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JMIR human factors 2024年 11卷 e69628页

作者： Aimee-Rose Wrightson-Hester Georgia Anderson Joel Dunstan Peter M McEvoy Christopher J Sutton Bronwyn Myers Sarah Egan Sara Tai Melanie Johnston-Hollitt Wai Chen Tom Gedeon Warren Mansell Curtin enAble Institute Faculty of Health Sciences Curtin University Perth Australia. Discipline of Psychology School of Population Health Curtin University Perth Australia. School of Arts and Humanities Edith Cowan University Perth Australia. Mental Health Commission of Western Australia Perth Australia. Curtin Institute for Data Science Curtin University Perth Australia. Centre for Clinical Interventions North Metropolitan Health Service Nedlands Australia. Centre for Biostatistics School of Health Sciences The University of Manchester Manchester United Kingdom. Alcohol Tobacco and Other Drug Research Unit South African Medical Research Council Parow South Africa. Division of Addiction Psychiatry Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa. Department of Clinical Psychology School of Health Sciences The University of Manchester Manchester United Kingdom. Mental Health Service Fiona Stanley Hospital Perth Australia. Curtin Medical School Curtin University Perth Australia. Centre of Excellence in Medical Biotechnology Faculty of Medical Science Naresuan University Phitsanulok Thailand. Optus-Curtin Centre of Excellence in AI School of Electronic Engineering Computing and Mathematical Sciences Curtin University Perth Australia.

[This corrects the article DOI: 10.2196/46849.].

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Author Correction: Carbon dioxide capture from open air using covalent organic frameworks

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Nature 2024年第8043期636卷 E10页

作者： Zihui Zhou Tianqiong Ma Heyang Zhang Saumil Chheda Haozhe Li Kaiyu Wang Sebastian Ehrling Raynald Giovine Chuanshuai Li Ali H Alawadhi Marwan M Abduljawad Majed O Alawad Laura Gagliardi Joachim Sauer Omar M Yaghi Department of Chemistry University of California Berkeley CA USA. Kavli Energy NanoScience Institute University of California Berkeley CA USA. Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley CA USA. KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh Saudi Arabia. 3P Instruments Leipzig Germany. Department of Chemistry Pritzker School of Molecular Engineering and Chicago Center for Theoretical Chemistry University of Chicago Chicago IL USA. Institut für Chemie Humboldt-Universität zu Berlin Berlin Germany. js@chemie.hu-berlin.de. Department of Chemistry University of California Berkeley CA USA. yaghi@berkeley.edu. Kavli Energy NanoScience Institute University of California Berkeley CA USA. yaghi@berkeley.edu. Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley CA USA. yaghi@berkeley.edu. KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh Saudi Arabia. yaghi@berkeley.edu.

Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging

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European heart journal. Imaging methods and practice 2024年第1期2卷 qyae042页

作者： Xiang Chen Yan Xia Erica Dall'Armellina Nishant Ravikumar Alejandro F Frangi School of Computing University of Leeds Woodhouse LS2 9JT Leeds UK. School of Medicine University of Leeds Woodhouse LS2 9JT Leeds UK. Christabel Pankhurst Institute The University of Manchester Oxford Rd M13 9PL Manchester UK. Department of Computer Science School of Engineering The University of Manchester Oxford Rd M13 9PL Manchester UK. Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester Oxford Rd M13 9PL Manchester UK. NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Oxford Rd M13 9PL Manchester UK. Medical Imaging Research Center (MIRC) University Hospital Gasthuisberg UZ Herestraat 49 - bus 7003 3000 Leuven Belgium. Department of Cardiovascular Sciences KU Leuven UZ Herestraat 49 - box 911 3000 Leuven Belgium. Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 postbus 2440 3001 Leuven Belgium. Alan Turing Institute British Library 96 Euston Rd. NW1 2DB London UK.

Aims:Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Cardiac image and mesh are two primary modalities to present the shape and structure of the heart and have been demonstrated to be efficient in CVD prediction and diagnosis. However, previous research has been generally focussed on a single modality (image or mesh), and few of them have tried to jointly consider the image and mesh representations of heart. To obtain efficient and explainable biomarkers for CVD prediction and diagnosis, it is needed to jointly consider both representations. Methods and results:We design a novel multi-channel variational auto-encoder, mesh-image variational auto-encoder, to learn joint representation of paired mesh and image. After training, the shape-aware image representation (SAIR) can be learned directly from the raw images and applied for further CVD prediction and diagnosis. We demonstrate our method on data from UK Biobank study and two other datasets via extensive experiments. In acute myocardial infarction prediction, SAIR achieves 81.43% accuracy, significantly higher than traditional biomarkers like metadata and clinical indices (left ventricle and right ventricle clinical indices of cardiac function like chamber volume, mass, and ejection fraction). Conclusion:Our mesh-image variational auto-encoder provides a novel approach for 3D cardiac mesh reconstruction from images. The extraction of SAIR is fast and without need of segmentation masks, and its focussing can be visualized in the corresponding cardiac meshes. SAIR archives better performance than traditional biomarkers and can be applied as an efficient supplement to them, which is of significant potential in CVD analysis.

