A Hierarchical Bayesian Framework for Constructing Sparsity-inducing Priors

Variable selection techniques have become increasingly popular amongst statisticians due to an increased number of regression and classification applications involving high-dimensional data where we expect some predictors to be unimportant. In this context, Bayesian variable selection techniques involving Markov chain Monte Carlo exploration of the posterior distribution over models can be prohibitively computationally expensive and so there has been attention paid to quasi-Bayesian approaches such as maximum a posteriori (MAP) estimation using priors that induce sparsity in such estimates. We focus on this latter approach, expanding on the hierarchies proposed to date to provide a Bayesian interpretation and generalization of state-of-the-art penalized optimization approaches and providing simultaneously a natural way to include prior information about parameters within this framework. We give examples of how to use this hierarchy to compute MAP estimates for linear and logistic regression as well as sparse precision-matrix estimates in Gaussian graphical models. In addition, an adaptive group lasso method is derived using the framework.Comment: Submitted for publication; corrected typo

Mechanical response tissue analyzer for estimating bone strength

One of the major concerns for extended space flight is weakness of the long bones of the legs, composed primarily of cortical bone, that functions to provide mechanical support. The strength of cortical bone is due to its complex structure, described simplistically as cylinders of parallel osteons composed of layers of mineralized collagen. The reduced mechanical stresses during space flight or immobilization of bone on Earth reduces the mineral content, and changes the components of its matrix and structure so that its strength is reduced. Currently, the established clinical measures of bone strength are indirect. The measures are based on determinations of mineral density by means of radiography, photon absorptiometry, and quantitative computer tomography. While the mineral content of bone is essential to its strength, there is growing awareness of the limitations of the measurement as the sole predictor of fracture risk in metabolic bone diseases, especially limitations of the measurement as the sole predictor of fracture risk in metabolic bone diseases, especially osteoporosis. Other experimental methods in clinical trials that more directly evaluate the physical properties of bone, and do not require exposure to radiation, include ultrasound, acoustic emission, and low-frequency mechanical vibration. The last method can be considered a direct measure of the functional capacity of a long bone since it quantifies the mechanical response to a stimulus delivered directly to the bone. A low frequency vibration induces a response (impedance) curve with a minimum at the resonant frequency, that a few investigators use for the evaluation of the bone. An alternative approach, the method under consideration, is to use the response curve as the basis for determination of the bone bending stiffness EI (E is the intrinsic material property and I is the cross-sectional moment of inertia) and mass, fundamental mechanical properties of bone

Contrôle par percolation dynamique de la conductivité de nanocomposites polymères-nanotubes de carbone

L'effet de la percolation dynamique sur la conductivité de composites polymères chargés en nanotubes de carbone a été étudié. La réalisation d'étapes de recuits thermiques à l'état fondu a permis de mettre en évidence une augmentation graduelle de plusieurs ordres de grandeurs de la conductivité. Du point de vue applicatif la maitrise de la cinétique de percolation dynamique autorise le contrôle fin et localisé de la structuration du réseau de charges. Ceci a notamment conduit à une réduction des taux de charges critiques, une augmentation de la conductivité maximale des systèmes ainsi qu'à l'invention et la réalisation de matériaux à gradient de conductivité électrique

Effects of Rewarding and Unrewarding Experiences on the Response to Host-induced Plant Odors of the Generalist Parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)

Associative learning is known to modify foraging behavior in numerous parasitic wasps. This is in agreement with optimal foraging theory, which predicts that the wasps will adapt their responses to specific cues in accordance with the rewards they receive while perceiving these cues. Indeed, the generalist parasitoid Cotesia marginiventris shows increased attraction to a specific plant odor after perceiving this odor during contact with hosts. This positive associative learning is common among many parasitoids, but little is known about the effects of unrewarding host searching events on the attractiveness of odors. To study this, preferences of female C. marginiventris for herbivore-induced odors of three plant species were tested in a six-arm olfactometer after the wasps perceived one of these odors either i) without contacting any caterpillars, ii) while contacting the host caterpillar Spodoptera littoralis, or iii) while contacting the non-host caterpillar Pieris rapae. The results confirm the effects of positive associative learning, but showed no changes in innate responses to the host-induced odors after "negative” experiences. Hence, a positive association is made during an encounter with hosts, but unsuccessful host-foraging experiences do not necessarily lead to avoidance learning in this generalist parasitoi

Optimisation et contrôle de la transition dynamique de percolation au sein de matériaux nanostructurés

International audienceL'ajout de charges carbonées au sein de matrices polymères permet la mise en œuvre de composites aux propriétés électriques optimisées. La conductivité de ces matériaux dépend en grande partie de l'organisation des charges dans la matrice, notamment de la présence de réseaux percolants. L'objectif du présent travail est de comprendre les mécanismes de structuration des nanotubes de carbone au sein de différents milieux. L'architecture de ces réseaux de charges a principalement été révélée par le biais de mesures électriques et diélectriques. L'originalité de nos travaux réside dans l'utilisation de matrices liquides, notamment des huiles de silicone, a?n de s'affranchir des contraintes présentes dans les plastiques d'une part, et de simpli?er les processus de mise en œuvre d'autre part. Nos travaux révèlent une agrégation des charges au cours du temps, plus connue sous le nom de percolation dynamique. La conductivité de ces matériaux a ensuite été modélisée en fonction du temps et du taux de charge à partir de l'équation statique de Kirkpatrick. L'application d'un champ électrique a permis une augmentation de la conductivité ainsi qu'une diminution du seuil de percolation. En?n, une étude des paramètres intrinsèques de la matrice a révélé une forte in?uence de la viscosité et de la tension de surface sur la dispersion et la vitesse d'agrégation des nanotubes de carbone