Robust classification via MOM minimization

We present an extension of Vapnik's classical empirical risk minimizer (ERM) where the empirical risk is replaced by a median-of-means (MOM) estimator, the new estimators are called MOM minimizers. While ERM is sensitive to corruption of the dataset for many classical loss functions used in classification, we show that MOM minimizers behave well in theory, in the sense that it achieves Vapnik's (slow) rates of convergence under weak assumptions: data are only required to have a finite second moment and some outliers may also have corrupted the dataset. We propose an algorithm inspired by MOM minimizers. These algorithms can be analyzed using arguments quite similar to those used for Stochastic Block Gradient descent. As a proof of concept, we show how to modify a proof of consistency for a descent algorithm to prove consistency of its MOM version. As MOM algorithms perform a smart subsampling, our procedure can also help to reduce substantially time computations and memory ressources when applied to non linear algorithms. These empirical performances are illustrated on both simulated and real datasets

Physical Simulation of Inarticulate Robots

In this note we study the structure and the behavior of inarticulate robots. We introduce a robot that moves by successive revolvings. The robot's structure is analyzed, simulated and discussed in detail

When dunes move together, structure of deserts emerges

Crescent shaped barchan dunes are highly mobile dunes that are usually presented as a prototypical model of sand dunes. Although they have been theoretically shown to be unstable when considered separately, it is well known that they form large assemblies in desert. Collisions of dunes have been proposed as a mechanism to redistribute sand between dunes and prevent the formation of heavily large dunes, resulting in a stabilizing effect in the context of a dense barchan field. Yet, no models are able to explain the spatial structures of dunes observed in deserts. Here, we use an agent-based model with elementary rules of sand redistribution during collisions to access the full dynamics of very large barchan dune fields. Consequently, stationnary, out of equilibrium states emerge. Trigging the dune field density by a sand load/lost ratio, we show that large dune fields exhibit two assymtotic regimes: a dilute regime, where sand dune nucleation is needed to maintain a dune field, and a dense regime, where dune collisions allow to stabilize the whole dune field. In this dense regime, spatial structures form: the dune field is structured in narrow corridors of dunes extending in the wind direction, as observed in dense barchan deserts

Search for a new short-range spin-dependent force with polarized Helium 3

Measuring the depolarization rate of a $^3$He hyperpolarized gas is a sensitive method to probe hypothetical short-range spin-dependent forces. A dedicated experiment is being set up at the Institute Laue Langevin in Grenoble to improve the sensitivity. We presented the status of the experiment at the 10th PATRAS Workshop on Axions, WIMPs and WISPs.Comment: Presented at the 10th PATRAS Workshop on Axions, WIMPs and WISP

Constraining short-range spin-dependent forces with polarized 3^3He

We have searched for a short-range spin-dependent interaction using the spin relaxation of hyperpolarized $^3$He. Such a new interaction would be mediated by a hypothetical light scalar boson with \CP-violating couplings to the neutron. The walls of the $^3$He cell would generate a pseudomagnetic field and induce an extra depolarization channel. We did not see any anomalous spin relaxation and we report the limit for interaction ranges $\lambda$ between $1$ and $100~\rm{\mu m}$: $g_sg_p \lambda ^2 \leq 2.6\times 10^{-28}~\mathrm{m^2}\, ( 95~\%\, \mathrm{C.L.})$, where $g_s$($g_p$) are the (pseudo)scalar coupling constant, improving the previous best limit by 1 order of magnitude

Génération automatique de HashTags

Les HashTags sont des mots-clés que les utilisateurs de réseaux sociaux choisissent de mettre en avant dans leurs messages. Ils ont été popularisés sur le réseau social Twitter, qui a permis à ses utilisateurs de sélectionner des HashTags à suivre et d'afficher l'ensemble des messages contenant un HashTag suivi. Ils sont aujourd'hui utilisés sur les principaux réseaux sociaux, tels que Facebook, Google+, Diaspora*, et sont un facteur important de la diffusion de l'information sur Internet. Dans cet article, nous proposons une méthode fondée sur des informations statistiques, syntaxiques et sémantiques pour générer des HashTags. (Résumé d'auteur

Description of the unsteady flow pattern from peak efficiency to near surge in subsonic centrifugal compressor stage

This paper aims to describe the flow structure modifications when the operating point moves from peak efficiency to near stall condition in a moderate pressure ratio centrifugal compressor stage consisted of a splittered unshrouded impeller and a vaned diffuser. The investigations are based on three-dimensional U-RANS simulation results. The flow is described in the impeller and in the vaned diffuser through time-averaged flow quantities and unsteady fluctuations. Results show that at low mass flow rate, the effects of secondary flow in the impeller are more pronounced, inducing both, high time-averaged values and temporal fluctuations of the flow angle near the shroud at the diffuser inlet, leading to vane suction side boundary layer separation. Pressure waves due to impeller diffuser interaction spread through the vaned diffuser generating unsteadiness which intensifies at near surge condition

Unsupervised mining of audiovisually consistent segments in videos with application to structure analysis

International audienceIn this paper, a multimodal event mining technique is proposed to discover repeating video segments exhibiting audio and visual consistency in a totally unsupervised manner. The mining strategy first exploits independent audio and visual cluster analysis to provide segments which are consistent in both their visual and audio modalities, thus likely corresponding to a unique underlying event. A subsequent modeling stage using discriminative models enables accurate detection of the underlying event throughout the video. Event mining is applied to unsupervised video structure analysis, using simple heuristics on occurrence patterns of the events discovered to select those relevant to the video structure. Results on TV programs ranging from news to talk shows and games, show that structurally relevant events are discovered with precisions ranging from 87% to 98% and recalls from 59% to 94%