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 & Orthogonal frequency-division multiplexing. 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.

Multiple Imputation for Multilevel Data with Continuous and Binary Variables

Abstract: We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data.

Benchmarking classification of earth-observation data: From learning explicit features to convolutional networks

Abstract: In this paper, we address the task of semantic labeling of multisource earth-observation (EO) data. Precisely, we benchmark several concurrent methods of the last 15 years, from expert classifiers, spectral support-vector classification and high-level features to deep neural networks. We establish that (1) combining multisensor features is essential for retrieving some specific classes, (2) in the image domain, deep convolutional networks obtain significantly better overall performances and (3) transfer of learning from large generic-purpose image sets is highly effective to build EO data classifiers.

Multiple-cohort genetic association study reveals CXCR6 as a new chemokine receptor involved in long-term nonprogression to AIDS

Abstract: a Background. The compilation of previous genomewide association studies of AIDS shows a major polymor- phism in the HCP5 gene associated with both control of the viral load and long-term nonprogression (LTNP) to AIDS. Methods. To look for genetic variants that affect LTNP without necessary control of the viral load, we reanalyzed the genomewide data of the unique LTNP Genomics of Resistance to Immunodeficiency Virus (GRIV) cohort by excluding "elite controller" patients, who were controlling the viral load at very low levels (!100 copies/mL). Results. The rs2234358 polymorphism in the CXCR6 gene was the strongest signal ( ; odds ratio, 7 P p 2.5 10 1.85) obtained for the genomewide association study comparing the 186 GRIV LTNPs who were not elite controllers with 697 uninfected control subjects. This association was replicated in 3 additional independent European studies, reaching genomewide significance of . This association with LTNP is independent of the 10 P p 9.7 10 combined CCR2-CCR5 locus and the HCP5 polymorphisms. Conclusions. The statistical significance, the replication, and the magnitude of the association demonstrate that CXCR6 is likely involved in the molecular etiology of AIDS and, in particular, in LTNP, emphasizing the power of extreme-phenotype cohorts. CXCR6 is a chemokine receptor that is known as a minor coreceptor in human immunodeficiency virus type 1 infection but could participate in disease progression through its role as a mediator of inflammation.

Mechanically Proving Termination Using Polynomial Interpretations

Abstract: For a long time, term orderings defined by polynomial interpretations were scarcely used in computer-aided termination proof of TRSs. But recently, the introduction of the dependency pairs approach achieved considerable progress w.r.t. automated termination proof, in particular by requiring from the underlying ordering much weaker properties than the classical approach. As a consequence, the noticeable power of a combination dependency pairs/polynomial orderings yielded a regain of interest for these interpretations. We describe criteria on polynomial interpretations for them to define weakly monotonic orderings. From these criteria, we obtain new techniques both for mechanically checking termination using a given polynomial interpretation and for finding such interpretations with full automation. With regard to automated search, we propose an original method for solving Diophantine constraints. We implemented these techniques into the CiME rewrite tool, and we provide some experimental results that show how useful polynomial orderings actually are in practice.