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Nombre de programmes trouvés : 161
Conférences

le (1h9m28s)

Numbers, computers and dynamical systems

A discrete dynamical system is defined as a set of states on which a transformation acts, considered as an evolution rule. The terminology discrete refers to the time that is discretized: at time n corresponds the nth iteration of this transformation. Dynamical systems are widely studied, for their modelling as well as for their computation power. We will focus here more specifically on trajectories of chaotic dynamical system from a computer science viewpoint (finite or periodic trajectories). A classical example is provided by the ...
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le (1h1m52s)

An extensionalist analysis of computer simulations

The current spreading of multi-scale and multi-process integrative simulations is challenging for the philosophy of science. What is the epistemic function of such a simulation? What does it really show? On what grounds? Is it an experimental design or a theoretical construction? To contribute to these debates, I suggest analyzing the ways computer simulations (CSs) are using symbols. To this end, I will present an extensionalist analysis of CSs. This approach puts the emphasis not on languages nor on models, but more precisely on symbols, ...
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le (1h14m31s)

A l’écoute du bruit – L’imagerie par corrélations croisées

Les techniques d’imagerie classiques utilisent des ondes pour sonder un milieu inconnu et sont employées pour des applications médicales (échographie) ou géophysiques (séismologie) par exemple. Ces ondes sont émises par des réseaux de sources et après propagation dans le milieu elles sont enregistrées par des réseaux de récepteurs.   Ces techniques sont généralement mises en défaut lorsqu’on les utilise dans des milieux diffusants contenant des inhomogénéités aléatoires, car les signaux cohérents venant des réflecteurs à imager et enregistrés par les réseaux ...
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le (1h7m1s)

Reasoning over large-scale biological systems with heterogeneous and incomplete data

Data produced by the domain of life sciences in the next decade are expected to be highly challenging. In addition to scalability issues which are shared with other applications domains, data produced in life sciences have very specific characteristics: multi-scale, incomplete, heterogeneous but somehow interdependent. This makes data-mining methods less efficient than expected to assist knowledge discovery. An example of such limitations is the study of biological systems in molecular and cellular biology, which cannot be uniquely identified with the data at hand. In this talk, we will introduce a strategy to study biological systems in the ...
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