检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Sirunyan, A.M. Tumasyan, A. Adam, W. Bergauer, T. Dragicevic, M. Erö, J. Escalante Del Valle, A. Frühwirth, R. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Madlener, T. Mikulec, I. Pitters, F.M. Rad, N. Schieck, J. Schöfbeck, R. Spanring, M. Templ, S. Waltenberger, W. Wulz, C.-E. Zarucki, M. Chekhovsky, V. Litomin, A. Makarenko, V. Suarez Gonzalez, J. Darwish, M.R. de Wolf, E.A. Di Croce, D. Janssen, X. Kello, T. Lelek, A. Pieters, M. Rejeb Sfar, H. van Haevermaet, H. van Mechelen, P. van Putte, S. van Remortel, N. Blekman, F. Bols, E.S. Chhibra, S.S. D’Hondt, J. de Clercq, J. Lontkovskyi, D. Lowette, S. Marchesini, I. Moortgat, S. Morton, A. Python, Q. Tavernier, S. van Doninck, W. van Mulders, P. Beghin, D. Bilin, B. Clerbaux, B. de Lentdecker, G. Dorney, B. Favart, L. Grebenyuk, A. Kalsi, A.K. Makarenko, I. Moureaux, L. Pétré, L. Popov, A. Postiau, N. Starling, E. Thomas, L. Vander Velde, C. Vanlaer, P. Vannerom, D. Wezenbeek, L. Cornelis, T. Dobur, D. Gruchala, M. Khvastunov, I. Niedziela, M. Roskas, C. Skovpen, K. Tytgat, M. Verbeke, W. Vermassen, B. Vit, M. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Delcourt, M. Donertas, I.S. Giammanco, A. Lemaitre, V. Mondal, K. Prisciandaro, J. Taliercio, A. Teklishyn, M. Vischia, P. Wertz, S. Wuyckens, S. Alves, G.A. Hensel, C. Moraes, A. Aldá Júnior, W.L. Belchior Batista Das Chagas, E. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Coelho, E. da Costa, E.M. da Silveira, G.G. de Jesus Damiao, D. Fonseca De Souza, S. Martins, J. Matos Figueiredo, D. Medina Jaime, M. Mora Herrera, C. Mundim, L. Nogima, H. Rebello Teles, P. Sanchez Rosas, L.J. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Torres Da Silva De Araujo, F. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Lemos, D.S. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Atanasov, I. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Bonchev, M. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Wien Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Institute of Modern Physics Key Laboratory of Nuclear Physics and Ion-beam Application MOE Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics CHEP-FU Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics University of Helsinki Helsinki Finland Helsinki Institute of Physics Helsinki Finland Lappe

Differential cross section measurements of W±γ production in proton-proton collisions at √s = 13 TeV are presented. The data set used in this study was collected with the CMS detector at the CERN LHC in 2016–2018 with an integrated luminosity of 138 fb'1. Candidate events containing an electron or muon, a photon, and missing transverse momentum are selected. The measurements are compared with standard model predictions computed at next-to-leading and next-to-next-to-leading orders in perturbative quantum chromodynamics. Constraints on the presence of TeV-scale new physics affecting the WWγ vertex are determined within an effective field theory framework, focusing on the O3W operator. A simultaneous measurement of the photon transverse momentum and the azimuthal angle of the charged lepton in a special reference frame is performed. This two-dimensional approach provides up to a factor of ten more sensitivity to the interference between the standard model and the O3W contribution than using the transverse momentum alone. © 2021, CC BY.

