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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Ultrastrong magnon-magnon coupling dominated by antiresonant interactions

arXiv

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arXiv 2020年

作者： Makihara, Takuma Hayashida, Kenji Noe, G. Timothy Li, Xinwei Peraca, Nicolas Marquez Ma, Xiaoxuan Jin, Zuanming Ren, Wei Ma, Guohong Katayama, Ikufumi Takeda, Jun Nojiri, Hiroyuki Turchinovich, Dmitry Cao, Shixun Bamba, Motoaki Kono, Junichiro Department of Physics and Astronomy Rice University HoustonTX United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Division of Applied Physics Graduate School Faculty of Engineering Hokkaido University Sapporo Hokkaido060-8628 Japan Department of Physics International Center of Quantum and Molecular Structures and Materials Genome Institute Shanghai University Shanghai200444 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology 516 JunGong Road Shanghai200093 China Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama Japan Institute for Materials Research Tohoku University Sendai Japan Fakultät für Physik Universität Bielefeld Universitätsstr. 25 Bielefeld33615 Germany Department of Physics Kyoto University Sakyo-ku Kyoto606-8502 Japan PRESTO Japan Science and Technology Agency Saitama332-0012 Japan Department of Materials Science and NanoEngineering Rice University HoustonTX United States

Exotic quantum vacuum phenomena are predicted in cavity quantum electrodynamics (QED) systems with ultrastrong light-matter interactions. Their ground states are predicted to be vacuum squeezed states with suppressed quantum fluctuations. The source of such phenomena is antiresonant terms in the Hamiltonian, yet antiresonant interactions are typically negligible compared to resonant interactions in light-matter systems. We report an unusual, ultrastrongly coupled matter-matter system of magnons that is analytically described by a unique Hamiltonian in which the relative importance of resonant and antiresonant interactions can be easily tuned and the latter can be made vastly dominant. We found a novel regime where vacuum Bloch-Siegert shifts, the hallmark of antiresonant interactions, greatly exceed analogous frequency shifts from resonant interactions. Further, we theoretically explored the system's ground state and calculated up to 5.9 dB of quantum fluctuation suppression. These observations demonstrate that magnonic systems provide an ideal platform for exploring exotic quantum vacuum phenomena predicted in ultrastrongly coupled light-matter systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Hamiltonians

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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Automated speech analysis for detection of cognitive and emotional changes

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Alzheimer's & Dementia 2021年第S5期17卷

作者： Alexandra König Inez H.G.B. Ramakers Nicklas Linz Radia Zeghari Philippe Robert CoBTeK (Cognition Behaviour Technology) Research Lab University Côte d’azur Nice France National Institute for Research in Computer Science and Automation (INRIA) Sophia Antipolis France School for Mental Health & Neuroscience Maastricht Netherlands Maastricht University Maastricht Netherlands Ki elements UG Saarbrücken Germany Université Côte d’Azur Cognition Behaviour Technology Lab (CoBTeK) Nice France Association Innovation Alzheimer Nice France Association IA Nice France

Background Many aspects of language and speech seem affected with increased dementia risk. Recently speech analysis has become mature enough to provide completely automatically extracted fine-grained features highly sensitive to early cognitive and affective changes. Method Several studies were performed during which over 150 speech samples of patients at different stages (controls, Mild Cognitive Impairment, Dementia) were collected at the clinic as well as remotely (over the phone and video-conference system). Subsamples of patients with apathy and depression were included. The spoken features extracted from the recordings were compared against data from classical assessment tools and manual annotations. Result Comparison to reference measures show firstly, highly accuracies in demonstrating speech differences based on clinical diagnosis (up to 90%) and secondly, significant correlations between automatically extracted and manually annotated measures ( r = 0.9). Conclusion Speech and language features represent a promising biomarker candidate as they can be automatically and remotely extracted even over the telephone. With the help of advanced machine learning and different computational techniques the most significant markers can be identified as early indicator for screening.

