563 research outputs found
Le Marais, quartier parisien riche en souvenirs historiques pharmaceutiques
No full text« Le Marais », ein an pharmazeutisch-historischen Erinnerungen reiches Pariser Viertel.
Wie bei einer Spaziergangsführung durch dieses Viertel, erklärt der Verfasser, Strasse nach Strasse, allerhand pharmazeutische Erinnerungen welche daran gebunden sind.The Marais, a Parisian quarter rich in reminders of the history of pharmacy.
In a kind of guided tour through the district, the author shows, street by street, the wide variety of pharmaceutical memories to be found therein.Labeÿ Robert. Le Marais, quartier parisien riche en souvenirs historiques pharmaceutiques. In: Revue d'histoire de la pharmacie, 77ᵉ année, n°283, 1989. pp. 355-372
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
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.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
Theater of love by Georges de Porto-Riche in front of the new paradigm: “drama-of-life”.
Get PDFGeorges de Porto-Riche has passed into posterity as an author of the modern
drama of sensual and painful love. Compared to the classical tradition of Racine or Corneille, this work seems at frst sight to conform to the traditional rules of drama, and more
particularly of boulevard theatre. However, from the frst plays that are part of the Theater
of Love (in this case: Françoise’ Luck, A Loving Wife) we are forced to note that the playwright moves away somewhat from the canonical form, choosing certain formal solutions
which announce the new dramatic paradigm (“drama-of-life”). The undermining manifests
itself above all through the intrusion of epic elements, the shaking of the fable and a new
approach to the characters. Rereading the dramas of Porto-Riche allows us to show how the
author attacks the Aristotelian “beautiful animal” and how he replaces the traditional hero
with a passive and refective character, processes that anticipate the advent of modern and
contemporary drama.Georges de Porto-Riche est passé à la postérité comme un auteur du drame
moderne de l’amour sensuel et douloureux. Comparée à la tradition classique des Racine ou
des Corneille, cette œuvre semble de prime abord se conformer aux règles traditionnelles du
drame, et plus particulièrement du théâtre de boulevard. Pourtant, dès les premières pièces qui
font partie du Théâtre d’amour (en l’occurrence : La Chance de Françoise et Amoureuse),
force nous est de noter que le dramaturge s’éloigne quelque peu de la forme canonique, en choisissant certaines solutions formelles qui annoncent un nouveau paradigme dramatique
(« drame-de-la-vie »). Le travail de sape de la forme se manifeste avant tout à travers l’intrusion d’éléments épiques, l’ébranlement de la fable et une nouvelle approche des personnages.
Relire les drames de Porto-Riche permet de montrer comment l’auteur s’en prend au « bel
animal » aristotélicien et comment il remplace le héros traditionnel par un personnage passif
et réfexif, procédés qui anticipent l’avènement du drame moderne et contemporain
Koszul duality for Coxeter groups
Get PDFWe construct a “Koszul duality” equivalence relating the (diagrammatic) Hecke category attached to a Coxeter system and a given realization to the Hecke category attached to the same Coxeter system and the dual realization. This extends a construction of Beĭlinson–Ginzburg–Soergel [8] and Bezrukavnikov–Yun [9] in a geometric context, and of the first author with Achar, Makisumi and Williamson [4]. As an application, we show that the combinatorics of the “tilting perverse sheaves” considered in [6] is encoded in the combinatorics of the canonical basis of the Hecke algebra of (W,S) attached to the dual realization.Fil: Riche, Simon. Centre National de la Recherche Scientifique; FranciaFil: Vay, Cristian Damian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentin
Hermann Grassmann’s contribution to Whitehead’s foundations of logic and mathematics
No full textAlfred North Whitehead, author of a Treatise on Universal Algebra almost entirely based on Hermann Grassmann’s Ausdehnungslehre, did not only advertised this work but he also incorporated some of its leading ideas into his main project, federating logic, mathematics and physics in his monumental cosmology. Here, through some historical account starting with Leibniz, we suggest some essential lines of thought underlying both works, emphasizing method and structure and their ethical connections.status: Publishe
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