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International audience; Data Mashup is a special class of applications mashup thatcombines data elements from multiple data sources (that are often ex-ported as data web services) to respond to transient business needs onthe y. In this paper, we propose a semantic model for data servicesalong with a declarative approach for creating data mashups withoutany programming involved. Given a query formulated over domain on-tologies, and a set of preferences modeled using the fuzzy set theory,our approach selects the relevant data services based on their semanticmodeling using an RDF query rewriting algorithm. Selected services arethen orchestrated using a ranking-aware algebra to rank their returnedresults based on users preferences at the data mashup execution time.

Abstract The serotonergic system plays a key modulatory role in the brain. This system is critical in many drug developments for brain disorders via interactions with the 14 subtypes of 5-HT receptors or through reuptake blockade. Positron emission tomography (PET) is an efficient tool for in vivo studies of physiological and pathological processes. Because of its pertinent radiochemical properties, fluorine-18 is one of the most used radioisotopes in PET imaging. This chapter will propose an overview of the 18F-radioligands targeting serotonin receptors, which have been developed over the last few years. Both radiosyntheses and pharmacological properties of these radiotracers will be described, with a specific emphasis on their potential medical applications.

International audience; We describe an approach to extract 13C-labeled sugars (glucose, fructose, maltose, sucrose, myo-inositol as well as glucose from starch) from plant tissues and to analyze their isotopomer distribution by gas chromatography–mass spectrometry (GC-MS). Sugars are derivatized with N,O-bis(trimethylsilyl) trifluoroacetamide (BSTFA) into their Si(CH3)3 derivatives. Electronic and chemical ionizations are used to obtain suitable fragments for metabolic flux analysis (MFA). Unique fragments are identified by computer simulation and experimental verification with labeled standards. Linear equations for separating information from glucosyl and fructosyl moieties of sucrose are presented. Finally, mass distributions are corrected for natural isotope abundance using a home-written program. The method is illustrated by sugar isotopomer analysis of 13C-labeled rapeseed embryos.

2013, IX, 349 p. 79 illus., 55 in color; International audience; We discuss models that astrochemists have developed to study the chemical composition of the interstellar medium. These models aim at computing the evolution of the chemical composition of a mixture of gas and dust under astrophysical conditions. These conditions, as well as the geometry and the physical dynamics, have to be adapted to the objects being studied because different classes of objects have very different characteristics (temperatures, densities, UV radiation fields, geometry, history etc); e.g., proto-planetary disks do not have the same characteristics as proto-stellar envelopes. Chemical models are being improved continually thanks to comparisons with observations but also thanks to laboratory and theoretical work in which the individual processes are studied.

International audience; Toxoplasma gondii is a highly successful parasite that is thought to infect nearly one third of the world's population. Molecular epidemiology studies have illuminated the population structure of T. gondii strains world-wide and enabled insights into how the parasite is transmitted and the frequency of genetic exchange. Toxoplasma population genetics studies have shown that in North America and Europe, three genotypes have predominated whereas other diverse genotypes have propagated in other parts of the world, particularly in Central and South America. This chapter summarizes the techniques used to understand the population structure of T. gondii and how these techniques have also been helpful in understanding outbreaks of toxoplasmosis. As technology has advanced, more in-depth studies have facilitated our understanding of how differing genotypes can be linked to biological phenotypes including human disease.

International audience; Orchestras of Digital Musical Instruments (DMIs) enable new musical collaboration possibilities, extending those of acoustic and electric orchestras. However the creation and development of these orchestras remain constrained. In fact, each new musical collaboration system or orchestra piece relies on a fixed number of musicians, a fixed set of instruments (often only one), and a fixed subset of possible modes of collaboration. In this paper, we describe a unified framework that enables the design of Digital Orchestras with potentially different DMIs and an expand-able set of collaboration modes. It relies on research done on analysis and classification of traditional and digital orchestras, on research in Collaborative Virtual Environments, and on interviews of musicians and composers. The BOEUF framework consists of a classification of modes of collaboration and a set of components for modelling digital orchestras. Integrating this framework into DMIs will enable advanced musical collaboration modes to be used in any digital orchestra, including spontaneous jam sessions.

Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledge-driven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

International audience; We study unlimited infinite churn in peer-to-peer overlay networks. Under this churn, arbitrary many peers may concurrently request to join or leave the overlay network; moreover these requests may never stop coming. We prove that unlimited adversarial churn, where processes may just exit the overlay network, is unsolvable. We focus on cooperative churn where exiting processes participate in the churn handling algorithm. We define the problem of unlimited infinite churn in this setting. We distinguish the fair version of the problem, where each request is eventually satisfied, from the unfair version that just guarantees progress. We focus on local solutions to the problem, and prove that a local solution to the Fair Infinite Unlimited Churn is impossible. We then present our algorithm UIUC that solves the Unfair Infinite Unlimited Churn Problem for a linearized peer-to-peer overlay network. We extend this solution to skip lists and skip graphs.

The standardization effort in vibration’s field has unfolded the interests of industries. This research aimed at better understanding of the impact of vibration on human body. In the past years, healthcare centers and manufacturers started to equip the workplace with Anti-fatigue Mats (AFM) for the improvement of the working conditions and quality at work. This study analyzed the impact of AFM on the reduction of vibrations transmitted during the weekly activity of manufacturing workers. Seven AFM marketed by Notrax Premium Superior Manufacturing Group were tested. The laser vibrometry technique was used in order to measure vibratory displacements at various points. Under the effect of the vibrations, AFM behaved differently according to their structure and geometries. In comparison, the values obtained showed that AFM absorbed the vibration. It permitted to decrease the risks of ligamentous, or articular pathologies, which can affect employees at work. Taking into account, different results obtained showing that AFM could be used effectively in different areas of workplace, such as in medical or manufacturing settings, in order to improve working conditions.

International audience; Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a parameterized policy space in order to maximize the associated value function averaged over some pre-defined distribution. The best one can hope in general from such an approach is to get a local optimum of this criterion. The first contribution of this article is the following surprising result: if the policy space is convex, any (approximate) local optimum enjoys a global performance guarantee. Unfortunately, the convexity assumption is strong: it is not satisfied by commonly used parameterizations and designing a parameterization that induces this property seems hard. A natural so-lution to alleviate this issue consists in deriving an algorithm that solves the local policy search problem using a boosting approach (constrained to the convex hull of the policy space). The resulting algorithm turns out to be a slight generalization of conservative policy iteration; thus, our second contribution is to highlight an original connection between local policy search and approximate dynamic pro-gramming.