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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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A SURVEY ON BRAIN-INSPIRED DEEP LEARNING VIA PREDICTIVE CODING

arXiv

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arXiv 2023年

作者： Salvatori, Tommaso Mali, Ankur Buckley, Christopher L. Lukasiewicz, Thomas Rao, Rajesh P.N. Friston, Karl Ororbia, Alexander VERSES AI Research Lab Los AngelesCA United States Institute of Logic and Computation Vienna University of Technology Austria University of South Florida TampaFL33620 United States Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Department of Computer Science University of Oxford United Kingdom Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States The Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London United Kingdom The Neural Adaptive Computing Laboratory Rochester Institute of Technology RochesterNY14623 United States

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, such an algorithm has always been considered biologically implausible. To this end, recent works have studied learning algorithms for deep neural networks inspired by the neurosciences. One such theory, called predictive coding (PC), has shown promising properties that make it potentially valuable for the machine learning community: it can model information processing in different areas of the brain, can be used in cognitive control and robotics, has a solid mathematical foundation in variational inference, and performs its computations asynchronously. Inspired by such properties, works that propose novel PC-like algorithms are starting to be present in multiple sub-fields of machine learning and artificial intelligence at large. Here, we survey such efforts by first providing a broad overview of the history of PC to provide a common ground for the understanding of the recent developments, then by surveying current efforts and results, and concluding with a large discussion of possible implications and ways forward. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

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The global spectrum of tree crown architecture

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Nature communications 2025年第1期16卷 4876页

作者： Tommaso Jucker Fabian Jörg Fischer Jérôme Chave David A Coomes John Caspersen Arshad Ali Grace Jopaul Loubota Panzou Ted R Feldpausch Daniel Falster Vladimir A Usoltsev Toby D Jackson Stephen Adu-Bredu Luciana F Alves Mohammad Aminpour Bhely Angoboy Ilondea Niels P R Anten Cécile Antin Yousef Askari Narayanan Ayyappan Lindsay F Banin Nicolas Barbier John J Battles Hans Beeckman Yannick E Bocko Ben Bond-Lamberty Frans Bongers Samuel Bowers Michiel van Breugel Arthur Chantrain Rajeev Chaudhary Jingyu Dai Michele Dalponte Kangbéni Dimobe Jean-Christophe Domec Jean-Louis Doucet Juan Manuel Dupuy Rada Remko A Duursma Moisés Enríquez Karin Y van Ewijk William Farfán-Rios Adeline Fayolle Marco Ferretti Eric Forni David I Forrester Hammad Gilani John L Godlee Matthias Haeni Jefferson S Hall Jie-Kun He Andreas Hemp José L Hernández-Stefanoni Steven I Higgins Robert J Holdaway Kiramat Hussain Lindsay B Hutley Tomoaki Ichie Yoshiko Iida Hai-Sheng Jiang Puspa Raj Joshi Hasan Kaboli Maryam Kazempour Larsary Tanaka Kenzo Brian D Kloeppel Takashi S Kohyama Suwash Kunwar Shem Kuyah Jakub Kvasnica Siliang Lin Emily R Lines Hongyan Liu Craig Lorimer Jean-Joël Loumeto Yadvinder Malhi Peter L Marshall Eskil Mattsson Radim Matula Jorge A Meave Sylvanus Mensah Xiangcheng Mi Stéphane T Momo Glenn R Moncrieff Francisco Mora Rodrigo Muñoz Sarath P Nissanka Zamah Shari Nur Hajar Kevin L O'Hara Steven Pearce Raphaël Pelissier Pablo L Peri Pierre Ploton Lourens Poorter Mohsen Javanmiri Pour Hassan Pourbabaei Sabina C Ribeiro Casey Ryan Anvar Sanaei Jennifer Sanger Michael Schlund Giacomo Sellan Alexander Shenkin Bonaventure Sonké Frank J Sterck Martin Svátek Kentaro Takagi Anna T Trugman Matthew A Vadeboncoeur Ahmad Valipour Mark C Vanderwel Alejandra G Vovides Peter Waldner Weiwei Wang Li-Qiu Wang Christian Wirth Murray Woods Wenhua Xiang Fabiano de Aquino Ximenes Yaozhan Xu Toshihiro Yamada Miguel A Zavala Niklaus E Zimmermann School of Biological Sciences University of Bristol Bristol BS8 1TQ UK. t.jucker@bristol.ac.uk. School of Biological Sciences University of Bristol Bristol BS8 1TQ UK. UMR5300 Centre de Recherche sur la Biodiversité et l'Environnement CNRS INPT IRD Université de Toulouse Bât 4R1 118 route de Narbonne 31062 Toulouse France. Conservation Research Institute University of Cambridge Cambridge CB2 3EA UK. Institute of Forestry and Conservation University of Toronto 33 Willcocks Street Toronto ON M5S 3B3 Canada. Forest Ecology Research Group College of Life Sciences Hebei University Baoding 071002 Hebei China. Institut Supérieur des Sciences Géographiques Environnementales et de l'Aménagement (ISSGEA) Université DENIS SASSOU-N'GUESSO Kintélé République du Congo. Laboratoire de Biodiversité de Gestion des Ecosystèmes et de l'Environnement (LBGE) Faculté des Sciences et Techniques Université Marien NGOUABI BP 69 Brazzaville Brazzaville République du Congo. Faculty of Environment Science and Economy University of Exeter Exeter EX4 4QE UK. Evolution & Ecology Research Centre University of New South Wales Sydney NSW Sydney Australia. Department of Forest Mensuration and Management Ural State Forest Engineering and Economic University Yekaterinburg Russia. Forestry Research Institute of Ghana Council for Scientific and Industrial Research University Kumasi Ghana. Center for Tropical Research Institute of the Environment and Sustainability University of California Los Angeles Los Angeles CA USA. Natural Recourses and Watershed Management Office West Azerbaijan Province Urmia Iran. Institut National pour l'Etude et la Recherche Agronomiques BP 2037 Kinshasa Democratic Republic of the Congo. Université Pédagogique Nationale BP 8815 Kinshasa-Ngaliema Democratic Republic of the Congo. Center for Crop Systems Analysis Wageningen University & Research Wageningen Netherlands. AMAP lab Montpellier University IRD CIRAD CNRS INRAE Montpellier France. Rese

