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A commuting model for the analysis of the impacts of a tramway project: application to the Lens area
No full text[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM [Axe_IRSTEA]DTAM1-REPRORoad congestion is not only an issue for major European urban agglomerations, but also for smaller ones. It is also the case of the Lens urban area, where car use is much higher than the average for medium-sized urban agglomerations in France. Local authorities put forward tramway projects to deal with the strongly negative externalities of congestion: travel time losses and pollution. To analyze its medium-term impact, we have developed a commuting with congestion model, inspired by the four-step traditional model, but with data made available from an origin- destination matrix. The results are encouraging but insufficient, and it is necessary to adopt supplementary measures in order to retrieve and justify the sizeable investments needed. Some measures prove to be very efficient, such as parking fees and urban tolls. Other measures, such as the subsidization of public transport, are partially efficient since they have an impact mainly on intra-urban commutes.La congestion routière n'est pas seulement un problème pour les grandes agglomérations urbaines européennes, mais aussi pour les plus petites. C'est également le cas de la zone urbaine de Lens, où l'utilisation des voitures est nettement supérieure à la moyenne des agglomérations urbaines de taille moyenne en France. Les autorités locales ont présenté des projets de tramway pour faire face aux externalités fortement négatives de la congestion, soit d'une part, les pertes de temps de déplacement et d'autre part, les problèmes liés à la pollution. Pour analyser l'impact à moyen terme de ce projet de tramway sur l'agglomération lensoise, nous avons développé un modèle de déplacements domicile-travail qui tient compte de la congestion, inspiré du modèle traditionnel en quatre étapes, utilisant des données disponibles à partir d'une matrice d'origine-destination. Les résultats sont encourageants mais insuffisants et il est nécessaire d'adopter des mesures complémentaires pour rentabiliser et justifier les investissements importants nécessaires d'un tel projet. Certaines mesures se réÏlent très efficaces, comme la tarification du stationnement et les péages urbains. D'autres mesures, telles que le subventionnement des transports publics, sont partiellement efficaces car elles ont un impact principalement sur les déplacements intra-urbains
Multispectral demosaicing using pseudo-panchromatic image
No full textInternational audienceSingle-sensor color cameras, which classically use a color filter array (CFA) to sample RGB channels, have recently been extended to the multispectral domain. To sample more than three wavelength bands, such systems use a multispectral filter array (MSFA) that provides a raw image in which a single channel value is available at each pixel. A demosaicing procedure is then needed to estimate a fully-defined multispectral image. In this paper, we review multispectral demosaicing methods and propose a new one based on the pseudo-panchromatic image (PPI). Pixel values in the PPI are computed as the average spectral values. Experimental results show that our method provides estimated images of better quality than classical ones
Alimentation et isolement des 60 ans et plus retraités. L'exemple de Paris et de la petite couronne
No full textNotre étude montre comment l'isolement social façonne les habitudes alimentaires des personnes âgées. Plus précisément, nous étudions les effets individuels et de voisinage sur la régularité et la fréquence des repas. Les facteurs individuels comprennent les caractéristiques sociodémographiques ainsi que les mesures objectives et subjectives de l'isolement social ; les facteurs au niveau du quartier incluent le statut socio-économique et l'accès à la nourriture
Apprentissage de représentation dirigée par la tâche
No full textMachine learning proposes numerous algorithms to solve the different tasks that can be extracted from real world prediction problems. To solve the different concerned tasks, most Machine learning algorithms somehow rely on relationships between instances. Pairwise instances relationships can be obtained by computing a distance between the vectorial representations of the instances. Considering the available vectorial representation of the data, none of the commonly used distances is ensured to be representative of the task that aims at being solved. In this work, we investigate the gain of tuning the vectorial representation of the data to the distance to more optimally solve the task. We more particularly focus on an existing graph-based algorithm for classification task. An algorithm to learn a mapping of the data in a representation space which allows an optimal graph-based classification is first introduced. By projecting the data in a representation space in which the predefined distance is representative of the task, we aim at outperforming the initial vectorial representation of the data when solving the task. A theoretical analysis of the introduced algorithm is performed to define the conditions ensuring an optimal classification. A set of empirical experiments allows us to evaluate the gain of the introduced approach and to temper the theoretical analysis.De nombreux algorithmes d'Apprentissage automatique ont été proposés afin de résoudre les différentes tâches pouvant être extraites des problèmes de prédiction issus d'un contexte réel. Pour résoudre les différentes tâches pouvant être extraites, la plupart des algorithmes d'Apprentissage automatique se basent d'une manière ou d'une autre sur des relations liant les instances. Les relations entre paires d'instances peuvent être définies en calculant une distance entre les représentations vectorielles des instances. En se basant sur la représentation vectorielle des données, aucune des distances parmi