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Journal of Shanghai Jiaotong university(science) 2018年第1期23卷 28-32页

作者：马鑫邓顺戈李新碗 State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering University of Michigan-Shanghai Joint Institute Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong UniversityShanghai 200240China

The deep understanding on sand and sand dunes scale can be useful to reveal the formation mechanism of the sandstorm for early sandstorm forecast. The current sandstorm observation methods are mainly based on conventional meteorological station and satellites remote sensing, which are difficult to acquire sand scale information. A wireless sensing network is implemented in the hinterland of desert, which includes ad hoc network,sensor, global positioning system(GPS) and system integration technology. The wireless network is a three-layer architecture and daisy chain topology network, which consists of control station, master robots and slave *** three robots including one master robot and its two slave robots forms an ad hoc network. Master robots directly communicate with radio base station. Information will be sent to remote information center. data sensing system including different kinds of sensors and desert robots is developed. A desert robot is designed and implemented as unmanned probing movable nodes and sensors' carrier. A new optical fiber sensor is exploited to measure vibration of sand in particular. The whole system, which is delivered to the testing field in hinterland of desert(25 km far from base station), has been proved efficient for data acquisition.

关键词： sandstorm sand-scale information optical fiber sensor desert robot wireless sensor network

The XRISM/Resolve View of the Fe K Region of Cyg X-3

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Astrophysical Journal Letters 2024年第2期977卷 L34-L34页

作者： Audard, Marc Awaki, Hisamitsu Ballhausen, Ralf Bamba, Aya Behar, Ehud Boissay-Malaquin, Rozenn Brenneman, Laura Brown, Gregory V. Corrales, Lia Costantini, Elisa Cumbee, Renata Trigo, María Díaz Done, Chris Dotani, Tadayasu Ebisawa, Ken Eckart, Megan E. Eckert, Dominique Eguchi, Satoshi Enoto, Teruaki Ezoe, Yuichiro Foster, Adam Fujimoto, Ryuichi Fujita, Yutaka Fukazawa, Yasushi Fukushima, Kotaro Furuzawa, Akihiro Gallo, Luigi García, Javier A. Gu, Liyi Guainazzi, Matteo Hagino, Kouichi Hamaguchi, Kenji Hatsukade, Isamu Hayashi, Katsuhiro Hayashi, Takayuki Hell, Natalie Hodges-Kluck, Edmund Hornschemeier, Ann Ichinohe, Yuto Ishida, Manabu Ishikawa, Kumi Ishisaki, Yoshitaka Kaastra, Jelle Kallman, Timothy Kara, Erin Katsuda, Satoru Kanemaru, Yoshiaki Kelley, Richard Kilbourne, Caroline Kitamoto, Shunji Kobayashi, Shogo Kohmura, Takayoshi Kubota, Aya Leutenegger, Maurice Loewenstein, Michael Maeda, Yoshitomo Markevitch, Maxim Matsumoto, Hironori Matsushita, Kyoko McCammon, Dan McNamara, Brian Mernier, François Miller, Eric D. Miller, Jon M. Mitsuishi, Ikuyuki Mizumoto, Misaki Mizuno, Tsunefumi Mori, Koji Mukai, Koji Murakami, Hiroshi Mushotzky, Richard Nakajima, Hiroshi Nakazawa, Kazuhiro Ness, Jan-Uwe Nobukawa, Kumiko Nobukawa, Masayoshi Noda, Hirofumi Odaka, Hirokazu Ogawa, Shoji Ogorzalek, Anna Okajima, Takashi Ota, Naomi Paltani, Stephane Petre, Robert Plucinsky, Paul Porter, Frederick S. Pottschmidt, Katja Sato, Kosuke Sato, Toshiki Sawada, Makoto Seta, Hiromi Shidatsu, Megumi Simionescu, Aurora Smith, Randall Suzuki, Hiromasa Szymkowiak, Andrew Takahashi, Hiromitsu Takeo, Mai Tamagawa, Toru Tamura, Keisuke Tanaka, Takaaki Tanimoto, Atsushi Tashiro, Makoto Terada, Yukikatsu Terashima, Yuichi Tsuboi, Yohko Tsujimoto, Masahiro Tsunemi, Hiroshi Tsuru, Takeshi Uchida, Hiroyuki Uchida, Nagomi Uchida, Yuusuke Uchiyama, Hideki Ueda, Yoshihiro Uno, Shinichiro Vink, Jacco Watanabe, Shin Williams, Brian J. Yamada, Satoshi Yamada, Shinya Yamaguchi, Hiroya Yamaoka, Kazutaka Yamasaki, Noriko Yamauchi, Makoto Yamauchi, Shig Department of Astronomy University of Geneva Versoix CH-1290 Switzerland Department of Physics Ehime University Ehime 790-8577 Japan Department of Astronomy University of Maryland College Park 20742 MD United States NASA Goddard Space Flight Center Greenbelt 20771 MD United States Center for Research and Exploration in Space Science and Technology NASA GSFC (CRESST II) Greenbelt 20771 MD United States Department of Physics University of Tokyo Tokyo 113-0033 Japan Department of Physics Technion Technion City Haifa 3200003 Israel Center for Space Sciences and Technology University of Maryland Baltimore County (UMBC) Baltimore 21250 MD United States Center for Astrophysics-Harvard-Smithsonian 02138 MA United States Lawrence Livermore National Laboratory 94550 CA United States Department of Astronomy University of Michigan 48109 MI United States SRON Netherlands Institute for Space Research Leiden Netherlands ESO Karl-Schwarzschild-Strasse 2 Garching bei München 85748 Germany Centre for Extragalactic Astronomy Department of Physics University of Durham South Road Durham DH1 3LE United Kingdom Institute of Space and Astronautical Science (ISAS) Japan Aerospace Exploration Agency (JAXA) Kanagawa 252-5210 Japan Department of Economics Kumamoto Gakuen University Kumamoto 862-8680 Japan Department of Physics Kyoto University Kyoto 606-8502 Japan Department of Physics Tokyo Metropolitan University Tokyo 192-0397 Japan Department of Physics Hiroshima University Hiroshima 739-8526 Japan Department of Physics Fujita Health University Aichi 470-1192 Japan Department of Astronomy and Physics Saint Mary's University B3H 3C3 NS Canada California Institute of Technology Pasadena 91125 CA United States European Space Agency (ESA) European Space Research and Technology Centre (ESTEC) Noordwijk 2200AG Netherlands Faculty of Engineering University of Miyazaki 1-1 Gakuen-Kibanadai-Nishi Miyazaki Miyazaki 889-2192 Japan RIKEN Nishina Ce

