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arXiv 2018年

作者： Dutt, Mousumi Biswas, Arindam Nagy, Benedek Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology 4 Diamond Harbour Road Kidderpore West Bengal Kolkata700023 India Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur West Bengal Howrah711103 India Department of Mathematics Eastern Mediterranean University W. Shakespeare Street Famagusta North Cyprus via Mersin-10 99628 Turkey Institute of Mathematics and Informatics Eszertházy Károly Catholic University Leányka utca 4 Eger3300 Hungary

The enumeration of shortest paths in cubic grid is presented herein, which could have importance in image processing and also in the network sciences. The cubic grid considers three neighborhoods — namely, 6-, 18- and 26-neighborhood related to face connectivity, edge connectivity and vertex connectivity, respectively. The formulation for distance metrics is given. L1, D18, and L∞ are the three metrics for 6-neighborhood, 18-neighborhood and 26-neighborhood. The task is to count the number of minimal paths, based on given neighborhood relations, from any given point to any other, in the three-dimensional cubic grid. Based on the coordinate triplets describing the grid, the formulations for the three neighborhoods are presented in this work. The problem both of theoretical importance and has several practical aspects. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Search for pair-production of vector-like quarks in pp collision events at s=13 TeV with at least one leptonically decaying Z boson and a third-generation quark with the ATLAS detector

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2023年 843卷

作者： Aad, G. Abbott, B. Abeling, K. Abicht, N.J. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B.S. Adam Bourdarios, C. Adamczyk, L. Addepalli, S.V. Addison, M.J. Adelman, J. Adiguzel, A. Adye, T. Affolder, A.A. Afik, Y. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Aizenberg, I. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Akiyama, D. Akolkar, N.N. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Albouy, G.L. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, F. Algren, M. Alhroob, M. Ali, B. Ali, H.M.J. Ali, S. Alibocus, S.W. Aliev, M. Alimonti, G. Alkakhi, W. Allaire, C. Allbrooke, B.M.M. Allen, J.F. Allendes Flores, C.A. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alvarez Estevez, M. Alvarez Fernandez, A. Alves Cardoso, M. Aly, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Amelung, C. Amerl, M. Ames, C.G. Amidei, D. Amor Dos Santos, S.P. Amos, K.R. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Anulli, F. Aoki, M. Aoki, T. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Appelt, C. Apyan, A. Aranzabal, N. Arbiol Val, S.J. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Assahsah, J. Assamagan, K. Astalos, R. Atashi, S. Atkin, R.J. Atkinson, M. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Auriol, A.D. Austrup, V.A. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahmani, M. Bailey, A.J. Bailey, V.R. Baines, J.T. Baines, L. Baker, O.K. Bakos, E. Bakshi Gupta, D. Balakrishnan, V. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F. Baltes, L.M. Balunas, W.K. Balz, J. Banas, E. Bandieramonte, M. Bandyopadh Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Istanbul Aydin University Application and Research Center for Advanced Studies Istanbul Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d'Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of

A search for the pair-production of vector-like quarks optimized for decays into a Z boson and a third-generation Standard Model quark is presented, using the full Run 2 dataset corresponding to 139fb⁻¹ of pp collisions at s=13TeV, collected in 2015–2018 with the ATLAS detector at the Large Hadron Collider. The targeted final state is characterized by the presence of a Z boson with high transverse momentum, reconstructed from a pair of same-flavour leptons with opposite-sign charges, as well as by the presence of b-tagged jets and high-transverse-momentum large-radius jets reconstructed from calibrated smaller-radius jets. Events with exactly two or at least three leptons are used, which are further categorized by the presence of boosted W, Z, and Higgs bosons and top quarks. The categorization is performed using a neural-network-based boosted object tagger to enhance the sensitivity to signal relative to the background. No significant excess above the background expectation is observed and exclusion limits at 95% confidence level are set on the masses of the vector-like partners T and B of the top and bottom quarks, respectively. The limits depend on the branching ratio configurations and, in the case of 100% branching ratio for T→Zt and 100% branching ratio for B→Zb, this search sets the most stringent limits to date, allowing m_T>1.60TeV and m_B>1.42TeV, respectively. © 2023 The Author(s)

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Cow behavior recognition based on image analysis and activities

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International Journal of Agricultural and Biological engineering 2017年第3期10卷 165-174页

作者： Gu Jingqiu Wang Zhihai Gao Ronghua Wu Huarui School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Key Laboratory of Agri-informatics Ministry of AgricultureBeijing 100097China Beijing Engineering Research Center of Agricultural Internet of Things Beijing 100097China

For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall *** method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex *** a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back ***,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy *** behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior *** experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,*** method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.

