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Search for singly produced vectorlike top partners in multilepton final states with 139 fb−1 of pp collision data at s=13 TeV with the ATLAS detector

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Physical Review D 2024年第11期109卷 112012-112012页

作者： G. Aad B. Abbott K. Abeling N. J. Abicht S. H. Abidi A. Aboulhorma H. Abramowicz H. Abreu Y. Abulaiti A. C. Abusleme Hoffman B. S. Acharya C. Adam Bourdarios L. Adamczyk L. Adamek S. V. Addepalli M. J. Addison J. Adelman A. Adiguzel T. Adye A. A. Affolder Y. Afik M. N. Agaras J. Agarwala A. Aggarwal C. Agheorghiesei A. Ahmad F. Ahmadov W. S. Ahmed S. Ahuja X. Ai G. Aielli M. Ait Tamlihat B. Aitbenchikh I. Aizenberg M. Akbiyik T. P. A. Åkesson A. V. Akimov D. Akiyama N. N. Akolkar K. Al Khoury G. L. Alberghi J. Albert P. Albicocco G. L. Albouy S. Alderweireldt M. Aleksa I. N. Aleksandrov C. Alexa T. Alexopoulos A. Alfonsi F. Alfonsi M. Algren M. Alhroob B. Ali H. M. J. Ali S. Ali S. W. Alibocus M. Aliev G. Alimonti W. Alkakhi C. Allaire B. M. M. Allbrooke J. F. Allen C. A. Allendes Flores P. P. Allport A. Aloisio F. Alonso C. Alpigiani M. Alvarez Estevez A. Alvarez Fernandez M. Alves Cardoso M. G. Alviggi M. Aly Y. Amaral Coutinho A. Ambler C. Amelung M. Amerl C. G. Ames D. Amidei S. P. Amor Dos Santos K. R. Amos V. Ananiev C. Anastopoulos T. Andeen J. K. Anders S. Y. Andrean A. Andreazza 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 T. Aoki J. A. Aparisi Pozo M. A. Aparo L. Aperio Bella C. Appelt A. Apyan N. Aranzabal C. Arcangeletti A. T. H. Arce E. Arena J-F. Arguin S. Argyropoulos J.-H. Arling O. Arnaez H. Arnold Z. P. Arrubarrena Tame G. Artoni H. Asada K. Asai S. Asai N. A. Asbah J. Assahsah K. Assamagan R. Astalos S. Atashi R. J. Atkin M. Atkinson N. B. Atlay H. Atmani P. A. Atmasiddha K. Augsten S. Auricchio A. D. Auriol V. A. Austrup G. Avolio K. Axiotis G. Azuelos D. Babal H. Bachacou K. Bachas A. Bachiu F. Backman A. Badea P. Bagnaia M. Bahmani A. J. Bailey V. R. Bailey J. T. Baines L. Baines C. Bakalis O. K. Baker E. Bakos D. Bakshi Gupta R. Balasubramanian E. M. Baldin P. Balek E. Ballabene F. Balli L. M. Baltes W. K. Balunas J. Balz E. Banas M. Bandieramonte A. Bandyopadhyay S. Bansal L. Barak M. Barakat E. L. Bar CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman Oklahoma USA II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physics Department Brookhaven National Laboratory Upton New York USA Faculté des sciences Université Mohammed V Rabat Morocco 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 Department of Physics New York University New York New York USA Departamento de Física Pontificia Universidad Católica de Chile Santiago Chile INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics University of Toronto Toronto Ontario Canada Department of Physics Brandeis University Waltham Massachusetts USA School of Physics and Astronomy University of Manchester Manchester United Kingdom Department of Physics Northern Illinois University DeKalb Illinois USA Department of Physics Istanbul University Istanbul Türkiye Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz California USA CERN Geneva Switzerland Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain INFN Sezione di Pavia Pavia Italy Dipartimento di Fisica Università di Pavia Pavia Italy Institut für Physik Universität Mainz Mainz Germany Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Affiliated with an international laboratory covered by a cooperation agreement with CERN Department of Physics McGill

A search for the single production of a vectorlike top partner (T) with mass greater than 1 TeV decaying into a Z boson and a top quark is presented, using the full Run 2 dataset corresponding to 139 fb−1 of pp collisions at s=13 TeV, collected in 2015–2018 with the ATLAS detector at the Large Hadron Collider. The targeted final state is characterized by the presence of a pair of electrons or muons with opposite-sign charges which form a Z-boson candidate, as well as by the presence of b-tagged jets and forward jets. Events with exactly two or at least three leptons are categorized into two independently optimized analysis channels. No significant excess above the background expectation is observed and the results from the two channels are statistically combined to set exclusion limits at 95% confidence level on the masses and couplings of T. The results are interpreted in several benchmark scenarios to set limits on the mass and universal coupling strength (κ) of the vectorlike quark. For singlet T quarks, κ values between 0.22 and 0.64 are excluded for masses between 1000 and 1975 GeV. For T quarks in the doublet scenario, where the production cross section is much lower, κ values between 0.54 and 0.88 are excluded for masses between 1000 and 1425 GeV.

