Parallelizing RRT on large-scale distributed-memory architectures

This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance

Enhancing the Transition-based RRT to deal with complex cost spaces

The Transition-based RRT (T-RRT) algorithm enables to solve motion planning problems involving configuration spaces over which cost functions are defined, or cost spaces for short. T-RRT has been successfully applied to diverse problems in robotics and structural biology. In this paper, we aim at enhancing T-RRT to solve ever more difficult problems involving larger and more complex cost spaces. We compare several variants of T-RRT by evaluating them on various motion planning problems involving different types of cost functions and different levels of geometrical complexity. First, we explain why applying as such classical extensions of RRT to T-RRT is not helpful, both in a mono-directional and in a bidirectional context. Then, we propose an efficient Bidirectional T-RRT, based on a bidirectional scheme tailored to cost spaces. Finally, we illustrate the new possibilities offered by the Bidirectional T-RRT on an industrial inspection problem

Parallelizing RRT on distributed-memory architectures

This paper addresses the problem of improving the performance of the Rapidly-exploring Random Tree (RRT) algorithm by parallelizing it. For scalability reasons we do so on a distributed-memory architecture, using the message-passing paradigm. We present three parallel versions of RRT along with the technicalities involved in their implementation. We also evaluate the algorithms and study how they behave on different motion planning problems

Suivi de visages par regroupement de détections : traitement séquentiel par blocs

Session "Posters"National audienceCet article décrit une méthode de partitionnement des visages d'une séquence vidéo; elle se base sur une méthode de type tracking-by-detections et utilise une modélisation probabiliste de type Maximum A Posteriori, résolu par un algorithme s'appuyant sur une recherche de flot de coût minimal sur un graphe. Face aux contraintes de densité, mouvement et taille des détections de visage issues de la vidéosurveillance, les travaux présentés apportent deux contributions : (1) la définition de différentes dissimilarités (spatiale, temporelle, apparence et mouvement) combinées de façon simple et (2) la mise en œuvre d'une version séquentielle par blocs d'images qui permet de traiter des flux vidéos. La méthode proposée est évaluée sur plusieurs séquences réelles annotées

Clustering de visages : vers la construction automatique d'un album photo à partir d'une s equence vidé o

National audienceThis paper presents a clustering method of detections of the same object seen on a video. We apply it to the context of the automatic construction of photo album. We use a global analysis, based on a probabilistic framework of data association problems. The solution is given by Maximum A Posteriori estimation. Our main contribution concerns the use of a local front-back tracking, applied to each detection ; to increase appearance information of detections with a spatial information, through local tracks construction. We introduce a new likelihood measure based on the spatio-temporal dissimilarity between tracks. The algorithm is then able to deal with situations in which the face detections are scattered. We propose to use criteria derived from purity and inverse purity of a clustering to assess performances of the proposed method. This method is compared to hierarchical clustering on two real test sequences.Cet article présente une méthode de regroupement de détections d'un même objet vu sur une séquence vidéo, en se plaçant dans le cadre applicatif plus précis de la construction automatique d'un album photo. Nous utilisons une méthode d'analyse globale, basée sur une formalisation probabiliste du problème d'association de données. La solution du problème est alors donnée par une estimation du Maximum A Posteriori (MAP). La principale contribution concerne l'utilisation d'une méthode de suivi locale avant-arrière appliquée à chaque détection. Cela afin d'enrichir l'information d'apparence issue de la détection, par une information spatiale provenant de la construction de pistes locales. Nous introduisons une nouvelle mesure de vraisemblance basée sur la dissimilarité spatio-temporelle entre les pistes. L'algorithme obtenu est alors capable d'adresser des situations où les détections de visages sont éparses. Nous proposons d'utiliser des critères dérivés de la pureté et la pureté inverse d'un clustering pour évaluer les performances de la méthode proposée. La méthode est ensuite comparée à un clustering ascendant hiérarchique, sur deux séquences test réelles

A multi-tree approach to compute transition paths on energy landscapes

Exploring the conformational energy landscape of a molecule is an important but challenging problem because of the inherent complexity of this landscape. As part of this theme, various methods have been developed to compute transition paths between stable states of a molecule. Besides the methods classically used in biophysics/biochemistry, a recent approach originating from the robotics community has proven to be an efficient tool for conformational exploration. This approach, called the Transition-based RRT (T-RRT) is based on the combination of an effective path planning algorithm (RRT) with a Monte-Carlo-like transition test. In this paper, we propose an extension to T-RRT based on a multi-tree approach, which we call Multi-T-RRT. It builds several trees rooted at different interesting points of the energy landscape and allows to quickly gain knowledge about possible conformational transition paths. We demonstrate this on the alanine dipeptide