关键词： cow behavior target segmentation image entropy image moment activities intelligent analysis

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DEA Model with Hesitant Fuzzy Polyhedral Set in Benchmarking 1

DEA Model with Hesitant Fuzzy Polyhedral Set in Benchmarking

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Abdullah, Dahlan Hartono, H. Erliana, Cut Ita Irwansyah, Defi Siregar, Nurintan Asyiah Sari, Ayu Esteka Hasibuan, Abdurrozzaq Abdussakir Kurniasih, Nuning Mas'Ut Rangkuti, Irma Yanti Nurmawati Buston, Erni Abdurrahman, Apridar Handayani, Tita Septi Hadikurniawati, Wiwien Winarno, Edy Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Faculty of Economic Universitas Islam Sumatera Utara Medan Indonesia Faculty of Medical Universitas Islam Sumatera Utara Medan Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Departement of Nursing Politeknik Kesehatan Kementerian Bengkulu Indonesia Department of Management Universitas Malikussaleh Aceh Indonesia Departemen of Nursing Universitas Dehasen Bengkulu Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

Data Envelopment Analysis (DEA) method is a linear programming approach that has been widely used as a framework for evaluating efficiency and measurement. DEA decision making is often faced with situations where the existing DMU has input and output that contains fuzzy and hesitant elements so that it is difficult to make efficiency measurements. This study will design a DEA model in the state of inputs and outputs that contain hesitant elements by using polyhedral uncertainty sets. The results of this study are expected to produce a benchmarking model with DEA that can overcome the hesitant element and have the advantage of using a polyhedral uncertainty set that can measure the linearity of the nominal problem. © Published under licence by IOP Publishing Ltd.

关键词： Data envelopment analysis

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Prediction of facebook post metrics using machine learning

arXiv

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arXiv 2018年

作者： Sam, Emmanuel Yarushev, Sergey Basterrech, Sebastián Averkin, Alexey Faculty of Computing and Information Technology Wisconsin International University College Accra Ghana Department of Informatics Plekhanov Russian University of Economics Moscow Russia Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Informatics Problems FRC CSC RAS Moscow Russia

In this short paper, we evaluate the performance of three well-known Machine Learning techniques for predicting the impact of a post in Facebook. Social medias have a huge influence in the social behaviour. Therefore to develop an automatic model for predicting the impact of posts in social medias can be useful to the society. In this article, we analyze the efficiency for predicting the post impact of three popular techniques: Support Vector Regression (SVR), Echo State Network (ESN) and Adaptive Network Fuzzy Inject System (ANFIS). The evaluation was done over a public and well-known benchmark dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Renewable Mobility in Smart Cities: Cloud-Based Services

Renewable Mobility in Smart Cities: Cloud-Based Services

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IEEE Symposium on computers and Communications

作者： Damianos Gavalas Dionisis Kehagias Damianos Kypriadis Kalliopi Giannakopoulou Charalampos Konstantopoulos Grammati Pantziou Christos Zaroliagis Vlasios Kasapakis Spyros Kontogiannis Andreas Paraskevopoulos Dept of Product & Systems Design Engineering University of the Aegean Syros Greece & CTI Patras Greece Dept of Computer Engineering & Informatics University of Patras Patras Greece & CTI Patras Greece Dept of Cultural Technology & Communication University of the Aegean Mytilene Greece & CTI Patras Greece Centre for Research and Technology Hellas/Information Technologies Institute (CERTH/ITI) Thessaloniki Greece Dept of Informatics University of Piraeus Piraeus Greece & CTI Patras Greece Dept of Computer Science & Engineering University of Ioannina Ioannina Greece & CTI Patras Greece Dept of Informatics Technological Educational Institution of Athens Athens Greece & CTI Patras Greece

