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Galactic transient sources with the Cherenkov Telescope Array Observatory

Monthly Notices of the Royal Astronomical Society 2025年第1期540卷 205–238页

作者： Abe, K Abe, S Abhir, J Abhishek, A Acero, F Acharyya, A Adam, R Aguasca-Cabot, A Agudo, I Aguirre-Santaella, A Alfaro, J Alfaro, R Alvarez-Crespo, N Alves Batista, R Amans, J-P Amato, E Ambrosi, G Ambrosino, F Angüner, E O Antonelli, L A Aramo, C Arcaro, C Arnesen, T T H Asano, K Ascasibar, Y Aschersleben, J Ashkar, H Augusto Stuani, L Baack, D Backes, M Balazs, C Balbo, M Baquero Larriva, A Barbosa Martins, V Barres de Almeida, U Barrio, J A Barrios-Jiménez, L Batković, I Batzofin, R Baxter, J Becerra González, J Becker Tjus, J Belmont, R Benbow, W Bernete, J Bernlöhr, K Berti, A Bertucci, B Beshley, V Bhattacharjee, P Bhattacharyya, S Bigongiari, C Bissaldi, E Blanch, O Blazek, J Bocchino, F Boisson, C Bolmont, J Bonnoli, G Bonollo, A Bordas, P Bosnjak, Z Bottacini, E Böttcher, M Bradascio, F Bronzini, E Brown, A M Brunelli, G Bulgarelli, A Burelli, I Burger-Scheidlin, C Burmistrov, L Burton, M Buscemi, M Cailleux, J Campoy-Ordaz, A Cantlay, B K Capasso, G Caproni, A Capuzzo-Dolcetta, R Caraveo, P Caroff, S Carosi, A Carosi, R Carquin, E Carrasco, M-S Cascone, E Cassol, F Castaldini, L Castrejon, N Castro-Tirado, A J Cerasole, D Ceribella, G Cerruti, M Chadwick, P M Chaty, S Chen, A W Chernyakova, M Chiavassa, A Chudoba, J Chytka, L Cicciari, G M Cifuentes, A Coimbra Araujo, C H Colapietro, M Conforti, V Contreras, J L Cortina, J Costa, A Costantini, H Cotter, G Cristofari, P Cuevas, O Curtis-Ginsberg, Z D’Aì, A D’Amico, G D’Ammando, F Dai, S Dazzi, F de Bony de Lavergne, M De Caprio, V De Cesare, G De Frondat Laadim, F de Gouveia Dal Pino, E M De Lotto, B De Lucia, M de Martino, D de Menezes, R de Naurois, M de Ona Wilhelmi, E de Souza, V del Peral, L del Valle, M V Delgado, C Dell’aiera, M Della Valle, M della Volpe, D Depaoli, D Devin, J Di Girolamo, T Di Piano, A Di Pierro, F Di Tria, R Di Venere, L Diebold, S Dignam, C Dima, R Dinesh, A Djuvsland, J Dominik, R M Dominis Prester, D Donini, A Dorner, D Dörner, J Doro, M Dubos, C Ducci, L Dwarkadas, V V Ebr, J Eckner, C Egberts, K Einecke, S Elsässer, D Emery Department of Physics Tokai University 4-1-1 Kita-Kaname Hiratsuka Kanagawa 259-1292 Japan Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba 277-8582 Japan ETH Zürich Institute for Particle Physics and Astrophysics Otto-Stern-Weg 5 CH-8093 Zürich Switzerland INFN and Università degli Studi di Siena Dipartimento di Scienze Fisiche della Terra e dell’Ambiente (DSFTA) Sezione di Fisica Via Roma 56 I-53100 Siena Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM F-91191 Gif-sur-Yvette Cedex France FSLAC IRL 2009 CNRS/IAC E-38205 La Laguna Tenerife Spain University of Alabama Tuscaloosa Department of Physics and Astronomy Gallalee Hall Box 870324 Tuscaloosa AL 35487-0324 USA Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange UF 06304 Nice France Laboratoire Leprince-Ringuet CNRS/IN2P3 École Polytechnique Institut Polytechnique de Paris F-91120 Palaiseau France Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 E-08028 Barcelona Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n E-18008 Granada Spain Institute for Computational Cosmology and Department of Physics Durham University South Road Durham DH1 3LE UK Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 8331150 Santiago Chile Universidad Nacional Autónoma de México Delegación Coyoacán 04510 Ciudad de México Mexico IPARCOS-UCM Instituto de Física de Partículas y del Cosmos and EMFTEL Department Universidad Complutense de Madrid E-28040 Madrid Spain Instituto de Física Teórica UAM/CSIC and Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM E-28049 Madrid Spain LUTH GEPI and LERMA Observatoire de Paris Université PSL Université Paris Cité CNRS 5 place Jules Janssen F-92190 Meudon France INAF – Osservatorio

