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SPT clusters with DES and HST weak lensing. I. Cluster lensing and Bayesian population modeling of multiwavelength cluster datasets

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Physical Review D 2024年第8期110卷 083509页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon B. Ansarinejad D. Bacon M. Bayliss K. Bechtol M. R. Becker B. A. Benson G. M. Bernstein M. Brodwin D. Brooks A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero R. Cawthon C. Chang R. Chen A. Choi J. Cordero M. Costanzi L. N. da Costa M. E. S. Pereira C. Davis J. DeRose S. Desai T. de Haan J. De Vicente H. T. Diehl S. Dodelson P. Doel C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. García-Bellido M. Gatti G. Giannini M. D. Gladders D. Gruen R. A. Gruendl I. Harrison W. G. Hartley K. Herner S. R. Hinton D. L. Hollowood W. L. Holzapfel K. Honscheid N. Huang E. M. Huff D. J. James M. Jarvis G. Khullar K. Kim R. Kraft K. Kuehn N. Kuropatkin F. Kéruzoré S. Lee P.-F. Leget N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. Mena-Fernández R. Miquel J. Myles A. Navarro-Alsina R. L. C. Ogando A. Palmese S. Pandey A. Pieres A. A. Plazas Malagón J. Prat M. Raveri C. L. Reichardt J. Roberson R. P. Rollins A. K. Romer C. Romero A. Roodman A. J. Ross E. S. Rykoff L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro T. Schrabback M. Schubnell L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin M. Smith T. Somboonpanyakul B. Stalder A. A. Stark V. Strazzullo E. Suchyta M. E. C. Swanson G. Tarle C. To M. A. Troxel I. Tutusaus T. N. Varga A. von der Linden N. Weaverdyck J. Weller P. Wiseman B. Yanny B. Yin M. Young Y. Zhang J. Zuntz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 6020 Innsbruck Austria Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstr. 1 85748 Garching Germany Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ-20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia Institute of Cosmology and Gravitation University of Portsmouth Portsmouth PO1 3FX United Kingdom Department of Physics University of Cincinnati Cincinnati Ohio 45221 USA Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue Madison Wisconsin 53706-1390 USA Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Fermi National Accelerator Laboratory Batavia Illinois 60510-0500 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Physics and Astronomy University of Missouri 5110 Rockhill Road Kansas City Missouri 64110 USA Department of Physics and Astronomy University Coll

We present a Bayesian population modeling method to analyze the abundance of galaxy clusters identified by the South Pole Telescope (SPT) with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). We discuss and validate the modeling choices with a particular focus on a robust, weak-lensing-based mass calibration using DES data. For the DES Year 3 data, we report a systematic uncertainty in weak-lensing mass calibration that increases from 1% at z=0.25 to 10% at z=0.95, to which we add 2% in quadrature to account for uncertainties in the impact of baryonic effects. We implement an analysis pipeline that joins the cluster abundance likelihood with a multiobservable likelihood for the Sunyaev-Zel’dovich effect, optical richness, and weak-lensing measurements for each individual cluster. We validate that our analysis pipeline can recover unbiased cosmological constraints by analyzing mocks that closely resemble the cluster sample extracted from the SPT-SZ, SPTpol ECS, and SPTpol 500d surveys and the DES Year 3 and HST-39 weak-lensing datasets. This work represents a crucial prerequisite for the subsequent cosmological analysis of the real dataset.

关键词： Cosmological parameters Gravitational lenses Large scale structure of the Universe

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Search for continuous gravitational waves from 20 accreting millisecond x-ray pulsars in O3 LIGO data

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Physical Review D 2022年第2期105卷 022002-022002页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain T. Akutsu S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer L. Cadonati G. Cagnoli C. Cahillane J. Calderón Bustillo J. D. Callaghan T. A. Callister E. Ca LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière F-69622 Villeurbanne France Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Un

