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arXiv 2021年

作者： Zhang, Michael Knutson, Heather A. Wang, Lile Dai, Fei Dos Santos, Leonardo A. Fossati, Luca Henry, Gregory W. Ehrenreich, David Alibert, Yann Hoyer, Sergio Wilson, Thomas G. Bonfanti, Andrea Department of Astronomy California Institute of Technology PasadenaCA91125 United States Division of Geological and Planetary Sciences California Institute of Technology United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Observatoire astronomique de l'Université de Genève Chemin Pegasi 51 Versoix1290 Switzerland Space Research Institute Austrian Academy of Sciences Schmiedlstrasse 6 GrazA-8042 Austria Center of Excellence in Information Systems Tennessee State University NashvilleTN37209 United States Universitat Bern Switzerland Aix Marseille Univ CNRS CNES LAM Marseille France SUPA School of Physics and Astronomy University of St. Andrews North Haugh FifeKY16 9SS United Kingdom

We detect Lyman α absorption from the escaping atmosphere of HD 63433c, a R = 2:67R⊕, P = 20:5 d mini Neptune orbiting a young (440 Myr) solar analogue in the Ursa Major Moving Group. Using HST/STIS, we measure a transit depth of 11:1 - 1:5% in the blue wing and 8 ± 3% in the red. This signal is unlikely to be due to stellar variability, but should be confirmed by an upcoming second transit observation with HST. We do not detect Lyman α absorption from the inner planet, a smaller R = 2:15R⊕ mini Neptune on a 7.1 d orbit. We use Keck/NIRSPEC to place an upper limit of 0.5% on helium absorption for both planets. We measure the host star's X-ray spectrum and MUV flux with XMM-Newton, and model the outflow from both planets using a 3D hydrodynamic code. This model provides a reasonable match to the light curve in the blue wing of the Lyman α line and the helium non-detection for planet c, although it does not explain the tentative red wing absorption or reproduce the excess absorption spectrum in detail. Its predictions of strong Lyman α and helium absorption from b are ruled out by the observations. This model predicts a much shorter mass loss timescale for planet b, suggesting that b and c are fundamentally different: while the latter still retains its hydrogen/helium envelope, the former has likely lost its primordial atmosphere. Copyright © 2021, The Authors. All rights reserved.

关键词： Planets

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Social physics

arXiv

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arXiv 2021年

作者： Jusup, Marko Holme, Petter Kanazawa, Kiyoshi Takayasu, Misako Romić, Ivan Wang, Zhen Geček, Sunčana Lipić, Tomislav Podobnik, Boris Wang, Lin Luo, Wei Klanjšček, Tin Fan, Jingfang Boccaletti, Stefano Perc, Matjaž Institute of Innovative Research Tokyo Institute of Technology Tokyo152-8550 Japan Department of Physics University of Rijeka RijekaHR-51000 Croatia Faculty of Engineering Information and Systems The University of Tsukuba Tennodai Tsukuba305-8573 Japan Japan Science and Technology Agency PRESTO Kawaguchi Saitama332-0012 Japan Statistics and Mathematics College Yunnan University of Finance and Economics Kunming650221 China Northwestern Polytechnical University Xi'An710072 China Division for Marine and Environmental Research Ruđer Bošković Institute ZagrebHR-10002 Croatia Division of Electronics Ruđer Bošković Institute ZagrebHR-10002 Croatia Faculty of Civil Engineering University of Rijeka Rijeka51000 Croatia Faculty of Information Studies in Novo Mesto Novo Mesto8000 Slovenia Zagreb School of Economics and Management Zagreb10000 Croatia Luxembourg School of Business 1450 Luxembourg Department of Genetics University of Cambridge CambridgeCB2 3EH United Kingdom Department of Geography Faculty of Arts & Social Sciences National University of Singapore Singapore117570 Singapore School of Systems Science Beijing Normal University Beijing100875 China Potsdam14412 Germany CNR-Institute for Complex Systems Florence50019 Italy Universidad Rey Juan Carlos Móstoles Madrid28933 Spain Moscow Institute of Physics and Technology National Research University Moscow Region 141701 Russia Faculty of Natural Sciences and Mathematics University of Maribor Maribor2000 Slovenia Complexity Science Hub Vienna Vienna1080 Austria Department of Medical Research China Medical University Hospital China Medical University Taichung404332 Taiwan

