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Traffic Optimization Algorithms in Optical Networks For Real Time Traffic Analysis

Optik 2022年

作者： Suresh Kumar. K Swati Vijay Shinde S. Venkata Lakshmi R.S. Sabeenian Koppisetti Durga Bhavani K.V.S.R. Murthy Department of Information Technology Saveetha Engineering College Tamil Nadu India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Dept. of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of ECE Sona College of Technology Salem Department of Management Studies Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India EEE Department & Dean (R&D) Aditya Group of Engineering Colleges

The rapid growth of metropolitan areas poses significant traffic control issues. Advanced telecommunication structures, such as the IoT (Internet of Things) technology, including wireless connections, produce enormous amounts of diverse traffic information. Conventional networking control approaches for tracking and information analysis confront certain obstacles and problems in specific systems, like reliability and efficient actual-time analysis of massive datasets. Furthermore, due to variables like gadget portability and connection diversity, the overall structure of internet activity, particularly in wireless networks, exhibits extremely complicated activity. The article aims to create a method for collecting high-quality, comprehensive information like transportation flowing strength, travel routes, and mean vehicle velocity. Simultaneously, information is gathered throughout the whole operational zone of junctions and neighboring highway segments under the city video security sensor perspective. More profound learning methods will be used within the communication domain for Networking Transport Management and Analytics (NTMA) activities such as traffic categorization and forecasting. The YOLOv3 artificial routing topology, as well as a SORT-accessible monitor, are used in our approach. Utilizing extended masking branches and improving its geometry of anchoring, overall essential efficiency in YOLO has been enhanced. We employed a technique of viewpoint conversion using parameters from actual pictures to global dimensions for computing overall vehicular velocities.

关键词： Networking Traffic Management and Analytics (NTMA) real-time surveillance traffic management and database processing

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Applications of Evolutionary Computation 1

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丛书名： Lecture Notes in Computer science

1000年

作者： Cecilia Chio Alexandros Agapitos Stefano Cagnoni Carlos Cotta Francisco Fernández Vega Gianni A. Caro Rolf Drechsler Anikó Ekárt Anna I. Esparcia-Alcázar Muddassar Farooq William B. Langdon Juan J. Merelo-Guervós Mike Preuss Hendrik Richter Sara Silva Anabela Simões Giovanni Squillero Ernesto Tarantino Andrea G. B. Tettamanzi Julian Togelius Neil Urquhart A. Şima Uyar Georgios N. Yannakakis

ISBN: (数字)9783642291784

ISBN: (纸本)9783642291777

This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 events EuroGP, EvoCOP, EvoBIO, and EvoMUSART. The 54 revised full papers presented were carefully reviewed and selected from 90 submissions. EvoApplications 2012 consisted of the following 11 tracks: EvoCOMNET (nature-inspired techniques for telecommunication networks and other parrallel and distributed systems), EvoCOMPLEX (algorithms and complex systems), EvoFIN (evolutionary and natural computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoHOT (bio-inspired heuristics for design automation), EvoIASP (evolutionary computation in image analysis and signal processing), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defense applications), EvoSTIM (nature-inspired techniques in scheduling, planning, and timetabling), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

关键词： Computation by Abstract Devices Programming Techniques Computer Communication Networks Math Applications in Computer science Image Processing and Computer Vision Artificial Intelligence

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DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