Results are presented of searches for continuous gravitational waves from 20 accreting millisecond x-ray pulsars with accurately measured spin frequencies and orbital parameters, using data from the third observing run of the Advanced LIGO and Advanced Virgo detectors. The search algorithm uses a hidden Markov model, where the transition probabilities allow the frequency to wander according to an unbiased random walk, while the J-statistic maximum-likelihood matched filter tracks the binary orbital phase. Three narrow subbands are searched for each target, centered on harmonics of the measured spin frequency. The search yields 16 candidates, consistent with a false alarm probability of 30% per subband and target searched. These candidates, along with one candidate from an additional target-of-opportunity search done for SAX J1808.4−3658, which was in outburst during one month of the observing run, cannot be confidently associated with a known noise source. Additional follow-up does not provide convincing evidence that any are a true astrophysical signal. When all candidates are assumed nonastrophysical, upper limits are set on the maximum wave strain detectable at 95% confidence, h095%. The strictest constraint is h095%=4.7×10−26 from IGR J17062−6143. Constraints on the detectable wave strain from each target lead to constraints on neutron star ellipticity and r-mode amplitude, the strictest of which are ε95%=3.1×10−7 and α95%=1.8×10−5 respectively. This analysis is the most comprehensive and sensitive search of continuous gravitational waves from accreting millisecond x-ray pulsars to date.

关键词： Gravitational waves Binary stars Neutron stars & pulsars

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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Erratum to: Search for single top-quark production via flavour-changing neutral currents at 8 TeV with the ATLAS detector

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The European Physical Journal C 2022年第1期82卷 1-15页

作者： Aad, G. Abbott, B. Abdallah, J. Abdinov, O. Aben, R. Abolins, M. AbouZeid, O. S. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adamczyk, L. Adams, D. L. Adelman, J. Adomeit, S. Adye, T. Affolder, A. A. Agatonovic-Jovin, T. Agricola, J. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akerstedt, H. Åkesson, T. P. A. Akimov, A. V. Alberghi, G. L. Albert, J. Albrand, S. Alconada Verzini, M. J. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Alimonti, G. Alio, L. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Altheimer, A. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amako, K. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amorim, A. Amoroso, S. Amram, N. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, G. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Anger, P. Angerami, A. Anghinolfi, F. Anisenkov, A. V. Anjos, N. Annovi, A. Antonelli, M. Antonov, A. Antos, J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Arce, A. T. H. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Arnaez, O. Arnal, V. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Asai, S. Asbah, N. Ashkenazi, A. Åsman, B. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Aurousseau, M. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baak, M. A. Baas, A. E. Baca, M. J. Bacci, C. Bachacou, H. Bachas, K. Backes, M. Backhaus, M. Bagiacchi, P. Bagnaia, P. Bai, Y. Bain, T. Baines, J. T. Baker, O. K. Baldin, E. M. Balek, P. Balestri, T. Balli, F. Banas, E. Banerjee, Sw. Bannoura, A. A. E. Bansil, H. S. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisonzi, M. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovska, Z. Baroncelli, A. Barone, G. Barr, A. J. Barreiro, F. Barreiro Guimarães da Costa, J. Bartol CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman USA Institute of Physics Academia Sinica Taipei Taiwan Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam The Netherlands Department of Physics and Astronomy Michigan State University East Lansing USA Department of Physics University of Toronto Toronto Canada 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 Center for High Energy Physics University of Oregon Eugene USA Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London UK AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Physics Department Brookhaven National Laboratory Upton USA Department of Physics Northern Illinois University De Kalb USA Fakultät für Physik Ludwig-Maximilians-Universität München Munich Germany Particle Physics Department Rutherford Appleton Laboratory Didcot UK Oliver Lodge Laboratory University of Liverpool Liverpool UK Institute of Physics University of Belgrade Belgrade Serbia II Physikalisches Institut Georg-August-Universität Göttingen Germany Laboratório de Instrumentação e Física Experimental de Partículas-LIP Lisbon Portugal Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada Spain Department of Physics Boston University Boston USA Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Rome Italy Dipartimento di Fisica Università di Roma Tor Vergata Rome Italy Fysiska institutionen Lunds uni

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Toward reliable validation of HPC network simulation models 17

Toward reliable validation of HPC network simulation models

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Simulation Winter Conference

