Localisation précise et fiable de véhicules par approche multisensorielle

Vehicle localization is a crucial step in the development of smart vehicles. The research in this domain has been growing in recent years. Generally, the effort is focused on the localization accuracy, we present here a localization method on existing map where the objective is to estimate the robot position not only with accuracy but also with confidence. To achieve this , the algorithm developed has two main steps : one, selection and perception of the most relevant informations and two, position estimation and confidence update. This last step also allows to detect and eliminate the previous errors. Environment perception is well achieved, thanks to different sensors associated with specific detectors. Humans use different senses, shifting automatically in order to localize themselves depending on the situation of the environment, for e.g if there is enough illumination we depend on eyes, else the ear or the touch otherwise. We have developed a similar approach for the robot that takes into account the specific environmental constraints and actual position estimation to select at each instant the most relevant set of sensor, landmark and detector. The perception step, led by a top-down process, can use already known informations allowing a focus on the searched landmark and an improvement of the detection and data associations steps. This top-down approach is well implemented, thanks to a Bayesian network. Bayesian network allows to model the interactions between the different probable events with management of the uncertainty. With this network, it is very easy to take into account those different events. Moreover, a Bayesian network has a great flexibility to take into consideration additional events that can cause false detections (like sensor failure, meteorological conditions and others). The environment data is obtained with a Georeferenced map (from GIS). With the already available maps on the internet, allows to exploit an already existing information. The use of a Georeferenced map facilitates the communication of informations between a vehicle and several other vehicles or with an element of the infrastructure, that can be very useful for multi vehicle coordination, for example. The results shows that the developed approach is very accurate and reliable for localization, whether static or dynamic, and can be applied for autonomous driving. Moreover, new sensors can be added at ease.La localisation d’un véhicule est une étape cruciale dans le développement des véhicules intelligents. Les recherches sur ce sujet ont connu un grand essor ces dernières années. L’accent est souvent porté sur la précision de la localisation, nous présentons ici une méthode de localisation sur carte existante dont l’objectif est d’estimer la position du robot non seulement de façon précise mais également de manière fiable. Pour ce faire, l’algorithme développé se présente en deux étapes principales : une étape de sélection et de perception des informations les plus pertinentes et une étape de mise à jour de la position estimée et de la fiabilité, cette dernière étape permet également de détecter et réparer ou éliminer les précédentes erreurs. La perception de l’environnement est réalisée à travers différents capteurs associés à des détecteurs spécifiques. L’humain utilise aussi différents capteurs pour se localiser et va intuitivement sélectionner le plus performant, s’il fait jour il utilisera ses yeux, sinon son oreille ou le toucher. Nous avons développé une approche similaire pour le robot qui tient compte des contraintes environnementales et de l’estimation actuelle pour sélectionner à chaque instant l’ensemble capteur, indice de la scène et détecteur le plus pertinent. La phase de perception, étant pilotée par un processus Top-Down, peut bénéficier d’informations déjà connues permettant ainsi de se focaliser sur l’indice recherché et d’améliorer les phases de détection et d’associations de données. Cette approche Top-Down s’appuie sur un réseau bayésien. Ce dernier permet de modéliser les interactions entre les différents événements qui se produisent en gérant l’incertitude. Il permet une prise en compte facile des différents événements. Par ailleurs le réseau bayésien présente une grande flexibilité pour l’ajout d’événements supplémentaires pouvant engendrer des non-détections (tels que dysfonctionnement de capteurs, conditions météorologiques, etc.). Les données de l’environnement sont rendues disponibles grâce à une carte géoréférencée préalablement fournie. Avec le développement de cartes disponibles facilement sur internet, cette façon de faire permet d’exploiter au mieux l’information déjà fournie. L’utilisation d’une carte géoréférencée permet d’avoir un référentiel commun entre tous les véhicules ou éléments de l’infrastructure facilitant ainsi l’échange d’informations et ouvrant du coup la possibilité d’interaction simplifiées dans le cas de flottes par exemple. Les résultats montrent que l’approche développée est pertinente pour une localisation précise et fiable aussi bien statique que dynamique. L’ajout de nouveaux capteurs se fait naturellement et sans nécessiter d’heuristique particulière. La localisation obtenue est suffisamment précise et fiable pour permettre des applications de conduite autonome utilisant, entre autres, cet algorithme

Stratégie de perception active pour l'interprétation de scènes : Application à une scène routière