Trees can differ enormously in their crown architectural traits, such as the scaling relationships between tree height, crown width and stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of terrestrial ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from 374,888 globally distributed trees, we explore how climate, disturbance, competition, functional traits, and evolutionary history constrain the height and crown width scaling relationships of 1914 tree species. We find that variation in height-diameter scaling relationships is primarily controlled by water availability and light competition. Conversely, crown width is predominantly shaped by exposure to wind and fire, while also covarying with functional traits related to mechanical stability and photosynthesis. Additionally, we identify several plant lineages with highly distinctive stem and crown forms, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legume trees in African savannas. Our study charts the global spectrum of tree crown architecture and pinpoints the processes that shape the 3D structure of woody ecosystems.

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pymdp: A Python library for active inference in discrete state spaces

arXiv

引用

arXiv 2022年

作者： Heins, Conor Millidge, Beren Demekas, Daphne Klein, Brennan Friston, Karl Couzin, Iain D. Tschantz, Alexander Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz78457 Germany Centre for the Advanced Study of Collective Behaviour Konstanz78457 Germany Department of Biology University of Konstanz Konstanz78457 Germany VERSES Research Lab Los AngelesCA United States MRC Brain Networks Dynamics Unit University of Oxford Oxford United Kingdom Department of Computing Imperial College London London United Kingdom Network Science Institute Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-Technical Systems Northeastern University Boston United States Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London LondonWC1N 3AR United Kingdom Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Sackler Centre for Consciousness Science University of Sussex Brighton United Kingdom

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Galactic transient sources with the Cherenkov Telescope Array Observatory

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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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MONAI: An open-source framework for deep learning in healthcare

arXiv

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arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research

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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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Piquasso: A Photonic Quantum Computer Simulation Software Platform

arXiv

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arXiv 2024年

作者： Kolarovszki, Zoltán Rybotycki, Tomasz Rakyta, Péter Kaposi, Ágoston Poór, Boldizsár Jóczik, Szabolcs Nagy, Dániel T.R. Varga, Henrik El-Safty, Kareem H. Morse, Gregory Oszmaniec, Michal Kozsik, Tamás Zimborás, Zoltán Quantum Computing and Quantum Information Research Group HUN-REN Wigner Research Centre for Physics Konkoly-Thege Miklós út 29-33 BudapestH-1525 Hungary Department of Programming Languages and Compilers Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Center for Theoretical Physics Polish Academy of Sciences Al. Lotników 32/46 Warszawa02-668 Poland Department of Physics of Complex Systems Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Quantinuum 17 Beaumont Street OxfordOX1 2NA United Kingdom Robert Bosch Kft. Gyömrői út 104. BudapestH-1103 Hungary Department of Computer Engineering Technical University of Munich Arcisstraße 21 München80333 Germany Algorithmiq Ltd. Kanavakatu 3C Helsinki00160 Finland

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends, Piquasso provides state-of-the-art performance in the simulation of photonic quantum computers. The Piquasso framework is supported by an intuitive web-based graphical user interface where the users can design quantum circuits, run computations, and visualize the results. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

引用

BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Modern computing: Vision and Challenges

arXiv

引用

arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

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