The X-ray binary system Cygnus X-3 (4U 2030+40, V1521 Cyg) is luminous but enigmatic owing to the high intervening absorption. High-resolution X-ray spectroscopy uniquely probes the dynamics of the photoionized gas in the system. In this Letter, we report on an observation of Cyg X-3 with the XRISM/Resolve spectrometer, which provides unprecedented spectral resolution and sensitivity in the 2-10 keV band. We detect multiple kinematic and ionization components in absorption and emission whose superposition leads to complex line profiles, including strong P Cygni profiles on resonance lines. The prominent Fe XXV Hea and Fe XXVI Lya emission complexes are clearly resolved into their characteristic fine-structure transitions. Self-consistent photoionization modeling allows us to disentangle the absorption and emission components and measure the Doppler velocity of these components as a function of binary orbital phase. We find a significantly higher velocity amplitude for the emission lines than for the absorption lines. The absorption lines generally appear blueshifted by ∼-500-600 km s^-1. We show that the wind decomposes naturally into a relatively smooth and large-scale component, perhaps associated with the background wind itself, plus a turbulent, denser structure located close to the compact object in its orbit. © 2024. The Author(s). Published by the American Astronomical Society.

关键词： High mass x-ray binary stars High resolution spectroscopy

Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

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arXiv 2020年

作者： Li, Wenhao Jin, Bo Wang, Xiangfeng Yan, Junchi Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai200062 China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a flexible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multiagent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

Why botnets work: Distributed brute-force attacks need no synchronization

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Why botnets work: Distributed brute-force attacks need no sy...

作者： Salamatian, Salman Huleihel, Wasim Beirami, Ahmad Cohen, Asaf Medard, Muriel Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering-Systems Tel-Aviv University Tel-Aviv6997801 Israel EA Digital Platform-Data and AI Electronic Arts Redwood CityCA94065 United States Department of Electrical Engineering Ben-Gurion University of the Negev Beersheba8410501 Israel

In September 2017, the McAfee Labs quarterly report estimated that brute-force attacks represent 20% of total network attacks, making them the most prevalent type of attack ex-aequo with browser-based vulnerabilities. These attacks have sometimes catastrophic consequences, and understanding their fundamental limits may play an important role in the risk assessment of password-secured systems and in the design of better security protocols. While some solutions exist to prevent online brute-force attacks that arise from one single IP address, attacks performed by botnets are more challenging. In this paper, we analyze these distributed attacks by using a simplified model. Our aim is to understand the impact of distribution and asynchronization on the overall computational effort necessary to breach a system. Our result is based on guesswork, a measure of the number of queries (guesses) before a correct sequence, such as a password, is found in an optimal attack. Guesswork is a direct surrogate for time and computational effort of guessing a sequence from a set of sequences with the associated likelihoods. We model the lack of synchronization by a worst-case optimization in which the queries made by multiple adversarial agents are received in the worst possible order for the adversary, resulting in a min-max formulation. We show that, even without synchronization, and for sequences of growing length, the asymptotic optimal performance is achievable by using randomized guesses drawn from an appropriate distribution. Therefore, randomization is key for the distributed asynchronous attacks. In other words, asynchronous guessers can asymptotically perform brute-force attacks as efficiently as synchronized guessers. © 2019 IEEE.