作者： Misbah Mubarak Nikhil Jain Jens Domke Noah Wolfe Caitlin Ross Kelvin Li Abhinav Bhatele Christopher D. Carothers Kwan-Liu Ma Robert B. Ross Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA Matsuoka Laboratory Tokyo Institute of Technology Tokyo Japan Computer Science Department Rensselaer Polytechnic Institute Troy NY USA Computer Science Department University of California Davis CA

ISBN: (纸本)9781538634271

While the high performance computing (HPC) community is relying on simulations increasingly to co-design and optimize HPC interconnects, the simulation community lacks a coherent set of practices to be followed when validating the simulators and network models. Validation of HPC network simulation models is a multi-step process starting with the selection of representative communication patterns, configuring the network model, followed by designing the set of experiments, and finally, documenting the outcome for reproducibility. In this paper, we present a set of recommended practices for each of these steps in the validation process. If the recommendations are followed, the end result should be a validated network model that can make reasonably accurate predictions and convince the community about the correctness of the model.

关键词： Benchmark testing Computational modeling Predictive models Computer science Network topology Mathematical model Protocols

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Search for Gravitational Waves Associated with Fast Radio Bursts Detected by CHIME/FRB during the LIGO-Virgo Observing Run O3a

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Astrophysical Journal 2023年第2期955卷 155-155页

作者： Abbott, R. Abbott, T.D. Acernese, F. Ackley, K. Adams, C. Adhikari, N. Adhikari, R.X. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Agatsuma, K. Aggarwal, N. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Allocca, A. Altin, P.A. Amato, A. Anand, C. Anand, S. Ananyeva, A. Anderson, S.B. Anderson, W.G. Ando, M. Andrade, T. Andres, N. Andrić, T. Angelova, S.V. Ansoldi, S. Antelis, J.M. Antier, S. Appert, S. Arai, Koya Arai, Y. Araki, S. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Aronson, S.M. Arun, K.G. Asada, H. Asali, Y. Ashton, G. Aso, Y. Assiduo, M. Aston, S.M. Astone, P. Aubin, F. Austin, C. Babak, S. Badaracco, F. Bader, M.K.M. Badger, C. Bae, S. Bae, Y. Baer, A.M. Bagnasco, S. Bai, Y. Baiotti, L. Baird, J. Bajpai, R. Ball, M. Ballardin, G. Ballmer, S.W. Balsamo, A. Baltus, G. Banagiri, S. Bankar, D. Barayoga, J.C. Barbieri, C. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Bartlett, J. Barton, M.A. Bartos, I. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Bejger, M. Belahcene, I. Benedetto, V. Beniwal, D. Bennett, T.F. Bentley, J.D. BenYaala, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bhandare, R. Bhardwaj, U. Bhattacharjee, D. Bhaumik, S. Bilenko, I.A. Billingsley, G. Bini, S. Birney, R. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boer, M. Bogaert, G. Boldrini, M. Bonavena, L.D. Bondu, F. Bonilla, E. Bonnand, R. Booker, P. Boom, B.A. Bork, R. Boschi, V. Bose, N. Bose, S. Bossilkov, V. Boudart, V. Bouffanais, Y. Boumerdassi, A. Bozzi, A. Bradaschia, C. Brady, P.R. Bramley, A. Branch, A. Branchesi, M. Brau, J.E. Breschi, M. Briant, T. Briggs, J.H. Brillet, A. Brinkmann, M. Brockill, P. Brooks, A.F. Brooks, J. Brown, D.D. Brunett, S. Bruno, G. Bruntz, R. Bryant, J. Buchanan, J. B LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Louisiana State University Baton Rouge 70803 LA United States Dipartimento di Farmacia Università di Salerno Fisciano Salerno I-84084 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia LIGO Livingston Observatory Livingston 70754 LA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena Jena D-07743 Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston 60208 IL United States Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino Torino I-10125 Italy Università di Napoli “Federico II Complesso Universitario di Monte S. Angelo Napoli I-80126 Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière Villeurbanne F-69622