National audienceCet article décrit une méthode générique pour reconnaitre des objets donnés en cherchant à utiliser au mieux toutes les connaissances a priori disponibles de la scène. Chaque objet est composé d'un ensemble de parties. A chacune de ces parties sont associés une primitive et un détecteur pour la trouver. Les différentes étapes de l'approche seront alors : la focalisation des parties (c'est à dire qu'on détermine la zone de recherche des primitives associées), la sélection de la "meilleure partie" (celle qui a priori doit apporter le plus pour la reconnaissance de l'objet), la détection des primitives dans la zone associée à cette partie et la sélection de la meilleure primitive (celle qui correspond le plus à nos attentes) et enfin la mise à jour de l'objet compte tenu de la réussite (ou de l'échec) de la détection précédente. Ce papier décrit cette approche avec une application dédiée à une scène routière comprenant une route et un panneau de limitation de vitesse

Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction

Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding.Comment: 13 pages, 7 Postscript figure

Community Structure Characterization

This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry

MYBL2 (B-Myb): a central regulator of cell proliferation, cell survival and differentiation involved in tumorigenesis

Limitless cell proliferation, evasion from apoptosis, dedifferentiation, metastatic spread and therapy resistance: all these properties of a cancer cell contribute to its malignant phenotype and affect patient outcome. MYBL2 (alias B-Myb) is a transcription factor of the MYB transcription factor family and a physiological regulator of cell cycle progression, cell survival and cell differentiation. When deregulated in cancer cells, MYBL2 mediates the deregulation of these properties. In fact, MYBL2 is overexpressed and associated with poor patient outcome in numerous cancer entities. MYBL2 and players of its downstream transcriptional network can be used as prognostic and/or predictive biomarkers as well as potential therapeutic targets to offer less toxic and more specific anti-cancer therapies in future. In this review, we summarize current knowledge on the physiological roles of MYBL2 and highlight the impact of its deregulation on cancer initiation and progression

Community evolution in patent networks: technological change and network dynamics

When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results

Community evolution in patent networks: technological change and network dynamics

When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results

High-Throughput Drug Screening Identifies Pazopanib and Clofilium Tosylate as Promising Treatments for Malignant Rhabdoid Tumors

Summary: Rhabdoid tumors (RTs) are aggressive tumors of early childhood characterized by SMARCB1 inactivation. Their poor prognosis highlights an urgent need to develop new therapies. Here, we performed a high-throughput screening of approved drugs and identified broad inhibitors of tyrosine kinase receptors (RTKs), including pazopanib, and the potassium channel inhibitor clofilium tosylate (CfT), as SMARCB1-dependent candidates. Pazopanib targets were identified as PDGFRα/β and FGFR2, which were the most highly expressed RTKs in a set of primary tumors. Combined genetic inhibition of both these RTKs only partially recapitulated the effect of pazopanib, emphasizing the requirement for broad inhibition. CfT perturbed protein metabolism and endoplasmic reticulum stress and, in combination with pazopanib, induced apoptosis of RT cells in vitro. In vivo, reduction of tumor growth by pazopanib was enhanced in combination with CfT, matching the efficiency of conventional chemotherapy. These results strongly support testing pazopanib/CfT combination therapy in future clinical trials for RTs. : Rhabdoid tumors (RTs) are aggressive pediatric tumors characterized by SMARCB1 inactivation. Chauvin et al. identify two SMARCB1-dependent targeted therapies for RT: pazopanib, which inhibits PDGFR and FGFR2, and the potassium channel inhibitor clofilium tosylate, which induces endoplasmic reticulum stress. Combining both drugs induces cell apoptosis and reduces PDX tumor growth. Keywords: rhabdoid tumors, SMARCB1, pazopanib, clofilium tosylate, high-throughput drug screening, tyrosine kinase inhibitor

Cooperation of cancer drivers with regulatory germline variants shapes clinical outcomes

Pediatric malignancies including Ewing sarcoma (EwS) feature a paucity of somatic alterations except for pathognomonic driver-mutations that cannot explain overt variations in clinical outcome. Here, we demonstrate in EwS how cooperation of dominant oncogenes and regulatory germline variants determine tumor growth, patient survival and drug response. Binding of the oncogenic EWSR1-FLI1 fusion transcription factor to a polymorphic enhancerlike DNA element controls expression of the transcription factor MYBL2 mediating these phenotypes. Whole-genome and RNA sequencing reveals that variability at this locus is inherited via the germline and is associated with variable inter-tumoral MYBL2 expression. High MYBL2 levels sensitize EwS cells for inhibition of its upstream activating kinase CDK2 in vitro and in vivo, suggesting MYBL2 as a putative biomarker for anti-CDK2-therapy. Collectively, we establish cooperation of somatic mutations and regulatory germline variants as a major determinant of tumor progression and highlight the importance of integrating the regulatory genome in precision medicine