Providing efficient, sustainable and personalized mobility services in urban environments that combine a spectrum of transport modes (e.g., public transport, electric vehicles, vehicle sharing, low energy and/or emission routes) constitutes a great challenge. In this work, we present MOVESMART, a holistic approach (and integrated platform) for the provision of renewable personal mobility services, leveraging crowd-sourcing data, tools for collecting real-time information by multimodal travelers, and traffic prediction mechanisms. MOVESMART guarantees real-time responses to renewable (on-demand) mobility queries for efficient multi-modal route planning that are time-dependent as well as sensitive to aperiodic incidents and traffic prediction forecasts. This paper focuses on the cloud-based (backend) services of the MOVESMART platform.

关键词： Sustainable mobility Renewable mobility Multimodal route planning Crowdsourcing Vehicle sharing Algorithm engineering

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Hard color-singlet exchange in dijet events in proton-proton collisions at s=13 TeV

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Physical Review D 2021年第3期104卷 032009-032009页

作者： A. M. Sirunyan A. Tumasyan W. Adam F. Ambrogi T. Bergauer M. Dragicevic J. Erö A. Escalante Del Valle R. Frühwirth M. Jeitler N. Krammer L. Lechner D. Liko T. Madlener I. Mikulec F. M. Pitters N. Rad J. Schieck R. Schöfbeck M. Spanring S. Templ W. Waltenberger C.-E. Wulz M. Zarucki V. Chekhovsky A. Litomin V. Makarenko J. Suarez Gonzalez M. R. Darwish E. A. De Wolf D. Di Croce X. Janssen T. Kello A. Lelek M. Pieters H. Rejeb Sfar H. Van Haevermaet P. Van Mechelen S. Van Putte N. Van Remortel F. Blekman E. S. Bols S. S. Chhibra J. D’Hondt J. De Clercq D. Lontkovskyi S. Lowette I. Marchesini S. Moortgat A. Morton Q. Python S. Tavernier W. Van Doninck P. Van Mulders D. Beghin B. Bilin B. Clerbaux G. De Lentdecker H. Delannoy B. Dorney L. Favart A. Grebenyuk A. K. Kalsi I. Makarenko L. Moureaux L. Pétré A. Popov N. Postiau E. Starling L. Thomas C. Vander Velde P. Vanlaer D. Vannerom L. Wezenbeek T. Cornelis D. Dobur M. Gruchala I. Khvastunov M. Niedziela C. Roskas K. Skovpen M. Tytgat W. Verbeke B. Vermassen M. Vit G. Bruno F. Bury C. Caputo P. David C. Delaere M. Delcourt I. S. Donertas A. Giammanco V. Lemaitre K. Mondal J. Prisciandaro A. Taliercio M. Teklishyn P. Vischia S. Wuyckens J. Zobec G. A. Alves G. Correia Silva C. Hensel A. Moraes W. L. Aldá Júnior E. Belchior Batista Das Chagas H. Brandao Malbouisson W. Carvalho J. Chinellato E. Coelho E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao S. Fonseca De Souza J. Martins D. Matos Figueiredo M. Medina Jaime M. Melo De Almeida C. Mora Herrera L. Mundim H. Nogima P. Rebello Teles L. J. Sanchez Rosas A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel E. J. Tonelli Manganote F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris T. R. Fernandez Perez Tomei E. M. Gregores D. S. Lemos P. G. Mercadante S. F. Novaes Sandra S. Padula A. Aleksandrov R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov M. Bonchev A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov W. Fang Q. Guo H. Wang L. Yuan M. Ahmad Z. Hu Y. Wang E. 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 University of Illinois at Chicago (UIC) Chicago Illinois USA Department of Physics Tsinghua University Beijing China University of Virginia Charlottesville Virginia USA Institute of High Energy Physics Beijing China Massachusetts Institute of Technology Cambridge Massachusetts USA State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China The University of Kansas Lawrence Kansas USA Sun Yat-Sen University Guangzhou China Institute of Modern Physics and 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 Physic

