Lidar detection of metallic species at the mesopause level

The measurement alkali species present in the atomic form at the mesopause level has been performed by lidar for more than ten years. Atomic and ionic calcium density profiles are obtained for 3 years by the same technique in the visible range, at 423 nm for atomic calcium, and 393 nm for ionic calcium Ca(+). The experimental set-up and the preliminary results have been presented elsewhere. The 423 nm wavelength is directly obtained by the emission of a dye laser pumped by the third harmonic of a Nd-YAG laser. For the generation of the 393 nm wavelength, frequency mixing was used: the emission at 624 nm of a dye laser pumped by the 2nd harmonic of a Nd-Yag laser is mixed with the fundamental infrared emission (remaining after frequency doubling), in a non-linear KDP crystal, which gives the 393 nm emission. The behavior of the two atomic species, calcium and sodium, which are in the same altitude range are compared. For 45% of the observations, no ionic calcium was detected: the ionic calcium abundance was thus below the detection threshold. Contrasting with the density profiles of the atomic species, sodium and calcium, the ionic calcium profile present important variations on small time scales. The main characteristics of theatomic and ionic calcium behaviors that can be deduced from the measurements made are given

Gossip-Based Video Streaming: Beyond Heterogeneous Bandwidth

Le stage porte sur les protocoles de dissémination de données en pair à pair. Les protocoles de dissémination de données sont des protocoles dont le but est de faire parvenir un flux de données émis en temps réel à un groupe de participants. Ils ont ainsi de nombreux domaines d'application, comme la mise a jour de bases de données, la diffusion de flux RSS ou encore le streaming audio ou vidéo. C'est sur ce dernier domaine que nous nous focaliserons. L'objectif du stage était d'améliorer HEAP, un protocole de diffusion de données pair-à-pair, dans plusieurs domaines, l'un d'entre eux étant la répartition de la charge en tenant compte de ces paramètres. Une autre amélioration était de doter HEAP d'un mécanisme d'estimation dynamique de la capacité des pairs, afin de lui permettre d'adapter dynamiquement la contribution de chaque pair en fonction de ses capacités réelles. En effet, HEAP adaptait la participation des noeuds à partir d'une valeur donnée au protocole, qui traduit généralement mal les capacités réelles des noeuds et ne permet pas de tenir compte des variations de celles-ci au cours du temps. Une troisième amélioration consistait à rendre le protocole conscient de la topologie du réseau. En effet, un réseau comme Internet est loin d'être homogène, certains points du réseau sont plus proches les uns des autres et certaines zones peuvent être surchargées, ce qui a un impact significatif sur la qualité de transferts des données. Il est donc important de tenir compte de ces irrégularités afin de favoriser les échanges de données entre pairs proches et d'éviter de générer du traffic supplémentaire dans les zones déjà surchargées

Compression of Deep Neural Networks on the Fly

Thanks to their state-of-the-art performance, deep neural networks are increasingly used for object recognition. To achieve these results, they use millions of parameters to be trained. However, when targeting embedded applications the size of these models becomes problematic. As a consequence, their usage on smartphones or other resource limited devices is prohibited. In this paper we introduce a novel compression method for deep neural networks that is performed during the learning phase. It consists in adding an extra regularization term to the cost function of fully-connected layers. We combine this method with Product Quantization (PQ) of the trained weights for higher savings in storage consumption. We evaluate our method on two data sets (MNIST and CIFAR10), on which we achieve significantly larger compression rates than state-of-the-art methods

Who'll Stop the Rain? Allocating Emissions Allowances for Free: Environmental Policy, Economics, and WTO Subsidy Law

This article investigates the environmental and economic impact of the free allocation of emissions allowances in Emissions Trading Schemes (ETSs) as well as its compatibility with trade law. Free allocation can facilitate the industry's gradual adjustment to an ETS and hence boost its acceptability. At the same time, however, the article shows that the economic and environmental benefits of free allocation are debatable. Moreover, the practice of free allocation possibly contravenes WTO law. The conclusion that free allowances may constitute an objectionable subsidy under WTO subsidy disciplines raises questions of law reform. Should the ETS be reformed to fit conventional trade imperatives, or should trade law be rethought so as to be responsive to contemporary environmental protection strategies? The article argues that, considering the questionable benefits of free allocation, any adjustment to trade law should be narrow and temporar

The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul

In the last two decades many random graph models have been proposed to extract knowledge from networks. Most of them look for communities or, more generally, clusters of vertices with homogeneous connection profiles. While the first models focused on networks with binary edges only, extensions now allow to deal with valued networks. Recently, new models were also introduced in order to characterize connection patterns in networks through mixed memberships. This work was motivated by the need of analyzing a historical network where a partition of the vertices is given and where edges are typed. A known partition is seen as a decomposition of a network into subgraphs that we propose to model using a stochastic model with unknown latent clusters. Each subgraph has its own mixing vector and sees its vertices associated to the clusters. The vertices then connect with a probability depending on the subgraphs only, while the types of edges are assumed to be sampled from the latent clusters. A variational Bayes expectation-maximization algorithm is proposed for inference as well as a model selection criterion for the estimation of the cluster number. Experiments are carried out on simulated data to assess the approach. The proposed methodology is then applied to an ecclesiastical network in Merovingian Gaul. An R code, called Rambo, implementing the inference algorithm is available from the authors upon request.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS691 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multi-scale Orderless Pooling of Deep Convolutional Activation Features

Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN). This scheme extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from image classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any joint training of prediction layers for a particular target dataset. In absolute terms, it achieves state-of-the-art results on the challenging SUN397 and MIT Indoor Scenes classification datasets, and competitive results on ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets

Determination of residual stress in nitrided steels with transmission angle-dispersive diffraction

Residual stress analysis with transmission angle-dispersive diffraction is carried out on nitrided steels. It aims evaluating residual stresses within the ferritic matrix but also within secondary phases such as nano-scale nitrides and polycrystalline cementite. Samples of various alloying elements contents are investigated. Results are compared to laboratory X-ray diffraction and micromechanical modeling of gas nitriding of steels

Optimisation de la nitruration gazeuse des aciers par une modélisation multiphysique

La nitruration gazeuse est un traitement thermochimique contrôlée par la température, le temps et le potentiel azote superficiel qui influencent de façon notable les propriétés méca- niques (dureté, contraintes résiduelles) et la durée de vie des pièces mécaniques traitées. Cet article propose une méthodologie permettant l’optimisation des paramètres de nitruration en utilisant un modèle multiphysique décrivant complètement le traitement : microstructure, mécanisme de diffusion/précipitation, dureté et génération des contraintes résiduelles.Thèse CIFRE Nit+