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1
The trans-ancestral genomic architecture of glycemic traits
Chen, J; Spracklen, CN; Marenne, G. - : Nature Research, 2021
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2
The trans-ancestral genomic architecture of glycemic traits.
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3
The trans-ancestral genomic architecture of glycemic traits.
In: Nature genetics, vol. 53, no. 6, pp. 840-860 (2021)
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4
Validation of a revised Mandarin Chinese language version of the Postoperative Quality of Recovery Scale
In: Anaesthesia and Intensive Care, Vol. 46, no. 3 (2018), pp. 278-289 (2018)
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5
Observation of $Z$ production in proton-lead collisions at LHCb
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6
Observation of $Z$ production in proton-lead collisions at LHCb
In: Symplectic Elements at Oxford ; Web of Science (Lite) (http://apps.webofknowledge.com/summary.do) ; Scopus (http://www.scopus.com/home.url) ; ArXiv (http://arxiv.org/) (2014)
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7
Literature review of health impact post-earthquakes in China 1906-2007
Chan, E. Y. Y.; Gao, Y.; Griffiths, S. M.. - : Oxford University Press, 2010
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8
Literature review of health impact post-earthquakes in China 1906-2007.
In: Symplectic Elements at Oxford ; Europe PubMed Central ; PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) ; Scopus (http://www.scopus.com/home.url) ; CrossRef (2010)
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9
Speech Recognition - Maximum Entropy Direct Models for Speech Recognition
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 14 (2006) 3, 873-881
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10
A haplotype map of the human genome
Belmont J.W.; Boudreau A.; Leal S.M.. - : NATURE PUBLISHING GROUP, 2005
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11
40th EASD Annual Meeting of the European Association for the Study of Diabetes : Munich, Germany, 5-9 September 2004.
Veitenhansl M, Stegner K; Chatellier G, Group D.E.S.I.R.; DIABHYCAR Study Group, Nichols GA. - : Springer, 2004. : country:DEU, 2004. : place:Berli, 2004
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12
An experimental study of $\gamma \gamma \rightarrow hadrons$ at LEP
Buskulic, D.; De Bonis, I.; Decamp, D.; Ghez, P.; Goy, C.; Lees, J P.; Minard, M N.; Pietrzyk, B.; Alemany, R.; Ariztizabal, F.; Comas, P.; Crespo, J M.; Delfino, M.; Efthymiopoulos, I.; Fernandez, E.; Fernandez-Bosman, M.; Gaitan, V.; Garrido, L.; Mattison, T.; Pacheco, A.; Padilla, C.; Pascual, A.; Creanza, D.; De Palma, M.; Farilla, A.; Iaselli, G.; Maggi, G.; Natali, S.; Nuzzo, S.; Quattromini, M.; Ranieri, A.; Raso, G.; Romano, F.; Ruggieri, F.; Selvaggi, G.; Silvestris, L.; Tempesta, P.; Zito, G.; Chai, Y.; Hu, H.; Huang, D.; Huang, X.; Lin, Jiali; Wang, T.; Xie, Y.; Xu, D.; Xu, R.; Zhang, J.; Zhang, L.; Zhao, W.; Blucher, E.; Bonvicini, G.; Boudreau, J.; Casper, D.; Drevermann, H.; Forty, R