关键词： Composite models Signatures with W or Z bosons Signatures with light leptons Signatures with new fermions Signatures with top quarks Hypothetical fermions

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Encoding classical information into quantum resources

arXiv

引用

arXiv 2019年

作者： Korzekwa, Kamil Puchala, Zbigniew Tomamichel, Marco Zyczkowski, Karol International Centre for Theory of Quantum Technologies University of Gdansk Gdańsk80-308 Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice44-100 Poland Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Centre for Quantum Software and Information School of Software University of Technology Sydney SydneyNSW2007 Australia Center for Theoretical Physics Polish Academy of Sciences Warszawa02-668 Poland

We introduce and analyse the problem of encoding classical information into different resources of a quantum state. More precisely, we consider a general class of communication scenarios characterised by encoding operations that commute with a unique resource destroying map and leave free states invariant. Our motivating example is given by encoding information into coherences of a quantum system with respect to a fixed basis (with unitaries diagonal in that basis as encodings and the decoherence channel as a resource destroying map), but the generality of the framework allows us to explore applications ranging from super-dense coding to thermodynamics. For any state, we find that the number of messages that can be encoded into it using such operations in a one-shot scenario is upper bounded in terms of the information spectrum relative entropy between the given state and its version with erased resources. Furthermore, if the resource destroying map is the twirling channel over some unitary group, we find matching one-shot lower bounds as well. In the asymptotic setting where we encode into many copies of the resource state, our bounds yield an operational interpretation of resource monotones such as the relative entropy of coherence and its corresponding relative entropy variance. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum optics

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Prediction of soft proton intensities in the near-Earth space using machine learning

arXiv

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arXiv 2021年

作者： Kronberg, Elena A. Hannan, Tanveer Huthmacher, Jens Münzer, Marcus Peste, Florian Zhou, Ziyang Berrendorf, Max Faerman, Evgeniy Gastaldello, Fabio Ghizzardi, Simona Escoubet, Philippe Haaland, Stein Smirnov, Artem Sivadas, Nithin Allen, Robert C. Tiengo, Andrea Ilie, Raluca Munich Theresienstr. 41 Munich80333 Germany Institute of Informatics LMU Munich Oettingenstraße 67 MunichD-80538 Germany Department of Statistics LMU Munich Ludwigstraße 33 MunichD-80539 Germany Milano via A. Corti 12 MilanoI-20133 Italy European Space Research and Technology Centre Keplerlaan 1 Noordwjik2201 AZ Netherlands Birkeland Centre for Space Science University of Bergen Allégaten 55 BergenNO-5007 Norway Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany German Research Centre for Geosciences Albert-Einstein-Straße 42-46 PotsdamD-14473 Germany Department of Electrical and Computer Engineering Boston University 8 Saint Mary's Street BostonMA02134 United States Johns Hopkins University Applied Physics Lab 11100 Johns Hopkins Road LaurelMD20723 United States Scuola Universitaria Superiore IUSS Pavia piazza della Vittoria 15 PaviaI-27100 Italy INFN Sezione di Pavia via A. Bassi 6 PaviaI-27100 Italy University of Illinois at Urbana-Champaign ECEB 306 N. Wright Street 5054 UrbanaIL61801 United States

The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have derived machine learning-based models to predict the proton intensities at energies from 28 to 1,885 keV in the 3D terrestrial magnetosphere at radial distances between 6 and 22 RE. We used the satellite location and indices for solar, solar wind and geomagnetic activity as predictors. The results demonstrate that the neural network (multi-layer perceptron regressor) outperforms baseline models based on the k-Nearest Neighbors and historical binning on average by ∼80% and ∼33%, respectively. The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere. In addition to a quantitative analysis of the prediction results, we also investigate parameter importance in our model. The most decisive parameters for predicting proton intensities are related to the location: ZGSE direction and the radial distance. Among the activity indices, the solar wind dynamic pressure is the most important. The results have a direct practical application, for instance, for assessing the contamination particle background in the X-Ray telescopes for X-ray astronomy orbiting above the radiation belts. To foster reproducible research and to enable the community to build upon our work we publish our complete code, the data, as well as weights of trained models. Further description can be found in the GitHub project at https://***/Tanveer81/deep horizon. © 2021, CC BY-NC-ND.

