Evidence for an Excess of Soft Photons in Hadronic Decays of Z^0

Soft photons inside hadronic jets converted in front of the DELPHI main tracker (TPC) in events of qqbar disintegrations of the Z^0 were studied in the kinematic range 0.2 < E_gamma < 1 GeV and transverse momentum with respect to the closest jet direction p_T < 80 MeV/c. A clear excess of photons in the experimental data as compared to the Monte Carlo predictions is observed. This excess (uncorrected for the photon detection efficiency) is (1.17 +/- 0.06 +/- 0.27) x 10^{-3} gamma/jet in the specified kinematic region, while the expected level of the inner hadronic bremsstrahlung (which is not included in the Monte Carlo) is (0.340 +/- 0.001 +/- 0.038) x 10^{-3} gamma/jet. The ratio of the excess to the predicted bremsstrahlung rate is then (3.4 +/- 0.2 +/- 0.8), which is similar in strength to the anomalous soft photon signal observed in fixed target experiments with hadronic beams.Comment: 37 pages, 9 figures, Accepted by Eur. Phys. J.

Search for Dark Matter and Supersymmetry with a Compressed Mass Spectrum in the Vector Boson Fusion Topology in Proton-Proton Collisions at root s=8 TeV

Peer reviewe

Development and validation of HERWIG 7 tunes from CMS underlying-event measurements

This paper presents new sets of parameters (“tunes”) for the underlying-event model of the HERWIG7 event generator. These parameters control the description of multiple-parton interactions (MPI) and colour reconnection in HERWIG7, and are obtained from a fit to minimum-bias data collected by the CMS experiment at s=0.9, 7, and 13Te. The tunes are based on the NNPDF 3.1 next-to-next-to-leading-order parton distribution function (PDF) set for the parton shower, and either a leading-order or next-to-next-to-leading-order PDF set for the simulation of MPI and the beam remnants. Predictions utilizing the tunes are produced for event shape observables in electron-positron collisions, and for minimum-bias, inclusive jet, top quark pair, and Z and W boson events in proton-proton collisions, and are compared with data. Each of the new tunes describes the data at a reasonable level, and the tunes using a leading-order PDF for the simulation of MPI provide the best description of the dat

Measurements of the t(t)over-bar production cross section in lepton plus jets final states in pp collisions at 8 and ratio of 8 to 7 TeV cross sections

Peer reviewe

Measurement of the mass of the top quark in decays with a J/ψ meson in pp collisions at 8 TeV

Peer reviewe

Techniques d’apprentissage pour la caractérisation de bibliothèques de cellules en vue du test et du diagnostic de circuits intégrés.

