Conservatoire national des arts et métiers

About: Conservatoire national des arts et métiers is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Context (language use). The organization has 3573 authors who have published 7127 publications receiving 141430 citations. The organization is also known as: CNAM & Conservatoire des arts et métiers.

Competing risks regression for clustered data

Abstract: A population average regression model is proposed to assess the marginal effects of covariates on the cumulative incidence function when there is dependence across individuals within a cluster in the competing risks setting. This method extends the Fine?Gray model proportional hazards model for the subdistribution to situations where individualswithin a cluster may be correlated due to unobserved shared factors. Estimators of the regression parameters in the marginal model are developed under an independence working assumption, where the correlation across individuals within a cluster is completely unspecified. The estimators are consistent and asymptotically normal, and variance estimation may be achieved without specifying the form of the dependence across individuals. A simulation study evidences that the inferential procedures perform well with realistic sample sizes. The practical utility of the methods is illustrated with data from the European Bone Marrow Transplant Registry.

Improvement of range-free localization technology by a novel DV-hop protocol in wireless sensor networks

Abstract: Localization is a fundamental issue for many applications in wireless sensor networks. Without the need of additional ranging devices, the range-free localization technology is a cost-effective solution for low-cost indoor and outdoor wireless sensor networks. Among range-free algorithms, DV-hop (Distance Vector-hop) has the advantage to localize the mobile nodes which has less than three neighbour anchors. Based on the original DV-hop algorithm, this paper presents two improved algorithms (Checkout DV-hop and Selective 3-Anchor DV-hop). Checkout DV-hop algorithm estimates the mobile node position by using the nearest anchor, while Selective 3-Anchor DV-hop algorithm chooses the best 3 anchors to improve localization accuracy. Then, in order to implement these DV-hop based algorithms in network scenarios, a novel DV-hop localization protocol is proposed. This new protocol is presented in detail in this paper, including the format of data payloads, the improved collision reduction method E-CSMA/CA, as well as parameters used in deciding the end of each DV-hop step. Finally, using our localization protocol, we investigate the performance of typical DV-hop based algorithms in terms of localization accuracy, mobility, synchronization and overhead. Simulation results prove that Selective 3-Anchor DV-hop algorithm offers the best performance compared to Checkout DV-hop and the original DV-hop algorithm.

Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part A: 2-D Contest

Abstract: In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the scientific results obtained by the winners of the 2-D contest, which studied either the complementarity of RGB and LiDAR with deep neural networks (winning team) or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data (runner-up team). The data and the previously undisclosed ground truth will remain available for the community and can be obtained at http://www.grss-ieee.org/community/technical-committees/data-fusion/2015-ieee-grss-data-fusion-contest/. The 3-D part of the contest is discussed in the Part-B paper [1].

The Centrality of Work

Abstract: This article briefly presents some of the main features of the notion of "centrality of work" within the framework of the "psychodynamic" approach to work developed by Christophe Dejours The paper argues that we should distinguish between at least four separate but related ways in which work can be said to be central: psychologically, in terms of gender relations, social-politically and epistemically