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

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Digital Biomarkers 2019年第3期3卷 116-132页

作者： Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape. © 2019 The Author(s

关键词： Metadata

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The influence of high-energy local orbitals and electron-phonon interactions on the band gaps and optical spectra of hexagonal boron nitride

arXiv

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arXiv 2020年

作者： Shen, Tong Zhang, Xiao-Wei Shang, Honghui Zhang, Min-Ye Wang, Xinqiang Wang, En-Ge Jiang, Hong Li, Xin-Zheng State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontier Science Center for Nano-optoelectronics School of Physics Peking University Beijing China International Center for Quantum Materials and School of Physics Peking University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing China Collaborative Innovation Center of Quantum Matter Peking University Beijing100871 China Ceramic Division Songshan Lake Lab Institute of Physics Chinese Academy of Sciences Guangdong China School of Physics Liaoning University Shenyang China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing China Collaborative Innovation Center of Quantum Matter Peking University Beijing China

We report ab initio band diagram and optical absorption spectra of hexagonal boron nitride (h-BN), focusing on unravelling how the completeness of basis set for GW calculations and how electron-phonon interactions (EPIs) impact on them. The completeness of basis set, an issue which was seldom discussed in previous optical spectra calculations of h-BN, is found crucial in providing converged quasiparticle band gaps. In the comparison among three different codes, we demonstrate that by including high-energy local orbitals in the all-electron linearized augmented plane waves based GW calculations, the quasiparticle direct and fundamental indirect band gaps are widened by ∼0.2 eV, giving values of 6.81 eV and 6.25 eV respectively at the GW0 level. EPIs, on the other hand, reduce them to 6.62 eV and 6.03 eV respectively at 0 K, and 6.60 eV and 5.98 eV respectively at 300 K. With clamped crystal structure, the first peak of the absorption spectrum is at 6.07 eV, originating from the direct exciton contributed by electron transitions around K in the Brillouin zone. After including the EPIs-renormalized quasiparticles in the Bethe-Salpeter equation, the exciton-phonon coupling shifts the first peak to 5.83 eV at 300 K, lower than the experimental value of ∼6.00 eV. This accuracy is acceptable to an ab initio description of excited states with no fitting parameter. Copyright © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

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Comparison for the effects of different components of temperature variability on mortality: A multi-country time-series study

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Environment International 2024年 187卷 108712页

作者： Wen, Bo Wu, Yao Guo, Yuming Gasparrini, Antonio Tong, Shilu Overcenco, Ala Urban, Aleš Schneider, Alexandra Entezari, Alireza Vicedo-Cabrera, Ana Maria Zanobetti, Antonella Analitis, Antonis Zeka, Ariana Tobias, Aurelio Nunes, Baltazar Alahmad, Barrak Armstrong, Ben Forsberg, Bertil Pan, Shih-Chun Íñiguez, Carmen Ameling, Caroline Valencia, César De la Cruz Åström, Christofer Houthuijs, Danny Van Dung, Do Royé, Dominic Indermitte, Ene Lavigne, Eric Mayvaneh, Fatemeh Acquaotta, Fiorella de'Donato, Francesca Rao, Shilpa Sera, Francesco Carrasco-Escobar, Gabriel Kan, Haidong Orru, Hans Kim, Ho Holobaca, Iulian-Horia Kyselý, Jan Madureira, Joana Schwartz, Joel Jaakkola, Jouni J.K. Katsouyanni, Klea Diaz, Magali Hurtado Ragettli, Martina S. Hashizume, Masahiro Pascal, Mathilde Coélho, Micheline de Sousa Zanotti Stagliorio Ortega, Nicolás Valdés Ryti, Niilo Scovronick, Noah Michelozzi, Paola Matus Correa, Patricia Goodman, Patrick Saldiva, Paulo Hilario Nascimento Raz, Raanan Abrutzky, Rosana Osorio, Samuel Dang, Tran Ngoc Colistro, Valentina Huber, Veronika Lee, Whanhee Seposo, Xerxes Honda, Yasushi Kim, Yoonhee Guo, Yue Leon Bell, Michelle L. Li, Shanshan Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Centre for Statistical Methodology London School of Hygiene & Tropical Medicine London United Kingdom Centre on Climate Change & Planetary Health London School of Hygiene & Tropical Medicine London United Kingdom Shanghai Children's Medical Centre Shanghai Jiao Tong University Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China Center for Global Health Nanjing Medical University Nanjing China School of Public Health and Social Work Queensland University of Technology Brisbane Australia National Agency for Public Health of the Ministry of Health Labour and Social Protection of the Republic of Moldova Moldova Institute of Atmospheric Physics Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic Institute of Epidemiology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany Faculty of Geography and Environmental Sciences Hakim Sabzevari University Sabzevar Iran Institute of Social and Preventive Medicine University of Bern Bern Switzerland Oeschger Center for Climate Change Research University of Bern Bern Switzerland Department of Environmental Health Harvard T.H. Chan School of Public Health Harvard University BostonMA United States Department of Hygiene Epidemiology and Medical Statistics National and Kapodistrian University of Athens Athens Greece Institute for Environment Health and Societies Brunel University London London United Kingdom Institute of Environmental Assessment and Water Research Spanish Council for Scientific Research Barcelona Spain School of Tropical Medicine and Global Health Nagasaki University Naga