关键词： Botnet

Industrial topics in urban labor system

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arXiv 2020年

作者： Park, Jaehyuk Frank, Morgan R. Sun, Lijun Youn, Hyejin Kellogg School of Management Northwestern University EvanstonIL United States Northwestern Institute on Complex Systems EvanstonIL United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh PittsburgePA United States Connection Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Digital Economy Lab Institute for Human-Centered AI Stanford University StanfordCA United States Department of Civil Engineering McGill University MontrealQC Canada Department of Civil and Environmental Engineering Northwestern University EvanstonIL60208 United States

Categorization is an essential component for us to understand the world for ourselves and to communicate it collectively. It is therefore important to recognize that classification systems is not necessarily static, especially for economic systems, and even more so in urban areas where most innovation takes place and is implemented. Out-of-date classification systems would potentially limit further understanding of the current economy because things constantly change. Here, we develop an occupation-based classification system for the US labor economy, called industrial topics, that satisfy adaptability and representability. By leveraging the distributions of occupations across the US urban areas, we identify industrial topics — clusters of occupations based on their co-existence pattern. Industrial topics indicate the mechanisms under the systematic allocation of different occupations. Considering the densely connected occupations as an industrial topic, our approach characterizes regional economies by their topical composition. Unlike the existing survey-based top-down approach, our method provides timely information about the underlying structure of the regional economy, which is critical for policymakers and business leaders especially in our fast-changing economy. Copyright © 2020, The Authors. All rights reserved.

关键词： Employment

PLUMED Tutorials: a collaborative, community-driven learning ecosystem

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arXiv 2024年

作者： Tribello, Gareth A. Bonomi, Massimiliano Bussi, Giovanni Camilloni, Carlo Armstrong, Blake I. Arsiccio, Andrea Aureli, Simone Ballabio, Federico Bernetti, Mattia Bonati, Luigi Brookes, Samuel G.H. Faidon Brotzakis, Z. Capelli, Riccardo Ceriotti, Michele Chan, Kam-Tung Cossio, Pilar Dasetty, Siva Donadio, Davide Ensing, Bernd Ferguson, Andrew L. Fraux, Guillaume Gale, Julian D. Gervasio, Francesco Luigi Giorgino, Toni Herringer, Nicholas S.M. Hocky, Glen M. Hoff, Samuel E. Invernizzi, Michele Languin-Cattoën, Olivier Leone, Vanessa Limongelli, Vittorio Lopez-Acevedo, Olga Marinelli, Fabrizio Martinez, Pedro Febrer Masetti, Matteo Mehdi, Shams Michaelides, Angelos Murtada, Mhd Hussein Parrinello, Michele Piaggi, Pablo M. Pietropaolo, Adriana Pietrucci, Fabio Pipolo, Silvio Pritchard, Claire Raiteri, Paolo Raniolo, Stefano Rapetti, Daniele Rizzi, Valerio Rydzewski, Jakub Salvalaglio, Matteo Schran, Christoph Seal, Aniruddha Zadeh, Armin Shayesteh Silva, Tomás F.D. Spiwok, Vojtěch Stirnemann, Guillaume Sucerquia, Daniel Tiwary, Pratyush Valsson, Omar Vendruscolo, Michele Voth, Gregory A. White, Andrew D. Wu, Jiangbo Centre for Quantum Materials and Technology School of Mathematics and Physics Queen’s University Belfast United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Via Bonomea 265 Trieste34136 Italy Department of Biosciences University of Milano Milano Italy School of Molecular and Life Sciences Curtin University GPO Box U1987 PerthWA6845 Australia Coriolis Pharma Fraunhoferstrasse 18b Martinsried82152 Germany School of Pharmaceutical Sciences University of Geneva Rue Michel-Servet 1 GenevaCH-1206 Switzerland Institute of Pharmaceutical Sciences of Western Switzerland University of Geneva GenevaCH-1206 Switzerland Swiss Bioinformatics Institute University of Geneva GenevaCH-1206 Switzerland Via Celoria 26 Milan20133 Italy Department of Biomolecular Sciences University of Urbino "Carlo Bo" Piazza Rinascimento 6 Urbino61029 Italy Department of Pharmacy and Biotechnology Alma Mater Studiorum University of Bologna Via Belmeloro 6 Bologna40126 Italy Atomistic Simulations Italian Institute of Technology Genova Italy Yusuf Hamied Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Institute for Bioinnovation Biomedical Sciences Research Center "Alexander Fleming" Vari16672 Greece Laboratory of Computational Science and Modeling Institut des Matériaux École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Chemistry University of California Davis DavisCA95616 United States Center for Computational Mathematics Flatiron Institute New YorkNY10010 United States Center for Computational Biology Flatiron Institute New YorkNY10010 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States AI4Science Lab Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Computational Chemistry Group Van’t Hoff Institute for Molecular Sciences Unive

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software’s functionalities. To address these issues, here we introduce "PLUMED Tutorials", a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources. © 2024, CC BY.