Recent decades have seen a rise in the use of physics methods to study different societal phenomena. This development has been due to physicists venturing outside of their traditional domains of interest, but also due to scientists from other disciplines taking from physics the methods that have proven so successful throughout the 19th and the 20th century. Here we dub this field 'social physics' and pay our respect to intellectual mavericks who nurtured it to maturity. We do so by reviewing the current state of the art. Starting with a set of topics that are at the heart of modern human societies, we review research dedicated to urban development and traffic, the functioning of financial markets, cooperation as the basis for our evolutionary success, the structure of social networks, and the integration of intelligent machines into these networks. We then shift our attention to a set of topics that explore potential threats to society. These include criminal behaviour, large-scale migrations, epidemics, environmental challenges, and climate change. We end the coverage of each topic with promising directions for future research. Based on this, we conclude that the future for social physics is bright. Physicists studying societal phenomena are no longer a curiosity, but rather a force to be reckoned with. Notwithstanding, it remains of the utmost importance that we continue to foster constructive dialogue and mutual respect at the interfaces of different scientific disciplines. © 2021, CC BY-NC-ND.

关键词： Thermodynamics

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Post Quantum-Resistant Blind Signature Scheme for Consumer Internet of Things Security

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IEEE Transactions on Consumer Electronics 2025年

作者： Seno, Mohammed E. Ramesh, Janjhyam Venkata Naga Quraishi, Aadam AlGhamdi, Azzah Prasad, Kdv Nimma, Divya Rajesh, Yelisela Berdieva, Uguloy Soni, Mukesh University of Al Maarif College of Sciences Department of Computer Sciences Al Anbar31001 Iraq Graphic Era Hill University Department of CSE Dehradun248002 India Department of CSE Graphic Era Deemed to Be University India Uttarakhand Dehradun248002 India Intervention Treatment Institute HoustonTX United States Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia Symbiosis Institute of Business Management Hyderabad Pune India University of Southern Mississippi United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Dist Andhra Pradesh Vaddeswaram522302 India Tashkent State University of Economics Department of Tax and Taxation Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India Dr. D. Y. Patil School of Science and Technology Dr. D. Y. Patil Vidyapeeth Tathawade Pune India

To overcome the issue of traditional blind signature schemes being vulnerable to quantum computing attacks during interactions over the consumer electronic Internet of Things (CIoT), a new blind signature scheme called CDBS has been developed. The post quantum digital signature technique CRYSTALS-Dilithium, which was standardized by NIST, serves as the foundation for the quantum-resistant blind signature scheme, CDBS, presented in this study. This system is appropriate for a variety of internet application scenarios, including e-ticketing, anonymous payments, and privacy protection, because it integrates the idea of blind signatures, which is not included in typical digital signature schemes. This scheme is designed to resist quantum computing attacks and is based on the post-quantum digital signature algorithm CRYSTALS-Dilithium, which NIST has selected. The whole technique utilizes the Fiat-Shamir signature structure, which consists of five stages: key creation, blinding, signing, unblinding, and verification. The security of the proposed scheme is dependent on the resolution of the Module Learning with Errors (MLWE) problem and the Short Integer Solution (SIS) problem. Additionally, the scheme must satisfy the criteria of correctness, blindness, and unforgeability. According to the test results, the CRYSTALS-Dilithium scheme requires an average of 401.89 μ s for a single round of verification and signing. The CDBS technique takes an average of 657.65 ls for one round of signing and verification since the blinding and unblinding modules are added on top of the original algorithm, increasing the algorithm’s complexity. The software testing results indicate that the average time required to complete a single full blind signature and verification operation is 657.65 microseconds. The suggested system offers a framework for expanding the CRYSTALS-Dilithium digital signature algorithm. © 1975-2011 IEEE.

关键词： Qubits

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From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence

arXiv

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arXiv 2023年

作者： Oniani, David Hilsman, Jordan Peng, Yifan Poropatich, Ronald K. Pamplin, Jeremy C. Legault, Gary L. Wang, Yanshan Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Division of Pulmonary Allergy & Critical Care Medicine University of Pittsburgh PittsburghPA United States Center for Military Medicine Research University of Pittsburgh PittsburghPA United States Telemedicine & Advanced Technology Research Center US Army Fort DetrickMD United States Department of Surgery Uniformed Services University BethesdaMD United States Virtual Medical Center Brooke Army Medical Center San AntonioTX United States Intelligent Systems Program University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center Hillman Cancer Center PittsburghPA United States

