286 research outputs found
Evidences that SARS-CoV-2 Vaccine-Induced apoplexy may not be solely due to ASIA or VITT syndrome', Commentary on Pituitary apoplexy and COVID-19 vaccination: A case report and literature review
Facteurs de risque cardiovasculaires au cours du lupus systémique
Cette étude a pour objectif d'évaluer la fréquence des facteurs de risque cardiovasculaires au cours du lupus et de préciser leur prévalence. Etude rétrospective portant sur 250 patients ayant un lupus, diagnostiqué selon les critères de l'ACR, hospitalisés entre 1970 et 2013. Les données cliniques et para cliniques ont été recueillies à partir des observations médicales. Il s'agit de 228 femmes et 22 hommes d'âge moyen au diagnostic du lupus de 30, 32 ans (extrêmes: 16-69). La durée moyenne du suivi des patients était de 64 mois (extrêmes: 7 jours- 382mois). Quatre vingt dix patients (36%) étaient hypertendus, 74% avaient une hypercholestérolémie et 22% étaient diabétiques. Pour les autres facteurs de risque cardiovasculaire traditionnels, un âge > 50 ans a été retrouvé dans 40% des cas, le sexe masculin dans 8% des cas, l'obésité dans 76% des cas et le tabagisme dans 11% des cas. Les facteurs de risque surajoutés sont représentés par la présence des anticorps antiphospholipides (47% des cas), la néphropathie lupique (49% des cas), l'insuffisance rénale (42% des cas), la corticothérapie au long cours (74% des cas) et la chronicité de la maladie dans 35% des cas. Les complications cardiovasculaires retrouvées dans notre série étaient: les accidents vasculaires cérébraux (2%) et l'insuffisance coronarienne (5,6%). Devant l'importance du risque cardiovasculaire au cours du lupus, une surveillance rapprochée des facteurs de risque cardio-vasculaires semble primordiale chez les lupiques
Anomaly detection with semi-supervised classification based on risk estimators
A significant limitation of one-class classification anomaly detection
methods is their reliance on the assumption that unlabeled training data only
contains normal instances. To overcome this impractical assumption, we propose
two novel classification-based anomaly detection methods. Firstly, we introduce
a semi-supervised shallow anomaly detection method based on an unbiased risk
estimator. Secondly, we present a semi-supervised deep anomaly detection method
utilizing a nonnegative (biased) risk estimator. We establish estimation error
bounds and excess risk bounds for both risk minimizers. Additionally, we
propose techniques to select appropriate regularization parameters that ensure
the nonnegativity of the empirical risk in the shallow model under specific
loss functions. Our extensive experiments provide strong evidence of the
effectiveness of the risk-based anomaly detection methods
Scores for Multivariate Distributions and Level Sets
Forecasts of multivariate probability distributions are required for a variety of applications. Scoring rules enable the evaluation of forecast accuracy and comparison between forecasting methods. We propose a theoretical framework for scoring rules for multivariate distributions that encompasses the existing quadratic score and multivariate continuous ranked probability score. We demonstrate how this framework can be used to generate new scoring rules. In some multivariate contexts, it is a forecast of a level set that is needed, such as a density level set for anomaly detection or the level set of the cumulative distribution as a measure of risk. This motivates consideration of scoring functions for such level sets. For univariate distributions, it is well established that the continuous ranked probability score can be expressed as the integral over a quantile score. We show that, in a similar way, scoring rules for multivariate distributions can be decomposed to obtain scoring functions for level sets. Using this, we present scoring functions for different types of level sets, including density level sets and level sets for cumulative distributions. To compute the scores, we propose a simple numerical algorithm. We perform a simulation study to support our proposals, and we use real data to illustrate usefulness for forecast combining and conditional value at risk estimation
Scores for Multivariate Distributions and Level Sets
Forecasts of multivariate probability distributions are required for a variety of applications. Scoring rules enable the evaluation of forecast accuracy and comparison between forecasting methods. We propose a theoretical framework for scoring rules for multivariate distributions that encompasses the existing quadratic score and multivariate continuous ranked probability score. We demonstrate how this framework can be used to generate new scoring rules. In some multivariate contexts, it is a forecast of a level set that is needed, such as a density level set for anomaly detection or the level set of the cumulative distribution as a measure of risk. This motivates consideration of scoring functions for such level sets. For univariate distributions, it is well established that the continuous ranked probability score can be expressed as the integral over a quantile score. We show that, in a similar way, scoring rules for multivariate distributions can be decomposed to obtain scoring functions for level sets. Using this, we present scoring functions for different types of level sets, including density level sets and level sets for cumulative distributions. To compute the scores, we propose a simple numerical algorithm. We perform a simulation study to support our proposals, and we use real data to illustrate usefulness for forecast combining and conditional value at risk estimation
Parallelization Strategies for Modern Computing Platforms: Application to Illustrative Image Processing and Computer Vision Applications
RÉSUMÉ
L’évolution spectaculaire des technologies dans le domaine du matériel et du logiciel a permis l’émergence des nouvelles plateformes parallèles très performantes. Ces plateformes ont marqué le début d’une nouvelle ère de la computation et il est préconisé qu’elles vont rester dans le domaine pour une bonne période de temps. Elles sont présentes déjà dans le domaine du calcul de haute performance (en anglais HPC, High Performance Computer) ainsi que dans le domaine des systèmes embarqués. Récemment, dans ces domaines le concept de calcul hétérogène a été adopté pour atteindre des performances élevées. Ainsi, plusieurs types de processeurs sont utilisés, dont les plus populaires sont les unités centrales de traitement ou CPU (de l’anglais Central Processing Unit) et les processeurs graphiques ou GPU (de l’anglais Graphics Processing Units).
