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le (1h10m13s)

Folding Turing is hard but feasible

We introduce and study the computational power of Oritatami, a theoretical model to explore greedy molecular folding, by which the molecule begins to fold before waiting the end of its production. This model is inspired by our recent experimental work demonstrating the construction of shapes at the nanoscale by folding an RNA molecule during its transcription from an engineered sequence of synthetic DNA. While predicting the most likely conformation is known to be NP-complete in other models, Oritatami sequences fold optimally in linear time. Although our model ...
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le (1h32m20s)

Mathématiques, Statistiques et Médecine: des collaborations plus que jamais nécessaires

A travers le paradigme d’une maladie chronique fréquente, le cancer du sein, nous souhaitons aborder les évolutions de formes de collaborations entre le domaine médical et les chercheurs en mathématiques appliquées. La capacité à observer et quantifier les états physiologiques et pathologiques et approcher leurs variabilités intra- et inter- individuelles a marqué l’entrée dans une médecine dite scientifique dès la fin de 18eme siècle. Dès la seconde moitié du 20eme siècle, le monde anglo-saxon, suivi rapidement par le monde scandinave a su mettre en ...
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le (1h1s)

Numerical Optimal Transport and Applications

Optimal transport (OT) has become a fundamental mathematical theoretical tool at the interface between calculus of variations, partial differential equations and probability. It took however much more time for this notion to become mainstream in numerical applications. This situation is in large part due to the high computational cost of the underlying optimization problems. There is however a recent wave of activity on the use of OT-related methods in fields as diverse as computer vision, computer graphics, statistical inference, machine learning and image ...
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le (1h1m21s)

Semantics in the Time of Computing

Much of the technical terminology of computer science betrays its logical heritage: ‘language’, ‘symbol’, ‘syntax’, ‘semantics’, ‘value’, ‘reference’, ‘identifier’, ‘data’, etc.  Classically, such terms were used to name essential phenomena underlying logic, human thought and language — phenomena, it was widely believed, that would never succumb to scientific (causal, mechanical) explanation.  Computer science, however, now uses all these terms in perfectly good scientific ways, to name respectable scientific (causally explicable, mathematically modellable) phenomena. There are two possibilities.  The first is that ...
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