Mode decomposition and renormalization in semiclassical gravity

We compute the influence action for a system perturbatively coupled to a linear scalar field acting as the environment. Subtleties related to divergences that appear when summing over all the modes are made explicit and clarified. Being closely connected with models used in the literature, we show how to completely reconcile the results obtained in the context of stochastic semiclassical gravity when using mode decomposition with those obtained by other standard functional techniques.

Nanometer-scale oxidation of Si(100) surfaces by tapping mode atomic force microscopy

The nanometer¿scale oxidation of Si(100) surfaces in air is performed with an atomic force microscope working in tapping mode. Applying a positive voltage to the sample with respect to the tip, two kinds of modifications are induced on the sample: grown silicon oxide mounds less than 5 nm high and mounds higher than 10 nm (which are assumed to be gold depositions). The threshold voltage necessary to produce the modification is studied as a function of the average tip¿to¿sample distance.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

L 'évolution des réserves énergétiques chez les ouvrières et les sexués de la fourmi d 'Argentine (Iridomyrmex humilis) (Mayr): une stratégie en relation avec le mode de fondation

Le poids sec, le rapport corps gras/poids sec et le contenu énergétique ou dernier stade larvaire. de le nymphe et de l'adulte ont été mesurés chez les reines. Les mâles et les ouvrières de la fourmi d'Argentine, une espèce caractérisée par une fondation de type dépendant. Deux principaux résultats ont été obtenus. En ce qui concerne les reines, nous avons montré qu'il existe une accumulation du corps gras entre l'émergence et l'accouplement toutefois cette augmentation du contenu énergétique se révèle moins importante chez cette espèce que chez celles qui possèdent une fondation de type indépendant. L'intérêt adaptatif de cette différence est discuté. En ce qui concerne les ouvrières adultes, c'est au moment de l'émergence que nous avons rencontré le plus de corps gras. En vieillissant les ouvrières perdent de l'énergie. On peut penser que ces variations sont en relation avec les tâches accomplies par les ouvrières, à l'rintéheur et à l'extérieur du nid.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Physiologie des sexués femelles de fourmis (Hymenoptera; Formicidae) en relation avec le mode de fondation

A study was undertaken to determine if there was a relation between the mode of colony founding in ants and the physiology of the new queens produced, in which mature gynes of 24 ant species were examined. Gynes of species utilizing independent colony founding had a far higher relative fat content (X±SD; 54±6%)(g fat/g dry weight) than gynes of species employing dependent colony founding(19±8%). Dimorphism between queens and workers was significantly higher in species employing independent colony founding. Thus independent colony founding not only results in production of queens with a relatively higher fat content and therefore with a higher energy content per g, but also results in the production of larger queens (in comparison with worker size). Of species employing independent colony founding, 80% were monogynous, whereas only 11% of the species employing dependent colony founding were monogynous. These results are discussed with regard to the social structure and life-history of ant species.