arXiv

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arXiv 2025年

作者： Huang, X. Baltasar, S. Ratier-Werbin, N. Storfer, C. Sheu, W. Agarwal, S. Tamargo-Arizmendi, M. Schlegel, D.J. Aguilar, J. Ahlen, S. Aldering, G. Banka, S. BenZvi, S. Bianchi, D. Bolton, A. Brooks, D. Cikota, A. Claybaugh, T. de la Macorra, A. Dey, A. Doel, P. Edelstein, J. Filipp, A. Forero-Romero, J.E. Gaztañaga, E. Gontcho, S.A. Gontcho Gu, A. Gutierrez, G. Honscheid, K. Jullo, E. Juneau, S. Kehoe, R. Kirkby, D. Kisner, T. Kremin, A. Kwon, K.J. Lambert, A. Landriau, M. Lang, D. Le Guillou, L. Liu, J. Meisner, A. Miquel, R. Moustakas, J. Myers, A.D. Perlmutter, S. Pérez-Ràfols, I. Prada, F. Rossi, G. Rubin, D. Sanchez, E. Schubnell, M. Shu, Y. Silver, E. Sprayberry, D. Suzuki, N. Tarlé, G. Weaver, B.A. Zou, H. Department of Physics & Astronomy University of San Francisco San FranciscoCA94117 United States Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics Complutense University of Madrid Madrid28040 Spain Department of Mathematics Complutense University of Madrid Madrid28040 Spain Institute for Astronomy University of Hawai’i HonoluluHI96822-1897 United States Department of Physics & Astronomy University of California Los Angeles Los AngelesCA90095 United States University of Chicago Department of Astronomy ChicagoIL60615 United States Department of Physics & Astronomy University of Pittsburgh PittsburghPA15260 United States Physics Dept. Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Electrical Engineering & Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics & Astronomy University of Rochester 206 Bausch and Lomb Hall P.O. Box 270171 RochesterNY14627-0171 United States Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 MilanoI-20133 Italy NSF’s National Optical-Infrared Astronomy Research Laboratory TucsonAZ85719 United States Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Gemini Observatory NSF’s NOIRLab Casilla 603 La Serena Chile Instituto de Física Universidad Nacional Autónoma de México Circuito de la Investigación Científica Ciudad Universitaria Cd. de MéxicoC. P. 04510 Mexico Space Sciences Laboratory University of California Berkeley 7 Gauss Way BerkeleyCA94720 United States Université de Montréal Physics Department 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Technical University Munich TUM School of Natural Sciences

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼ 3500 new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging. © 2025, CC BY.

关键词： Dark energy

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Investigation of SrTiO3 barrier layers for RF sputter‐deposited Y‐Ba‐Cu‐O films on Si and sapphire

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AIP Conference Proceedings 1989年第1期182卷 65-73页

作者： J. K. Truman M. Leskela C. H. Mueller P. H. Holloway Dept. of Materials Science and Engineering University of Florida Gainesville FL 32611 Films of Y‐Ba‐Cu‐O were deposited by RF planar magnetron sputtering from single sintered oxide targets onto silicon and sapphire substrates with and without RF sputter‐deposited SrTiO3 barrier layers. The composition of YBCO films was determined by electron microprobe analysis. The effectiveness of the barrier layers was determined from AES XRD room temperature resistivity and optical and electron microscopy data. Films of SrTiO3 were found to exhibit {111} preferential orientation parallel to {100} Si but no preferential orientation was observed on sapphire. Silicon was prevented by SrTiO3 from reaching the Y‐Ba‐Cu‐O but was not a suitable barrier layer since Ba migrated to the Si interface. For sapphire SrTiO3 prevented interaction between Y‐Ba‐Cu‐O and the substrate although Al did migrate a limited distance into the SrTiO3.

J. K. Truman, M. Leskela, C. H. Mueller, P. H. Holloway; Investigation of SrTiO3 barrier layers for RF sputter‐deposited Y‐Ba‐Cu‐O films on Si and sapphire, AIP

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Preface

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Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2001年 2037卷 V-VI页

作者： Boers, Egbert J. W. Cagnoni, Stefano Gottlieb, Jens Hart, Emma Lanzi, Pier Luca Raidl, Günther R. Smith, Robert E. Tijink, Harald Leiden Institute of Advanced Computer Science Leiden University Niels Bohrweg 1 Leiden Netherlands Dept. of Computer Engineering University of Parma Parco Area delle Scienze 181/a Parma Italy SAP AG Neurottstrasse 16 Walldorf Germany Napier University School of Computing 219 Colinton Road Edinburgh United Kingdom Polytechnic of Milan Piazza Leonardo da Vinci 32 Milan Italy Algorithms and Data Structur es Group Institute of Computer Graphics Vienna University of Technology Favoritenstrasse 9-11/186 Vienna Austria The Intelligent Computer Systems Centre The University of The West of England Coldharbour Lane Frenchay Bristol United Kingdom Data and Knowledge Systems Department National Aerospace Laboratory NLR P.O. Box 153 Emmeloord Netherlands

