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L’enfance populaire à l’épreuve de l’éducation populaire : une autre expérience de la domination
No full textInternational audienceÀ partir d’une ethnographie au sein d’accueils de loisirs se réclamant de l’éducation populaire, l’article se propose d’interroger la confrontation d’enfants de classes populaires avec des activités construites dans une perspective d’éducation à la citoyenneté. Il montre comment les appropriations enfantines, produits des modalités populaires de socialisation familiale et entre pairs, se heurtent aux attentes socialement situées de l’institution concernant la participation aux dispositifs mis en œuvre. Cet article, en investiguant les expériences enfantines de l’animation, permet d’interroger à nouveau frais la relation qui s’établit entre éducation populaire et public populaire
Hide Exposures by Removing Mastermind’s External Sources on Social Network (Student Abstract)
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Pour une co-construction du sens, hommage à Alain Rabatel
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Hybrid simulation modelling of emergency departments for resource scheduling
No full textInternational audienceThis paper addresses the problem of resource scheduling of emergency departments in an uncertain environment. An innovative approach based on Agent-Based Simulation (ABS) and Discrete Event Simulation (DES) is proposed. To do so, this paper first proposes a framework integrating ABS and DES. Subsequently, we provide a detailed description of the DES and ABS parts of the model of the ED. Five combinations of strategies – namely, “FIFO+Random” (i:e:FIFO: the queue of patients operates in FIFO; Random: the patients are randomly assigned to doctors), “FIFO+Centralised”, “Random+Centralised” (“Centralised” implies the assignment of the most appropriate doctor to a patient is only determined by the Platform agent via optimising all the resources), “Random+Random” and “Autonomous” – are implemented to schedule the resources of the ED. Finally, experiments analyse the efficiency of the five combinations of strategies on the duration of patients’ stay. The results show that “FIFO +Centralised” outperforms others regarding the average for the duration of patients’ stay without hospitalisation: 3.75% of the time has been saved; “Random+Centralised” outperforms others regarding the average for the duration of patients’ stay with hospitalisation: 0.57% of the time has been saved
Saperi medici, saperi naturalistici
No full textInternational audienceEmbrassant d'un seul regard la médecine et l'histoire naturelle, mon chapitre a tenté de proposer de manière claire et accessible une lecture originale du développement du savoir dans l’Italie du XVIe siècle qui va au-delà d’une série de lieux communs historiographiques répandus, encore aujourd’hui, dans les ouvrages de vulgarisation scientifique, tels que l’opposition entre savoir livresque et pratique empirique ou celle entre science et religion, des dichotomies forgées à partir d’une époque ultérieure et, en particulier, sur le récit de la prétendue « révolution scientifique » du XVIIe siècl
Entropy-Guided Self-Regulated Learning Without Forgetting for Distribution-Shift Continual Learning with blurred task boundaries
No full textInternational audienceContinual Learning (CL) aims to endow machines with the human-like ability to continuously acquire novel knowledge while retaining previously learned experiences. Recent research on CL has focused on Domain-Incremental Learning (DIL) or Class-Incremental Learning (CIL) with well-defined task boundaries. However, for real-life applications, e.g., waste sorting, robotic grasping, etc., the model needs to be constantly updated to fit new data. Additionally, there is usually an overlap between new and old data. Thus, task boundaries may not be well defined, and a more smooth scenario is needed. In this paper, we propose a more general scenario, namely Distribution-Shift Incremental Learning (DS-IL), which enables soft task boundaries with possible mixtures of data distributions over tasks and thereby subsumes the two previous CL scenarios: DIL and CIL are simply DS-IL. Moreover, given the increasingly greater importance of data privacy in real-life applications and, incidentally, data storage efficiency, we further introduce an entropy-guided self-regulated distillation process \textbf{without memory}, which leverages data similarities between tasks with soft-boundaries. Experimented on a variety of datasets, our proposed method outperforms or matches state-of-the-art continual learning methods
Player-Centric Shot Maps in Table Tennis
No full textInternational audienceShot maps are popular in many sports as they typically plot events and player positions in the way they are collected, using a pitch or a table as an absolute coordinate system. We introduce a variation of a table tennis shot map that shifts the point of view from the table to the player. This results in a new reference system to plot incoming balls relative to the player's position rather than on the table. This approach aligns with how table tennis tactical analysis is conducted, focusing on identifying empty spaces and weak spots around the players. We describe the motivation behind this work, built through close collaboration with two table tennis experts, and demonstrate how this approach aligns with the way they analyze games to reveal key tactical aspects. We also present the design rationale and the computer vision pipeline used to accurately collect data, leveraging recent table tennis data extracted from broadcast videos. Our findings show the technique enables to capture insights that were not visible with the absolute coordinate system
Soirée de présentation du dossier thématique de Critique Internationale "Élites minorisées : comparaisons internationales et circulations transnationales"
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