158 research outputs found
Monographie du palais de Fontainebleau
No full textFontainebleau, Dungeon (roof detail) Cour Ovale (Plate # 3); Monographie du Palais de Fontainebleau, dessinée Gravée par M. Rodolphe Pfnor, published by Vve. A. Morel et Cie Éditeurs, Paris in 1873, 2 volumes. Source: University of Toronto Libraries; http://main.library.utoronto.ca/ (accessed 2/1/2008
Ressenya de: Christin, Rodolphe (2018). Manual del Anti-turismo. València: Fuera de Ruta
No full textEn paraules del seu autor, autodefinit com “un turista més”, aquest és un llibre dirigit, amb mala intenció, als amants dels viatges i del món. El seu objectiu és analitzar el “drama del turisme”, com ell mateix l’anomena. És el turista un destructor marginal? La pregunta ens trasllada a l’univers on els turistes es converteixen en els principals actius de la “món-fàgia”. Literalment, menjar-se el món. El turisme com a consumidor i principal depredador d’una espècie que resulta el món sencer. Partint del principi de racionalització absoluta introduït per Max Weber a l’era científica i tècnica que significà la industrialització de finals del segle XIX i principis del XX, el sociòleg Rodolphe Christin ens planteja un espai convertit en parc, escenificat i modelitzat per la tecnosfera, on el turista assumeix el paper d’espectador-consumidor, i l’hàbitat el d’actor.In the words of its author, self-described as "another tourist", this is a book intended, with a bad intention, for travel lovers and the world. His goal is to analyze the "tourism drama", as he calls it. Is the tourist a marginal destroyer? The question takes us to the world where tourists become the main assets of the "world-phagia". Literally eating the world. Tourism as the main consumer and predator of a species that is the whole world. Based on the principle of absolute rationalization introduced by Max Weber into the scientific and technical era of industrialization in the late nineteenth and early twentieth centuries, sociologist Rodolphe Christin proposes a park-turned space, staged and modeled by the technosphere, where the tourist assumes the role of spectator-consumer, and the habitat the actor. Book Review. ISBN: 9788494789724.Ressenya de: Christin, Rodolphe (2018). Manual del Anti-turismo. València: Fuera de Rut
La nourriture au service de la littérature : le banquet d’inauguration du monument à Karamzine à Simbirsk en 1845
No full textCelebrating Literature with Food : The Inauguration Banquet for Karamzin’s Monument in Simbirsk in 1845.
the present paper analyzes the reference to food in Mikhail Pogodin’s depiction of the banquet given by the nobility of Simbirsk on the occasion of the inauguration of the monument to nikolay Karamzin in 1845. After reminding how the different genres of classical poetry deal with the depiction of food, particularly with fish, the author shows how Pogodin used the specific features of the odic tradition, from the hyperbole to the enthusiastic tone, in his depiction of the Simbirsk banquet.Sapchenko Lioubov, Baudin Rodolphe. La nourriture au service de la littérature : le banquet d’inauguration du monument à Karamzine à Simbirsk en 1845. In: Revue Russe n°44, 2015. Manger russe. pp. 65-73
Bayesian optimization with derivatives acceleration
No full textGuillaume Perrin, first author. Rodolphe Le Riche, second author, invited speaker of the workshop.National audienceBayesian optimization algorithms form an important class of methods to minimize functions that are costly to evaluate, which is a very common situation. These algorithms iteratively infer Gaussian processes from past observations of the function and decide where new observations should be made through the maximization of an acquisition criterion. Often, in particular in engineering practice, the objective function is defined on a compact set such as in a hyper-rectangle of a d-dimensional real space, and the bounds are chosen wide enough so that the optimum is inside the search domain. In this situation, this work provides a way to integrate in the acquisition criterion the a priori information that these functions, once modeled as GP trajectories, should be evaluated at their minima, and not at any point as usual acquisition criteria do. We propose an adaptation of the widely used Expected Improvement acquisition criterion that accounts only for GP trajectories where the first order partial derivatives are zero and the Hessian matrix is positive definite. The new acquisition criterion keeps an analytical, computationally efficient, expression. This new acquisition criterion is found to improve Bayesian optimization on a test bed of functions made of Gaussian process trajectories in dimensions 2, 3 and 5. The addition of first and second order derivative information is particularly useful for multimodal functions
Bayesian optimization with derivatives acceleration
No full textGuillaume Perrin, first author. Rodolphe Le Riche, second author, invited speaker of the workshop.International audienceBayesian optimization algorithms form an important class of methods to minimize functions that are costly to evaluate, which is a very common situation. These algorithms iteratively infer Gaussian processes from past observations of the function and decide where new observations should be made through the maximization of an acquisition criterion. Often, in particular in engineering practice, the objective function is defined on a compact set such as in a hyper-rectangle of a d-dimensional real space, and the bounds are chosen wide enough so that the optimum is inside the search domain. In this situation, this work provides a way to integrate in the acquisition criterion the a priori information that these functions, once modeled as GP trajectories, should be evaluated at their minima, and not at any point as usual acquisition criteria do. We propose an adaptation of the widely used Expected Improvement acquisition criterion that accounts only for GP trajectories where the first order partial derivatives are zero and the Hessian matrix is positive definite. The new acquisition criterion keeps an analytical, computationally efficient, expression. This new acquisition criterion is found to improve Bayesian optimization on a test bed of functions made of Gaussian process trajectories in dimensions 2, 3 and 5. The addition of first and second order derivative information is particularly useful for multimodal functions
Bayesian optimization with derivatives acceleration
