Carnaval : le discours populaire et l'art du bonimenteur chez Bonaventure Des Périers ; suivi de Propos et contes avinés

Ce mémoire de maîtrise en recherche-création explore aussi bien la teneur populaire que l'aspect proprement théâtral associés aux figures narratives que sont les bonimenteurs. À partir d'une lecture critique des Nouvelles récréations et joyeux devis (1558) de Bonaventure Des Périers, le volet essai du mémoire tente de cerner quelle est la posture particulière que présente ce narrateur bonimenteur de la Renaissance française. Nous avons cette fois tenté de voir comment ce discours employé par Des Périers, grâce à une stylistique et une structure discursive qui lui sont propres, participe de la figure du bonimenteur s’adressant au lecteur comme il le ferait sur une place publique, usant de cette posture pour jouer avec son lecteur, grâce à des effets paradoxaux de rapprochement et de mise à distance. Cette façon ludique de se mettre en valeur propre aux bonimenteurs aura des échos jusque dans le volet création du mémoire. Propos et contes avinés est un recueil de nouvelles inspiré de la tradition boccaccienne dans lequel des protagonistes se racontent des histoires pour convaincre leurs interlocuteurs du bien-fondé de leur position. S’y juxtapose aussi bien la parole dialoguée (dans les « propos ») que la parole narrative (dans les « contes »), dans une atmosphère carnavalesque.This master’s thesis in research/creation explores the popular scope as well as the theatrical aspect associated with the narrative figures that are the pitchmen. Following a critical reading of the Nouvelles récréations et joyeux devis (1558) from Bonaventure Des Périers, the essay portion of the thesis attempts to identify the particular posture presented by the pitchman narrator from the French Renaissance. We have attempted to illustrate how this discourse used by Des Périers, through his particular stylistic and discursive structure, partakes in the pitchman figure who addresses the readers directly as he would do in the public square and uses this posture to play with them, using paradoxical effects of proximity and detachment. This playful manner through which the pitchmen distinguish themselves will fin echoes in the creation portion of this thesis. Propos et contes avinés is a collection of short stories inspired by the boccaccian tradition in which the protagonists tell stories in order to convince their listeners of the validity of their opinions. Their dialogue (the discourse) is juxtaposed with narrative (the tale) in a carnivalesque atmosphere

ThinResNet: A New Baseline for Structured Convolutional Networks Pruning

Pruning is a compression method which aims to improve the efficiency of neural networks by reducing their number of parameters while maintaining a good performance, thus enhancing the performance-to-cost ratio in nontrivial ways. Of particular interest are structured pruning techniques, in which whole portions of parameters are removed altogether, resulting in easier to leverage shrunk architectures. Since its growth in popularity in the recent years, pruning gave birth to countless papers and contributions, resulting first in critical inconsistencies in the way results are compared, and then to a collective effort to establish standardized benchmarks. However, said benchmarks are based on training practices that date from several years ago and do not align with current practices. In this work, we verify how results in the recent literature of pruning hold up against networks that underwent both state-of-the-art training methods and trivial model scaling. We find that the latter clearly and utterly outperform all the literature we compared to, proving that updating standard pruning benchmarks and re-evaluating classical methods in their light is an absolute necessity. We thus introduce a new challenging baseline to compare structured pruning to: ThinResNet.Comment: 11 pages, 2 figures, 3 table

Rethinking Weight Decay For Efficient Neural Network Pruning

Introduced in the late 80's for generalization purposes, pruning has now become a staple to compress deep neural networks. Despite many innovations brought in the last decades, pruning approaches still face core issues that hinder their performance or scalability. Drawing inspiration from early work in the field, and especially the use of weight-decay to achieve sparsity, we introduce Selective Weight Decay (SWD), which realizes efficient continuous pruning throughout training. Our approach, theoretically-grounded on Lagrangian Smoothing, is versatile and can be applied to multiple tasks, networks and pruning structures. We show that SWD compares favorably to state-of-the-art approaches in terms of performance/parameters ratio on the CIFAR-10, Cora and ImageNet ILSVRC2012 datasets.Comment: 16 pages, 14 figures, submitted at ICML 2021, update : added new results, rewrit

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Élagage de réseaux de neurones convolutifs et son application aux systèmes embarqués de vision par ordinateur

