Décomposition en valeurs singulières randomisée et positionnement multidimensionel à base de tâches

The multidimensional scaling (MDS) is an important and robust algorithm for representing individual cases of a dataset out of their respective dissimilarities. However, heuristics, possibly trading-off with robustness, are often preferred in practice due to the potentially prohibitive memory and computational costs of the MDS. The recent introduction of random projection techniques within the MDS allowed it to be become competitive on larger testcases. The goal of this manuscript is to propose a high-performance distributed-memory MDS based on random projection for processing data sets of even larger size (up to one million items). We propose a task-based design of the whole algorithm and we implement it within an efficient software stack including state-of-the-art numerical solvers, runtime systems and communication layers. The outcome is the ability to efficiently apply robust MDS to large datasets on modern supercomputers. We assess the resulting algorithm and software stack to the point cloud visualization for analyzing distances between sequencesin metabarcoding.Le positionnement multidimensionnel (MDS) est un algorithme important et robuste pour représenter les cas individuels d’un ensemble de données en fonction de leurs dissimilarités respectives. Cependant, les heuristiques, qui peuvent être un compromis avec la robustesse, sont souvent préférées en pratique en raison de sa consommation mémoire et de ses coûts potentiellement prohibitifs. L’introduction récente de techniques de projection aléatoire dans le MDS lui a permis de devenir compétitif sur des cas test plus importants. L’objectif de ce manuscrit est de proposer un MDS haute performance basé sur la projection aléatoire pour le traitement d’ensembles de données de taille encore plus grande (jusqu’à un million d’éléments). Nous proposons une conception de l’algorithme et nous l’implémentons dans une pile logicielle efficace, comprenant des solveurs numériques de pointe ainsi des systèmes d’exécution et des couches de communication optimisés. L’aboutissement de ce travail résultat est la capacité d’appliquer efficacement le MDS robuste à de grands ensembles de données sur des super-ordinateurs modernes. Nous évaluons l’algorithme etla pile logicielle résultants à la visualisation de nuages de points pour l’analyse des distances entre séquences de metabarcoding

Using FRAM to determine enrichment of shielded uranium by portable electrically cooled HPGe detectors

The capability of the FRAM software to accurately determine the enrichment of shielded uranium by portable electrically cooled HPGe detectors was studied. This can have applications in the future, e.g., for the verification of aged uranium-bearing products, scrap, and waste materials, especially during short notice random or unannounced inspections, when detector cooling with liquid nitrogen is not feasible. More than 7000 high-resolution gamma spectra of certified reference materials were taken by the ORTEC "Detective" detector under well-defined measurement conditions. Up to 16 mm of steel was used for shielding. The 235U enrichment of the reference materials varied from 0.31% to 4.46%. The settings of an existing FRAM parameter set were optimized and all the collected spectra were analysed using the default and the optimized parameter sets. The results obtained with these parameter sets are compared in this paper.JRC.G.II.6-Nuclear Safeguards and Forensic

Contribution to continuum estimation in gamma spectrum by observation of local minima

International audienceThis paper presents a method to estimate the continuum of a gamma rays spectrum through the observation of local minima. The method is simple, automatable and has a large scope of application. Indeed, it is not limited by the peaks width, and consequently it is usable with GeHP as well as with scintillators spectra. In the extent where the method exploits signal properties, its operation is easily explainable. It involves a limited set of meaningful parameters for which an adjustment is proposed. The potential of this method is demonstrated through simulations but also through real gamma spectrometry measurements

Baseline removal in spectrometry gamma by observation of local minima

International audienceThis paper presents a Baseline Removal method in the context of spectrometry gamma. The method implements an estimator for the full continuum based on the observation of local minima. This estimator is constructed from the statistical properties of the signal and is therefore easily explainable. The method involves a limited number of fixed parameters, which allows the automation of the process. Moreover, the method is adaptable to any peaks width, which makes it suitable for both HPGe spectrometers and scintillators. Application to real gamma spectrometry measurements are presented, as well as a discussion about the choice of the parameters, for which an adjustment is proposed

Task-based randomized singular value decomposition and multidimensional scaling

Le positionnement multidimensionnel (MDS) est un algorithme important et robuste pour représenter les cas individuels d’un ensemble de données en fonction de leurs dissimilarités respectives. Cependant, les heuristiques, qui peuvent être un compromis avec la robustesse, sont souvent préférées en pratique en raison de sa consommation mémoire et de ses coûts potentiellement prohibitifs. L’introduction récente de techniques de projection aléatoire dans le MDS lui a permis de devenir compétitif sur des cas test plus importants. L’objectif de ce manuscrit est de proposer un MDS haute performance basé sur la projection aléatoire pour le traitement d’ensembles de données de taille encore plus grande (jusqu’à un million d’éléments). Nous proposons une conception de l’algorithme et nous l’implémentons dans une pile logicielle efficace, comprenant des solveurs numériques de pointe ainsi des systèmes d’exécution et des couches de communication optimisés. L’aboutissement de ce travail résultat est la capacité d’appliquer efficacement le MDS robuste à de grands ensembles de données sur des super-ordinateurs modernes. Nous évaluons l’algorithme etla pile logicielle résultants à la visualisation de nuages de points pour l’analyse des distances entre séquences de metabarcoding.The multidimensional scaling (MDS) is an important and robust algorithm for representing individual cases of a dataset out of their respective dissimilarities. However, heuristics, possibly trading-off with robustness, are often preferred in practice due to the potentially prohibitive memory and computational costs of the MDS. The recent introduction of random projection techniques within the MDS allowed it to be become competitive on larger testcases. The goal of this manuscript is to propose a high-performance distributed-memory MDS based on random projection for processing data sets of even larger size (up to one million items). We propose a task-based design of the whole algorithm and we implement it within an efficient software stack including state-of-the-art numerical solvers, runtime systems and communication layers. The outcome is the ability to efficiently apply robust MDS to large datasets on modern supercomputers. We assess the resulting algorithm and software stack to the point cloud visualization for analyzing distances between sequencesin metabarcoding

