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Studies of new Higgs boson interactions through nonresonant HH production in the bb¯γγ final state in pp collisions at s = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2024年第1期2024卷 66页

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. Aktas, S. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Albouy, G.L. Alderweireldt, S. Alegria, Z.L. 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. Alviggi, M.G. Aly, M. 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. 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. Baer, T.M. Bagnaia, P. Bahmani, M. Bahner, D. 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 Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France APC Université Paris Cité CNRS/IN2P3 Paris 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 Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan 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 School of Science Shenzhen Campus of Sun Yat-sen University Shenzhen 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 University of California Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziant

A search for nonresonant Higgs boson pair production in the bb¯γγ final state is performed using 140 fb−1 of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the C... 详细信息

A search for nonresonant Higgs boson pair production in the bb¯γγ final state is performed using 140 fb⁻¹ of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. This analysis supersedes and expands upon the previous nonresonant ATLAS results in this final state based on the same data sample. The analysis strategy is optimised to probe anomalous values not only of the Higgs (H) boson self-coupling modifier κ_λ but also of the quartic HHVV (V = W, Z) coupling modifier κ_2V. No significant excess above the expected background from Standard Model processes is observed. An observed upper limit μ_HH < 4.0 is set at 95% confidence level on the Higgs boson pair production cross-section normalised to its Standard Model prediction. The 95% confidence intervals for the coupling modifiers are −1.4 < κ_λ < 6.9 and −0.5 < κ_2V < 2.7, assuming all other Higgs boson couplings except the one under study are fixed to the Standard Model predictions. The results are interpreted in the Standard Model effective field theory and Higgs effective field theory frameworks in terms of constraints on the couplings of anomalous Higgs boson (self-)interactions. © The Author(s) 2024.

关键词： Hadron-Hadron Scattering Higgs Physics

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A search for R-parity-violating supersymmetry in final states containing many jets in pp collisions at s = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2024年第5期2024卷 3页

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. Aktas, S. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Albouy, G.L. Alderweireldt, S. Alegria, Z.L. 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. Alviggi, M.G. Aly, M. 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. 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. Baer, T.M. Bagnaia, P. Bahmani, M. Bahner, D. Bai, K. 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, Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France APC Université Paris Cité CNRS/IN2P3 Paris 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 Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan 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 School of Science Shenzhen Campus of Sun Yat-sen University Shenzhen 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 University of California Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziant

A search for R-parity-violating supersymmetry in final states with high jet multiplicity is presented. The search uses 140 fb−1 of proton-proton collision data at s = 13 TeV collected by the ATLAS experiment during Ru... 详细信息

A search for R-parity-violating supersymmetry in final states with high jet multiplicity is presented. The search uses 140 fb⁻¹ of proton-proton collision data at s = 13 TeV collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature prompt gluino-pair production decaying directly to three jets each or decaying to two jets and a neutralino which subsequently decays promptly to three jets. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence level are extracted. Gluinos with masses up to 1800 GeV are excluded when decaying directly to three jets. In the cascade scenario, gluinos with masses up to 2340 GeV are excluded for a neutralino with mass up to 1250 GeV. © The Author(s) 2024.

关键词： Beyond Standard Model Hadron-Hadron Scattering Jets Supersymmetry

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Fighting administrative corruption with digital government in sub-saharan Africa 18

Fighting administrative corruption with digital government i...

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18th European Conference on Digital Government, ECDG 2018

作者： Walle, Yelkal Mulualem Janowski, Tomasz Estevez, Elsa Department of Information Technology Faculty of Informatics University of Gondar Ethiopia Department of Applied Informatics in Management Gdańsk University of Technology Poland Department for E-Governance and Administration Danube University Krems Austria Bahía Blanca Argentina Department of Computer Science and Engineering Universidad Nacional del Sur Bahía Blanca Argentina

ISBN: (纸本)9781912764037

Administrative corruption is a pervasive problem and a major threat to economic and social development around the world, especially in Sub-Saharan Africa which lags behind other regions in various development indicators and is seen as one of the most corrupt regions globally. This paper examines a hypothesis that digital government – the use of digital technology to transform public administration organizations and their relationships with citizens, businesses and each other – helps reduce administrative corruption in Sub-Saharan Africa. To this end, the paper relates the United Nation’s e-Government Development Index (EDI) and the Transparency International’s Corruption Perception Index (CPI) for 45 countries in the region in the period from 2012 to 2016, performing linear regression with EDI as an independent and CPI as a dependent variable. The estimated results show that the effects of digital government adoption on corruption reduction are statistically significant, with 37% of corruption reduction due to digital government development. The paper further explores the nature of this relationship using the case of Ethiopia, whose performance according to the EDI and CPI indexes improved slightly over the 2012-2016 period. As part of this case, the paper examines common applications such as e-procurement or e-tax that cut horizontally across ministries. Our findings suggest that the adoption of digital government has the potential for reducing administrative corruption in Sub-Saharan Africa but realizing such potential require strengthening of law enforcement and anti-corruption institutions, and political will. © 2018, Academic Conferences Limited. All rights reserved.