A wide variety of Galactic sources show transient emission at soft and hard X-ray energies: low- and high-mass X-ray binaries containing compact objects, isolated neutron stars exhibiting extreme variability as magnetars as well as pulsar-wind nebulae. Although most of them can show emission up to MeV and/or GeV energies, many have not yet been detected in the TeV domain by Imaging Atmospheric Cherenkov Telescopes. In this paper, we explore the feasibility of detecting new Galactic transients with the Cherenkov Telescope Array Observatory (CTAO) and the prospects for studying them with Target of Opportunity observations. We show that CTAO will likely detect new sources in the TeV regime, such as the massive microquasars in the Cygnus region, low-mass X-ray binaries with low-viewing angle, flaring emission from the Crab pulsar-wind nebula or other novae explosions, among others. Since some of these sources could also exhibit emission at larger time-scales, we additionally test their detectability at longer exposures. We finally discuss the multiwavelength synergies with other instruments and large astronomical facilities.

关键词： binaries: general stars: magnetars novae cataclysmic variables pulsars: general gamma-rays: general

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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Understanding the pattern of wind direction in Peninsular Malaysia

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AIP Conference Proceedings 2023年第1期2500卷

作者： Nor Hafizah Moslim Yong Zulina Zubairi Abdul Ghapor Hussin Siti Fatimah Hassan Nurkhairany Amyra Mokhtar 1)Institute of Advanced Studies University of Malaya 50603 Kuala Lumpur Malaysia 2)Centre of Mathematical Sciences College of Computing & Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Pahang Malaysia 3)Centre for Foundation Studies in Science University of Malaya 50603 Kuala Lumpur Malaysia 4)Faculty of Defence Sciences and Technology National Defence University of Malaysia Kem Sungai Besi 57000 Kuala Lumpur Malaysia 5)Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Johor Kampus Segamat 85000 Segamat Johor Malaysia

Understanding the pattern of wind direction is important in meteorological studies. In exploratory data analysis, summary statistics provide basic information such as the measures of central tendency and the spread of the maximum wind direction. In this study, daily wind direction data recorded at maximum wind speed of northeast and southwest monsoons from three stations in Peninsular Malaysia from the year of 2014 to 2019 are used. The wind direction data is modeled using von Mises distribution where confidence interval for concentration parameter is estimated using bootstrap-t method. Based on the performance measure of expected length, the confidence interval provides an efficient measure of the concentration parameter. Findings of this study suggest that there are variations with respect to monsoons and locations as some are dense and some are sparsely distributed. This information is useful in providing a better-understanding of the pattern of wind direction in Malaysia.

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Toward the end-to-end optimization of particle physics instruments with differentiable programming

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Reviews in Physics 2023年 10卷

作者： Dorigo, Tommaso Giammanco, Andrea Vischia, Pietro Aehle, Max Bawaj, Mateusz Boldyrev, Alexey de Castro Manzano, Pablo Derkach, Denis Donini, Julien Edelen, Auralee Fanzago, Federica Gauger, Nicolas R. Glaser, Christian Baydin, Atılım G. Heinrich, Lukas Keidel, Ralf Kieseler, Jan Krause, Claudius Lagrange, Maxime Lamparth, Max Layer, Lukas Maier, Gernot Nardi, Federico Pettersen, Helge E.S. Ramos, Alberto Ratnikov, Fedor Röhrich, Dieter de Austri, Roberto Ruiz del Árbol, Pablo Martínez Ruiz Savchenko, Oleg Simpson, Nathan Strong, Giles C. Taliercio, Angela Tosi, Mia Ustyuzhanin, Andrey Zaraket, Haitham MODE Collaboration Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy Université catholique de Louvain Belgium Chair for Scientific Computing Technische Universität Kaiserslautern Germany Università di Perugia and INFN Sezione di Perugia Italy HSE University Russia Université Clermont Auvergne Laboratoire de Physique de Clermont CNRS/IN2P3 France SLAC National Accelerator Laboratory United States Department of Physics and Astronomy Uppsala University Sweden Department of Computer Science University of Oxford United Kingdom Physik-Department Technische Universität München Germany Center for Technology and Transfer University of Applied Sciences Worms Germany CERN Switzerland NHETC Department of Physics and Astronomy Rutgers University United States Università di Napoli "Federico II" Italy Germany Università degli Studi di Padova Italy Department of Oncology and Medical Physics Haukeland University Hospital Norway Instituto de Física Corpuscular UV-CSIC Spain Department of Physics and Technology University of Bergen Norway Instituto de Física de Cantabria UC-CSIC Spain Lund University Sweden Multi-Disciplinary Physics Laboratory Optics and Fiber Optics Group Faculty of Sciences Lebanese University Lebanon the World Constructor University Bremen gGmbH Campus Ring 1 Bremen 28759 Germany