Events where the two leading jets are separated by a pseudorapidity interval devoid of particle activity, known as jet-gap-jet events, are studied in proton-proton collisions at s=13 TeV. The signature is expected from hard color-singlet exchange. Each of the highest transverse momentum (pT) jets must have pTjet>40 GeV and pseudorapidity 1.4<|ηjet|<4.7, with ηjet1ηjet2<0, where jet1 and jet2 are the leading and subleading jets in pT, respectively. The analysis is based on data collected by the CMS and TOTEM experiments during a low luminosity, high-β* run at the CERN LHC in 2015, with an integrated luminosity of 0.66 pb−1. Events with a low number of charged particles with pT>0.2 GeV in the interval |η|<1 between the jets are observed in excess of calculations that assume only color-exchange. The fraction of events produced via color-singlet exchange, fCSE, is measured as a function of pTjet2, the pseudorapidity difference between the two leading jets, and the azimuthal angular separation between the two leading jets. The fraction fCSE has values of 0.4–1.0%. The results are compared with previous measurements and with predictions from perturbative quantum chromodynamics. In addition, the first study of jet-gap-jet events detected in association with an intact proton using a subsample of events with an integrated luminosity of 0.40 pb−1 is presented. The intact protons are detected with the Roman pot detectors of the TOTEM experiment. The fCSE in this sample is 2.91±0.70(stat)−1.01+1.08(syst) times larger than that for inclusive dijet production in dijets with similar kinematics.

关键词： Quark & gluon jets Hadron colliders

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Measurement of cross sections for production of a Z boson in association with a flavor-inclusive or doubly b-tagged large-radius jet in proton-proton collisions at s=13 TeV with the ATLAS experiment

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Physical Review D 2023年第1期108卷 012022-012022页

作者： G. Aad B. Abbott D. C. Abbott A. Abed Abud K. Abeling D. K. Abhayasinghe S. H. Abidi O. S. AbouZeid N. L. Abraham H. Abramowicz H. Abreu Y. Abulaiti B. S. Acharya B. Achkar L. Adam C. Adam Bourdarios L. Adamczyk L. Adamek J. Adelman A. Adiguzel S. Adorni T. Adye A. A. Affolder Y. Afik C. Agapopoulou M. N. Agaras A. Aggarwal C. Agheorghiesei J. A. Aguilar-Saavedra A. Ahmad F. Ahmadov W. S. Ahmed X. Ai G. Aielli S. Akatsuka M. Akbiyik T. P. A. Åkesson E. Akilli A. V. Akimov K. Al Khoury G. L. Alberghi J. Albert M. J. Alconada Verzini S. Alderweireldt M. Aleksa I. N. Aleksandrov C. Alexa T. Alexopoulos A. Alfonsi F. Alfonsi M. Alhroob B. Ali S. Ali M. Aliev G. Alimonti C. Allaire B. M. M. Allbrooke P. P. Allport A. Aloisio F. Alonso C. Alpigiani E. Alunno Camelia M. Alvarez Estevez M. G. Alviggi Y. Amaral Coutinho A. Ambler L. Ambroz C. Amelung D. Amidei S. P. Amor Dos Santos S. Amoroso C. S. Amrouche F. An C. Anastopoulos N. Andari T. Andeen J. K. Anders S. Y. Andrean A. Andreazza V. Andrei C. R. Anelli S. Angelidakis A. Angerami A. V. Anisenkov A. Annovi C. Antel M. T. Anthony E. Antipov M. Antonelli D. J. A. Antrim F. Anulli M. Aoki J. A. Aparisi Pozo M. A. Aparo L. Aperio Bella N. Aranzabal V. Araujo Ferraz R. Araujo Pereira C. Arcangeletti A. T. H. Arce J-F. Arguin S. Argyropoulos J.-H. Arling A. J. Armbruster A. Armstrong O. Arnaez H. Arnold Z. P. Arrubarrena Tame G. Artoni H. Asada K. Asai S. Asai T. Asawatavonvanich N. A. Asbah E. M. Asimakopoulou L. Asquith J. Assahsah K. Assamagan R. Astalos R. J. Atkin M. Atkinson N. B. Atlay H. Atmani P. A. Atmasiddha K. Augsten V. A. Austrup G. Avolio M. K. Ayoub G. Azuelos D. Babal H. Bachacou K. Bachas F. Backman P. Bagnaia H. Bahrasemani A. J. Bailey V. R. Bailey J. T. Baines C. Bakalis O. K. Baker P. J. Bakker E. Bakos D. Bakshi Gupta S. Balaji R. Balasubramanian E. M. Baldin P. Balek F. Balli W. K. Balunas J. Balz E. Banas M. Bandieramonte A. Bandyopadhyay Sw. Banerjee L. Barak W. M. Barbe E. L. Barberio D. Barberis M. Barbero G. Barbour T. Barillari M-S. Barisits CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman Oklahoma USA Department of Physics University of Massachusetts Amherst Massachusetts USA CERN Geneva Switzerland II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Department of Physics Royal Holloway University of London Egham United Kingdom Physics Department Brookhaven National Laboratory Upton New York USA Niels Bohr Institute University of Copenhagen Copenhagen Denmark Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion Israel Institute of Technology Haifa Israel High Energy Physics Division Argonne National Laboratory Argonne Illinois USA INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Institut für Physik Universität Mainz Mainz Germany LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics University of Toronto Toronto Ontario Canada Department of Physics Northern Illinois University DeKalb Illinois USA Department of Physics Bogazici University Istanbul Türkiye Département de Physique Nucléaire et Corpusculaire Université de Genève Genève Switzerland Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz California USA IJCLab Université Paris-Saclay CNRS/IN2P3 91405 Orsay France LPC Université Clermont Auvergne CNRS/IN2P3 Clermont-Ferrand France Institute for Mathematics Astrophysics and Particle Physics Radboud University/Nikhef Nijmegen Netherlands Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania D