关键词： Public administration

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Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Interactive control approach to 3D shape reconstruction

arXiv

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arXiv 2019年

作者： Islam, Bipul Liu, Ji Yezzi, Anthony Sandhu, Romeil Department of Computer Science & Biomedical Informatics Stony Brook University Stony BrookNY11794 United States Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY11794 United States Department of Electrical and Computer Engineering Georgia Tech AtlantaGA30309 United States Departments of Applied Mathematics & Statistics Stony Brook University Stony BrookNY11794 United States

The ability to accurately reconstruct the 3D facets of a scene is one of the key problems in robotic vision. However, even with recent advances with machine learning, there is no high-fidelity universal 3D reconstruction method for this optimization problem as schemes often cater to specific image modalities and are often biased by scene abnormalities. Simply put, there always remains an "information" gap due to the dynamic nature of real-world scenarios. To this end, we demonstrate a feedback control framework which invokes operator inputs (also prone to errors) in order to augment existing reconstruction schemes. For proof-of-concept, we choose a classical region-based stereoscopic reconstruction approach and show how an ill-posed model can be augmented with operator input to be much more robust to scene artifacts. We provide necessary conditions for stability via Lyapunov analysis and perhaps more importantly, we show that the stability depends on a notion of absolute curvature. Mathematically, this aligns with previous work that has shown Ricci curvature as proxy for functional robustness of dynamical networked systems. We conclude with results that show how our method can improve standalone reconstruction schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Stereo image processing

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Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2021年 736 LNEE卷 v-vi页

作者： Thampi, Sabu M. Gelenbe, Erol Atiquzzaman, Mohammed Chaudhary, Vipin Li, Kuan-Ching School of CSE Indian Institute of Information Technology and Management-Kerala Trivandrum Kerala India School of Computer Science University of Oklahoma NormanOK United States Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Computer Science University at Buffalo State University BuffaloNY United States Case Western Reserve University ClevelandOH United States

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Building an international consortium for tracking coronavirus health status (vol 78, pg 931, 2020)

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NATURE MEDICINE 2020年第8期26卷 1309-1309页

作者： Segal, Eran Zhang, Feng Lin, Xihong King, Gary Shalem, Ophir Shilo, Smadar Allen, William E. Alquaddoomi, Faisal Altae-Tran, Han Anders, Simon Balicer, Ran Bauman, Tal Bonilla, Ximena Booman, Gisel Chan, Andrew T. Cohen, Ori Coletti, Silvano Davidson, Natalie Dor, Yuval Drew, David A. Elemento, Olivier Evans, Georgina Ewels, Phil Gale, Joshua Gavrieli, Amir Geiger, Benjamin Grad, Yonatan H. Greene, Casey S. Hajirasouliha, Iman Jerala, Roman Kahles, Andre Kallioniemi, Olli Keshet, Ayya Kocarev, Ljupco Landua, Gregory Meir, Tomer Muller, Aline Nguyen, Long H. Oresic, Matej Ovchinnikova, Svetlana Peterson, Hedi Prodanova, Jana Rajagopal, Jay Raetsch, Gunnar Rossman, Hagai Rung, Johan Sboner, Andrea Sigaras, Alexandros Spector, Tim Steinherz, Ron Stevens, Irene Vilo, Jaak Wilmes, Paul Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Howard Hughes Medical Institute Chevy Chase Maryland USA Broad Institute of MIT and Harvard Cambridge MA USA Departments of Biostatistics and Statistics Harvard T.H. Chan School of Public Health Harvard University Cambridge MA USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Pediatric Diabetes Unit Ruth Rappaport Children’s Hospital Rambam Healthcare Campus Haifa Israel Harvard Society of Fellows Harvard University Cambridge MA USA ETH Zurich NEXUS Personalized Health Technologies Zurich Switzerland Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Center for Molecular Biology (ZMBH) University of Heidelberg Heidelberg Germany Clalit Research Institute Clalit Health Services Ramat Gan Israel Mapping and Geo-Information Engineering Civil and Environmental Engineering Faculty The Technion Haifa Israel ETH Zurich Department for Computer Science Zurich University Hospital Zurich Medical Informatics Zurich and SIB Swiss Institute of Bioinformatics Zurich Switzerland Regen Network Mar del Plata Argentina Massachusetts General Hospital (MGH) Boston MA USA Chelonia Applied Science Allschwil Switzerland IMRIC Developmental Biology and Cancer Research School of Medicine The Hebrew University Jerusalem Israel Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Science for Life Laboratory (SciLifeLab) Department of Biochemistry and Biophysics Stockholm University Stockholm Sweden http://*** Canada Department of Immunology Weizmann Institute of Scienc

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Computational platforms and environments Databases Infectious diseases

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Developing self-adaptive microservice systems: Challenges and directions

arXiv

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arXiv 2019年

作者： Mendonça, Nabor C. Jamshidi, Pooyan Garlan, David Pahl, Claus Post Graduate Program in Applied Informatics University of Fortaleza FortalezaCE Brazil Computer Science and Engineering Department University of South Carolina ColumbiaSC United States School of Computer Science Carnegie Mellon University PittsburghPA United States Faculty of Computer Science Free University of Bozen-Bolzano Bozen-Bolzano Italy

A self-adaptive system can dynamically monitor and adapt its behavior to preserve or enhance its quality attributes under uncertain operating conditions. This article identifies key challenges for the development of microservice applications as self-adaptive systems, using a cloud-based intelligent video surveillance application as a motivating example. It also suggests potential new directions for addressing most of the identified challenges by leveraging existing microservice practices and technologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Adaptive systems

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The need to implement FAIR principles in biomolecular simulations

arXiv

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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein Data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

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