Task-based randomized singular value decomposition and multidimensional scaling

Le positionnement multidimensionnel (MDS) est un algorithme important et robuste pour représenter les cas individuels d’un ensemble de données en fonction de leurs dissimilarités respectives. Cependant, les heuristiques, qui peuvent être un compromis avec la robustesse, sont souvent préférées en pratique en raison de sa consommation mémoire et de ses coûts potentiellement prohibitifs. L’introduction récente de techniques de projection aléatoire dans le MDS lui a permis de devenir compétitif sur des cas test plus importants. L’objectif de ce manuscrit est de proposer un MDS haute performance basé sur la projection aléatoire pour le traitement d’ensembles de données de taille encore plus grande (jusqu’à un million d’éléments). Nous proposons une conception de l’algorithme et nous l’implémentons dans une pile logicielle efficace, comprenant des solveurs numériques de pointe ainsi des systèmes d’exécution et des couches de communication optimisés. L’aboutissement de ce travail résultat est la capacité d’appliquer efficacement le MDS robuste à de grands ensembles de données sur des super-ordinateurs modernes. Nous évaluons l’algorithme etla pile logicielle résultants à la visualisation de nuages de points pour l’analyse des distances entre séquences de metabarcoding.The multidimensional scaling (MDS) is an important and robust algorithm for representing individual cases of a dataset out of their respective dissimilarities. However, heuristics, possibly trading-off with robustness, are often preferred in practice due to the potentially prohibitive memory and computational costs of the MDS. The recent introduction of random projection techniques within the MDS allowed it to be become competitive on larger testcases. The goal of this manuscript is to propose a high-performance distributed-memory MDS based on random projection for processing data sets of even larger size (up to one million items). We propose a task-based design of the whole algorithm and we implement it within an efficient software stack including state-of-the-art numerical solvers, runtime systems and communication layers. The outcome is the ability to efficiently apply robust MDS to large datasets on modern supercomputers. We assess the resulting algorithm and software stack to the point cloud visualization for analyzing distances between sequencesin metabarcoding

L'eau à découvert

Indispensable à la régulation du climat, au développement de la vie sur Terre, au maintien des écosystèmes, aux populations, au développement de l'agriculture, de l'industrie comme à la production d'énergie, l'eau est un élément vital. Il convient donc, dans un contexte de changement global, d'analyser dans toute sa diversité la place et le rôle de l'eau et de se donner ainsi les moyens de mieux la préserver. Autour de cet enjeu qui engage toute l'humanité, Agathe Euzen, Catherine Jeandel et Rémy Mosseri ont réuni près de cent cinquante contributions, visant à apporter un éclairage sur chacun des domaines et des approches que couvre cette thématique. Quelle est l'origine de l'eau ? Son rapport avec l'apparition de la vie ? Quel rôle a-t-elle joué dans l'histoire de la planète et dans le développement de la vie végétale, animale et humaine ? Quel est son cycle ? Quelles sont ses propriétés chimiques ? Comment les sociétés se sont-elles emparées de cet élément précieux ? Allons-nous manquer d'eau ? L'eau est-elle source de conflits ? Comment l'eau est-elle gérée ? Comment recycle-t-on une eau polluée ? Quels sont les risques pour la santé mondiale ? Quels sont les grands enjeux liés à l'eau au xxie siècle ? Comprendre et proposer des solutions à ces défis majeurs est l'intention de cet ouvrage

Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey

Background: Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes. Methods: An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile. Results: 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. Discussion: The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI

Time for a paradigm shift in shared decision-making in trauma and emergency surgery? Results from an international survey

Background Shared decision-making (SDM) between clinicians and patients is one of the pillars of the modern patient-centric philosophy of care. This study aims to explore SDM in the discipline of trauma and emergency surgery, investigating its interpretation as well as the barriers and facilitators for its implementation among surgeons. Methods Grounding on the literature on the topics of the understanding, barriers, and facilitators of SDM in trauma and emergency surgery, a survey was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was sent to all 917 WSES members, advertised through the society’s website, and shared on the society’s Twitter profile. Results A total of 650 trauma and emergency surgeons from 71 countries in five continents participated in the initiative. Less than half of the surgeons understood SDM, and 30% still saw the value in exclusively engaging multidisciplinary provider teams without involving the patient. Several barriers to effectively partnering with the patient in the decision-making process were identified, such as the lack of time and the need to concentrate on making medical teams work smoothly. Discussion Our investigation underlines how only a minority of trauma and emergency surgeons understand SDM, and perhaps, the value of SDM is not fully accepted in trauma and emergency situations. The inclusion of SDM practices in clinical guidelines may represent the most feasible and advocated solutions