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Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

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arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Sadeghi, Delaram Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Hussain, Sadiq Zare, Assef Sani, Zahra Alizadeh Khozeimeh, Fahime Nahavandi, Saeid Rajendra Acharya, U. Gorriz, Juan M. The Data Science and Computational Intelligence Institute University of Granada Spain The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Deakin University VIC3217 Australia The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran System Administrator at Dibrugarh University Assam786004 India Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran The School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Dept. of Psychiatry University of Cambridge United Kingdom

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed. © 2020, CC BY-NC-ND.

关键词： COVID-19

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

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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

Sampling Free TDOA Localization in Millimeter Wave Networks

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arXiv 2019年

作者： Shree Prasad, M. Panigrahi, Trilochan Hassan, Mahbub Ding, Ming Dept. of Electronics and Communication Engineering National Institute of Technology Goa School of Computer Science and Engineering University of New South Wales Sydney Australia Data61 Csiro Sydney Australia

Time difference of arrival (TDOA) is a widely used technique for localizing a radio transmitter from the difference in signal arrival times at multiple receivers. For TDOA to work, the individual receivers must estimate the respective signal arrival times precisely, which requires sampling the signal at least double the rate of its highest frequency content, commonly known as the Nyquist rate. Such sampling is less practical for the millimeter wave band comprising of frequencies in 30-300 GHz range. In this paper, we propose an energy detection architecture for accurately estimating the time of arrival from a single picosecond Gaussian pulse, which enables TDOA localization without sampling at the receiver. We derive the closed form expression of the estimated time of arrival and validate it via simulation. We demonstrate that the proposed sampling-free TDOA can localize millimeter wave transmitters as accurately as the conventional samplingbased TDOA. Copyright © 2019, The Authors. All rights reserved.

关键词： Time difference of arrival

An OpenMetBuoy dataset of Marginal Ice Zone dynamics collected around Svalbard in 2022 and 2023

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arXiv 2024年

作者： Rabault, Jean Taelman, Catherine Idžanović, Martina Hope, Gaute Nose, Takehiko Kristoffersen, Yngve Jensen, Atle Breivik, Øyvind Bryhni, Helge Thomas Hoppmann, Mario Demchev, Denis Korosov, Anton Johansson, Malin Eltoft, Torbjørn Dagestad, Knut-Frode Röhrs, Johannes Eriksson, Leif Moro, Marina Durán Rikardsen, Edel S.U. Waseda, Takuji Kodaira, Tsubasa Lohse, Johannes Desjonquères, Thibault Olsen, Sveinung Gundersen, Olav de Aguiar, Victor Cesar Martins Karlsen, Truls Babanin, Alexander Voermans, Joey Park, Jeong-Won Müller, Malte Norwegian Meteorological Institute IT Department Oslo0313 Norway UiT The Arctic University of Norway Dept. of Physics and Technology Tromsø9037 Norway Norwegian Meteorological Institute Division for Ocean and Ice Oslo0371 Norway Norwegian Meteorological Institute Division for Oceanography and Marine Meteorology Bergen5007 Norway The University of Tokyo Graduate School of Frontier Sciences Kashiwa277-8561 Japan University of Bergen Department of Earth Science Bergen5007 Norway University of Oslo Department of Mathematics Oslo0313 Norway University of Bergen Geophysical Institute Bergen5007 Norway Alfred-Wegener-Institut Helmholtz-Zentrum fuer Polar- und Meeresforschung Bremerhaven27580 Germany Department of Space Earth and Environment Chalmers University of Technology Gothenburg41296 Sweden Ocean and Sea Ice Remote Sensing Group Nansen Environmental and Remote Sensing Center Bergen5006 Norway Norwegian Meteorological Institute Remote sensing and data management division Oslo Norway University of Oslo Department of Geosciences Oslo0313 Norway Japan Agency for Marine-Earth Science and Technology Yokosuka237-0061 Japan Gothenburg University Gothenburg40508 Sweden Department of Infrastructure Engineering University of Melbourne Melbourne Australia Center of Remote Sensing and GIS Korea Polar Research Institute Incheon21990 Korea Republic of Norwegian Meteorological institute R&D Department Oslo0313 Norway

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms. © 2024, CC BY.

