"Organisation, anti-organisation" : un changement de paradigme pour penser l’innovation et la prospective

Partant d’une relecture d’un ouvrage (Stourdzé, 1973), nous soulignerons à la fois l’apport des pro­positions contenues dans cet opus et la richesse des perspectives qu’il ouvre. Dans la première partie de cet article, l’organisation apparait comme la question qui introduit une approche anthropologique originale du pouvoir. Dans la seconde partie nous verrons com­ment la mutation du système de production s’envisage comme une limitation du sacrifice. En conclusion, nous observerons en quoi la position de Stourdzé oscille entre une critique active et un pessimisme foncier. Cette ambivalence prépare une rupture qu’inscrira par la suite le CESTA. Stourdzé entrera finalement à la fin des années 70 dans une nouvelle phase de son par­cours : il devient lui-même organisateur et pratiquera l’innovation et la prospective.Based on a rereading of a book by Stourdzé (1973), we will discuss the contribution it makes and the wealth of perspectives it opens up. In the first part of the article, the topic of the organization introduces an original anthropological approach to power. In the second part, we see how changes in the system of production can be understood as the limitation of sacrifice. Finally, we note how Stourdzé’s position oscillates between an active critique and a fundamental pessimism. This ambivalence explains a rupture introduced by CESTA later. In the late 1970s, Stourdzé entered a new stage in his own intellectual trajectory, becoming an organizer and practitioner of innovation and forecasting

Challenge MICROTRANSAT

L'IUT de Nantes et l'ENSICA de Toulouse développent conjointement un voilier autonome. Ce projet pédagogique collaboratif a permis de concevoir, fabriquer et d'instrumenter un voilier de 2,4m. Notre démonstrateur a pour but de démontrer la possibilité de faire réaliser une traversée transatlantique à un petit voilier autonome. Nous lançons par cet article le challenge "Microtransat" afin de susciter des projets similaires dans d’autres écoles ou universités

Improving Online Continual Learning Performance and Stability with Temporal Ensembles

Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.1345(2) showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature.Comment: CoLLAs 2023 accepted pape

Improving Online Continual Learning Performance and Stability with Temporal Ensembles

Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al. 2022, Lange et al. 2023) showed that replay methods used in continual learning suffer from the {\textbackslash}textit\{stability gap\}, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature

Evaluation of different heat transfer conditions on an automotive turbocharger

This paper presents a combination of theoretical and experimental investigations for determining the main heat fluxes within a turbocharger. These investigations consider several engine speeds and loads as well as different methods of conduction, convection, and radiation heat transfer on the turbocharger. A one-dimensional heat transfer model of the turbocharger has been developed in combination with simulation of a turbocharged engine that includes the heat transfer of the turbocharger. Both the heat transfer model and the simulation were validated against experimental measurements. Various methods were compared for calculating heat transfer from the external surfaces of the turbocharger, and one new method was suggested. The effects of different heat transfer conditions were studied on the heat fluxes of the turbocharger using experimental techniques. The different heat transfer conditions on the turbocharger created dissimilar temperature gradients across the turbocharger. The results show that changing the convection heat transfer condition around the turbocharger affects the heat fluxes more noticeably than changing the radiation and conduction heat transfer conditions. Moreover, the internal heat transfers from the turbine to the bearing housing and from the bearing housing to the compressor are significant, but there is an order of magnitude difference between these heat transfer rates.The Swedish Energy Agency and KTH Royal Institute of Technology sponsored this work within the Competence Centre for Gas Exchange (CCGEx).Aghaali, H.; Angström, H.; Serrano Cruz, JR. (2015). Evaluation of different heat transfer conditions on an automotive turbocharger. International Journal of Engine Research. 16(2):137-151. doi:10.1177/1468087414524755S137151162Romagnoli, A., & Martinez-Botas, R. (2012). Heat transfer analysis in a turbocharger turbine: An experimental and computational evaluation. Applied Thermal Engineering, 38, 58-77. doi:10.1016/j.applthermaleng.2011.12.022Romagnoli, A., & Martinez-Botas, R. (2009). Heat Transfer on a Turbocharger Under Constant Load Points. Volume 5: Microturbines and Small Turbomachinery; Oil and Gas Applications. doi:10.1115/gt2009-59618Baines, N., Wygant, K. D., & Dris, A. (2010). The Analysis of Heat Transfer in Automotive Turbochargers. Journal of Engineering for Gas Turbines and Power, 132(4). doi:10.1115/1.3204586Serrano, J. R., Olmeda, P., Páez, A., & Vidal, F. (2010). An experimental procedure to determine heat transfer properties of turbochargers. Measurement Science and Technology, 21(3), 035109. doi:10.1088/0957-0233/21/3/035109Bohn, D., Heuer, T., & Kusterer, K. (2005). Conjugate Flow and Heat Transfer Investigation of a Turbo Charger. Journal of Engineering for Gas Turbines and Power, 127(3), 663-669. doi:10.1115/1.1839919Galindo, J., Luján, J. M., Serrano, J. R., Dolz, V., & Guilain, S. (2006). Description of a heat transfer model suitable to calculate transient processes of turbocharged diesel engines with one-dimensional gas-dynamic codes. Applied Thermal Engineering, 26(1), 66-76. doi:10.1016/j.applthermaleng.2005.04.010Sirakov, B., & Casey, M. (2012). Evaluation of Heat Transfer Effects on Turbocharger Performance. Journal of Turbomachinery, 135(2). doi:10.1115/1.4006608Serrano, J., Olmeda, P., Arnau, F., Reyes-Belmonte, M., & Lefebvre, A. (2013). Importance of Heat Transfer Phenomena in Small Turbochargers for Passenger Car Applications. SAE International Journal of Engines, 6(2), 716-728. doi:10.4271/2013-01-0576Larsson, P.-I., Westin, F., Andersen, J., Vetter, J., & Zumeta, A. (2009). Efficient turbo charger testing. MTZ worldwide, 70(7-8), 16-21. doi:10.1007/bf03226965Aghaali, H., & Ångström, H.-E. (2012). Turbocharged SI-Engine Simulation With Cold and Hot-Measured Turbocharger Performance Maps. Volume 5: Manufacturing Materials and Metallurgy; Marine; Microturbines and Small Turbomachinery; Supercritical CO2 Power Cycles. doi:10.1115/gt2012-68758Leufven, O., & Eriksson, L. (2012). Investigation of compressor correction quantities for automotive applications. International Journal of Engine Research, 13(6), 588-606. doi:10.1177/146808741243901

A Comprehensive Empirical Evaluation on Online Continual Learning

Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at https://github.com/AlbinSou/ocl_survey based on the avalanche framework to reproduce our results and encourage future research

A Comprehensive Empirical Evaluation on Online Continual Learning

Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at https://github.com/AlbinSou/ocl_survey based on the avalanche framework to reproduce our results and encourage future research.Comment: ICCV Visual Continual Learning Workshop 2023 accepted pape

L’école et l’avenir de la culture digitale

International audienceCette contribution propose d’interroger les rapports des institutions éducatives aux technologies digitales à partir d’une mise en perspective des politiques publiques et des innovations technologiques qui en ont jalonné l’histoire. Un perspective qui met en lumière la nécessité d’intégrer au cœur des prérogatives de l’Ecole la formation à de nouveaux modes d’écriture et de lecture ; de nouvelles modalités d’acquisition et de production des savoirs. C’est en esquissant les contours d’une nouvelle figure d’apprenant, celle du « Lettré digital », que nous voudrions réinterroger non pas tant la place du digital à l’école que celle d’un apprenant immergé dans un environnement et une culture de plus en plus façonnés par le digital

Autoroute A85

La construction de l’autoroute A85 se faisant en deux sections successives (première section : Corzé-Vivy, deuxième section : Vivy-Restigné), l’étude archéologique a suivi le même découpage. Il s’agit ici des prospections qui se sont déroulées sur la partie de la seconde section située en Maine-et-Loire. Sur cette partie du tracé, des prescriptions particulières, concernant les modalités de réalisation de l’ouvrage, font que la bande autoroutière ne sera pas terrassée, le revêtement devant êt..