关键词：

CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis

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arXiv 2022年

作者： Prazuch, Wojciech Suwalska, Aleksandra Socha, Marek Tobiasz, Joanna Foszner, Pawel Jaroszewicz, Jerzy Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Cieszanowski, Andrzej Nowak, Mateusz Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital of The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Imaging Diagnostics National Institute of Oncology Warsaw Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland PDepartment of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States

Background: Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. However, regardless of the numerous proposed solutions using lung X-rays, none of them is a product that can be used in the clinic. Methods: Five different datasets (clinical POLCOVID and AIforCOVID databases, a public collection of images called COVIDx set, pneumonia patients from NIH, and artificially generated data) were used to construct a representative dataset of 23 799 CXRs for model training, 1 050 images as a hold-out test set and 44 247 CXRs as independent test set (BIMCV database). A U-Net-based model was developed to identify a clinically relevant region of the CXR that contains the lungs and each class (normal, pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian mixture model. A decision tree was used to aggregate predictions from the InceptionV3 convolutional network based on processed CXRs and a dense 7-layer neural network on radiomic features determined independently for the upper, middle, and lower lung fragments. Results: The lung segmentation model gave the Sorensen-Dice coefficient of 94.86% in the validation dataset, and 93.36% in the testing dataset. In 5-fold cross-validation, the accuracy for all classes ranged from 91% to 93%, keeping slightly higher specificity than sensitivity. Also, NPV was higher than PPV for each class to guarantee a low rate of false negative cases. In the hold-out test set, the balanced accuracy ranged between 68% and 100%. The highest performance was obtained for the subtypes N1, P1, and C1, while images assigned to subtypes N3, P3, or C3 have been wrongly predicted among themselves. A similar performance was obtained on the independent dataset for normal and COVID-19 class

关键词： COVID-19

A nonparametric Bayesian framework for uncertainty quantification in stochastic simulation

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arXiv 2019年

作者： Xie, Wei Li, Cheng Wu, Yuefeng Zhang, Pu Department of Mechanical and Industrial Engineering Northeastern University Department of Statistics and Data Science National University of Singapore Department of Mathematics and Computer Science University of Missouri St. Louis United States Department of Industrial and Systems Engineering Rensselaer Polytechnic Institute

When we use simulation to assess the performance of stochastic systems, the input models used to drive simulation experiments are often estimated from finite real-world data. There exist both input model and simulation estimation uncertainties in the system performance estimates. Without strong prior information on the input models and the system mean response surface, in this paper, we propose a Bayesian nonparametric framework to quantify the impact from both sources of uncertainty. Specifically, since the real-world data often represent the variability caused by various latent sources of uncertainty, Dirichlet Processes Mixtures (DPM) based nonparametric input models are introduced to model a mixture of heterogeneous distributions, which can faithfully capture the important features of real-world data, such as multi-modality and skewness. Bayesian posteriors of flexible input models characterize the input model estimation uncertainty, which automatically accounts for both model selection and parameter value uncertainty. Then, input model estimation uncertainty is propagated to outputs by using direct simulation. Thus, under very general conditions, our framework delivers an empirical credible interval accounting for both input and simulation uncertainties. A variance decomposition is further developed to quantify the relative contributions from both sources of uncertainty. Our approach is supported by rigorous theoretical and empirical study. © 2019, CC BY.

关键词： Bayesian networks

Communications in Computer and Information Science

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Preface

Communications in Computer and Information science 2025年 2194 CCIS卷 v-vi页

作者： Singh, Mayank Tyagi, Vipin Gupta, P.K. Flusser, Jan Ören, Tuncer Cherif, Amar Ramdane Tomar, Ravi G.L. Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Academics and Research Department Jaypee University of Engineering and Technology Madhya Pradesh Raghogarh India School of Computing Department of Data Science and Engineering Mohan Babu University Andhra Pradesh Tirupati India Institute of Information Theory and Automation Prague Czech Republic School of Electrical Engineering and Computer Science OttawaON Canada University of Paris-Saclay Paris France Persistent Systems Uttarakhand Dehradun India