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Écrire du point de vue de l'ennemi : Les Perses, Triomphe de l’Empathie de Chokri Ben Chikha
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Pratiques déclarées des enseignants français d’élémentaire en Unités Localisées d’Inclusion Scolaire : comment enseignent-il le décodage à leurs élèves ?
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Scolarisation des élèves sourds en France : Etat des lieux des formations des enseignants dans une perspective inclusive
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Rancière, le queer, le blackface et l’appropriation culturelle : repenser les lignes de partage du théâtre public
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Learning professional taste: an ethnographic and multimodal study in a culinary arts workshop in French Guiana
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From Florence to Lyon and Geneva fairs: the Pazzi family, the King of France and the shifting economic geography during the late fifteenth century
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Et l’art appliqué dans tout ça ? Entre droit des dessins ou modèles et droit d’auteur : quelle protection ? quel cumul ?,
No full textInternational audienceLe chapitre envisage l'évolution du principe du cumul de protection entre droit d'auteur et droit des dessins et modèles en France et dans l'UE depuis sa consécration jusqu'aux derniers textes européens, et son évolution dans la jurisprudence française
Enhancing Fluorescence Correlation Spectroscopy with machine learning to infer anomalous molecular motion
No full textInternational audienceThe random motion of molecules in living cells has consistently been reported to deviate from standard Brownian motion, a behavior coined as ``anomalous diffusion''. To study this phenomenon in living cells, Fluorescence Correlation Spectroscopy (FCS) and Single-Particle Tracking (SPT) are the two main methods of reference. In opposition to SPT, FCS with its classical analysis methodology cannot consider models of motion for which no analytical expression of the auto-correlation function is known. This excludes for instance anomalous Continuous-Time Random Walks (CTRW) and Random Walk on fractal (RWf). Moreover, the whole acquisition sequence of the classical FCS methodology takes several tens of minutes. Here, we propose a new analysis approach that frees FCS of these limitations. Our approach associates each individual FCS recording with a vector of features based on an estimator of the auto-correlation function and uses machine learning to infer the underlying model of motion and to estimate the values of the motion parameters. Using simulated recordings, we show that this approach endows FCS with the capacity to distinguish between a range of standard and anomalous random motions, including CTRW and RWf. Our approach exhibits performances comparable to the best-in-class state-of-the-art algorithms for SPT and can be used with a range of FCS setup parameters. Since it can be applied on individual recordings of short duration, we show that with our method, FCS can be used to monitor rapid changes of the motion parameters. Finally, we apply our method on experimental FCS recordings of calibrated fluorescent beads in increasing concentrations of glycerol in water. Our results accurately predict that the beads follow Brownian motion with a diffusion coefficient and anomalous exponent which agree with classical predictions from Stokes-Einstein law even at large glycerol concentrations. Taken together, our approach significantly augments the analysis power of FCS to capacities that are similar to state-of-the-art SPT approaches