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has captivated the research community, leading to debates about its application in healthcare, mainly due to concerns about transparency and related issues. Meanwhile, concerns about the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied, and decision-makers often fail to consider the significance of generative AI. In this paper, we propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding practical ethical principles that have been useful in the military to healthcare for generative AI, based on contrasting their ethical concerns and risks. We aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI in healthcare. C

关键词： Artificial intelligence

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COVID-19 collaborative modelling for policy response in the Philippines, Malaysia and Vietnam

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The Lancet Regional Health - Western Pacific 2022年 29卷 100563-100563页

作者： Hughes, Angus Ragonnet, Romain Jayasundara, Pavithra Ngo, Hoang-Anh de Lara-Tuprio, Elvira Estuar, Maria Regina Justina Teng, Timothy Robin Boon, Law Kian Peariasamy, Kalaiarasu M. Chong, Zhuo-Lin Ghazali, Izzuna Mudla M Fox, Greg J. Nguyen, Thu-Anh Le, Linh-Vi Abayawardana, Milinda Shipman, David McBryde, Emma S. Meehan, Michael T. Caldwell, Jamie M. Trauer, James M. School of Public Health and Preventive Medicine Monash University Melbourne Australia Woolcock Institute of Medical Research Hanoi Viet Nam Usher Institute The University of Edinburgh Edinburgh United Kingdom Department of Mathematics Ateneo de Manila University Manila Philippines Department of Information Systems and Computer Science Ateneo de Manila University Manila Philippines Institute for Clinical Research National Institutes of Health Ministry of Health Malaysia Kuala Lumpur Malaysia Institute for Public Health National Institutes of Health Ministry of Health Malaysia Kuala Lumpur Malaysia Malaysian Health Technology Assessment Section Medical Development Division Ministry of Health Malaysia Kuala Lumpur Malaysia Central Clinical School Faculty of Medicine and Health The University of Sydney Sydney Australia WHO Regional Office for the Western Pacific Manila Philippines Australian Institute of Tropical Health and Medicine James Cook University Townsville Australia High Meadows Environmental Institute Princeton University NJ United States

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An implementation of relay function based on LTE technology for multi-UAV communication 1st

An implementation of relay function based on LTE technology ...

引用

1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Tsai, Wen-Chung Huang, Nien-Ting Lin, Shih-Hsuan Chiang, Mao-Lun Department of Information and Communication Engineering Chaoyang University of Technology Taichung City Taiwan Aeronautical Systems Research Division National Chung-Shan Institute of Science and Technology Taichung City Taiwan

ISBN: (纸本)9783319707297

This paper presents a design of relay function for multi-UAV communication and evaluates its performance in an implemented simulation environment. The proposed relay function design is based on the popular and commonly used LTE technology. Accordingly, the implementation can be low-cost and performance-efficient. Through the use of relay function, UAVs can forward data messages among UAVs when direct communication is available between a ground console and a remote UAV. Consequently, UAVs can be dispatched to areas that cannot reach before to monitor environment and collect information, then transmit data backward to ground consoles by the proposed relay function in real time. © Springer International Publishing AG 2018.

关键词： Unmanned aerial vehicles (UAV)

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How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