Background: Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. Objectives: We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. Methods: We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Results: Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0–7 (0.9 °C). An IQR increase in inter-day TV0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0–7 and inter-day TV0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Conclusions: Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health. © 2024

关键词： Location

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Formation Dynamics of Potassium-Based Graphite Intercalation Compounds: An Ab Initio Study

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Physical Review Applied 2018年第4期9卷 044015-044015页

作者： Xiankai Jiang Bo Song David Tománek Terahertz Technology Innovation Research Institute Shanghai Key Lab of Modern Optical System Terahertz Science Cooperative Innovation Center School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 People’s Republic of China Physics and Astronomy Department Michigan State University East Lansing Michigan 48824-2320 USA

This paper is a contribution to the Physical Review Applied collection in memory of Mildred S. Dresselhaus.

关键词： Energy storage Surface diffusion Alkali metals Batteries Carbon-based materials Energy materials Multilayer thin films Molecular dynamics

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Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]

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Medical image analysis 2025年 100卷 103363页

作者： Matthias Ivantsits Leonid Goubergrits Jan-Martin Kuhnigk Markus Huellebrand Jan Bruening Tabea Kossen Boris Pfahringer Jens Schaller Andreas Spuler Titus Kuehne Yizhuan Jia Xuesong Li Suprosanna Shit Bjoern Menze Ziyu Su Jun Ma Ziwei Nie Kartik Jain Yanfei Liu Yi Lin Anja Hennemuth Charité Universitätsmedizin Berlin Augustenburger Pl. 1 Berlin 13353 Germany. Electronic address: matthias.ivantsits@dhzc-charite.de. Charité Universitätsmedizin Berlin Augustenburger Pl. 1 Berlin 13353 Germany Einstein Center Digital Future Wilhelmstrae 67 Berlin 10117 Germany. Fraunhofer MEVIS Am Fallturm 1 Bremen 28359 Germany. Charité Universitätsmedizin Berlin Augustenburger Pl. 1 Berlin 13353 Germany. Helios Hospital Berlin-Buch Schwanebecker Chaussee 50 Berlin 13125 Germany. German Heart Centre Berlin Augustenburger Pl. 1 Berlin 13353 Germany DZHK (German Centre for Cardiovascular Research) Berlin Germany. Mediclouds Medical Technology Beijing China. School of Computer Science and Technology Beijing Institute of Technology Beijing China. Departments of Informatics Technical University Munich Germany TranslaTUM Center for Translational Cancer Research Munich Germany. TranslaTUM Center for Translational Cancer Research Munich Germany Department of Quantitative Biomedicine of UZH Zurich Switzerland. Department of Mathematics Nanjing University of Science and Technology Nanjing China. Department of Mathematics Nanjing University Nanjing China. Faculty of Engineering Technology University of Twente P.O. Box 217 Enschede 7500 AE the Netherlands. Jarvis Lab Tencent Shenzhen China Shenzhen United Imaging Research Institute of Innovative Medical Equipment Innovation Research Shenzhen China. Jarvis Lab Tencent Shenzhen China. Fraunhofer MEVIS Am Fallturm 1 Bremen 28359 Germany

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