关键词： Photons

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Layerwise perturbation-based adversarial training for hard drive health degree prediction

arXiv

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arXiv 2018年

作者： Zhang, Jianguo Wang, Ji He, Lifang Li, Zhao Yu, Philip S. Department of Computer Science University of Illinois at Chicago ChicagoIL United States College of Systems Engineering National University of Defense Technology Changsha Hunan China Weill Cornell Department of Healthcare Policy & Research Cornell University NY United States Alibaba Group Hangzhou Zhejiang China Shanghai Institute for Advanced Communication and Data Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China

With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning algorithms based on SMART attributes are effective methods to predict hard drive failures. In this paper, we use SMART attributes to predict hard drive health degrees which are helpful for taking different fault tolerant actions in advance. Given the highly imbalanced SMART datasets, it is a nontrivial work to predict the health degree precisely. The proposed model would encounter overfitting and biased fitting problems if it is trained by the traditional methods. In order to resolve this problem, we propose two strategies to better utilize imbalanced data and improve performance. Firstly, we design a layerwise perturbation-based adversarial training method which can add perturbations to any layers of a neural network to improve the generalization of the network. Secondly, we extend the training method to the semi-supervised settings. Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model. Our extensive experiments on two real-world hard drive datasets demonstrate the superiority of the proposed schemes for both supervised and semi-supervised classification. The model trained by the proposed method can correctly predict the hard drive health status 5 and 15 days in advance. Finally, we verify the generality of the proposed training method in other similar anomaly detection tasks where the dataset is imbalanced. The results argue that the proposed methods are applicable to other domains. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Search for a heavy resonance decaying into a top quark and a W boson in the lepton+jets final state at √s = 13 TeV

arXiv

引用

arXiv 2021年

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Del Valle, A. Escalante Frühwirth, R. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Paulitsch, P. Pitters, F.M. Schieck, J. Schöfbeck, R. Schwarz, D. Templ, S. Waltenberger, W. Wulz, C.-E. Chekhovsky, V. Litomin, A. Makarenko, V. Darwish, M.R. De Wolf, E.A. Janssen, T. Kello, T. Lelek, A. Sfar, H. Rejeb Van Mechelen, P. Van Putte, S. Van Remortel, N. Blekman, F. Bols, E.S. D’Hondt, J. Delcourt, M. El Faham, H. Lowette, S. Moortgat, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Doninck, W. Beghin, D. Bilin, B. Clerbaux, B. De Lentdecker, G. Favart, L. Kalsi, A.K. Mahdavikhorrami, M. Makarenko, I. Moureaux, L. Pétré, L. Popov, A. Postiau, N. Starling, E. Thomas, L. Bemden, M. Vanden Velde, C. Vander Vanlaer, P. Cornelis, T. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Niedziela, M. Rendón, C. Roskas, C. Samalan, A. Skovpen, K. Tytgat, M. Vermassen, B. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Mondal, K. Prisciandaro, J. Taliercio, A. Teklishyn, M. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Hensel, C. Moraes, A. Teles, P. Rebello Júnior, W.L. Aldá Pereira, M. Alves Gallo Filho, M. Barroso Ferreira Malbouisson, H. Brandao Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. De Souza, S. Fonseca Herrera, C. Mora Amarilo, K. Mota Mundim, L. Nogima, H. Santoro, A. Do Amaral, S.M. Silva Sznajder, A. Thiel, M. De Araujo, F. Torres Da Silva Pereira, A. Vilela Bernardes, C.A. Calligaris, L. Tomei, T.R. Fernandez Perez Gregores, E.M. Lemos, D.S. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Cheng, T. Javaid, T. Mittal, M. Yuan, L. Ahmad, M. Bauer, G. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics University of Helsinki Helsinki Finland Helsinki Institute of Physics Helsinki Finland Lappeenranta University of Technology Lappeenranta Finland IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Laboratoire Leprince-R

A search for a heavy resonance decaying into a top quark and a W boson in proton-proton collisions at √s = 13 TeV is presented. The data analyzed were recorded with the CMS detector at the LHC and correspond to an integrated luminosity of 138 fb−1. The top quark is reconstructed as a single jet and the W boson, from its decay into an electron or muon and the corresponding neutrino. A top quark tagging technique based on jet clustering with a variable distance parameter and simultaneous jet grooming is used to identify jets from the collimated top quark decay. The results are interpreted in the context of two benchmark models, where the heavy resonance is either an excited bottom quark b∗ or a vector-like quark B. A statistical combination with an earlier search by the CMS Collaboration in the all-hadronic final state is performed to place upper cross section limits on these two models. The new analysis extends the lower range of resonance mass probed from 1.4 down to 0.7 TeV. For left-handed, right-handed, and vector-like couplings, b∗ masses up to 3.0, 3.0, and 3.2 TeV are excluded at 95% confidence level, respectively. The observed upper limits represent the most stringent constraints on the b∗ model to date. © 2021, CC BY.