The rapid growth of worldwide semiconductors demand leads to numerous innovations and improvements in cost, speed, and power consumption.The constant shrinking of the transistors feature size allows the development of new applications and capabilities but reveals new types of manufacturing defects.Efficient test and precise diagnosis are crucial to guarantee the creation of quality products and to improve the production yield.To keep test and diagnosis in phase with the new types of defects, new methodologies and faults models have been invented and deployed.The Cell-Aware (CA) fault models abstract the subtle defects found inside the standard cells used to design digital ICs, at the transistor level.Cell-aware test uses these fault models with ATPG to create test patterns explicitly targeting cells internal defects.Cell-aware diagnosis tools use the CA data to identify the location and type of intra-cell defects, providing valuable insights in the context of large and complex standard cells.While becoming an industry standard, the CA methodology has a large and costly development overhead, involving numerous electrical simulations to characterize standard cells.Due to the high number of standard cells to be characterized, and, for each cell, the high numbers of potential intra-cell defects and cell-level patterns to consider, the CA characterization phase represents a heavy usage and cost of simulator licenses and computational power.This thesis presents an innovative flow using Machine-Learning (ML) to reduce the CA test method runtime and ease its adoption for industrial usage.Experiments using different technology nodes demonstrated an over 99% runtime reduction for 80% of combinational cells.To ensure the generation of a quality CA model for all cells, while decreasing the CA characterization time, a hybrid flow is proposed, mixing ML-based CA models prediction method with the conventional method using electrical simulations.This hybrid-flow includes a decision algorithm, which leverage ML techniques to decide whether the CA characterization of a new standard cell should be ML-based or simulation-based, thus allowing to decrease the CA characterization runtime while maintaining high quality CA models for all cells.Experimental results demonstrate the high performance of the new decision algorithm and the quality of the obtained CA models.The coverage of real cell-internal defects of ATPG patterns using ML-predicted CA data proves that our predicted CA data can accurately replace those obtained by running extensive analog simulations, thus proving the effectiveness and pertinence of the proposed methodology.La croissance rapide de la demande mondiale de semi-conducteurs conduit à de nombreuses innovations et améliorations en matière de coût, de vitesse et de consommation d'énergie.La réduction continue de la taille des transistors permet le développement de nouvelles applications et fonctionnalités, mais fait apparaître de nouveaux types de défauts de fabrication.Pour garantir la fabrication de produits de qualité et améliorer le rendement de production, il est nécessaire de disposer d'outils de test efficaces et de diagnostic précis.Pour permettre la manipulation des nouveaux types de défauts par les outils de test et de diagnostic, de nouvelles méthodes et de nouveaux modèles de fautes ont été inventés et développés.Les modèles de fautes Cell-Aware (CA) représentent les défauts subtils situés à l'intérieur des cellules standards utilisées pour concevoir des circuits intégrés numériques, au niveau transistor.Le test CA fournit ces modèles de fautes à un ATPG pour créer des vecteurs de test ciblant explicitement les défauts internes des cellules.Les outils de diagnostic CA utilisent les données CA pour identifier l'emplacement et le type des défauts intra-cellules, fournissant de précieuses informations pour les cellules standards les plus larges et complexesBien que devenant un standard dans l'industrie, la méthodologie CA a un surcoût de développement important et coûteux, impliquant de nombreuses simulations électriques pour caractériser les cellules standards.En raison du nombre élevé de cellules standards à caractériser et, pour chaque cellule, des nombres élevés de défauts intra-cellules potentiels et de stimuli possibles à appliquer aux entrées de la cellule, la phase de caractérisation CA représente un coût important en matière de licences de simulateur à utiliser et de puissance de calcul nécessaire.Ce manuscrit présente une méthode innovante qui utilise l'apprentissage automatique (Machine-Learning --- ML) pour réduire le temps d'exécution du test CA et faciliter son adoption par l'industrie.Des expériences utilisant différents nœuds technologiques ont démontré une réduction de plus de 99% du temps d'exécution pour 80% des cellules combinatoires.Pour assurer la génération d'un modèle CA de qualité pour toutes les cellules standards, tout en diminuant le temps de caractérisation CA, un flot hybride est proposé, mélangeant la méthode de prédiction des modèles CA basés sur l'apprentissage automatique avec la méthode conventionnelle utilisant des simulations électriques.Ce flot hybride comprend un algorithme de décision, qui tire également parti des techniques d'apprentissage automatique pour décider si la caractérisation CA d'une nouvelle cellule standard doit être basée sur des méthodes ML ou bien basée sur des simulations analogiques, permettant ainsi de réduire le temps d'exécution de la caractérisation CA tout en obtenant des modèles CA de haute qualité pour toutes les cellules.Les résultats expérimentaux ont démontré les bonnes performances du nouvel algorithme de décision.Le taux de couverture de fautes, sur des défauts intra-cellules réels, de vecteurs de test obtenus par ATPG en utilisant des modèles CA générés par les méthodes d'apprentissage automatique démontre le fait que nos données CA obtenues par prédiction peuvent efficacement remplacer celles obtenues par simulations.Ce point prouve l'efficacité et la pertinence de la méthodologie proposée

