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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.

This paper describes an evaluation of a mobile exertion application, developed to promote physical activity among adult users. To extend previous work, we aimed to identify changes in walking behavior over time, and to determine the efficacy of non-visual cues presented via the phone. Data was gathered using three methods: automated logging, participant-maintained diaries and interviews. Findings revealed that participants were able to respond most effectively to multimodal icons to modify their pace over a two week period. However, their preferences were noted to change depending on the situational context. The study has also highlighted the complexities associated with data collection techniques for mobile evaluations. In proceedings of INTERACT 2011, Part IV, LNCS 6949, Lisbon, Portugal

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; Focused proof systems provide means for reducing and structuring the non-determinism involved in searching for sequent calculus proofs. We present a focused proof system for a first-order logic with inductive and co-inductive definitions in which the introduction rules are partitioned into an asynchronous phase and a synchronous phase. These focused proofs allow us to naturally see proof search as being organized around interleaving intervals of computation and more general deduction. For example, entire Prolog-like computations can be captured using a single synchronous phase and many model-checking queries can be captured using an asynchronous phase followed by a synchronous phase. Leveraging these ideas, we have developed an interactive proof assistant, called Tac, for this logic. We describe its high-level design and illustrate how it is capable of automatically proving many theorems using induction and coinduction. Since the automatic proof procedure is structured using focused proofs, its behavior is often rather easy to anticipate and modify. We illustrate the strength of Tac with several examples of proved theorems, some achieved entirely automatically and others achieved with user guidance.

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.

This paper argues in favor of more national and global histories of computing, describes the author’s current research, and presents preliminary findings about the global diffusion of IT with particular emphasis on developments in the pan-Atlantic community of North America and Europe from to 1940s to 2000. The author introduces a new model for dating events in the diffusion of computing.

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.