355 research outputs found

    Criel-sur-Mer – Le Mont Jolibois

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    La campagne de prospections menĂ©e Ă  la fin de l’hiver sur une parcelle boisĂ©e du Mont Jolibois visait Ă  retrouver, Ă  partir du seul tĂ©moignage oral d’un habitant, Mr Pierre Lecuyer, nĂ© en 1923, un ancien cimetiĂšre amĂ©nagĂ© durant la PremiĂšre Guerre mondiale. Ce cimetiĂšre aurait accueilli les sĂ©pultures des chiens employĂ©s par l’armĂ©e belge pour tracter attelages, mitrailleuses et munitions jusqu’au Centre d’Instruction des Mitrailleurs, amĂ©nagĂ© sur la commune de Criel-sur-Mer et utilisĂ© entre ..

    ThinResNet: A New Baseline for Structured Convolutional Networks Pruning

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    Pruning is a compression method which aims to improve the efficiency of neural networks by reducing their number of parameters while maintaining a good performance, thus enhancing the performance-to-cost ratio in nontrivial ways. Of particular interest are structured pruning techniques, in which whole portions of parameters are removed altogether, resulting in easier to leverage shrunk architectures. Since its growth in popularity in the recent years, pruning gave birth to countless papers and contributions, resulting first in critical inconsistencies in the way results are compared, and then to a collective effort to establish standardized benchmarks. However, said benchmarks are based on training practices that date from several years ago and do not align with current practices. In this work, we verify how results in the recent literature of pruning hold up against networks that underwent both state-of-the-art training methods and trivial model scaling. We find that the latter clearly and utterly outperform all the literature we compared to, proving that updating standard pruning benchmarks and re-evaluating classical methods in their light is an absolute necessity. We thus introduce a new challenging baseline to compare structured pruning to: ThinResNet.Comment: 11 pages, 2 figures, 3 table

    Rethinking Weight Decay For Efficient Neural Network Pruning

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    Introduced in the late 80's for generalization purposes, pruning has now become a staple to compress deep neural networks. Despite many innovations brought in the last decades, pruning approaches still face core issues that hinder their performance or scalability. Drawing inspiration from early work in the field, and especially the use of weight-decay to achieve sparsity, we introduce Selective Weight Decay (SWD), which realizes efficient continuous pruning throughout training. Our approach, theoretically-grounded on Lagrangian Smoothing, is versatile and can be applied to multiple tasks, networks and pruning structures. We show that SWD compares favorably to state-of-the-art approaches in terms of performance/parameters ratio on the CIFAR-10, Cora and ImageNet ILSVRC2012 datasets.Comment: 16 pages, 14 figures, submitted at ICML 2021, update : added new results, rewrit

    La Rue-Saint-Pierre – Parc d’activitĂ© du Moulin d’Écalles (tranche 1)

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    Cette opĂ©ration de fouilles archĂ©ologiques s’est dĂ©roulĂ©e sur la commune de La Rue-Saint-Pierre au nord-est de Rouen. Le projet est encadrĂ© par l’autoroute A28 Ă  l’est et la RN28 Ă  l’ouest, voies reliant Rouen Ă  Abbeville. Il s’agit d’une extension de l’actuel parc d’activitĂ© du Moulin d’Écalles, dont la premiĂšre tranche est fonctionnelle depuis une dizaine d’annĂ©es. Deux ans se sont Ă©coulĂ©s depuis le diagnostic qui avait conduit Ă  l’identification d’un site oĂč deux occupations distinctes ava..

    Bois-Guillaume – Rue Herbeuse

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    Le diagnostic a rĂ©vĂ©lĂ© un certain nombre de vestiges fossoyĂ©s, pour la plupart datĂ©s de trois grandes pĂ©riodes. Les plus nombreux et les plus structurĂ©s appartiennent Ă  un habitat gaulois, matĂ©rialisĂ© par un systĂšme de fossĂ©s formant, semble-t-il, un enclos au moins partiellement curviligne. Ce dernier est installĂ© au niveau de la rupture de pente, sur le cĂŽtĂ© sud d’un thalweg trĂšs marquĂ©. Plusieurs fosses et trous de poteaux signent la nature domestique de l’occupation, qui peut ĂȘtre datĂ©e ..

    Fleury-sur-Orne – Rue Louise-Michel, centre de maintenance du tramway

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    La fouille a permis de mettre en Ă©vidence les fossĂ©s correspondant Ă  deux monuments funĂ©raires nĂ©olithiques partiels et un monument double entier de type Passy. Ils s’inscrivent dans la continuitĂ© de la nĂ©cropole de Fleury-sur-Orne « Les Hauts de l’Orne », avec un des monuments du diagnostic dĂ©jĂ  partiellement fouillĂ© en 2014 (mon. 24). L’autre monument partiel, no 7, est inscrit dans la partie nord de l’emprise. Il mesure plus de 118 m de long pour 15 de large Ses fossĂ©s sont sub-parallĂšles...

    Saint-Aubin-lùs-Elbeuf – Les Hauts de Novalle

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    L’opĂ©ration de fouille prĂ©ventive rĂ©alisĂ©e sur la commune de Saint-Aubin-lĂšs-Elbeuf a Ă©tĂ© motivĂ©e par un projet de lotissement rues du Docteur-Villers, Paul-Doumer et avenue de l’Europe portĂ© par la sociĂ©tĂ© Nexity. Elle fait suite Ă  un diagnostic archĂ©ologique conduit en avril 2019, qui a rĂ©vĂ©lĂ© deux occupations principales, l’une concernant le NĂ©olithique final et l’autre, plus inattendue, de la Seconde Guerre mondiale. Cette derniĂšre correspond Ă  une position de Flack allemande pratiquemen..

    La carriĂšre Saingt Ă  Fleury-sur-Orne

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    Depuis 2014, la carriĂšre Saingt, l’une des nombreuses carriĂšres-refuges utilisĂ©es par les civils pris sous les bombes lors de la Bataille de Caen (juin-juillet 1944), offre l’opportunitĂ© de mettre en place une opĂ©ration archĂ©ologique Ă  caractĂšre expĂ©rimental permettant de confronter diffĂ©rents types d’analyses, au croisement de l’archĂ©ologie, de l’histoire et de la sociologie. Ce programme de recherche, dĂ©butĂ© en 2015, associe des chercheurs de l’Inrap, du CNRS, de l’INSA-Strasbourg et des sp..

    Fine-mapping identifies multiple prostate cancer risk loci at 5p15, one of which associates with TERT expression

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    Associations between single nucleotide polymorphisms (SNPs) at 5p15 and multiple cancer types have been reported. We have previously shown evidence for a strong association between prostate cancer (PrCa) risk and rs2242652 at 5p15, intronic in the telomerase reverse transcriptase (TERT) gene that encodes TERT. To comprehensively evaluate the association between genetic variation across this region and PrCa, we performed a fine-mapping analysis by genotyping 134 SNPs using a custom Illumina iSelect array or Sequenom MassArray iPlex, followed by imputation of 1094 SNPs in 22 301 PrCa cases and 22 320 controls in The PRACTICAL consortium. Multiple stepwise logistic regression analysis identified four signals in the promoter or intronic regions of TERT that independently associated with PrCa risk. Gene expression analysis of normal prostate tissue showed evidence that SNPs within one of these regions also associated with TERT expression, providing a potential mechanism for predisposition to disease
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