W.; Ganis, G.; Gay, C.; Hagelberg, R.; Harvey, J.; Haywood, S.; Hilgart, J.; Jacobsen, R.; Jost, B.; Knobloch, J.; Lehraus, I.; Lohse, T.; Maggi, M.; Markou, C.; Martinez, M.; Mato, P.; Meinhard, H.; Minten, A.; Miotto, A.; Miquel, R.; Moser, H G.; Palazzi, P.; Pater, J R.; Perlas, J A.; Pusztaszeri, J F.; Ranjard, F.; Redlinger, G.; Rolandi, L.; Rothberg, J.; Ruan, T.; Saich, M.; Schlatter, D.; Schmelling, M.; Sefkow, F.; Tejessy, W.; Veenhof, R.; Wachsmuth, H.; Wiedenmann, W.; Wildish, T.; Witzeling, W.; Wotschack, J.; Ajaltouni, Ziad,; Badaud, F.; Bardadin-Otwinowska, M.; El-Fellous, R.; Falvard, Alain; Gay, Pascal; Guicheney, C.; Henrard, Pierre; Jousset, J.; Michel, B.; Montret, Jean-Claude; Pallin, D.; Perret, Pascal; Podlyski, F.; Proriol, Joseph; Prulhiere, F.; Saadi, F.; Fearnley, T.; Hansen, J D.; Hansen, J R.; Hansen, P H.; Mollerud, R.; Nilsson, B S.; Kyriakis, A.; Simopoulou, E.; Vayaki, A.; Zachariadou, K.; Badier, J.; Blondel, A.; Bonneaud, G.; Brient, J C.; Fouque, G.; Orteu, S.; Rouge, A.; Rumpf, M.; Tanaka, R.; Verderi, M.; Videau, H.; Candlin, D J.; Parsons, M I.; Veitch, E.; Focardi, E.; Moneta, L.; Parrini, G.; Corden, M.; Georgiopoulos, C.; Ikeda, M.; Lannutti, J.; Levinthal, D.; Sawyer, L.; Wasserbaech, S.; Antonelli, A.; Baldini, R.; Bencivenni, G.; Bologna, G.; Bossi, F.; Campana, P.; Capon, G.; Cerutti, F.; Chiarella, V.; D'Ettorre Piazzoli, B.; Felici, G.; Laurelli, P.; Mannocchi, G.; Murtas, F.; Murtas, G P.; Passalacqua, L.; Pepe-Altarelli, M.; Picchi, P.; Colrain, P.; Ten Have, I.; Lynch, J G.; Maitland, W.; Morton, W T.; Raine, C.; Reeves, P.; Scarr, J M.; Smith, K.; Smith, M G.; Thompson, A S.; Turnbull, R M.; Brandl, B.; Braun, O.; Geweniger, C.; Hanke, P.; Hepp, V.; Kluge, E E.; Maumary, Y.; Putzer, A.; Rensch, B.; Stahl, A.; Tittel, K.; Wunsch, M.; Beuselinck, R.; Binnie, D M.; Cameron, W.; Cattaneo, M.; Colling, D J.; Dornan, P J.; Greene, A M.; Hassard, J F.; Lieske, N M.; Moutoussi, A.; Nash, J.; Patton, S.; Payne, D G.; Phillips, M J.; San Martin, G.; Sedgbeer, J K.; Tomalin, I R.; Wright, A G.; Girtler, P.; Kneringer, E.; Kuhn, D.; Rudolph, G.; Bowdery, C K.; Brodbeck, T J.; Finch, A J.; Foster, F.; Hughes, G.; Jackson, D.; Keemer, N R.; Nuttall, M.; Patel, A D.; Sloan, T.; Snow, S W.; Whelan, E P.; Kleinknecht, K.; Raab, J.; Renk, B.; Sander, H G.; Schmidt, H.; Steeg, F.; Walther, S M.; Wanke, R.; Wolf, B.; Bencheikh, A.M.; Benchouk, C.; Bonissent, A.; Carr, J.; Coyle, P.; Drinkard, J.; Etienne, F.; Nicod, D.; Papalexiou, S.; Payre, P.; Roos, L.; Rousseau, D.; Schwemling, P.; Talby, M.; Adlung, S.; Assmann, R W.; Bauer, C.; Blum, W.; Brown, D.; Cattaneo, P W.; Dehning, B.; Dietl, H.; Dydak, F.; Frank, M.; Halley, A W.; Jacobs, K.; Lauber, J.; Luetjens, G.; Lutz, G.; Manner, W.; Richter, R H.; Schroeder, J.; Schwarz, A S.; Settles, R.; Seywerd, H.; Stierlin, U.; Stiegler, U.; Wolf, G.; Boucrot, J.; Callot, O.; Cordier, A.; Davier, M.; Duflot, L.; Grivaz, J F.; Heusse, P.; Jaffe, D E.; Janot, P.; Kim, D W.; Le Diberder, F.; Lefrancois, J.; Lutz, A M.; Schune, M H.; Veillet, J J.; Videau, I.; Zhang, Zhiqing; Abbaneo, D.; Bagliesi, G.; Batignani, G.; Bottigli, U.; Bozzi, C.; Calderini, G.; Carpinelli, M.; Ciocci, M A.; Dell'Orso, R.; Ferrante, I.; Fidecaro, F.; Foa, L.; Forti, F.; Giassi, A.; Giorgi, M