关键词： Forecasting

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2019年 811卷 v-vi页

作者： Chakraborty, Mohuya Chakrabarti, Satyajit Balas, Valentina Emilia Mandal, J.K. Department of Information Technology Institute of Engineering and Management Kolkata India Institute of Engineering and Management Kolkata India Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad Romania Department of Computer Science and Engineering University of Kalyani KalyaniWest Bengal India

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Observation of Wγγ triboson production in proton-proton collisions at s=13 TeV with the ATLAS detector

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2024年 848卷

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. AbouZeid, O.S. Abraham, N.L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Adelman, J. Adersberger, M. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akilli, E. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allen, B.W. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amrouche, C.S. An, F. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Andrei, V. Anelli, C.R. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal Barrio, N. Araujo Ferraz, V. Araujo Pereira, R. Arcangeletti, C. Arce, A.T.H. Arduh, F.A. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asawatavonvanich, T. Asbah, N. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Augsten, K. Aukerman, A.T. Austrup, V.A. Avolio, G. Ayoub, M.K. Azuelos, G. Bachacou, H. Bachas, K. Backes, M. Backman, F. Bagnaia, P. Bahmani, M. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Balli, F. Balunas, CPPM Aix Marseille Université CNRS-IN2P3 Marseille France Department of Physics and Astronomy University of Oklahoma Norman OK United States Department of Physics University of Massachusetts Amherst MA United States The CERN Geneva Switzerland CERN Geneva Switzerland II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Department of Physics Royal Holloway University of London Egham United Kingdom Department of Physics University of Toronto Toronto ON Canada 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 Institute for Fundamental Science The University of Oregon Eugene OR United States High Energy Physics Division Argonne National Laboratory Argonne IL United States INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom Institut für Physik Universität Mainz Mainz Germany LAPP Université Grenoble Alpes 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 Northern Illinois University DeKalb IL United States Fakultät für Physik Ludwig-Maximilians-Universität München München Germany 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 CA United States IJCLab Université Paris Saclay CNRS-IN2P3 Orsay 91405 France LPC Université Clermont Auvergne CNRS/IN2P3 Cler

This letter reports the observation of W(ℓν)γγ production in proton-proton collisions. This measurement uses the full Run 2 sample of events recorded at a center-of-mass energy of s=13 TeV by the ATLAS detector at the LHC, corresponding to an integrated luminosity of 140 fb⁻¹. Events with a leptonically-decaying W boson and at least two photons are considered. The background-only hypothesis is rejected with an observed and expected significance of 5.6 standard deviations. The inclusive fiducial production cross section of W(eν)γγ and W(μν)γγ events is measured to be σ_fid=13.8±1.1(stat)+2.1−2.0(syst)±0.1(lumi) fb, in agreement with the Standard Model prediction. © 2023 The Author(s)

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A new colluded adversarial VNet embeddings attack in cloud 18

A new colluded adversarial VNet embeddings attack in cloud

引用

18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017

作者： Liu, I-Hsien Fang, Tay-Jiun Li, Jung-Shian Sun, Meng-Wei Liu, Chuan-Gang Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan Department of Applied Informatics and Multimedia Chia-Nan University of Pharmacy and Science Tainan Taiwan

ISBN: (纸本)9781538631515

Nowadays, network virtualization has been widely investigated in order to prevent Internet ossification, and develop future emerging network applications flexibly. However, prior work by Pignolet et al. shows the possible attacking methodology with which the attackers can disclose the whole cloud topology while deploying virtual networks in cloud named Topology Disclosure Attack. In this attack model, the attacker pretends to deploy virtual networks in cloud by issuing the graph requests to service provider. And the service provider responds the requests to the attacker after examining his/her topology resources. With this request/reply model, Pignolet et al. believe this attack eventually infers the targeted topology. However, one vital reason leads this attack to the failure- too many virtual requests from one adversary in a time. This paper tries to provide a new topology disclosure attack model, which multiple attackers launch attacks at the same time with the assistance of proposed Query-Trie and network tomography technique. Hence, in this paper, we propose much more possible attack model in cloud and this topic also encourages the network researchers to develop resistance mechanism against it in the future. © 2017 IEEE.

关键词： Topology

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Community-based security for the internet of things

arXiv

引用

arXiv 2018年

作者： Zhu, Quanyan Rass, Stefan Schartner, Peter Department of Electrical and Computer Engineering New York University 2 Metrotech Center Brooklyn11201 United States Institute of Applied Informatics System Security Group Universitaet Klagenfurt Universitaetsstrasse 65-67 Klagenfurt9020 Austria

With more and more devices becoming connectable to the internet, the number of services but also a lot of threats increases dramatically. Security is often a secondary matter behind functionality and comfort, but the problem has already been recognized. Still, with many IoT devices being deployed already, security will come step-by-step and through updates, patches and new versions of apps and IoT software. While these updates can be safely retrieved from app stores, the problems kick in via jailbroken devices and with the variety of untrusted sources arising on the internet. Since hacking is typically a community effort these days, security could be a community goal too. The challenges are manifold, and one reason for weak or absent security on IoT devices is their weak computational power. In this chapter, we discuss a community based security mechanism in which devices mutually aid each other in secure software management. We discuss game-theoretic methods of community formation and light-weight cryptographic means to accomplish authentic software deployment inside the IoT device community. Copyright © 2018, The Authors. All rights reserved.

关键词： Internet of things

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