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Exploring nutritional supplement use for countering respiratory tract infections through an X (formerly Twitter)-based survey

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Current Research in Biotechnology 2025年 9卷

作者： Singla, Rajeev K. Mondal, Himel Singla, Shailja De, Ronita Behzad, Sahar Găman, Mihnea-Alexandru Sai Chandragiri, Siva Cenanovic, Merisa Patra, Jayanta Kumar Depew, Jennifer R. Boyina, Hemanth Kumar Maigoro, Abdulkadir Yusif Lee, Soojin Atrooz, Omar M. Das, Gitishree Schultz, Fabien Abdallah, Emad Mohamed Chopra, Hitesh Ahmad, Jamil Gautam, Rupesh K. Patnaik, Sourav S. Goh, Bey Hing Babiaka, Smith B. Vats, Sharad Okoh, Michael P. Ahmed, Atallah F. Dubey, Ankit Kumar Lordan, Ronan Subramani, Parasuraman Aiya Singh, Amit Kumar Alvarez-Suarez, José M. Chellappan, Dinesh Kumar Paswan, Shravan Kumar Semwal, Prabhakar Khan, Johra Sheshe, Sadeeq Sethiya, Neeraj Kumar Karpiński, Tomasz M. Riaz, Muhammad Emam-Djomeh, Zahra Gupta, Girish Kumar Madaan, Reecha Kumar, Suresh Choudhary, Neeraj Parisi, Salvatore Willschke, Harald Pirgozliev, Vasil Rayan, Rehab A. Ritschl, Valentin Mondal, Shaikat Zengin, Gokhan Verma, Pritt Kapoor, Bhupinder Gulati, Monica Balla, Gareeballah Osman Adam Le, Dan Khoa Pittalà, Valeria El-Demerdash, Amr Khalid, Garba M. Simal-Gandara, Jesus Alzahrani, Qushmua E. Russo, Gian Luigi Kharat, Kiran R. Bishayee, Anupam Wang, Dongdong Orhan, Ilkay Erdogan Ullah, Hammad Heinrich, Michael Baral, Bikash Tzvetkov, Nikolay T. Yeung, Andy Wai Kan Dias-Ferreira, João M.L. Olea, Scarlett Perez Mohanta, Yugal Kishore Kureshi, Azazahemad A. Supuran, Claudiu T. Rani, Neeraj Gundamaraju, Rohit Mulholland, Eoghan Joseph Lonardo, Sara Di Dinkova-Kostova, Albena T. González-Burgos, Elena Hritcu, Lucian Badhe, Pravin Singh, Abhilasha Al-Rimawi, Fuad Sureda, Antoni Abiri, Rambod Braidy, Nady Kapral, Lorenz Abdullahi, A.N. Medina, Christhian Delfino Villanueva Sheridan, Helen Lucarini, Massimo Durazzo, Alessandra Giampieri, Francesca Barreca, Davide Maria, Witkowska Anna Andrade, José Carlos Fimognari, Carmela Akram, Faizan Tikhonov, Aleksei Battino, Maurizio Oladipupo, Akolade R. Emerald, Mila Efferth, Thomas Amrani, Said Echeverría, Javier Maria Louka, Anna Tripathi, Surya Kant Fiebich, Bernd L. Es-Safi, Nour Eddine Khan Department of Critical Care Medicine and Institutes for Systems Genetics Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu China Department of Physiology All India Institute of Medical Sciences Jharkhand Deoghar India iGlobal Research and Publishing Foundation New Delhi India Evidence-based Phytotherapy and Complementary Medicine Research Center Alborz University of Medical Sciences Karaj Iran Department of Hematology Center for Clinical and Basic Research (CCBR Clinic) Bucharest Romania Department of Cellular and Molecular Pathology Stefan S. Nicolau Institute of Virology Romanian Academy Bucharest Romania Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK United States Sarajevo Bosnia and Herzegovina Research Institute of Integrative Life Sciences Dongguk University-Seoul Goyang-Si South Korea United States University Institute of Pharma Sciences Chandigarh University Punjab Mohali 140413 India Department of Microbiology and Molecular Biology College of Bioscience and Biotechnology Chungnam National University Daejeon 34134 South Korea Department of Biological Sciences Mutah University Jordan Ethnopharmacology & Zoopharmacognosy Research Group Department of Agriculture and Food Sciences Neubrandenburg University of Applied Sciences Germany Department of Biology College of Science Qassim University Buraydah 51452 Saudi Arabia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai 602105 India Department of Human Nutrition The University of Agriculture Peshawar Khyber Pakhtunkhwa 25120 Pakistan Department of Pharmacology Indore Institute of Pharmacy IIST Campus Rau (M.P.) Indore 453331 India The University of Texas Southwestern Medical Center Dallas TX United States Sunway Biofunctional Molecules Discovery Centre (SBMDC) School of Medical and Life Sciences Sunway University S