关键词： Water waves

Using persistent homology to quantify a diurnal cycle in hurricanes

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arXiv 2019年

作者： Tymochko, Sarah Munch, Elizabeth Dunion, Jason Corbosiero, Kristen Torn, Ryan Michigan State University Dept. of Computational Mathematics Science and Engineering Michigan State University Dept. of Mathematics Cooperative Institute for Marine and Atmospheric Studies University of Miami Hurricane Research Division NOAA/Atlantic Oceanographic and Meteorological Laboratory University at Albany SUNY Albany Dept. of Atmospheric and Environmental Sciences

The diurnal cycle of tropical cyclones (TCs) is a daily cycle in clouds that appears in satellite images and may have implications for TC structure and intensity. The diurnal pattern can be seen in infrared (IR) satellite imagery as cyclical pulses in the cloud field that propagate radially outward from the center of nearly all Atlantic-basin TCs. These diurnal pulses, a distinguishing characteristic of this diurnal cycle, begin forming in the storm's inner core near sunset each day, appearing as a region of cooling cloud-top temperatures. The area of cooling takes on a ring-like appearance as cloud-top warming occurs on its inside edge and the cooling moves away from the storm overnight, reaching several hundred kilometers from the circulation center by the following afternoon. The state-of-the-art TC diurnal cycle measurement in IR satellite imagery has a limited ability to analyze the behavior beyond qualitative observations. We present a method for quantifying the TC diurnal cycle using one-dimensional persistent homology, a tool from Topological data Analysis, by tracking maximum persistence and quantifying the cycle using the discrete Fourier transform. Using Geostationary Operational Environmental Satellite IR imagery from Hurricanes Felix and Ivan, our method is able to detect an approximate daily cycle. Copyright © 2019, The Authors. All rights reserved.

关键词： Hurricanes

A Model for Recommending Actions during the Collaborative Design of Visual Analytics Flows 4

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A Model for Recommending Actions during the Collaborative De...

4th International Conference on Information Systems and Computer science, INCISCOS 2019

作者： Arce-Orozco, Armando Gonzalez-Torres, Antonio Mata-Montero, Erick Bener, Ayse Jara-Watson, Hans School of Computing Costa Rica Institute of Technology Cartago Costa Rica Costa Rica School of Computing National University Heredia Costa Rica Costa Rica Dept. of Computer Engineering Costa Rica Institute of Technology Cartago Costa Rica School of Computing ULACIT Costa Rica Data Science Lab Ryerson University Toronto Ontario Canada

ISBN: (纸本)9781728155814

Visual Analytics (VA) is a process to facilitate the uncovering of knowledge from large and complex datasets to enable users to make informed decisions. However, the design of VA tools is frequently performed by multidisciplinary teams that can be physically in the same place or distributed across different locations. Therefore, there is an intrinsic collaboration need between team members and also of proposing methods to support these teams to work collaboratively during the design and implementation of VA systems. Although some proposals have made on the use of visual data flows and recommender systems, its combination with a Multi-Device Environment can benefit the discussion and exchange of knowledge during design and programming. Therefore, this article presents the design of an architecture that combines a Multi-Device Environment (MDE) with visual data flows and recommender systems for the design of VA tools in a collaborative way. The intention is to support designers and programmers during the design of visual data flows through the sharing and analysis of ideas in a collaborative environment supported by a recommender system that aids decision making during the design of flows. © 2019 IEEE.

关键词： Recommender systems

Comparative Performance Evaluation of High-performance data Transfer Tools 12

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Comparative Performance Evaluation of High-performance Data ...