关键词： CVD diagnosis CVD prediction cardiac biomarker mesh reconstruction multi-channel VAE

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

Undoped Layered Perovskite Oxynitride Li2LaTa2O6N for Photocatalytic CO2Reduction with Visible Light

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Angewandte Chemie 2018年第27期130卷

作者： Takayoshi Oshima Tom Ichibha Ken Sinkou Qin Kanemichi Muraoka Junie Jhon M. Vequizo Keisuke Hibino Ryo Kuriki Shunsuke Yamashita Kenta Hongo Tomoki Uchiyama Kotaro Fujii Daling Lu Ryo Maezono Akira Yamakata Hideki Kato Koji Kimoto Masatomo Yashima Yoshiharu Uchimoto Masato Kakihana Osamu Ishitani Hiroshi Kageyama Kazuhiko Maeda Department of Chemistry School of Science Tokyo Institute of Technology 2-12-1-NE-2 Ookayama Meguro-ku Tokyo 152-8550 Japan Japan Society for the Promotion of Science Kojimachi Business Center Building 5-3-1 Kojimachi Chiyoda-ku Tokyo 102-0083 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Graduate School of Engineering Toyota Technological Institute 2-12-1 Hisakata Tempaku Nagoya 468-8511 Japan Research Center for Advanced Measurement and Characterization National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan PRESTO (Japan) Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama 322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan Graduate school of Human and Environmental Studies Kyoto University Yoshida-nihonmatsu-cho Sakyo-ku Kyoto 606-8501 Japan Suzukakedai Materials Analysis Division Technical Department Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan Institute of Multidisciplinary Research for Advanced Materials Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan

Oxynitrides are promising visible‐light‐responsive photocatalysts, but their structures are almost confined with three‐dimensional (3D) structures such as perovskites. A phase‐pure Li 2 LaTa 2 O 6 N with a layered perovskite structure was successfully prepared by thermal ammonolysis of a lithium‐rich oxide precursor. Li 2 LaTa 2 O 6 N exhibited high crystallinity and visible‐light absorption up to 500 nm. As opposed to well‐known 3D oxynitride perovskites, Li 2 LaTa 2 O 6 N supported by a binuclear Ru II complex was capable of stably and selectively converting CO 2 into formate under visible light ( λ >400 nm). Transient absorption spectroscopy indicated that, as compared to 3D oxynitrides, Li 2 LaTa 2 O 6 N possesses a lower density of mid‐gap states that work as recombination centers of photogenerated electron/hole pairs, but a higher density of reactive electrons, which is responsible for the higher photocatalytic performance of this layered oxynitride.

关键词： CO2-Reduktion Oxynitride Perowskite Photokatalyse Sichtbares Licht

Octopus, a computational framework for exploring light-driven phenomena and quantum dynamics in extended and finite systems

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arXiv 2019年

作者： Tancogne-Dejean, Nicolas Oliveira, Micael J.T. Andrade, Xavier Appel, Heiko Borca, Carlos H. Le Breton, Guillaume Buchholz, Florian Castro, Alberto Corni, Stefano Correa, Alfredo A. de Giovannini, Umberto Delgado, Alain Eich, Florian G. Flick, Johannes Gil, Gabriel Gomez, Adrián Helbig, Nicole Hübener, Hannes Jestädt, René Jornet-Somoza, Joaquim Larsen, Ask H. Lebedeva, Irina V. Lüders, Martin Marques, Miguel A.L. Ohlmann, Sebastian T. Pipolo, Silvio Rampp, Markus Rozzi, Carlo A. Strubbe, David A. Sato, Shunsuke A. Schäfer, Christian Theophilou, Iris Welden, Alicia Rubio, Angel Max Planck Institute for the Structure and Dynamics of Matter Luruper Chaussee 149 HamburgD-22761 Germany Quantum Simulations Group Lawrence Livermore National Laboratory LivermoreCA94551 United States Département de Physique École Normale Supérieure de Lyon 46 Allée d’Italie Lyon Cedex 07 France Institute for Biocomputation and Physics of Complex Systems University of Zaragoza Calle Mariano Esquillor Zaragoza50018 Spain ARAID Foundation Avda. de Ranillas 1-D Zaragoza50018 Spain Dipartimento di Scienze Chimiche Università degli studi di Padova via F. Marzolo 1 Padova35131 Italy CNR – Istituto Nanoscienze via Campi 213a Modena41125 Italy Xanadu 777 Bay Street TorontoONM5G 2C8 Canada John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Instituto de Cibernética Matemática y Física Calle E 309 La Habana10400 Cuba Nanomat/Qmat/CESAM and ETSF Université de Liège Sart-TilmanB-4000 Belgium Nano-Bio Spectroscopy Group ETSF Universidad del País Vasco San Sebastián20018 Spain 06120 Germany Max Planck Computing and Data Facility Gießenbachstraße 2 Garching85741 Germany Universit de Lille CNRS Centrale Lille ENSCL Universit d Artois UMR 8181 UCCS Unit de Catalyse et Chimie du Solide LilleF-59000 France Department of Physics School of Natural Sciences University of California MercedCA95343 United States Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan

Over the last years extraordinary advances in experimental and theoretical tools have allowed us to monitor and control matter at short time and atomic scales with a high-degree of precision. An appealing and challenging route towards engineering materials with tailored properties is to find ways to design or selectively manipulate materials, especially at the quantum level. To this end, having a state-of-the-art ab initio computer simulation tool that enables a reliable and accurate simulation of light-induced changes in the physical and chemical properties of complex systems is of utmost importance. The first principles real-space-based Octopus project was born with that idea in mind, providing an unique framework allowing to describe non-equilibrium phenomena in molecular complexes, low dimensional materials, and extended systems by accounting for electronic, ionic, and photon quantum mechanical effects within a generalized time-dependent density functional theory framework. The present article aims to present the new features that have been implemented over the last few years, including technical developments related to performance and massive parallelism. We also describe the major theoretical developments to address ultrafast light-driven processes, like the new theoretical framework of quantum electrodynamics density-functional formalism (QEDFT) for the description of novel light-matter hybrid states. Those advances, and other being released soon as part of the Octopus package, will enable the scientific community to simulate and characterize spatial and time-resolved spectroscopies, ultrafast phenomena in molecules and materials, and new emergent states of matter (QED-materials). Copyright © 2019, The Authors. All rights reserved.

关键词： Density functional theory

Radio detection of an elusive millisecond pulsar in the Globular Cluster NGC 6397

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arXiv 2022年

作者： Zhang, Lei Ridolfi, Alessandro Blumer, Harsha Freire, Paulo C.C. Manchester, Richard N. McLaughlin, Maura Kremer, Kyle Cameron, Andrew D. Zhang, Zhiyu Behrend, Jan Burgay, Marta Buchner, Sarah Champion, David J. Chen, Weiwei Dai, Shi Feng, Yi Fu, Xiaoting Guo, Meng Hobbs, George Keane, Evan F. Kramer, Michael Levin, Lina Li, Xiangdong Ni, Mengmeng Pan, Jingshan Padmanabh, Prajwal V. Possenti, Andrea Ransom, Scott M. Tsai, Chao-Wei Krishnan, Vivek Venkatraman Wang, Pei Zhang, Jie Zhi, Qijun Zhang, Yongkun Li, Di National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia SelargiusI-09047 Italy Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Max-Planck Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany CSIRO Space and Astronomy Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia TAPIR California Institute of Technology PasadenaCA91125 United States The Observatories of the Carnegie Institution for Science PasadenaCA91101 United States Swinburne University of Technology Mail H74 PO Box 218 VIC3122 Australia School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210093 China South African Radio Astronomy Observatory Fir Street Black River Park Cape Town7925 South Africa School of Science Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China National Supercomputing Center in Jinan Qilu University of Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China School of Physics Trinity College Dublin College Green Dublin Ireland Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom HannoverD-30167 Germany MonserratoI-09042 Italy NRAO 520 Edgemont Road CharlottesvilleVA22903 United States College of Physics and Electronic Engineering Qilu Normal University 2 Wenbo Road Zhangqiu District Jinan250200 China Guizhou Provincial Key Laboratory of Radio

We report the discovery of a new 5.78 ms-period millisecond pulsar (MSP), PSR J1740−5340B (NGC 6397B), in an eclipsing binary system discovered with the Parkes radio telescope (now also known as Murriyanga), Australia, and confirmed with the MeerKAT radio telescope in South Africa. The measured orbital period, 1.97 days, is the longest among all eclipsing binaries in globular clusters (GCs) and consistent with that of the coincident X-ray source U18, previously suggested to be a ‘hidden MSP’. Our XMM-Newton observations during NGC 6397B’s radio quiescent epochs detected no X-ray flares. NGC 6397B is either a transitional MSP or an eclipsing binary in its initial stage of mass transfer after the companion star left the main sequence. The discovery of NGC 6397B potentially reveals a subgroup of extremely faint and heavily obscured binary pulsars, thus providing a plausible explanation to the apparent dearth of binary neutron stars in core-collapsed GCs as well as a critical constraint on the evolution of GCs. Copyright © 2022, The Authors. All rights reserved.