关键词： Resonance

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Measurement of the top quark mass using events with a single reconstructed top quark in pp collisions at √s = 13 TeV

arXiv

引用

arXiv 2021年

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Dragicevic, M. Escalante Del Valle, A. Frühwirth, R. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Paulitsch, P. Pitters, F.M. Schieck, J. Schöfbeck, R. Spanring, M. Templ, S. Waltenberger, W. Wulz, C.-E. Chekhovsky, V. Litomin, A. Makarenko, V. Darwish, M.R. De Wolf, E.A. Janssen, X. Kello, T. Lelek, A. Rejeb Sfar, H. Van Mechelen, P. Van Putte, S. Van Remortel, N. Blekman, F. Bols, E.S. D'Hondt, J. De Clercq, J. Delcourt, M. El Faham, H. Lowette, S. Moortgat, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Doninck, W. Van Mulders, P. Beghin, D. Bilin, B. Clerbaux, B. De Lentdecker, G. Favart, L. Grebenyuk, A. Kalsi, A.K. Lee, K. Mahdavikhorrami, M. Makarenko, I. Moureaux, L. Pétré, L. Popov, A. Postiau, N. Starling, E. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. Vannerom, D. Wezenbeek, L. Cornelis, T. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Niedziela, M. Roskas, C. Samalan, A. Skovpen, K. Tytgat, M. Verbeke, W. Vermassen, B. Vit, M. Bethani, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Mondal, K. Prisciandaro, J. Taliercio, A. Teklishyn, M. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Hensel, C. Moraes, A. Aldá, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Fonseca De Souza, S. Matos Figueiredo, D. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Rebello Teles, P. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Da Silva De Araujo, F. Torres Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Lemos, D.S. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Cheng, T. Guo, Q. Javaid, T. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Wien Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Universite Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade Do Estado Do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal Do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics University of Helsinki Helsinki Finland Helsinki Institute of Physics Helsinki Finland Lappeenranta University of Technology Lappeenranta Finland IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Laboratoire Lepr

A measurement of the top quark mass is performed using a data sample enriched with single top quark events produced in the t channel. The study is based on protonproton collision data, corresponding to an integrated luminosity of 35.9 fb-1, recorded at √s = 13 TeV by the CMS experiment at the LHC in 2016. Candidate events are selected by requiring an isolated high-momentum lepton (muon or electron) and exactly two jets, of which one is identified as originating from a bottom quark. Multivariate discriminants are designed to separate the signal from the background. Optimized thresholds are placed on the discriminant outputs to obtain an event sample with high signal purity. The top quark mass is found to be 172.13+0.76-0.77GeV, where the uncertainty includes both the statistical and systematic components, reaching sub-GeV precision for the first time in this event topology. The masses of the top quark and antiquark are also determined separately using the lepton charge in the final state, from which the mass ratio and difference are determined to be 0.9952+0.0079-0.0104and 0.83+1.79-1.35GeV, respectively. The results are consistent with CPT invariance. © 2021, CC BY.

关键词： Tellurium compounds

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Measurements of the pp → W±γγ and pp → Zγγ cross sections at √s = 13 TeV and limits on anomalous quartic gauge couplings