arXiv

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arXiv 2024年

作者： Bunne, Charlotte Roohani, Yusuf Rosen, Yanay Gupta, Ankit Zhang, Xikun Roed, Marcel Alexandrov, Theo AlQuraishi, Mohammed Brennan, Patricia Burkhardt, Daniel B. Califano, Andrea Cool, Jonah Dernburg, Abby F. Ewing, Kirsty Fox, Emily B. Haury, Matthias Herr, Amy E. Horvitz, Eric Hsu, Patrick D. Jain, Viren Johnson, Gregory R. Kalil, Thomas Kelley, David R. Kelley, Shana O. Kreshuk, Anna Mitchison, Tim Otte, Stephani Shendure, Jay Sofroniew, Nicholas J. Theis, Fabian Theodoris, Christina V. Upadhyayula, Srigokul Valer, Marc Wang, Bo Xing, Eric Yeung-Levy, Serena Zitnik, Marinka Karaletsos, Theofanis Regev, Aviv Lundberg, Emma Leskovec, Jure Quake, Stephen R. Department of Computer Science Stanford University StanfordCA United States Genentech South San FranciscoCA United States Chan Zuckerberg Initiative Redwood CityCA United States School of Computer and Communication Sciences School of Life Sciences EPFL Lausanne Switzerland Arc Institute Palo AltoCA United States KTH Royal Institute of Technology Science for Life Laboratory Department of Protein Science Stockholm Sweden Department of Bioengineering Stanford University StanfordCA United States Department of Pharmacology University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Systems Biology Columbia University New YorkNY United States Cellarity SomervilleMA United States Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New YorkNY United States Chan Zuckerberg Biohub New YorkNY United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA United States Department of Statistics Stanford University StanfordCA United States Chan Zuckerberg Biohub San Francisco CA United States Chan Zuckerberg Institute for Advanced Biological Imaging Redwood CityCA United States Department of Bioengineering University of California BerkeleyCA United States Microsoft Research RedmondWA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Google Research Mountain ViewCA United States NewLimit San FranciscoCA United States Schmidt city United States Calico Life Sciences LLC San FranciscoCA United States Chan Zuckerberg Biohub Chicago IL United States Northwestern University EvanstonIL United States Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany Department of Systems Biology Harvard Medical School BostonMA United States Department of Genome Sciences University of Washington SeattleWA Unit

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach. © 2024, CC BY.

关键词： Virtual environments

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Semi-Parametric Kernel-Based Identification of Wiener systems 57

Semi-Parametric Kernel-Based Identification of Wiener System...

引用

57th IEEE Conference on Decision and Control, CDC 2018

作者： Sven Risuleo, Riccardo Lindsten, Fredrik Hjalmarsson, Hakan Department of Automatic Control School of Electrical Engineering and Computer Science KTH - Royal Institute of Technology Stockholm Sweden Department of Information Technology Division of Systems and Control Uppsala University Uppsala Sweden

ISBN: (纸本)9781538613955

We present a technique for kernel-based identification of Wiener systems. We model the impulse response of the linear block with a Gaussian process. The static nonlinearity is modeled with a combination of basis functions. The coefficients of the static nonlinearity are estimated, together with the hyperparameters of the covariance function of the Gaussian process model, using an iterative algorithm based on the expectation-maximization method combined with elliptical slice sampling to sample from the posterior distribution of the impulse response given the data. The same sampling method is then used to find the posterior-mean estimate of the impulse response. We test the proposed algorithm on a benchmark of randomly-generated Wiener systems. © 2018 IEEE.

关键词： Impulse response

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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990-2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

引用

LANCET INFECTIOUS DISEASES 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H. A. Abd ElHafeez, Samar Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ali Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Syed Shujait Ali Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwari, Saleha Anwar Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishna Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age-sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings In 2021, diarrhoeal diseases caused an estimated 1.17 million (95% uncertainty interval 0.793-1.62) deaths globally, representing a 60.3% (50.6-69.0) decrease since 1990 (2.93 million [2.31-3.73] deaths). The most pronounced decline was in children younger than 5 years, with a 79.2% (72.4-84.6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147-221) in 1990 to 51.4 million (39.9-65.9) in 2021. In 2021, an esti

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MammoFL: Mammographic Breast Density Estimation using Federated Learning

arXiv

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arXiv 2022年

作者： Muthukrishnan, Ramya Heyler, Angelina Katti, Keshava Pati, Sarthak Mankowski, Walter Alahari, Aprupa Sanborn, Michael Conant, Emily F. Scott, Christopher Winham, Stacey Vachon, Celine Chaudhari, Pratik Kontos, Despina Bakas, Spyridon Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Massachusetts Institute of Technology CambridgeMA United States Research and Development GSK CambridgeMA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indiana IN United States Research Center for Federated Learning in Medicine Indiana University School of Medicine Indiana IN United States University of Pennsylvania PhiladelphiaPA United States Department of Pathology & Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Health Sciences Research Mayo Clinic RochesterMN United States

In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were separately trained on algorithm-generated labels to perform segmentation of the breast and dense tissue from these images and subsequently calculate breast percent density (PD). The networks were trained with federated learning and compared to three non-federated baselines, one trained on each single-institution dataset and one trained on the aggregated multi-institution dataset. We demonstrate that training on multi-institution datasets is critical to algorithm generalizability. We further show that federated learning on multi-institutional datasets improves model generalization to unseen data at nearly the same level as centralized training on multi-institutional datasets, indicating that federated learning can be applied to our method to improve algorithm generalizability while maintaining patient privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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