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this white paper, we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications. © 2023 The Author(s)

关键词： Astrophysics

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COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

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Sensors 2024年第5期24卷 1557页

作者： Bougourzi, Fares Distante, Cosimo Dornaika, Fadi Taleb-Ahmed, Abdelmalik Hadid, Abdenour Chaudhary, Suman Yang, Wanting Qiang, Yan Anwar, Talha Breaban, Mihaela Elena Hsu, Chih-Chung Tai, Shen-Chieh Chen, Shao-Ning Tricarico, Davide Chaudhry, Hafiza Ayesha Hoor Fiandrotti, Attilio Grangetto, Marco Spatafora, Maria Ausilia Napoli Ortis, Alessandro Battiato, Sebastiano Institute of Applied Sciences and Intelligent Systems National Research Council of Italy Lecce73100 Italy Laboratoire LISSI University Paris-Est Creteil Paris Vitry sur Seine94400 France Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Manuel Lardizabal 1 San Sebastian20018 Spain IKERBASQUE Basque Foundation for Science Bilbao48011 Spain UMR 8520 Universite Polytechnique Hauts-de-France Université de Lille CNRS Valenciennes59313 France Sorbonne Center for Artificial Intelligence Sorbonne University of Abu Dhabi P.O. Box 38044 Abu Dhabi United Arab Emirates College of Computer Science and Technology Taiyuan University of Technology Taiyuan030024 China School of Computing National University of Computer and Emerging Sciences Islamabad44000 Pakistan Faculty of Computer Science Alexandru Ioan Cuza University Iasi700506 Romania Institute of Data Science National Cheng Kung University No. 1 University Rd. East Dist. Tainan City701 Taiwan Dipartimento di Informatica Universita degli Studi di Torino Corso Svizzera 185 Torino10149 Italy Department of Mathematics and Computer Science University of Catania Catania95125 Italy

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient’s state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. © 2024 by the authors.

关键词： COVID-19

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Big Data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update

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Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 2023年第1期43卷 3-25页

作者： Ronney B Panerai Patrice Brassard Joel S Burma Pedro Castro Jurgen Ahr Claassen Johannes J van Lieshout Jia Liu Samuel Je Lucas Jatinder S Minhas Georgios D Mitsis Ricardo C Nogueira Shigehiko Ogoh Stephen J Payne Caroline A Rickards Andrew D Robertson Gabriel D Rodrigues Jonathan D Smirl David M Simpson Department of Cardiovascular Sciences University of Leicester and NIHR Biomedical Research Centre Leicester UK. Department of Kinesiology Faculty of Medicine and Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec Université Laval Québec QC Canada. Faculty of Kinesiology University of Calgary Calgary AB Canada. Department of Neurology Centro Hospitalar Universitário de São João Faculty of Medicine University of Porto Porto Portugal. Department of Geriatric Medicine and Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen The Netherlands. Department of Internal Medicine Amsterdam UMC The Netherlands and Division of Physiology Pharmacology and Neuroscience School of Life Sciences University of Nottingham Medical School Queen's Medical Centre UK. Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen University Town Shenzhen China. School of Sport Exercise and Rehabilitation Sciences and Centre for Human Brain Health University of Birmingham Birmingham UK. Department of Bioengineering McGill University Montreal Québec QC Canada. Neurology Department School of Medicine Hospital das Clinicas University of São Paulo São Paulo Brazil. Department of Biomedical Engineering Toyo University Kawagoe-Shi Saitama Japan. Institute of Applied Mechanics National Taiwan University Taipei. Department of Physiology & Anatomy University of North Texas Health Science Center Fort Worth Texas USA. Department of Kinesiology and Health Sciences University of Waterloo Waterloo ON Canada. Department of Clinical Sciences and Community Health University of Milan Milan Italy. Institute of Sound and Vibration Research University of Southampton Southampton UK.

Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

关键词： Cerebral hemodynamics consensus guidelines reference values transfer function analysis

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Observation of a giant Goos-Hänchen shift for matter waves

arXiv

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arXiv 2024年

作者： McKay, S. de Haan, V.O. Leiner, J. Parnell, S.R. Dalgliesh, R.M. Boeni, P. Bannenberg, L.J. Le Thien, Q. Baxter, D.V. Ortiz, G. Pynn, R. Department of Physics Indiana University BloomingtonIN47405 United States Center for Exploration of Energy and Matter Indiana University Bloomington47408 United States BonPhysics Research and Investigations BV Laan van Heemstede 38 Puttershoek 3297AJ Netherlands Neutron Sciences Directorate Oak Ridge National Laboratory Oak RidgeTN37830 United States Faculty of Applied Sciences Delft University of Technology Mekelweg 15 Delft2629 JB Netherlands ISIS Rutherford Appleton Laboratory Oxfordshire ChiltonOX11 0QX United Kingdom Physics Department E21 Technical University of Munich GarchingD-85748 Germany SwissNeutronics AG Brühlstrasse 28 KlingnauCH-5313 Switzerland Quantum Science and Engineering Center Indiana University BloomingtonIN47408 United States Institute for Advanced Study PrincetonNJ08540 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada

The Goos-Hänchen (GH) shift describes a phenomenon in which a specularly-reflected beam is laterally translated along the reflecting surface such that the incident and reflected rays no longer intersect at the surface. Using a neutron spin-echo technique and a specially-designed magnetic multilayer mirror, we have measured the relative phase between the reflected up and down neutron spin states in total reflection. The relative GH shift calculated from this phase shows a strong resonant enhancement at a particular incident neutron wavevector, which is due to a waveguiding effect in one of the magnetic layers. Calculations based on the observed phase difference between the neutron states indicate a propagation distance along the waveguide layer of 0.65 mm for the spin-down state, which we identify with the magnitude of the giant GH shift. The existence of a physical GH shift is confirmed by the observation of neutron absorption in the waveguide layer. We propose ways in which our experimental method may be exploited for neutron quantum-enhanced sensing of thin magnetic layers. Copyright © 2024, The Authors. All rights reserved.

关键词： Neutrons

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Optimizing a parameterized controlled gate with Free Quaternion Selection

arXiv

引用

arXiv 2024年

作者： Kurogi, Hiroyoshi Endo, Katsuhiro Sato, Yuki Sugawara, Michihiko Wada, Kaito Sugisaki, Kenji Kanno, Shu Watanabe, Hiroshi C. Nakano, Haruyuki Department of Chemistry Graduate School of Science Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Toyota Central R & D Labs. Inc. Koraku Mori Building 10F 1-4-14 Koraku Bunkyo-ku Tokyo112-0004 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama223-8522 Japan Graduate School of Science and Technology Keio University 7-1 Shinkawasaki Saiwai-ku Kawasaki212-0032 Japan Keio University 2-15-45 Mita Minato-ku Tokyo108-8345 Japan Centre for Quantum Engineering Research and Education TCG Centres for Research and Education in Science and Technology Salt Lake Sector V Kolkata700091 India Mitsubishi Chemical Corporation Science & Innovation Center Yokohama227-8502 Japan Department of Chemistry Faculty of Science Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan

In variational algorithms, quantum circuits are conventionally parametrized with respect to single-qubit gates. In this study, we parameterize a generalized controlled gate and propose an algorithm to estimate the optimal parameters for locally minimizing the cost value, where we extend the free quaternion selection method, an optimization method for a single-qubit gate. To benchmark the performance, we apply the proposed method to various optimization problems, including the Variational Quantum Eigensolver (VQE) for Ising and molecular Hamiltonians, Variational Quantum Algorithms (VQA) for fidelity maximization, and unitary compilation of time evolution operators. In these applications, the proposed method shows efficient optimization and greater expressibility with shallower circuits than other methods. Furthermore, this method is also capable of generalizing and fully optimizing particle-number-conserving gates, which are in demand in chemical systems applications. Taking advantage of this property, we have actually approximated time evolution operators of molecular Hamiltonian and simulated the dynamics with shallower circuits in comparison to the standard implementation by Trotter decomposition. © 2024, CC BY-NC-SA.

关键词： Hamiltonians

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