We present measurements of cross sections for production of a leptonically decaying Z boson in association with a large-radius jet in 13 TeV proton-proton collisions at the LHC, using 36 fb−1 of data from the ATLAS detector. Integrated and differential cross sections are measured at particle level in both a flavor inclusive and a doubly b-tagged fiducial phase space. The large-radius jet mass and transverse momentum, its kinematic relationship to the Z boson, and the angular separation of b-tagged small-radius track jets within the large-radius jet are measured. This measurement constitutes an important test of perturbative quantum chromodynamics in kinematic and flavor configurations relevant to several Higgs boson and beyond-Standard-Model physics analyses. The results highlight issues with modeling of additional hadronic activity in the flavor-inclusive selection, and a distinction between flavor-number schemes in the b-tagged phase space.

关键词： Jets & heavy flavor physics Perturbative QCD W & Z bosons

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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Search for heavy particles in the b-tagged dijet mass distribution with additional b-tagged jets in proton-proton collisions at s=13 TeV with the ATLAS experiment

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Physical Review D 2022年第1期105卷 012001-012001页

作者： G. Aad B. Abbott D. C. Abbott A. Abed Abud K. Abeling D. K. Abhayasinghe S. H. Abidi H. Abramowicz H. Abreu Y. Abulaiti A. C. Abusleme Hoffman B. S. Acharya B. Achkar L. Adam C. Adam Bourdarios L. Adamczyk L. Adamek J. Adelman A. Adiguzel S. Adorni T. Adye A. A. Affolder Y. Afik C. Agapopoulou M. N. Agaras J. Agarwala A. Aggarwal C. Agheorghiesei J. A. Aguilar-Saavedra A. Ahmad F. Ahmadov W. S. Ahmed X. Ai G. Aielli S. Akatsuka M. Akbiyik T. P. A. Åkesson A. V. Akimov K. Al Khoury G. L. Alberghi J. Albert M. J. Alconada Verzini S. Alderweireldt M. Aleksa I. N. Aleksandrov C. Alexa T. Alexopoulos A. Alfonsi F. Alfonsi M. Alhroob B. Ali S. Ali M. Aliev G. Alimonti C. Allaire B. M. M. Allbrooke P. P. Allport A. Aloisio F. Alonso C. Alpigiani E. Alunno Camelia M. Alvarez Estevez M. G. Alviggi Y. Amaral Coutinho A. Ambler L. Ambroz C. Amelung D. Amidei S. P. Amor Dos Santos S. Amoroso C. S. Amrouche C. Anastopoulos N. Andari T. Andeen J. K. Anders S. Y. Andrean A. Andreazza V. Andrei S. Angelidakis A. Angerami A. V. Anisenkov A. Annovi C. Antel M. T. Anthony E. Antipov M. Antonelli D. J. A. Antrim F. Anulli M. Aoki J. A. Aparisi Pozo M. A. Aparo L. Aperio Bella N. Aranzabal V. Araujo Ferraz C. Arcangeletti A. T. H. Arce E. Arena J-F. Arguin S. Argyropoulos J.