La programmation efficace pour ces nouvelles plateformes parallèles amène actuellement non seulement des opportunités mais aussi des défis importants pour les concepteurs. Par conséquent, l’industrie a besoin de l’appui de la communauté de recherche pour assurer le succès de ce nouveau changement de paradigme vers le calcul parallèle. Trois défis principaux présents pour les processeurs GPU massivement parallèles (ou “many-cores”) ainsi que pour les processeurs CPU multi-coeurs sont: (1) la sélection de la meilleure plateforme parallèle pour une application donnée, (2) la sélection de la meilleure stratégie de parallèlisation et (3) le réglage minutieux des performances (ou en anglais performance tuning) pour mieux exploiter les plateformes existantes.
Dans ce contexte, l’objectif global de notre projet de recherche est de définir de nouvelles solutions pour aider à la programmation efficace des applications complexes sur les plateformes parallèles modernes.
Les principales contributions Ă la recherche sont:
1. L’évaluation de l’efficacité d’accélération pour plusieurs plateformes parallèles, dans le cas des applications de calcul intensif.
2. Une analyse quantitative des stratégies de parallèlisation et implantation sur les plateformes à base de processeurs CPU multi-cœur ainsi que pour les plateformes à base de processeurs GPU massivement parallèles.
3. La définition et la mise en place d’une approche de réglage de performances (en Anglais performance tuning) pour les plateformes parallèles.
Les contributions proposées ont été validées en utilisant des applications réelles illustratives et un ensemble varié de plateformes parallèles modernes.----------ABSTRACT
With the technology improvement for both hardware and software, parallel platforms started a new computing era and they are here to stay. Parallel platforms may be found in High Performance Computers (HPC) or embedded computers. Recently, both HPC and embedded computers are moving toward heterogeneous computing platforms. They are employing both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) to achieve the highest performance. Programming efficiently for parallel platforms brings new opportunities but also several challenges. Therefore, industry needs help from the research community to succeed in its recent dramatic shift to parallel computing.
Parallel programing presents several major challenges. These challenges are equally present whether one programs on a many-core GPU or on a multi-core CPU. Three of the main challenges are: (1) Finding the best platform providing the required acceleration (2) Select the best parallelization strategy (3) Performance tuning to efficiently leverage the parallel platforms.
In this context, the overall objective of our research is to propose a new solution helping designers to efficiently program complex applications on modern parallel architectures. The contributions of this thesis are:
1. The evaluation of the efficiency of several target parallel platforms to speedup compute-intensive applications.
2. The quantitative analysis for parallelization and implementation strategies on multicore CPUs and many-core GPUs.
3. The definition and implementation of a new performance tuning framework for heterogeneous parallel platforms.
The contributions were validated using real computation intensive applications and modern parallel platform based on multi-core CPU and many-core GPU
Scoring Functions for Multivariate Distributions and Level Sets
Interest in predicting multivariate probability distributions is growing due
to the increasing availability of rich datasets and computational developments.
Scoring functions enable the comparison of forecast accuracy, and can
potentially be used for estimation. A scoring function for multivariate
distributions that has gained some popularity is the energy score. This is a
generalization of the continuous ranked probability score (CRPS), which is
widely used for univariate distributions. A little-known, alternative
generalization is the multivariate CRPS (MCRPS). We propose a theoretical
framework for scoring functions for multivariate distributions, which
encompasses the energy score and MCRPS, as well as the quadratic score, which
has also received little attention. We demonstrate how this framework can be
used to generate new scores. For univariate distributions, it is
well-established that the CRPS can be expressed as the integral over a quantile
score. We show that, in a similar way, scoring functions for multivariate
distributions can be "disintegrated" to obtain scoring functions for level
sets. Using this, we present scoring functions for different types of level
set, including those for densities and cumulative distributions. To compute the
scoring functions, we propose a simple numerical algorithm. We illustrate our
proposals using simulated and stock returns data
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Global visibility of publications through Digital Object Identifiers
This brief research report analyzes the availability of Digital Object Identifiers (DOIs) worldwide, highlighting the dominance of large publishing houses and the need for unique persistent identifiers to increase the visibility of publications from developing countries. The study reveals that a considerable amount of publications from developing countries are excluded from the global flow of scientific information due to the absence of DOIs, emphasizing the need for alternative publishing models. The authors suggest that the availability of DOIs should receive more attention in scholarly communication and scientometrics, contributing to a necessary debate on DOIs relevant for librarians, publishers, and scientometricians
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