MoMA-LigPath: A web server to simulate protein-ligand unbinding

Protein-ligand interactions taking place far away from the active site, during ligand binding or release, may determine molecular specificity and activity. However, obtaining information about these interactions with experimental or computational methods remains difficult. The computational tool presented in this paper, MoMA-LigPath, is based on a mechanistic representation of the molecular system, considering partial flexibility, and on the application of a robotics-inspired algorithm to explore the conformational space. Such a purely geometric approach, together with the efficiency of the exploration algorithm, enables the simulation of ligand unbinding within very short computing time. Ligand unbinding pathways generated by MoMA-LigPath are a first approximation that can provide very useful information about protein-ligand interactions. When needed, this approximation can be subsequently refined and analyzed using state-of-the-art energy models and molecular modeling methods. MoMA-LigPath is available at http://moma.laas.fr. The web server is free and open to all users, with no login requirement

Emotional and attention-deficit/hyperactivity disorder symptoms of preterm vs. full-term children during COVID-19 pandemic restrictions

BACKGROUND: Preterm children are at higher risk of developing mental health problems than full-term children. Deterioration of children's mental health was observed during COVID-19 pandemic restrictive measures. Our study compared emotional and attention-deficit/hyperactivity disorder (ADHD) symptoms during school closure between preterm and full-term children. METHODS: Data from two French birth cohorts-ELFE and EPIPAGE-2-were used. In 2011, infants born ≥22 weeks' gestation were recruited. Parents completed the Strengths and Difficulties Questionnaire when the children were 9 years old and experiencing school closure. Multivariate multinomial logistic regression models were used. RESULTS: Subjects included 4164 full-term and 1119 preterm children. In univariate analyses, compared to full-term children: extremely and very preterm children more frequently had abnormal and borderline ADHD scores (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.50-2.30, OR 1.42, 95% CI 1.08-1.85, respectively) and abnormal emotional scores (OR 1.86, 95% CI 1.43-2.40); moderate to late preterm children more often had abnormal ADHD scores (OR 1.33, 95% CI 1.01-1.78). The associations did not remain when previous symptoms at 5 years old were considered. CONCLUSIONS: School closure during lockdown did not appear to increase the risk of mental health problems in preterm compared to full-term children. IMPACT STATEMENT: Preterm children are at higher risk of developing mental health problems than full-term children. Deterioration in children's mental health was observed during COVID-19 pandemic restrictions. However, whether preterm children were a particularly vulnerable subgroup during school closure is unclear. In univariate analyses, extremely and very preterm children more often had abnormal and borderline ADHD symptoms and abnormal emotional symptom scores than full-term children. The associations did not remain significantly associated when previous symptoms were considered. Preterm compared to full-term children more often suffer from ADHD and emotional symptoms, but school closure during lockdown did not appear to increase this risk.Santé, perception, pratiques, relations et inégalités sociales en population générale pendant la crise COVID-1

Compléments à l’estimation de la variance pour l’enquête Elfe

L’étude Elfe est une enquête nationale dont l’objet est de suivre environ 18000 enfants nés en France en 2011 jusqu’à l’âge adulte. Le plan d’échantillonnage utilisé pour sélectionner les nourrissons en maternité est complexe. L’analyse de la précision des résultats issue des différentes phases d’enquêtes également. Les procédures classiques des logiciels statistiques standards ne permettent pas de calculer cette précision. Ce travail fait suite au « Document de travail INED 226 - Estimation de la variance pour l’enquête ELFE Hélène Juillard » publié en 2016 (https://www.ined.fr/fr/publications/editions/document-travail/estimationvariance-enquete-elfe/) et propose une simplification du concept même du plan de sondage mis en oeuvre. L’étude de cette simplification, de son impact dans le cadre du calcul de variance, ainsi que l’estimation de la précision d’une cinquantaine d’indicateurs permettent de proposer des préconisations pour estimer avec des procédures simplifiées les variances dans le cadre de l’analyse des résultats issus de l’enquête Elfe, et donc d’en approcher simplement l’incertitude. Les résultats sont illustrés grâce au logiciel SAS