10^11 M_sun objects). Such systems observed at z ~ 1 in VIPERS, therefore, represent the most plausible progenitors of the subsequent emerging class of larger MPGs
Sparse supernodal solver with low-rank compression for solving the frequency-domain Maxwell equations discretized by a high order HDG method
No full textNational audienceIn this talk, we present the use of PaStiX sparse direct solver in a Schwartz method for solving the frequencydomainMaxwell equations discretized by a high order HDG method. More precisely, the sparse solver is usedto solve a system on sub-domains while iterative refinement is performed to get the global solution. Recently,low-rank compression have been added to PaStiX in order to reduce the time-to-solution or the memory footprintof the solver. The resulting low-rank solver can be used either as a direct solver at a lower accuracy oras a good preconditionner for iterative methods. We will investigate the use of low-rank compression for thefrequency-domain Maxwell equations on large systems to experiment the compressibility of this equation
Defect-free InGaN nanowires on silicon whatever the indium composition
No full textInternational audienc
Making Sense of Indoor Spaces Using Semantic Web Mining and Situated Robot Perception
No full textInternational audienceIntelligent Autonomous Robots deployed in human environments must have understanding of the wide range of possible semantic identities associated with the spaces they inhabit – kitchens, living rooms, bathrooms, offices, garages, etc. We believe robots should learn this information through their own exploration and situated perception in order to uncover and exploit structure in their environments – structure that may not be apparent to human engineers, or that may emerge over time during a deployment. In this work, we combine semantic web-mining and situated robot perception to develop a system capable of assigning semantic categories to regions of space. This is accomplished by looking at web-mined relationships between room categories and objects identified by a Convolutional Neural Network trained on 1000 categories. Evaluated on real-world data, we show that our system exhibits several conceptual and technical advantages over similar systems, and uncovers semantic structure in the environment overlooked by ground-truth annotators