A.; Gregorio, A.; Ligabue, F.; Lusiani, A.; Mannelli, E B.; Marrocchesi, P S.; Messineo, A.; Palla, F.; Rizzo, G.; Sanguinetti, G.; Spagnolo, P.; Steinberger, J.; Tenchini, R.; Tonelli, G.; Triggiani, G.; Vannini, C.; Venturi, A.; Verdini, P G.; Walsh, J.; Betteridge, A P.; Carter, J M.; Gao, Y.; Green, M G.; March, P V.; Mir, L M.; Medcalf, T.; Quazi, I S.; Strong, J A.; West, L R.; Botterill, D R.; Clifft, R W.; Edgecock, T R.; Norton, P R.; Thompson, J C.; Bloch-Devaux, B.; Colas, P.; Duarte, H.; Emery, S.; Kozanecki, W.; Lancon, E.; Lemaire, M C.; Locci, E.; Marx, B.; Perez, P.; Rander, J.; Renardy, J F.; Rosowsky, A.; Roussarie, A.; Schuller, J P.; Schwindling, J.; Si Mohand, D.; Vallage, B.; Johnson, R P.; Litke, A M.; Taylor, G.; Wear, J.; Ashman, J G.; Babbage, W.; Booth, C N.; Buttar, C.; Cartwright, S L.; Combley, F.; Dawson, I.; Thompson, L F.; Barbiero, E.; Boehrer, A.; Brandt, S.; Cowan, G.; Grupen, C.; Lutters, G.; Rivera, F.; Schaefer, U.; Smolik, L.; Bosisio, L.; Della Marina, R.; Giannini, G.; Gobbo, B.; Ragusa, F.; Bellantoni, L.; Chen, W.,; Cinabro, D.; Conway, J S.; Feng, Z.; Ferguson, D P S.; Gao, Y S.; Grahl, J.; Harton, J L.; LeClaire, B W.; Lishka, C.; Pan, Y B.; Saadi, Y.; Schmitt, M.; Sharma, V.; Shi, Z H.; Walsh, A M.; Weber, F V.; Wu Sau, Lan.; Wu, X.; Zheng, M.; Zobernig, G.
In: ISSN: 0370-2693 ; Physics Letters B ; http://hal.in2p3.fr/in2p3-00004532 ; Physics Letters B, Elsevier, 1993, 313, pp.509-519 (1993)
Abstract: In this paper, we treat the problems of Part-of-Speech (PoS) tagging of unannotated corpora of specialty. The existing taggers are trained on non-specialized corpora, and most often give inconsistent results on specialized texts. In order to learn rules adapted to a specialized field, the usual approach labels manually a large corpus of this field. This is extremely time-consuming. We propose here a semi-automatic approach for PoS tagging corpora of specialty. ETIQ, the new tagger we are building, make it possible to correct the base of rules obtained by Brill‘s tagger and to adapt it to a corpus of specialty. The expert of the field visualizes a basic tagging and corrects it by the insertion of specialized contextual lexical rules. The inserted rules are more expressive than Brill‘s rules. To help the user in this task, we designed an inductive algorithm biased by the "correct" knowledge acquired beforehand by the user. By using machine learning techniques while allowing the expert to incorporate knowledge of the field in an interactive and convivial way, we improve the tagging of a specialty corpus. Our approach has been applied to a molecular biology corpus.
Keyword: [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]; Part-of-Speech tagging; specialized corpus; text-mining
URL: http://hal.in2p3.fr/in2p3-00004532
http://hal.in2p3.fr/in2p3-00004532/document
http://hal.in2p3.fr/in2p3-00004532/file/democrite-00004532.pdf
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13
Search for a non-minimal Higgs boson produced in the reaction e$^+$e$^-$ $\rightarrow$ hZ$^{\star}$
In: ISSN: 0370-2693 ; Physics Letters B ; http://hal.in2p3.fr/in2p3-00004593 ; Physics Letters B, Elsevier, 1993, 313, pp.312-325 (1993)
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