355 research outputs found
Criel-sur-Mer â Le Mont Jolibois
La campagne de prospections menĂ©e Ă la fin de lâhiver sur une parcelle boisĂ©e du Mont Jolibois visait Ă retrouver, Ă partir du seul tĂ©moignage oral dâun 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 lâarmĂ©e belge pour tracter attelages, mitrailleuses et munitions jusquâau Centre dâInstruction des Mitrailleurs, amĂ©nagĂ© sur la commune de Criel-sur-Mer et utilisĂ© entre ..
ThinResNet: A New Baseline for Structured Convolutional Networks Pruning
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
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 â Parc dâactivitĂ© du Moulin dâĂcalles (tranche 1)
Cette opĂ©ration de fouilles archĂ©ologiques sâest dĂ©roulĂ©e sur la commune de La Rue-Saint-Pierre au nord-est de Rouen. Le projet est encadrĂ© par lâautoroute A28 Ă lâest et la RN28 Ă lâouest, voies reliant Rouen Ă Abbeville. Il sâagit dâune extension de lâactuel parc dâactivitĂ© du Moulin dâĂcalles, dont la premiĂšre tranche est fonctionnelle depuis une dizaine dâannĂ©es. Deux ans se sont Ă©coulĂ©s depuis le diagnostic qui avait conduit Ă lâidentification dâun site oĂč deux occupations distinctes ava..
Bois-Guillaume â Rue Herbeuse
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 dâun thalweg trĂšs marquĂ©. Plusieurs fosses et trous de poteaux signent la nature domestique de lâoccupation, qui peut ĂȘtre datĂ©e ..
Fleury-sur-Orne â Rue Louise-Michel, centre de maintenance du tramway
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 sâinscrivent dans la continuitĂ© de la nĂ©cropole de Fleury-sur-Orne « Les Hauts de lâOrne », avec un des monuments du diagnostic dĂ©jĂ partiellement fouillĂ© en 2014 (mon. 24). Lâautre monument partiel, no 7, est inscrit dans la partie nord de lâemprise. Il mesure plus de 118 m de long pour 15 de large Ses fossĂ©s sont sub-parallĂšles...
Saint-Aubin-lĂšs-Elbeuf â Les Hauts de Novalle
LâopĂ©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 lâEurope 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, lâune concernant le NĂ©olithique final et lâautre, plus inattendue, de la Seconde Guerre mondiale. Cette derniĂšre correspond Ă une position de Flack allemande pratiquemen..
La carriĂšre Saingt Ă Fleury-sur-Orne
Depuis 2014, la carriĂšre Saingt, lâune des nombreuses carriĂšres-refuges utilisĂ©es par les civils pris sous les bombes lors de la Bataille de Caen (juin-juillet 1944), offre lâopportunitĂ© de mettre en place une opĂ©ration archĂ©ologique Ă caractĂšre expĂ©rimental permettant de confronter diffĂ©rents types dâanalyses, au croisement de lâarchĂ©ologie, de lâhistoire et de la sociologie. Ce programme de recherche, dĂ©butĂ© en 2015, associe des chercheurs de lâInrap, du CNRS, de lâINSA-Strasbourg et des sp..
Fine-mapping identifies multiple prostate cancer risk loci at 5p15, one of which associates with TERT expression
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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