Médaille Fields

Résumé

La médaille Fields est la plus prestigieuse récompense en mathématiques avec le prix Abel. Elle est considérée comme équivalente à un prix Nobel inexistant pour cette discipline. Elle est attribuée tous les quatre ans depuis 1936 au cours du congrès international des mathématiciens à quatre mathématiciens au plus, tous de moins de . Les lauréats reçoivent chacun une médaille et . Origine et premières attributions John Charles Fields, mathématicien canadien, propose la création de cette médaille en 1923 lors d'une réunion internationale à Toronto. À sa mort, , il lègue ses biens à la science afin de contribuer au financement de la médaille. L'attribution des deux premières médailles a lieu . La Seconde Guerre mondiale interrompt la délivrance de la distinction jusqu'en 1950. Au départ, seules deux médailles sont décernées tous les quatre ans. En 1966, la décision est prise de passer à quatre lauréats au plus. Liste des lauréats Lauréat de la médaille Fields

Source officielle

https://fr.wikipedia.org/wiki/M%C3%A9daille_Fields

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Many fields make use nowadays of machine learning (ML) enhanced applications for cost optimization, scheduling or forecasting, in- cluding the energy sector. However, these very ML algorithms consume a significant amount of energy, sometimes going against the whole purpose of their employment. To this day, solutions for an energy-efficient execution of these algorithms have not been addressed adequately. In this paper, we demonstrate the advantage of executing ML algorithms on mobile devices (ARM) over a stan- dard server machine (RISC), from the perspective of energy. To do so, we first propose a novel methodology to quantify the amount of energy consumed by an ML algorithm. Then, we compare the energy consumption of existing algorithms running on mobile de- vices and server machines. To motivate running ML algorithms on mobile devices, we also propose a new peer-to-peer personalized ML algorithm (P3) that shows better convergence properties than related works, and provably converging to a ball centered at a criti- cal point of a non-convex cost function, under mild assumptions. Most importantly, we show that running the P3 algorithm on mo- bile devices is extremely energy-efficient, consuming 2700x, 200x and 20x less energy than centralized learning algorithms for 10, 100, and 300 peers respectively. Finally, unlike centralized learning algorithms, the proposed P2P algorithm can generate personalized models, and does not have issues of single-point-of-failure nor data privacy. Thus, we give evidence on the supremacy of our proposed P3 algorithm over the other state-of-the-art centralized ML ones.

ACM2022

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Autoradiographic mapping of [mono[125I]iodo-Tyr10, MetO17]vasoactive intestinal peptide binding sites in the rat brain

Pierre Magistretti, Jean-Luc Martin

The distribution of vasoactive intestinal peptide binding sites in the rat brain was examined by in vitro autoradiography on slide-mounted sections. A fully characterized monoiodinated form of vasoactive intestinal peptide (M-[125I]VIP) previously shown to maintain in the central nervous system the full biological activity of native vasoactive intestinal peptide was used for this study. In initial kinetic and pharmacological experiments the binding of M-[125I]vasoactive intestinal peptide to slide-mounted sections was shown to be time-dependent, saturable and reversible. Association of M-[125I]VIP specific binding was maximal within 90-120 minutes. Specific binding, corresponding to approximately 50% of total binding was saturable, of high affinity (Kd of 76.6 pM) and low capacity (fmol/mg prot range). Dissociation of M-[125I]VIP was maximal at 10 minutes. Unlabeled vasoactive intestinal peptide and the two structurally related peptides "peptide-histidine-isoleucine" (PHI) and secretin competed in a concentration-dependent manner for sites labeled by M-[125I]vasoactive intestinal peptide with the following rank order of potencies: vasoactive intestinal peptide greater than PHI greater than secretin. Vasoactive intestinal peptide receptors, as revealed by quantitative autoradiography, are present at various levels of the neuraxis. High densities were observed in olfactory bulb, cerebral cortex (highest in layers I, II, IV and VI), dentate gyrus, subiculum, various thalamic and hypothalamic nuclei, superior colliculus, locus coeruleus, area postrema, subependymal layer and pineal gland. Intermediate densities were found in the amygdala, nucleus accumbens, caudate-putamen, septum, bed nucleus of the stria terminalis, CA1 to CA4 fields of the hippocampus and central gray. No specific binding of M-[125I]vasoactive intestinal peptide was observed in white matter tracts such as corpus callosum, anterior commissure, medial forebrain bundle and fornix. The mapping of M-[125I]vasoactive intestinal peptide binding sites as revealed by autoradiography on slide-mounted sections indicates an association, although not exclusive, of vasoactive intestinal peptide receptors with brain regions involved in the processing of specific sensory inputs.

1987

Chargement

Autoradiographic mapping of [mono[125I]iodo-Tyr10, MetO17]vasoactive intestinal peptide binding sites in the rat brain

Pierre Magistretti, Jean-Luc Martin

1987