12th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2018

作者： Nadig, Deepak Jung, Eun-Sung Kettimuthu, Rajkumar Fosterz, Ian Nageswara Rao, S.V. Ramamurthy, Byrav Dept. of Computer Science Engineering University of Nebraska-Lincoln United States Dept. of Computer and Information Communication Hongik University Korea Republic of Data Science and Learning Division Argonne National Laboratory United States Dept. of Computer Science University of Chicago United States Computer Science and Mathematics Division Oak Ridge National Laboratory United States

ISBN: (纸本)9781538681343

data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However, few studies have shown the full capabilities of existing data transfer tools from the perspective of whether such tools have fully adopted state-of-the-art techniques through meticulous comparative evaluations. In this paper, we evaluate the performance of the four highperformance data transfer tools (GridFTP, FDT, mdtmFTP, and XDD) in various environments. Our evaluation suggests that each tool has strengths and weaknesses. FDT and GridFTP perform consistently in diverse environments. XDD and mdtmFTP show improved performance in limited environments and datasets during our evaluation. Unlike other studies on data transfer tools, we also evaluate the predictability of the tools' performance, an important factor for scheduling different stages of science workflows. Performance predictability also helps in (auto)tuning the configurable parameters of the data transfer tool. We apply statistical learning techniques such as linear/polynomial regression, and k-nearest neighbors (kNN), to assess the performance predictability of each tool using its control parameters. Our results show that we can achieve good prediction performance for GridFTP and mdtmFTP using linear regression and kNN, respectively. © 2018 IEEE.

关键词： data transfer

On the design rationale of simon block cipher: Integral attacks and impossible differential attacks against simon variants

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On the design rationale of simon block cipher: Integral atta...

作者： Kondo, Kota Sasaki, Yu Todo, Yosuke Iwata, Tetsu Dept. of Computational Science and Engineering Nagoya University Nagoya-shi464-8603 Japan NTT Secure Platform Laboratories NTT Corporation Musashino-shi180-8585 Japan Dept. of Information and Communication Engineering Nagoya University Nagoya-shi464-8603 Japan

Simon is a lightweight block cipher designed by NSA in 2013. NSA presented the specification and the implementation efficiency, but they did not provide detailed security analysis nor the design rationale. The original Simon has rotation constants of (1;8;2), and Kölbl et al. regarded the constants as a parameter (a;b;c), and analyzed the security of Simon block cipher variants against differential and linear attacks for all the choices of (a;b;c). This paper complements the result of Kölbl et al. by considering integral and impossible differential attacks. First, we search the number of rounds of integral distinguishers by using a supercomputer. Our search algorithm follows the previous approach byWang et al., however, we introduce a new choice of the set of plaintexts satisfying the integral property. We show that the new choice indeed extends the number of rounds for several parameters. We also search the number of rounds of impossible differential characteristics based on the miss-in-the-middle approach. Finally, we make a comparison of all parameters from our results and the observations by Kölbl et al. Interesting observations are obtained, for instance we find that the optimal parameters with respect to the resistance against differential attacks are not stronger than the original parameter with respect to integral and impossible differential attacks. Furthermore, we consider the security against differential attacks by considering differentials. From the result, we obtain a parameter that is potential to be better than the original parameter with respect to security against these four attacks. © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Supercomputers

αs from the QCD static energy

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αs from the QCD static energy

2019 Alphas Workshop on Precision Measurements of the QCD Coupling Constant, ALPHAS 2019

作者： Brambilla, Nora Bazavov, Alexei Garcia i Tormo, Xavier Petreczky, Péter Soto, Joan Vairo, Antonio Weber, Johannes Heinrich Physik Department Technische Universität München GarchingD-85748 Germany Dept. of Computational Mathematics Science and Engineering Dept. of Physics and Astronomy Michigan State University East LansingMI48824 United States Albert Einstein Center for Fundamental Physics. Institut für Theoretische Physik Universität Bern Sidlerstrasse 5 BernCH-3012 Switzerland Physics Department Brookhaven National Laboratory UptonNY11973 United States Dept. d’Estructura i Constituents de la Matèria and Institut de Ciències del Cosmos Universitat de Barcelona Barcelona Catalonia08028 Spain

We present our latest determination of the strong coupling constant αs from the Quantum Chro-modynamics static energy: αs(mZ) = 0.1166+−0000080012, extracted at three loops with leading ultrasoft log resummation. The determination is based on a combination of lattice data on the static energy at small quark-antiquark distance and perturbative high-order calculations of the static energy for small quark-antiquark distance. We discuss further improvements from an upcoming extraction based on new lattice data, at smaller lattice spacings reaching shorter distances, and on lattice data on the singlet free energy at finite temperature at very small distances. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Free energy