celles communément utilisées n'est assurée d'être représentative de la tâche à résoudre. Dans ce document, nous étudions l'intérêt d'adapter la représentation vectorielle des données à la distance utilisée pour une meilleure résolution de la tâche. Nous nous concentrons plus précisément sur l'algorithme existant résolvant une tâche de classification en se basant sur un graphe. Nous décrivons d'abord un algorithme apprenant une projection des données dans un espace de représentation permettant une résolution, basée sur un graphe, optimale de la classification. En projetant les données dans un espace de représentation dans lequel une distance préalablement définie est représentative de la tâche, nous pouvons surpasser la représentation vectorielle des données lors de la résolution de la tâche. Une analyse théorique de l'algorithme décrit est développée afin de définir les conditions assurant une classification optimale. Un ensemble d'expériences nous permet finalement d'évaluer l'intérêt de l'approche introduite et de nuancer l'analyse théorique
Contributions au carnet de recherche du projet TALIE (Traditions [Textes, Traces] de l'Antiquité à Lille et dans l'Eurorégion)
No full textContributions au carnet de recherche du projet TALIE (Traditions [Textes, Traces] de l'Antiquité à Lille et dans l'Eurorégion) : http://talie.hypotheses.org/author/talie
bf-pd: Enabling Mediated Communication and Cooperation in Improvised Digital Orchestras
No full textInternational audienceDigital musical instruments enable new musical collaboration possibilities, extending those of acoustic ensembles. However, the use of these new possibilities remains constrained due to a lack of a common terminology and technical framework for implementing them. Bf-pd is a new software library built in the PureData (Pd) language which enables communication and cooperation between digital instruments. Its design is based on the BOEUF conceptual framework which consists of a classification of modes of collaboration used in collective music performance , and a set of components which affords them. Bf-pd can be integrated into any digital instrument built in Pd, and provides a " collaboration window " from which musicians can easily view each others' activity and share control of instrument parameters and other musical data. We evaluate the implementation and design of bf-pd through workshops and a preliminary study and discuss its impact on collaboration within improvised ensembles of digital instruments
BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts
No full textInternational audienceThe data management needs of the neuroimaging community are currently addressed by several specialized software platforms, which automate repetitive data import, archiving and processing tasks. The BIOMedical Imaging SemanTic data management (BIOMIST) project aims at creating such a framework, yet with a radically different approach: the key insight behind it is the realization that the data management needs of the neuroimaging community—organizing the secure and convenient storage of large amounts of large files, bringing together data from different scientific domains, managing workflows and access policies, ensuring traceability and sharing data across different labs—are actually strikingly similar to those already expressed by the manufacturing industry. The BIOMIST neuroimaging data management framework is built around the same systems as those that were designed in order to meet the requirements of the industry. Product Lifecycle Management (PLM) systems rely on an object-oriented data model and allow the traceability of data and workflows throughout the life of a product, from its design to its manufacturing, maintenance, and end of life, while guaranteeing data consistency and security. The BioMedical Imaging—Lifecycle Management data model was designed to handle the specificities of neuroimaging data in PLM systems, throughout the lifecycle of a scientific study. This data model is both flexible and scalable, thanks to the combination of generic objects and domain-specific classes sourced from publicly available ontologies. The data integrated management and processing method was then designed to handle workflows of processing chains in PLM. Following these principles, workflows are parameterized and launched from the PLM platform onto a computer cluster, and the results automatically return to the PLM where they are archived along with their provenance information. Third, to transform the PLM into a full-fledged neuroimaging framework, we developed a series of external modules: DICOM import, XML form data import web services, flexible graphical querying interface, and SQL export to spreadsheets. Overall, the BIOMIST platform is well suited for the management of neuroimaging cohorts, and it is currently used for the management of the BIL&GIN dataset (300 participants) and the ongoing magnetic resonance imaging-Share cohort acquisition of 2,000 participants
Multi-view Sequential Games: The Helper-Agent Problem
No full textProblems where agents wish to cooperate for a common goal, but disagree on their view of reality are frequent. Of particular interest are settings where one agent is an AI ``helper agent'' and the other is a human. The AI wants to help the human to complete a task but the AI and human may disagree about the world model. This may come about for example because of the limited rationality and biases of the human or because of misaligned reward models. In this paper, we formalize this as the multi-view sequential game, and show that even when the human's model is far from correct, an AI can still steer their behavior to more beneficial outcomes. In particular, we develop a number of algorithms, based on dynamic programming to discover helper policies for the AI, under different assumptions about the AI's knowledge. Experimentally, we show that the AI's beliefs about human model are not required to be accuratein order to act as a useful helper agent