Crossmodal integration of emotional stimuli in alcohol dependence

Face–voice integration has been extensively explored among healthy participants during the last decades. Nevertheless, while binding alterations constitute a core feature of many psychiatric diseases, these crossmodal processing have been very little explored in these populations. This chapter presents three studies offering an integrative use of behavioural, electrophysiological and neuroimaging techniques to explore the audio–visual integration of emotional stimuli in alcohol dependence. These results constitute a preliminary step towards a multidisciplinary exploration of crossmodal processing in psychiatry, extending to other stimulations, sensorial modalities and populations. The exploration of impaired crossmodal abilities could renew the knowledge on “normal” audio–visual integration and could lead to innovative therapeutic programs

Eye tracking correlates of acute alcohol consumption: A systematic and critical review

Eye tracking has emerged as a reliable neuroscience tool indexing the eye movements' correlates of impairments resulting from alcohol-use disorders, ranging from perceptive abilities to high-level cognitive functions. This systematic review, following PRISMA guidelines, encompasses all human studies using eye tracking in participants presenting acute alcohol consumption. A literature search was conducted in PsycINFO, PubMed and Scopus, and a standardized methodological quality assessment was performed. Eye tracking studies were classified according to the processes measured (perception, attentional bias, memory, executive functions, prevention message processing). Eye tracking data centrally showed a global visuo-motor impairment (related to reduced cerebellar functioning) following alcohol intoxication, together with reduced memory and inhibitory control of eye movements. Conversely, the impact of such intoxication on alcohol-related attentional bias is still debated. The limits of this literature have been identified, leading to the emergence of new research avenues to increase the understanding of eye movements during alcohol intoxication, and to the proposal of guidelines for future research. Copyright © 2019 Elsevier Ltd. All rights reserved

Accelerating Cell-Aware Model Generation Through Machine Learning

International audienceINTRODUCTIONTo achieve the highest product quality, Cell-Aware (CA) test has become mandatory for semiconductor industry. In this methodology, a cell-internal-fault dictionary or CA model, describing the detection conditions of each potential defect affecting a cell, is used [1-2]. However, the generation of CA models for all standard cells is a time- and resource-consuming task that limits the deployment of CA test.Typical CA model generation flow starts with a SPICE netlist representation of a standard cell. This representation is used by an electrical simulator to simulate each potential defect against an exhaustive set of stimuli. The stimuli detecting defects are synthetized into a CA model. As thousands of standard cells, with various complexities, are used for a given technology, the generation time of CA models for complete standard cell libraries may reach up to several months, thus drastically increasing the library characterization process cost.To improve the generation run time of CA models and ease the characterization, this work proposes a methodology to predict the behavior of cell-internal defects using Machine Learning (ML) [3]. More widely, the goal is to use existing CA models from various standard cell libraries developed using different technologies to predict CA models for new standard cells independently of the technology

A Comprehensive Learning-Based Flow for Cell-Aware Model Generation

International audienceAs the semiconductor industry continues to shrink the transistor feature size, new fault models need to be invented and deployed to ensure manufacturing test and diagnostic of the highest quality. The Cell-Aware (CA) test and diagnosis methodology targets the detection of defects inside standard (std) cells, at the transistor level. While becoming an industry standard, the CA methodology, has a large and costly deployment overhead, involving numerous analog simulations. In [1], we presented an innovative flow using Machine-Learning (ML) to reduce the CA test method runtime and ease its adoption for industrial usage. Experiments using different technology nodes demonstrated an over 99% runtime reduction for 80% of combinational cells. In this paper, new elements are presented to more widely take advantage of the ML flow for CA characterization. This includes a new decision algorithm, leveraging ML techniques to decide whether the CA characterization of a new std cell should be MLbased or simulation-based, thus allowing to decrease the CA characterization runtime while maintaining high quality CA models for all cells. Experimental results demonstrate the high performance of the new decision algorithm. The fault coverage on real cell-internal defects of ATPG patterns using ML predicted CA data proves that our predicted CA data can accurately replace those obtained by running extensive analog simulations, thus proving the effectiveness and pertinence of the proposed methodology