We search for gravitational-wave (GW) transients associated with fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst Project, during the first part of the third observing run of Advanced LIGO and Advanced Virgo (2019 April 1 15:00 UTC-2019 October 1 15:00 UTC). Triggers from 22 FRBs were analyzed with a search that targets both binary neutron star (BNS) and neutron star-black hole (NSBH) mergers. A targeted search for generic GW transients was conducted on 40 FRBs. We find no significant evidence for a GW association in either search. Given the large uncertainties in the distances of our FRB sample, we are unable to exclude the possibility of a GW association. Assessing the volumetric event rates of both FRB and binary mergers, an association is limited to 15% of the FRB population for BNS mergers or 1% for NSBH mergers. We report 90% confidence lower bounds on the distance to each FRB for a range of GW progenitor models and set upper limits on the energy emitted through GWs for a range of emission scenarios. We find values of order 10⁵¹-10⁵⁷ erg for models with central GW frequencies in the range 70-3560 Hz. At the sensitivity of this search, we find these limits to be above the predicted GW emissions for the models considered. We also find no significant coincident detection of GWs with the repeater, FRB 20200120E, which is the closest known extragalactic FRB. © 2023. The Author(s). Published by the American Astronomical Society.

关键词： Gravitational wave astronomy Radio transient sources

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Shadowing, asymptotic shadowing and s-limit shadowing

arXiv

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arXiv 2017年

作者： Good, Chris Oprocha, Piotr Puljiz, Mate School of Mathematics University of Birmingham BirminghamB15 2TT United Kingdom AGH University of Science and Technology Faculty of Applied Mathematics al. Mickiewicza 30 Kraków30-059 Poland National Supercomputing Centre IT4Innovations Division of the University of Ostrava Institute for Research and Applications of Fuzzy Modeling 30. dubna 22 Ostrava70103 Czech Republic University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb10000 Croatia

We study three notions of shadowing: classical shadowing, limit (or asymptotic) shadowing, and s-limit shadowing. We show that classical and s-limit shadowing coincide for tent maps and, more generally, for piecewise linear interval maps with constant slopes, and are further equivalent to the linking property introduced by Chen in [15]. We also construct a system which exhibits shadowing but not limit shadowing, and we study how shadowing properties transfer to maximal transitive subsystems and inverse limits (sometimes called natural extensions). Where practicable, we show that our results are best possible by means of *** Codes 37E05 (Primary), 37C50, 37B10, 54H20, 26A18 (Secondary) Copyright © 2017, The Authors. All rights reserved.

关键词： Piecewise linear techniques

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Recent developments in the PySCF program package

arXiv

引用

arXiv 2020年

作者： Sun, Qiming Zhang, Xing Banerjee, Samragni Bao, Peng Barbry, Marc Blunt, Nick S. Bogdanov, Nikolay A. Booth, George H. Chen, Jia Cui, Zhi-Hao Eriksen, Janus Juul Gao, Yang Guo, Sheng Hermann, Jan Hermes, Matthew R. Koh, Kevin Koval, Peter Lehtola, Susi Li, Zhendong Liu, Junzi Mardirossian, Narbe McClain, James D. Motta, Mario Mussard, Bastien Pham, Hung Q. Pulkin, Artem Purwanto, Wirawan Robinson, Paul J. Ronca, Enrico Sayfutyarova, Elvira Scheurer, Maximilian Schurkus, Henry F. Smith, James E.T. Sun, Chong Sun, Shi-Ning Upadhyay, Shiv Wagner, Lucas K. Wang, Xiao White, Alec Whitfield, James Daniel Williamson, Mark J. Wouters, Sebastian Yang, Jun Yu, Jason M. Zhu, Tianyu Berkelbach, Timothy C. Sharma, Sandeep Sokolov, Alexander Yu. Chan, Garnet Kin-Lic AxiomQuant Investment Management LLC Shanghai200120 China Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry and Biochemistry The Ohio State University ColumbusOH43210 United States Beijing National Laboratory for Molecular Sciences State Key Laboratory for Structural Chemistry of Unstable and Stable Species Institute of Chemistry Chinese Academy of Sciences Beijing100190 China Simbeyond B.V. P.O. Box 513 EindhovenNL-5600 MB Netherlands Department of Chemistry Lensfield Road CambridgeCB2 1EW United Kingdom Max Planck Institute for Solid State Research Heisenbergstraße 1 Stuttgart70569 Germany Department of Physics King's College London Strand LondonWC2R 2LS United Kingdom Department of Physics University of Florida GainesvilleFL32611 United States Quantum Theory Project University of Florida GainesvilleFL32611 United States School of Chemistry University of Bristol Cantock's Close BristolBS8 1TS United Kingdom Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Google Inc. Mountain ViewCA94043 United States FU Berlin Department of Mathematics and Computer Science Arnimallee 6 Berlin14195 Germany TU Berlin Machine Learning Group Marchstr. 23 Berlin10587 Germany Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemistry and Biochemistry The University of Notre Dame du Lac 251 Nieuwland Science Hall Notre DameIN46556 United States Simune Atomistics S.L. Avenida Tolosa 76 Donostia-San Sebastian Spain HelsinkiFI-00014 Finland Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Chemistry The Johns Hopkins University BaltimoreMD21218 United States AMGEN