arXiv

引用

arXiv 2021年

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Dragicevic, M. Del Valle, A. Escalante Frühwirth, R. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Paulitsch, P. Pitters, F.M. Schieck, J. Schöfbeck, R. Spanring, M. Templ, S. Waltenberger, W. Wulz, C.-E. Chekhovsky, V. Litomin, A. Makarenko, V. Darwish, M.R. de Wolf, E.A. Janssen, X. Kello, T. Lelek, A. Sfar, H. Rejeb van Mechelen, P. van Putte, S. van Remortel, N. Blekman, F. Bols, E.S. D’Hondt, J. de Clercq, J. Delcourt, M. El Faham, H. Lowette, S. Moortgat, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. van Doninck, W. van Mulders, P. Beghin, D. Bilin, B. Clerbaux, B. de Lentdecker, G. Favart, L. Grebenyuk, A. Kalsi, A.K. Lee, K. Mahdavikhorrami, M. Makarenko, I. Moureaux, L. Pétré, L. Popov, A. Postiau, N. Starling, E. Thomas, L. Bemden, M. Vanden Velde, C. Vander Vanlaer, P. Vannerom, D. Wezenbeek, L. Cornelis, T. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Niedziela, M. Roskas, C. Samalan, A. Skovpen, K. Tran, T.T. Tytgat, M. Verbeke, W. Vermassen, B. Vit, M. Bethani, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Lemaitre, V. Mondal, K. Prisciandaro, J. Taliercio, A. Teklishyn, M. Vischia, P. Wertz, S. Wuyckens, S. Alves, G.A. Hensel, C. Moraes, A. Aldá, W.L. Pereira, M. Alves Gallo Filho, M. Barroso Ferreira Malbouisson, H. Brandao Carvalho, W. Chinellato, J. da Costa, E.M. da Silveira, G.G. de Jesus Damiao, D. de Souza, S. Fonseca Figueiredo, D. Matos Herrera, C. Mora Amarilo, K. Mota Mundim, L. Nogima, H. Teles, P. Rebello Santoro, A. Do Amaral, S.M. Silva Sznajder, A. Thiel, M. da Silva De Araujo, F. Torres Pereira, A. Vilela Bernardes, C.A. Calligaris, L. Tomei, T.R. Fernandez Perez Gregores, E.M. Lemos, D.S. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Cheng, T. Fang, W. Guo, Q. Javaid, T. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Wien Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics University of Helsinki Helsinki Finland Helsinki Institute of Physics Helsinki Finland Lappeenranta University of Technology Lappeenranta Finland IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Laboratoire Lepr

The cross section for W or Z boson production in association with two photons is measured in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data set corresponds to an integrated luminosity of 137 fb−1 collected by the CMS experiment at the LHC. The W → `ν and Z → `` decay modes (where ` = e, µ) are used to extract the Wγγ and Zγγ cross sections in a phase space defined by electron (muon) with transverse momentum larger than 30 GeV and photon transverse momentum larger than 20 GeV. All leptons and photons are required to have absolute pseudorapidity smaller than 2.5. The measured cross sections in this phase space are σ(Wγγ) = 13.6+−1.91.9 (stat)+−4.04.0 (syst) ± 0.08 (PDF + scale) fb and σ(Zγγ) = 5.41+−0.580.55 (stat)+−0.640.70 (syst) ± 0.06 (PDF + scale) fb. Limits on anomalous quartic gauge couplings are set in the framework of an effective field theory with dimension-8 operators. © 2021, CC BY.

关键词： Phase space methods

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Relation Model of Burden Operation and State Variables of Blast Furnace Based on Low Frequency Feature Extraction*

引用

IFAC-PapersOnLine 2017年第1期50卷 13796-13801页

作者： Zhang, Kexin Wu, Min An, Jianqi Cao, Weihua Liu, Zhentao Ning, Fulong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

In the blast furnace ironmaking process, the running state of the blast furnace can be directly adjusted by the burden operation. Therefore, the explicit relation between the burden operation and the running state variables is vital for the blast furnace operators. However, the state variables of the blast furnace are affected by many factors and the burden matrix is complicated. In this paper, a support vector regression(SVR) predicting model based on low frequency feature extraction is proposed to solve these problems. First, the low frequency components of state variables time series which are mostly affected by the burden operation are extracted. Then, the dimensions of the burden matrix are reduced by a devised simple expression method. Finally, the relation model based on SVR is proposed to predict the values of the expected state variables according to the simplified burden matrix and the value of initial state variables. The simulation on real factory data indicates that the model reflects the accurate quantitative relation between the burden operation and the state variables. © 2017

关键词： Blast furnaces Blast furnace practice Extraction Feature extraction Support vector regression Blast furnace iron making Blast furnace operators burden operation Initial state variables Low frequency components Relation models State variables Support vector regression (SVR)