Background: Respiratory tract infections are a common health issue, driving interest in preventive strategies like nutritional supplements, while evidence on their usage and effectiveness remains limited. In this context, social media platforms, particularly X (formerly Twitter), provide a unique opportunity to gather large-scale public health-related data. Objectives: In this study, we aimed to survey participants’ uses and opinions on nutritional supplements in prevention or treatment of respiratory tract infections, by using X. Methods: A survey was conducted between 1st and 15th December 2022. A single open-ended question “Which are the best dietary supplements to counteract respiratory infections?“ was asked. One week after the start of the survey, a poll was posted to get more relevant information and boost the survey's reach. Total endorsements were calculated for each tweet posted as the total sum of replies, retweets, and likes. Results: The open-ended question received a total of 118 retweets, 39 quotes, and 371 likes, while the poll received 56 retweets, 13 quotes, and 67 likes. A total of 495 replies, 2,251 retweets, 5,118 likes, and 148 quotes were received for the question and its related tweets. Vitamin D (1,607 endorsements), zinc (1,347 endorsements), vitamin C (803 endorsements), magnesium (694 endorsements), and honey (661 endorsements) were the nutritional supplements that received most endorsements. Conclusion: Various foods, drinks, and natural ingredients have been suggested as potentially helpful for counteracting respiratory infections. Approximately half of respondents indicated using such supplements for themselves. The result of this study supports the idea that the X platform can be used as an effective survey tool to study global health-related behaviours and trends. © 2025 The Authors

关键词： Bacteria COVID-19 Nutritional supplements Phytomedicine Respiratory tract infections X (formerly Twitter)

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Mass supply from Io to Jupiter's magnetosphere

arXiv

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arXiv 2024年

作者： Roth, L. Blöcker, A. de Kleer, K. Goldstein, D. Lellouch, E. Saur, J. Schmidt, C. Strobel, D.F. Tao, C. Tsuchiya, F. Dols, V. Huybrighs, H. Mura, A. Szalay, J.R. Badman, S.V. de Pater, I. Dott, A.-C. Kagitani, M. Klaiber, L. Koga, R. McEwen, A. Milby, Z. Retherford, K.D. Schlegel, S. Thomas, N. Tseng, W.L. Vorburger, A. KTH Royal Institute of Technology Space and Plasma Physics Stockholm Sweden Department of Earth and Environmental Sciences Ludwig Maximilian University of Munich Munich Germany Division of Geological and Planetary Sciences California Institute of Technology PasadenaCA91125 United States Dept. Aerospace Engineering and Engineering Mechanics The University of Texas at Austin AustinTX United States Observatoire de Paris Meudon France Institute of Geophysics and Meteorology University of Cologne Cologne Germany Center for Space Physics Boston University BostonMA United States Departments of Earth & Planetary Science and Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States National Institute of Information and Communications Technology Koganei Japan Graduate School of Science Tohoku University Sendai Japan Institute for Space Astrophysics and Planetology National Institute for Astrophysics Italy School of Cosmic Physics DIAS Dunsink Observatory Dublin Institute for Advanced Studies Dublin15 Ireland Space and Planetary Science Center Khalifa University Abu Dhabi United Arab Emirates Department of Mathematics Khalifa University Abu Dhabi United Arab Emirates Roma Italy Department of Astrophysical Sciences Princeton University PrincetonNJ United States Department of Physics Lancaster University LancasterLA1 4YB United Kingdom Department of Astronomy Department of Earth & Planetary Science University of California BerkeleyCA94720 United States Physics Institute University of Bern Bern3012 Switzerland School of Data Science Nagoya University Aichi Nagoya464-8601 Japan Department of Planetary Sciences University of Arizona TucsonAZ United States Southwest Research Institute San AntonioTX United States University of Texas at San Antonio San AntonioTX United States Department of Earth Sciences National Taiwan Normal University Taiwan

