• Publicationsclose
• Research softwareclose
• Other research productsclose
• Part of book or chapter of book close
• US close
• IL close
• HAL AMU close

Genome-wide screens are a powerful technique to dissect the complex network of genes regulating diverse cellular phenotypes. The recent adaptation of the CRISPR-Cas9 system for genome engineering has revolutionized functional genomic screening. Here, we present protocols used to introduce Cas9 into human lymphoma cell lines, produce high-titer lentivirus of a genome-wide sgRNA library, transduce and culture cells during the screen, isolate genomic DNA, and prepare a custom library for next-generation sequencing. These protocols were tailored for loss-of-function CRISPR screens in human lymphoma cell lines but are highly amenable for other experimental purposes.

Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical to resolve conflicts and discover values that reflect the real world; this task is called {\em data fusion}. This paper describes a novel approach that finds true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a case study on real-world data showing that the described algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources. WAIM 2013

International audience; This chapter introduces several state of the art techniques that could help to make deep underwater archaeological photogrammetric surveys easier, faster, more accurate, and to provide more visually appealing representations in 2D and 3D for both experts and public. We detail how the 3D captured data is analysed and then represented using ontologies, and how this facilitates interdisciplinary interpretation and cooperation. Towards more automation, we present a new method that adopts a deep learning approach for the detection and the recognition of objects of interest, amphorae for example. In order to provide more readable, direct and clearer illustrations, we describe several techniques that generate different styles of sketches out of orthophotos developed using neural networks. In the same direction, we present the Non-Photorealistic Rendering (NPR) technique, which converts a 3D model into a more readable 2D representation that is more useful to communicate and simplifies the identification of objects of interest. Regarding public dissemination, we demonstrate how recent advances in virtual reality to provide an accurate, high resolution, amusing and appropriate visualization tool that offers the public the possibility to ‘visit’ an unreachable archaeological site. Finally, we conclude by introducing the plenoptic approach, a new promising technology that can change the future of the photogrammetry by making it easier and less time consuming and that allows a user to create a 3D model using only one camera shot. Here, we introduce the concepts, the developing process, and some results, which we obtained with underwater imaging.

International audience; Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals in the field is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2020 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: location-based prediction of species based on environmental and occurrence data, and (iv) SnakeCLEF: image-based snake identification.

International audience; Ce début du 21e siècle est marqué par l'explosion des réseaux sociaux mais également par une nouvelle approche plus informelle, massive et ouverte de l'apprentissage. La dimension sociale (Springer, 2009), que l'on peut déduire de la prise en compte de l'action sociale (CECR, 2001), doit être interprétée dans toute sa dimension à la fois physique et virtuelle. L'acteur social est aujourd'hui connecté, confronté à des « textes » qui ne relèvent plus uniquement du mode écrit et de la littératie classique, ni même de la plurilittératie. La question de l'intégration linguistique des migrants doit ainsi prendre en compte la nouvelle donne numérique. Or, la didactique des langues vise avant tout la maitrise de la littératie écrite, malgré l'affirmation de l'importance de la communication orale et l'accent mis sur l'éducation plurilingue comme finalité. Il en va de même pour les échelles de performances du CECR ainsi que pour les certifications en langues, et en particulier pour les certifications du public migrant très marquées par l'alphabétisation (Huver et Springer, 2011). Les nouvelles vagues de migration, auxquelles nous assistons, montrent l'importance croissante de l'e-migration et des réseaux sociaux qui accompagnent les migrants dans leur fuite de la guerre et de la barbarie. Diminescu (2005) décrit les réseaux mis en place par les migrants connectés ; Bashi (2007), de son côté, montre que l'intégration sociale (la survie sociale) dépend de la qualité des liens (survival of the knitted) qu'ils entretiennent avec leurs communautés. La théorie des réseaux de liens (Granovetter, 1973) exprime cette nouvelle réalité. La compétence plurilingue ne peut donc plus suffire. Elle est portée et renforcée par de nouvelles compétences qui relèvent de la translittératie (digital literacy) et de la multimodalité. De nouvelles recherches (Kress, 2009) montrent que le monde numérique actuel n'est plus fondé sur le mode écrit. La toile propose en effet des « textes » multimodaux qui relèvent plus d'une sémiotique sociale (Halliday, 1978) que d'une linguistique structurale et textuelle ou d'une sociolinguistique. Dès lors, il semble indispensable de faire évoluer le cadrage théorique qui sous-tend la didactique des langues, le CECR ainsi que les certifications pour les migrants adultes. Nous appuierons cette problématique sur une analyse de discours de forums de migrants (Facebook) ainsi que sur les résultats d'une enquête auprès d'associations de migrants à Marseille. La Recommandation 2034 (2014) alinéa 7.2. suggère de « proposer des options autres que les tests de langue/d'intégration pour promouvoir et mesurer l'intégration et améliorer les perspectives d'intégration des migrants et des candidats à l'immigration ». Il nous semble que ces options devraient prendre en compte les nouvelles compétences numériques pour accompagner une société de la connaissance réellement inclusive (Stratégie de Lisbonne, 2000, et Europe 2020).

Robot Systems. This paper considers distributed systems of autonomous robots that can move freely on the two-dimensional Euclidean space, have visibility sensors (to see other robots, obstacles etc.) and can perform computations. One of the fundamental problems in distributed coordination of robots is to gather the robots at a single location. The gathering problem has been studied under various models with the objective of determining the minimal set of assumptions that still allows the robots to gather successfully within a finite time. For example, it is known that gathering can be solved even if the robots are anonymous (indistinguishable from each-other), oblivious (no persistent memory of the past), and cannot communicate explicitly with each other (except for indirect signaling using movement). Further, the robots may not share a common sense of direction. Robots operate in cycles that comprise look, compute, and move phases. The look phase consists in taking a snapshot of the other robots positions. In the compute phase, a robot computes a target destination, based on the previous observation, using a deterministic algorithm and in the move phase, the robot moves toward the computed destination (although the move may end before reaching the target destination). We consider the semi-synchronous ATOM model [4], where each cycle is considered to be atomic but only a subset of the robots may be active in each cycle. The robots are modeled as points on the Euclidean plane and the objective is to gather all robots at a single point.

International audience; Robot learning is a challenging – and somewhat unique – research domain. If a robot behavior is defined as a mapping between situations that occurred in the real world and actions to be accomplished, then the supervised learning of a robot behavior requires a set of representative examples (situation, desired action). In order to be able to gather such learning base, the human operator must have a deep understanding of the robot-world interaction (i.e., a model). But, there are many application domains where such models cannot be obtained, either because detailed knowledge of the robot’s world is unavailable (e.g., spatial or underwater exploration, nuclear or toxic waste management), or because it would be to costly. In this context, the automatic synthesis of a representative learning base is an important issue. It can be sought using reinforcement learning techniques – in particular Q-learning which does not require a model of the robot-world interaction. Compared to supervised learning, Q-learning examples are triplets (situation, action, Q value), where the Q value is the utility of executing the action in the situation. The supervised learning base is obtained by recruiting the triplets with the highest utility.

International audience; Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2021 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, and (iv) SnakeCLEF: Automatic Snake Species Identification with Country-Level Focus.