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

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Spin-current-mediated rapid magnon localisation and coalescence after ultrafast optical pumping of ferrimagnetic alloys

arXiv

引用

arXiv 2018年

作者： Iacocca, E. Liu, T-M. Reid, A.H. Fu, Z. Ruta, S. Granitzka, P.W. Jal, E. Bonetti, S. Gray, A.X. Graves, C.E. Kukreja, R. Chen, Z. Higley, D.J. Chase, T. Le Guyader, L. Hirsch, K. Ohldag, H. Schlotter, W.F. Dakovski, G.L. Coslovich, G. Hoffmann, M.C. Carron, S. Tsukamoto, A. Savoini, M. Kirilyuk, A. Kimel, A.V. Rasing, T. Stöhr, J. Evans, R.F.L. Ostler, T. Chantrel, R.W. Hoefer, M.A. Silva, T.J. Dürr, H.A. Department of Applied Mathematics University of Colorado BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States Department of Physics Division for Theoretical Physics Chalmers University of Technology Gothenburg412 96 Sweden SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States School of Physics Science and Engineering Tongji University Shanghai200092 China Department of Physics University of York YorkYO10 5DD United Kingdom Spectroscopy and Coherent Scattering European X-Ray Free-Electron Laser Facility GmbH Holzkoppel 4 Schenefeld22869 Germany Department of Electronics and Computer Science Nihon University 7-24-1 Narashino-dai Funabashi Chiba274-8501 Japan Radboud University Institute for Molecules and Materials Heyendaalseweg 135 AJ Nijmegen6525 Netherlands Université de Liège Physique des Matériaux et Nanostructures Liège Sart TilmanB-4000 Belgium Faculty of Arts Computing Engineering and Sciences Sheffield Hallam University Howard Street SheffieldS1 1WB United Kingdom Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden

Sub-picosecond magnetisation manipulation via femtosecond optical pumping has attracted wide attention ever since its original discovery in 1996. However, the spatial evolution of the magnetisation is not yet well understood, n part due to the difficulty in experimentally probing such rapid dynamics. Here, we find evidence of rapid magnetic order recovery in materials with perpendicular magnetic anisotropy via nonlinear magnon processes. We identify both localisation and coalescence regimes, whereby localised magnetic textures nucleate and subsequently evolve in accordance with a power law formalism. Coalescence is observed for optical excitations both above and below the switching threshold. Simulations indicate that the ultrafast generation of noncollinear magnetisation via optical pumping establishes exchange-mediated spin currents with an equivalent 100% spin polarised charge current density of 108A/cm2. Such large spin currents precipitate rapid recovery of magnetic order after optical pumping. These processes suggest an ultrafast optical route for the stabilization of desired meta-stable states, e.g., solated skyrmions. Copyright © 2018, The Authors. All rights reserved.

关键词： Optical pumping

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