关键词： Mass transfer

TensorFlow quantum: A software framework for quantum machine learning

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arXiv 2020年

作者： Broughton, Michael Verdon, Guillaume McCourt, Trevor Martinez, Antonio J. Yoo, Jae Hyeon Isakov, Sergei V. Massey, Philip Halavati, Ramin Niu, Murphy Yuezhen Zlokapa, Alexander Peters, Evan Lockwood, Owen Skolik, Andrea Jerbi, Sofiene Dunjko, Vedran Leib, Martin Streif, Michael Von Dollen, David Chen, Hongxiang Cao, Shuxiang Wiersema, Roeland Huang, Hsin-Yuan McClean, Jarrod R. Babbush, Ryan Boixo, Sergio Bacon, Dave Ho, Alan K. Neven, Hartmut Mohseni, Masoud Google Quantum Ai Mountain ViewCA United States Sandbox Alphabet Mountain ViewCA United States Google Mountain ViewCA United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Division of Physics Mathematics and Astronomy Caltech PasadenaCA91125 United States Fermi National Accelerator Laboratory P.O. Box 500 BataviaIL605010 United States Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Data:Lab Volkswagen Group Ungererstr. 69 München80805 Germany Leiden University Niels Bohrweg 1 Leiden2333 CA Netherlands Institute for Theoretical Physics University of Innsbruck Technikerstr. 21a InnsbruckA-6020 Austria Institute of Theoretical Physics Staudtstr. 7 Erlangen91058 Germany Volkswagen Group Advanced Technologies San FranciscoCA94108 United States Rahko Ltd. Finsbury Park LondonN4 3JP United Kingdom Department of Computer Science University College London WC1E 6BT United Kingdom Clarendon Laboratory Department of Physics University of Oxford OxfordOX1 3PU United Kingdom Vector Institute MaRS Centre TorontoONM5G 1M1 Canada Department of Physics and Astronomy University of Waterloo ONN2L 3G1 Canada Institute for Quantum Information and Matter Caltech PasadenaCA United States Department of Computing and Mathematical Sciences Caltech PasadenaCA United States

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage. Copyright © 2020, The Authors. All rights reserved.

关键词： Open source software

Roadmap on Electronic Structure Codes in the Exascale Era

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arXiv 2022年

作者： Gavini, Vikram Baroni, Stefano Blum, Volker Bowler, David R. Buccheri, Alexander Chelikowsky, James R. Das, Sambit Dawson, William Delugas, Pietro Dogan, Mehmet Draxl, Claudia Galli, Giulia Genovese, Luigi Giannozzi, Paolo Giantomassi, Matteo Gonze, Xavier Govoni, Marco Gulans, Andris Gygi, François Herbert, John M. Kokott, Sebastian Kühne, Thomas D. Liou, Kai-Hsin Miyazaki, Tsuyoshi Motamarri, Phani Nakata, Ayako Pask, John E. Plessl, Christian Ratcliff, Laura E. Richard, Ryan M. Rossi, Mariana Schade, Robert Scheffler, Matthias Schütt, Ole Suryanarayana, Phanish Torrent, Marc Truflandier, Lionel Windus, Theresa L. Xu, Qimen Yu, Victor W.-Z. Perez, Danny Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States SISSA ISAS Trieste Trieste34136 Italy IOM-CNR Trieste Trieste34136 Italy Duke University DurhamNC27708 United States London Centre for Nanotechnology LondonWC1H 0AH United Kingdom WPI-MANA National Institute for Materials Science Ibaraki305-0044 Japan Humboldt-Universität zu Berlin Berlin10117 Germany McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States Center for Computational Materials Oden Institute University of Texas at Austin AustinTX78712 United States Department of Physics University of Texas at Austin AustinTX78712 United States RIKEN Center for Computational Science Hyogo Kobe650-0047 Japan Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Université Grenoble Alpes MEM L_Sim GrenobleF-38000 France University of Udine Udine33100 Italy IMCN Université catholique de Louvain Louvain-la-neuve Belgium University of Latvia RīgaLV-1586 Latvia University of California DavisCA95616 United States Department of Chemistry & Biochemistry The Ohio State University ColumbusOH43210 United States Berlin14195 Germany Dynamics of Condensed Matter Center for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University D-33098 Germany Paderborn Center for Parallel Computing Paderborn University D-33098 Germany Department of Computational and Data Sciences Indian Institute of Science Karnataka Bengaluru560012 India Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science Paderborn University D-33098 Germany Centre for Computational Chemistry School of Chemistry University of Bristol BristolBS8 1TS United Kingdom Ames National Laboratory AmesIA50011 United States Iowa State University AmesIA50

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic structure