Being at the state of the art in many domains, such as computer vision, convolutional neural networks became a staple for many industrial applications, such as autonomous vehicles—about which Stellantis have ambitions. However, neural networks can bear a great algorithmic complexity, as well as a large memory footprint, which makes them potentially unusable on embedded hardware such as those equipped on such vehicles. In order to reduce this complexity, while keeping the performance that said complexity is supposed to enable, the domain of neural networks compression proposed multiple families of methods, such as pruning that aims at simplifying networks by removing parts deemed unnecessary. Yet, the apparent simplicity of this principle actually hides many subtle implications that have a decisive impact on the efficiency of pruning. In order to clarify the unsuspected complexity of this method and to answer the question of its true efficiency, this manuscript tackles thematically each aspect of pruning and discusses both its theoretical foundations and its practical consequences. It also details the academical and industrial implications of various original contributions of this thesis about parameters supression, layers interdependencies and the energetic efficiency of pruned networks.A l’état de l’art dans de nombreux domaines tels que la vision par ordinateur, les réseaux à convolution sont devenus indispensables pour de nombreux types d'applications industrielles, comme la conception de véhicules autonomes – qui est l’une des ambitions de Stellantis. Toutefois, les réseaux de neurones peuvent présenter une grande complexité algorithmique, couplée à une importante empreinte mémoire, ce qui les rend potentiellement inutilisables sur le type de matériel embarqué que l’on peut trouver dans ces véhicules. Afin de réduire cette complexité, tout en conservant la performance d’origine le domaine de la compression de réseaux de neurones a proposé plusieurs types de méthodes, comme l’élagage qui vise à simplifier les réseaux en retirant des parties jugées inutiles. Cependant, derrière ce principe simple se cache en réalité de nombreuses considérations beaucoup plus subtiles ayant chacune de lourdes implications sur l’efficacité d’une telle méthode. Afin de mettre au clair toute la complexité insoupçonnée de l’élagage et de répondre à la question de son efficacité réelle, ce manuscrit aborde chaque aspect de la méthode de façon thématique et en discute à la fois les fondements théoriques et les conséquences pratiques. Il détaille également les implications académiques et industrielles de plusieurs contributions de cette thèse, portant notamment sur la suppression de paramètres, les interdépendances entre couches et l’efficacité énergétique des réseaux élagués

Élagage de réseaux de neurones convolutifs et son application aux systèmes embarqués de vision par ordinateur

A l’état de l’art dans de nombreux domaines tels que la vision par ordinateur, les réseaux à convolution sont devenus indispensables pour de nombreux types d'applications industrielles, comme la conception de véhicules autonomes – qui est l’une des ambitions de Stellantis. Toutefois, les réseaux de neurones peuvent présenter une grande complexité algorithmique, couplée à une importante empreinte mémoire, ce qui les rend potentiellement inutilisables sur le type de matériel embarqué que l’on peut trouver dans ces véhicules. Afin de réduire cette complexité, tout en conservant la performance d’origine le domaine de la compression de réseaux de neurones a proposé plusieurs types de méthodes, comme l’élagage qui vise à simplifier les réseaux en retirant des parties jugées inutiles. Cependant, derrière ce principe simple se cache en réalité de nombreuses considérations beaucoup plus subtiles ayant chacune de lourdes implications sur l’efficacité d’une telle méthode. Afin de mettre au clair toute la complexité insoupçonnée de l’élagage et de répondre à la question de son efficacité réelle, ce manuscrit aborde chaque aspect de la méthode de façon thématique et en discute à la fois les fondements théoriques et les conséquences pratiques. Il détaille également les implications académiques et industrielles de plusieurs contributions de cette thèse, portant notamment sur la suppression de paramètres, les interdépendances entre couches et l’efficacité énergétique des réseaux élagués.Being at the state of the art in many domains, such as computer vision, convolutional neural networks became a staple for many industrial applications, such as autonomous vehicles—about which Stellantis have ambitions. However, neural networks can bear a great algorithmic complexity, as well as a large memory footprint, which makes them potentially unusable on embedded hardware such as those equipped on such vehicles. In order to reduce this complexity, while keeping the performance that said complexity is supposed to enable, the domain of neural networks compression proposed multiple families of methods, such as pruning that aims at simplifying networks by removing parts deemed unnecessary. Yet, the apparent simplicity of this principle actually hides many subtle implications that have a decisive impact on the efficiency of pruning. In order to clarify the unsuspected complexity of this method and to answer the question of its true efficiency, this manuscript tackles thematically each aspect of pruning and discusses both its theoretical foundations and its practical consequences. It also details the academical and industrial implications of various original contributions of this thesis about parameters supression, layers interdependencies and the energetic efficiency of pruned networks