-H. Arling A. J. Armbruster A. Armstrong O. Arnaez H. Arnold Z. P. Arrubarrena Tame G. Artoni H. Asada K. Asai S. Asai N. A. Asbah E. M. Asimakopoulou L. Asquith J. Assahsah K. Assamagan R. Astalos R. J. Atkin M. Atkinson N. B. Atlay H. Atmani P. A. Atmasiddha K. Augsten S. Auricchio V. A. Austrup G. Avner G. Avolio M. K. Ayoub G. Azuelos D. Babal H. Bachacou K. Bachas F. Backman A. Badea P. Bagnaia H. Bahrasemani A. J. Bailey V. R. Bailey J. T. Baines C. Bakalis O. K. Baker P. J. Bakker E. Bakos D. Bakshi Gupta S. Balaji R. Balasubramanian E. M. Baldin P. Balek E. Ballabene F. Balli W. K. Balunas J. Balz E. Banas M. Bandieramonte A. Bandyopadhyay L. Barak E. L. Barberio D. Barberis M. Barbero G. Barbour K. N. Barends T. Barillari M-S. Barisits J. Barkeloo T. Bark CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman Oklahoma USA Department of Physics University of Massachusetts Amherst Massachusetts USA CERN Geneva Switzerland II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Department of Physics Royal Holloway University of London Egham United Kingdom Physics Department Brookhaven National Laboratory Upton New York USA Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion Israel Institute of Technology Haifa Israel High Energy Physics Division Argonne National Laboratory Argonne Illinois USA Departamento de Física Pontificia Universidad Católica de Chile Santiago Chile INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Institut für Physik Universität Mainz Mainz Germany LAPP University Savoie Mont Blanc CNRS/IN2P3 Annecy France AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics University of Toronto Toronto Ontario Canada Department of Physics Northern Illinois University DeKalb Illinois USA Department of Physics Bogazici University Istanbul Turkey Département de Physique Nucléaire et Corpusculaire Université de Genève Genève Switzerland Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz California USA IJCLab Université Paris-Saclay CNRS/IN2P3 91405 Orsay France Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain INFN Sezione di Pavia Italy Dipartimento di Fisica Università di Pavia Pavia Italy Institute for Mathematics Astrophysics and Particle Physics Radboud University/Nikhef Nijmegen Netherlands Department of Physics Ale

A search optimized for new heavy particles decaying to two b-quarks and produced in association with additional b-quarks is reported. The sensitivity is improved by b-tagging at least one lower-pT jet in addition to the two highest-pT jets. The data used in this search correspond to an integrated luminosity of 103 fb−1 collected with a dedicated trijet trigger during the 2017 and 2018 s=13 TeV proton-proton collision runs with the ATLAS detector at the LHC. The search looks for resonant peaks in the b-tagged dijet invariant mass spectrum over a smoothly falling background. The background is estimated with an innovative data-driven method based on orthonormal functions. The observed b-tagged dijet invariant mass spectrum is compatible with the background-only hypothesis. Upper limits at 95% confidence level on a heavy vector-boson production cross section times branching ratio to a pair of b-quarks are derived.

关键词： Extensions of Higgs sector Extensions of gauge sector

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