Since the Voyager mission flybys in 1979, we have known the moon Io to be both volcanically active and the main source of plasma in the vast magnetosphere of Jupiter. Material lost from Io forms neutral clouds, the Io plasma torus and ultimately the extended plasma sheet. This material is supplied from Io’s upper atmosphere and atmospheric loss is likely driven by plasma-interaction effects with possible contributions from thermal escape and photochemistry-driven escape. Direct volcanic escape is negligible. The supply of material to maintain the plasma torus has been estimated from various methods at roughly one ton per second. Most of the time the magnetospheric plasma environment of Io is stable on timescales from days to months. Similarly, Io’s atmosphere was found to have a stable average density on the dayside, although it exhibits lateral (longitudinal and latitudinal) and temporal (both diurnal and seasonal) variations. There is potential positive feedback in the Io torus supply: collisions of torus plasma with atmospheric neutrals are probably a significant loss process, which increases with torus density. The stability of the torus environment may be maintained by limiting mechanisms of either torus supply from Io or the loss from the torus by centrifugal interchange in the middle magnetosphere. Various observations suggest that occasionally (roughly 1 to 2 detections per decade) the plasma torus undergoes major transient changes over a period of several weeks, apparently overcoming possible stabilizing mechanisms. Such events (as well as more frequent minor changes) are commonly explained by some kind of change in volcanic activity that triggers a chain of reactions which modify the plasma torus state via a net change in supply of new mass. However, it remains unknown what kind of volcanic event (if any) can trigger events in torus and magnetosphere, whether Io’s atmosphere undergoes a general change before or during such events, and what processes could

关键词： Magnetosphere

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AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT

arXiv

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arXiv 2023年

作者： Lee, Edward H. Kelly, Brendan Altinmakas, Emre Dogan, Hakan Mohammadzadeh, Maryam Colak, Errol Fu, Steve Choudhury, Olivia Ratan, Ujjwal Kitamura, Felipe Chaves, Hernan Zheng, Jimmy Said, Mourad Reis, Eduardo Lim, Jaekwang Yokoo, Patrícia Mitchell, Courtney Houshmand, Golnaz Ghassemi, Marzyeh Killeen, Ronan Qiu, Wendy Hayden, Joel Rafiee, Farnaz Klochko, Chad Bevins, Nicholas Sazgara, Faeze Wong, S. Simon Moseley, Michael Halabi, Safwan Yeom, Kristen W. School of Medicine Stanford University United States Insight Centre for Data Analytics University College Dublin Ireland Icahn School of Medicine at Mount Sinai United States Koç University School of Medicine Turkey Division of Neuroradiology Amiralam Hospital Tehran University of Medical Sciences Iran Unity Health Toronto St. Michael’s Hospital University of Toronto Canada Amazon Web Services United States Universidade Federal de São Paulo Brazil Fleni Argentina Radiology Department Centre International Carthage Médicale Monastir Tunisia Hospital Israelita Albert Einstein SP São Paulo Brazil Department of Radiology Kyungpook National University Hospital Korea Republic of Department of Radiology St. Joseph’s Hospital and Medical Center United States Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Iran Dept. of Electrical Engineering and Computer Science at MIT United States Department of Radiology Henry Ford Health System DetroitMI United States Department of Radiology Guilan University of Medical Sciences Iran Department of Electrical Engineering Stanford University United States Ann & Robert H. Lurie Children's Hospital of Chicago United States

While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We also propose an FL strategy that leverages synthetically generated data to overcome class and size imbalances. We also describe the sources of data heterogeneity in the context of FL, and show how even among the correctly labeled populations, disparities can arise due to these biases. Copyright © 2023, The Authors. All rights reserved.

关键词： Hospitals

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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-dept. 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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