No full textGuillaume Perrin, first author. Rodolphe Le Riche, second author, invited speaker of the workshop.National audienceBayesian optimization algorithms form an important class of methods to minimize functions that are costly to evaluate, which is a very common situation. These algorithms iteratively infer Gaussian processes from past observations of the function and decide where new observations should be made through the maximization of an acquisition criterion. Often, in particular in engineering practice, the objective function is defined on a compact set such as in a hyper-rectangle of a d-dimensional real space, and the bounds are chosen wide enough so that the optimum is inside the search domain. In this situation, this work provides a way to integrate in the acquisition criterion the a priori information that these functions, once modeled as GP trajectories, should be evaluated at their minima, and not at any point as usual acquisition criteria do. We propose an adaptation of the widely used Expected Improvement acquisition criterion that accounts only for GP trajectories where the first order partial derivatives are zero and the Hessian matrix is positive definite. The new acquisition criterion keeps an analytical, computationally efficient, expression. This new acquisition criterion is found to improve Bayesian optimization on a test bed of functions made of Gaussian process trajectories in dimensions 2, 3 and 5. The addition of first and second order derivative information is particularly useful for multimodal functions
Bayesian optimization with derivatives acceleration
No full textGuillaume Perrin, first author. Rodolphe Le Riche, second author, invited speaker of the workshop.National audienceBayesian optimization algorithms form an important class of methods to minimize functions that are costly to evaluate, which is a very common situation. These algorithms iteratively infer Gaussian processes from past observations of the function and decide where new observations should be made through the maximization of an acquisition criterion. Often, in particular in engineering practice, the objective function is defined on a compact set such as in a hyper-rectangle of a d-dimensional real space, and the bounds are chosen wide enough so that the optimum is inside the search domain. In this situation, this work provides a way to integrate in the acquisition criterion the a priori information that these functions, once modeled as GP trajectories, should be evaluated at their minima, and not at any point as usual acquisition criteria do. We propose an adaptation of the widely used Expected Improvement acquisition criterion that accounts only for GP trajectories where the first order partial derivatives are zero and the Hessian matrix is positive definite. The new acquisition criterion keeps an analytical, computationally efficient, expression. This new acquisition criterion is found to improve Bayesian optimization on a test bed of functions made of Gaussian process trajectories in dimensions 2, 3 and 5. The addition of first and second order derivative information is particularly useful for multimodal functions
Bayesian optimization with derivatives acceleration
No full textGuillaume Perrin, first author. Rodolphe Le Riche, second author, invited speaker of the workshop.International audienceBayesian optimization algorithms form an important class of methods to minimize functions that are costly to evaluate, which is a very common situation. These algorithms iteratively infer Gaussian processes from past observations of the function and decide where new observations should be made through the maximization of an acquisition criterion. Often, in particular in engineering practice, the objective function is defined on a compact set such as in a hyper-rectangle of a d-dimensional real space, and the bounds are chosen wide enough so that the optimum is inside the search domain. In this situation, this work provides a way to integrate in the acquisition criterion the a priori information that these functions, once modeled as GP trajectories, should be evaluated at their minima, and not at any point as usual acquisition criteria do. We propose an adaptation of the widely used Expected Improvement acquisition criterion that accounts only for GP trajectories where the first order partial derivatives are zero and the Hessian matrix is positive definite. The new acquisition criterion keeps an analytical, computationally efficient, expression. This new acquisition criterion is found to improve Bayesian optimization on a test bed of functions made of Gaussian process trajectories in dimensions 2, 3 and 5. The addition of first and second order derivative information is particularly useful for multimodal functions
Bayesian optimization with derivatives acceleration
No full textGuillaume Perrin, first author. Rodolphe Le Riche, second author, invited speaker of the workshop.International audienceBayesian optimization algorithms form an important class of methods to minimize functions that are costly to evaluate, which is a very common situation. These algorithms iteratively infer Gaussian processes from past observations of the function and decide where new observations should be made through the maximization of an acquisition criterion. Often, in particular in engineering practice, the objective function is defined on a compact set such as in a hyper-rectangle of a d-dimensional real space, and the bounds are chosen wide enough so that the optimum is inside the search domain. In this situation, this work provides a way to integrate in the acquisition criterion the a priori information that these functions, once modeled as GP trajectories, should be evaluated at their minima, and not at any point as usual acquisition criteria do. We propose an adaptation of the widely used Expected Improvement acquisition criterion that accounts only for GP trajectories where the first order partial derivatives are zero and the Hessian matrix is positive definite. The new acquisition criterion keeps an analytical, computationally efficient, expression. This new acquisition criterion is found to improve Bayesian optimization on a test bed of functions made of Gaussian process trajectories in dimensions 2, 3 and 5. The addition of first and second order derivative information is particularly useful for multimodal functions
Voies biochimiques impliquées dans la mort des thymocytes de souris après activation du récepteur purinergique P2X7
No full textLE KREMLIN-B.- PARIS 11-BU Méd (940432101) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF
- …