Élagage de réseaux de neurones convolutifs et son application aux systèmes embarqués de vision par ordinateur

Being at the state of the art in many domains, such as computer vision, convolutional neural networks became a staple for many industrial applications, such as autonomous vehicles—about which Stellantis have ambitions. However, neural networks can bear a great algorithmic complexity, as well as a large memory footprint, which makes them potentially unusable on embedded hardware such as those equipped on such vehicles. In order to reduce this complexity, while keeping the performance that said complexity is supposed to enable, the domain of neural networks compression proposed multiple families of methods, such as pruning that aims at simplifying networks by removing parts deemed unnecessary. Yet, the apparent simplicity of this principle actually hides many subtle implications that have a decisive impact on the efficiency of pruning. In order to clarify the unsuspected complexity of this method and to answer the question of its true efficiency, this manuscript tackles thematically each aspect of pruning and discusses both its theoretical foundations and its practical consequences. It also details the academical and industrial implications of various original contributions of this thesis about parameters supression, layers interdependencies and the energetic efficiency of pruned networks.A l’état de l’art dans de nombreux domaines tels que la vision par ordinateur, les réseaux à convolution sont devenus indispensables pour de nombreux types d'applications industrielles, comme la conception de véhicules autonomes – qui est l’une des ambitions de Stellantis. Toutefois, les réseaux de neurones peuvent présenter une grande complexité algorithmique, couplée à une importante empreinte mémoire, ce qui les rend potentiellement inutilisables sur le type de matériel embarqué que l’on peut trouver dans ces véhicules. Afin de réduire cette complexité, tout en conservant la performance d’origine le domaine de la compression de réseaux de neurones a proposé plusieurs types de méthodes, comme l’élagage qui vise à simplifier les réseaux en retirant des parties jugées inutiles. Cependant, derrière ce principe simple se cache en réalité de nombreuses considérations beaucoup plus subtiles ayant chacune de lourdes implications sur l’efficacité d’une telle méthode. Afin de mettre au clair toute la complexité insoupçonnée de l’élagage et de répondre à la question de son efficacité réelle, ce manuscrit aborde chaque aspect de la méthode de façon thématique et en discute à la fois les fondements théoriques et les conséquences pratiques. Il détaille également les implications académiques et industrielles de plusieurs contributions de cette thèse, portant notamment sur la suppression de paramètres, les interdépendances entre couches et l’efficacité énergétique des réseaux élagués

Pipelined Architecture for a Semantic Segmentation Neural Network on FPGA

Many machine vision tasks like urban sceneunderstanding rely on machine learning, and more specifically deep neural networks to provide accurate enough results to make technology like autonomous vehicles possible. FPGAs have proven to be an excellent target for deploying highly parallel, low-latency and low-power deep neural network architectures for embedded and cloud applications. Many FPGA implementations use recursive architectures based on Deep Processing Units (DPUs) for fast and resource-efficient solutions which usually come at the cost of a higher latency. On the other hand, pipelined dataflow architectures have the potential to offer scalable, lowlatency implementations. In this work, we have explored implementing a semantic segmentation network as a pipelined architecture and evaluated the achievable performances. Our model, a convolutional encoder-decoder based on U-Net, achieves 62.9 % mIoU on the Cityscapes dataset with a 4-bit integer quantization. Once deployed on the Xilinx Alveo U250 FPGA board, the implemented neural network architecture is able to output close to 23 images per second with 44 ms latency per input. The code of this work is open-source and was released publicly

Compression par régularisation sélective de réseaux de neurones pour la conduite autonome

Propriétaire : PSA Automobiles Date de dépôt : 18/09/2020 N° de dépôt : FR20200009459The invention relates to the autonomous driving of a vehicle, and in particular the generation of a driving instruction from data obtained from at least one sensor of the vehicle, by execution (34) on a device comprising in the vehicle of an algorithm based on a neural network[0076]L’invention concerne la conduite autonome d’un véhicule, et en particulier la génération d’une instruction de conduite à partir d’une donnée obtenue à partir d’au moins un capteur du véhicule, par exécution (34) sur un dispositif compris dans le véhicule d’un algorithme fondé sur un réseau de neurones FIG.