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La morale a mauvaise réputation. Synonyme de prêche ou de bonne conduite, cette magnifique notion est pourtant fondamentale au vivre-ensemble.  Markus Gabriel, le philosophe allemand connu dans le monde entier, montre combien la morale sait s'adapter, et transformer ses valeurs en impératifs : ainsi la « compassion » est-elle devenue « solidarité » lors de la crise du Covid. Cette dernière a également signé l'avènement de nouveaux principes moraux, comme la certitude qu'on ne peut pas soumettre nos hôpitaux à une logique de marché.  Face au péril sanitaire, mais aussi technologique et démocratique, il devient urgent de repenser nos valeurs morales afin de garantir la dignité humaine.  Un manuel de philosophie indispensable, qui s'oppose au nihilisme de notre époque et dessine le projet de Nouvelles Lumières.  Traduit de l'allemand par Georges Sturm

Morale. --- Vivre-ensemble. --- Morale --- Valeurs (philosophie)

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12 millions de personnes méritent bien un livre... Sophie Cluzel, la secrétaire d'Etat aux Personnes handicapées, a interrogé, pendant un an, des personnalités autour d'un thème méconnu lié au handicap. Claude Chirac parle pour la première fois de son rôle auprès de sa famille, et de l'interrogation que génère ce soutien : qui aide les aidants ? Le chef Yannick Alléno se confronte à la question du recrutement des personnes handicapées. L'athlète Marie-Amélie Le Fur raconte la féminité lorsque le corps porte la trace d'un accident. Gilbert Montagné rit de la technologie, qui complique parfois la vie au lieu de la simplifier. La philosophe Alexandre Jollien interroge le rejet que suscite trop souvent le handicap. Dominique Farrugia tempête contre la galère d'un fauteuil roulant dans une ville. Le rappeur Gringe évoque la fratrie face à la différence. Marc-Olivier Fogiel répond au reproche concernant la faible présence des personnes handicapées dans les médias. Eléonore Laloux révèle son quotidien de première élue trisomique de France, à Arras. Damien Seguin, 6e au Vendée Globe, démontre que l'impossible se réalise avec une seule main. Tahar Ben Jelloun aborde le lien père-fils qui se réinvente lorsque l'enfant a des besoins spécifiques. Face-à-face ou tête-à-tête, débat ou confidence, Sophie Cluzell mène ces entretiens avec la conviction que le collectif gagnera à inclure le particulier. Un autre visage du handicap se dessine. Celui de la force.

Disabilities. --- Handicap --- Célébrités --- Vivre-ensemble --- Histoires de vie.

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This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation.

Earth sciences --- Probability & statistics --- Bayesian inference --- Data Assimilation --- Parameter Estimation --- Ensemble Kalman Filter --- 4DVar --- Representer Method --- Ensemble Methods --- Particle Filter --- Particle Flow

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Ce travail a pour objet l’intégration des Community Land Trust à leur territoire. Cette notion désigne à la fois l’intégration sociale des habitants du CLT au sein de la communauté locale et à la fois l’intégration des infrastructures du CLT dans son territoire. Au travers l’étude des fondements du modèle sur lequel se basent les CLT nous étudierons comment cet enjeu est inscrit au cœur de son fonctionnement. Le CLTB servira ensuite de support à une étude plus concrète de la façon donc les interactions entre un ensemble de logements et son quartier peuvent être favorisées. Différents dispositifs seront relevés et ensuite étudiés à partir de l’étude de trois réalisations du CLTB à Bruxelles, à savoir Arc-en-Ciel à Molenbeek, le Nid à Anderlecht et Calico à Forest.

développement territorial --- modèle du CLT --- territoire --- Bruxelles --- processus de montage de projet --- participation --- ensemble de logements abordables --- dynamiques collectives --- liens sociaux --- Sciences sociales & comportementales, psychologie > Sociologie & sciences sociales

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This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.

Artificial intelligence --- Data mining --- Machine learning --- Metalearning --- Automating Machine Learning (AutoML) --- Machine Learning --- Artificial Intelligence --- algorithm selection --- algorithm recommendation --- algorithm configuration --- hyperparameter optimization --- automating the workflow/pipeline design --- metalearning in ensemble construction --- metalearning in deep neural networks --- transfer learning --- algorithm recommendation for data streams --- automating data science --- Open Access