Publications

Sciences du Vivant

Folding and Intrinsic Disorder of the Receptor Tyrosine Kinase KIT Insert Domain Seen by Conventional Molecular Dynamics Simulations

The kinase insert domain (KID) of RTK KIT is the key recruitment region for downstream signalling proteins. KID, studied by molecular dynamics simulations as a cleaved polypeptide and as a native domain fused to KIT, showed intrinsic disorder…

Julie Ledoux, Alain Trouvé, Luba Tchertanov

International Journal of Molecular Sciences - 2021

Sciences du Vivant

Folding and Intrinsic Disorder of the Receptor Tyrosine Kinase KIT Insert Domain Seen by Conventional Molecular Dynamics Simulations

The kinase insert domain (KID) of RTK KIT is the key recruitment region for downstream signalling proteins. KID, studied by molecular dynamics simulations as a cleaved polypeptide and as a native domain fused to KIT, showed intrinsic disorder…

Julie Ledoux, Alain Trouvé, Luba Tchertanov

International Journal of Molecular Sciences - 2021

Neurobiologie

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex

A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general…

Anton V Chizhov, Lyle J Graham

PLoS Computational Biology - 2021

Mathématiques

Single pass computation of first seismic wave travel time in three dimensional heterogeneous media with general anisotropy

We present a numerical method for computing the first arrival time of seismic waves in media defined by a general Hooke tensor, in contrast with previous methods which are limited to a specific subclass such as the "tilted transversally isotropic" …

François Desquilbet, Jian Cao, Paul Cupillard, Ludovic Métivier, Jean-Marie Mirebeau

Journal of Scientific Computing - 2021

Applications

Epidemic Models for COVID-19 during the First Wave from February to May 2020: a Methodological Review

We review epidemiological models for the propagation of the COVID-19 pandemic during the early months of the outbreak: from February to May 2020. The aim is to propose a methodological review that highlights the following characteristics: (i) the…

Marie Garin, Myrto Limnios, Alice Nicolaï, Ioannis Bargiotas, Olivier Boulant, Stephen E Chick, Amir Dib, Theodoros Evgeniou, Mathilde Fekom, Argyris Kalogeratos, Christophe Labourdette, Anton Ovchinnikov, Raphaël Porcher, Camille Pouchol, Nicolas Vayatis

Vision par ordinateur et reconnaissance de formes

Automatic detection of repeated objects in images

The definition of an "object" through the presentation of several of its instances is certainly one of the most efficient ways for humans and machines to learn. An object can be "learned" from a single image, just because it is repeating. In this…

Mariano Rodríguez, Jean-Michel Morel, Julie Delon

IEEE International Conference on Image Processing - 2021

Mathématiques

Decentralized Learning in Online Queuing Systems

Motivated by packet routing in computer networks, online queuing systems are composed of queues receiving packets at different rates. Repeatedly, they send packets to servers, each of them treating only at most one packet at a time. In the…

Flore Sentenac, Etienne Boursier, Vianney Perchet

Mathématiques

Making the most of your day: online learning for optimal allocation of time

We study online learning for optimal allocation when the resource to be allocated is time. Examples of possible applications include a driver filling a day with rides, a landlord renting an estate, etc. Following our initial motivation, a driver…

Etienne Boursier, Tristan Garrec, Vianney Perchet, Marco Scarsini

Machine Learning

Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics

The ability to collect and store ever more massive databases has been accompanied by the need to process them efficiently. In many cases, most observations have the same behavior, while a probable small proportion of these observations are abnormal…

Myrto Limnios, Nathan Noiry, Stéphan Clémençon

Proceedings of Machine Learning Research - 2021

Optimisation et contrôle

Adversarial Weighting for Domain Adaptation in Regression

We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift. The approach developed in this paper is based on the assumption that the task on the target…

Antoine de Mathelin, Guillaume Richard, François Deheeger, Mathilde Mougeot, Nicolas Vayatis