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DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval

arXiv

引用

arXiv 2017年

作者： Pang, Liang Lan, Yanyan Guo, Jiafeng Xu, Jun Xu, Jingfang Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Sogou Inc Beijing China

This paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to generate ranking scores, without explicit understandings of the relevance. According to the human judgement process, a relevance label is generated by the following three steps: 1) relevant locations are detected;2) local relevances are determined;3) local relevances are aggregated to output the relevance label. In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process. Firstly, a detection strategy is designed to extract the relevant contexts. Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU). Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score. DeepRank well captures important IR characteristics, including exact/semantic matching signals, proximity heuristics, query term importance, and diverse relevance requirement. Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Information retrieval

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A deep relevance matching model for ad-hoc retrieval

arXiv

引用

arXiv 2017年

作者： Guo, Jiafeng Fan, Yixing Ai, Qingyao Bruce Croft, W. CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Center for Intelligent Information Retrieval University of Massachusetts Amherst MA United States

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models. Copyright © 2017, The Authors. All rights reserved.

关键词： Natural language processing systems

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A User-Centric data Secure Creation Scheme in Cloud computing

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Chinese Journal of Electronics 2016年第4期25卷 753-760页

作者： SU Mang LI Fenghua SHI Guozhen GENG Kui XIONG Jinbo School of Computer Science and Engineering Nanjing University of Science and Technology State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences Department of Information Security Beijing Electronic Science and Technology Institute State Key Laboratory of Integrated Services Network Xidian University

Due to the use of the cloud computing technology,the ownership is separated from the administration of the data in cloud and the shared data might be migrated between different clouds,which would bring new challenges to data secure creation,especially for the data privacy *** propose a User-centric data secure creation scheme(UCDSC) for the security requirements of resource owners in *** this scheme,a data owner first divides the users into different *** data owner encrypts data and defines different secure managing policies for the data according to *** encrypt the data in UCDSC,we present an algorithm based on Access control conditions proxy re-encryption(ACC-PRE),which is proved to be master secret secure and Chosen-ciphertext attack(CCA) secure in random oracle *** give the application protocols and make the comparisons between some existing approaches and UCDSC.

关键词： User-centric ACC-PRE data creation Master secret secure CCA

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On Capacity-Based Codebook Design and Advanced Decoding for Sparse Code Multiple Access Systems

On Capacity-Based Codebook Design and Advanced Decoding for ...

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作者： Xiao, Kexin Xia, Bin Chen, Zhiyong Xiao, Baicen Chen, Dageng Ma, Shaodan Shanghai Institute for Advanced Communication and Data Science Institute of Wireless Communication Technologies Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Wireless Communication Technologies Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering University of Washington SeattleWA98105 United States Cooperative Medianent Innovation Center Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai200240 China Communications Technology Laboratory Huawei Technologies Co. Ltd. Shanghai201206 China Department of Electrical and Computer Engineering University of Macau C-Macau999078 China

Sparse code multiple access (SCMA) is a promising non-orthogonal air-interface technology for its ability to support massive connections. In this paper, we design the multiuser codebook and the advanced decoding from the perspective of the theoretical capacity and the system feasibility. First, different from the lattice-based constellation in point-to-point channels, we propose a novel codebook for maximizing the constellation constrained capacity. We optimize a series of 1-D superimposed constellations to construct multi-dimensional codewords. An effective dimensional permutation switching algorithm is proposed to further obtain the capacity gain. Consequently, it shows that the performance of the proposed codebook approaches the Shannon limit and achieves significant gains over the other existing ones. Furthermore, we provide a symbol-based extrinsic information transfer tool to analyze the convergence of SCMA iterative detection, where the complex codewords are considered in modeling the a priori probabilities instead of assuming the binary inputs in previous literature. Finally, to approach the capacity, we develop a low-density parity-check code-based SCMA receiver. Most importantly, by utilizing the EXIT charts, we propose an iterative joint detection and decoding scheme with only partial inner iterations, which exhibits significant performance gain over the traditional one with separate detection and decoding. © 2002-2012 IEEE.

关键词： Iterative decoding

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