检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Han, Bingnan Lin, Yuxuan Yang, Yafang Mao, Nannan Li, Wenyue Wang, Haozhe Fatemi, Valla Zhou, Lin Wang, Joel I-Jan Ma, Qiong Cao, Yuan Rodan-Legrain, Daniel Bie, Ya-Qing Navarro-Moratalla, Efrén Klein, Dahlia MacNeill, David Wu, Sanfeng Leong, Wei Sun Kitadai, Hikari Ling, Xi Jarillo-Herrero, Pablo Palacios, Tomás Yin, Jihao Kong, Jing Image Processing Center School of Astronautics Beihang University Beijing100191 China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemistry Boston University BostonMA02215 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Instituto de Ciencia Molecular Universidad de Valencia c/Catedrático José Beltrán 2 Paterna46980 Spain Division of Materials Science and Engineering Boston University BostonMA02215 United States

Characterization of nanomaterial morphologies with advanced microscopy and/or spectroscopy tools plays an indispensable role in nanoscience and nanotechnology research 1 2, 3, 4, 5, as rich information about the chemical compositions, crystallography, other physical and chemical properties, as well as the growth mechanism can be extracted from morphology analysis. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features are often unused because of difficulties in processing the data and finding the correlations. Such difficulties can be well addressed by deep learning 6 7, 8, 9. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural network based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can be used to predict physical properties of the materials. Finally, a transfer learning technique is applied to adapt the pretrained network to more optical characterization applications such as identifying layer numbers of chemically synthesized graphene domains. Copyright © 2019, The Authors. All rights reserved.

关键词： data handling

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A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters (vol 14, pg 352, 2018)

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NATURE CHEMICAL BIOLOGY 2018年第9期14卷 901-901页

作者： Piatkevich, Kiryl D. Jung, Erica E. Straub, Christoph Linghu, Changyang Park, Demian Suk, Ho-Jun Hochbaum, Daniel R. Goodwin, Daniel Pnevmatikakis, Eftychios Pak, Nikita Kawashima, Takashi Yang, Chao-Tsung Rhoades, Jeffrey L. Shemesh, Or Asano, Shoh Yoon, Young-Gyu Freifeld, Limor Saulnier, Jessica L. Riegler, Clemens Engert, Florian Hughes, Thom Drobizhev, Mikhail Szabo, Balint Ahrens, Misha B. Flavell, Steven W. Sabatini, Bernardo L. Boyden, Edward S. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA USA Department of Electrical Engineering and Computer Science MIT Cambridge MA USA Harvard-MIT Division of Health Sciences and Technology MIT Cambridge MA USA Simons Center Data Analysis Simons Foundation New York NY USA Department of Mechanical Engineering MIT Cambridge MA USA Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences MIT Cambridge MA USA Department of Molecular and Cellular Biology and Center for Brain Science Harvard University Cambridge MA USA Department of Neurobiology Faculty of Life Sciences University of Vienna Wien Austria Department of Cell Biology and Neuroscience Montana State University Bozeman Montana USA Department of Biological Physics Eotvos University Budapest Hungary Department of Biological Engineering MIT Cambridge MA USA MIT Center for Neurobiological Engineering MIT Cambridge MA USA Department of Brain and Cognitive Sciences MIT Cambridge MA USA MIT McGovern Institute for Brain Research MIT Cambridge MA USA

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

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NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

arXiv

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arXiv 2020年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Ahn, Namhyuk Bai, Dongwoon Cai, Jie Cao, Yun Chen, Junyang Cheng, Kaihua Chun, SeYoung Deng, Wei El-Khamy, Mostafa Man Ho, Chiu Ji, Xiaozhong Kheradmand, Amin Kim, Gwantae Ko, Hanseok Lee, Kanghyu Lee, Jungwon Li, Hao Liu, Ziluan Liu, Zhi-Song Liu, Shuai Lu, Yunhua Meng, Zibo Navarrete Michelini, Pablo Micheloni, Christian Prajapati, Kalpesh Ren, Haoyu Hyeok Seo, Yong Siu, Wan-Chi Sohn, Kyung-Ah Tai, Ying Muhammad Umer, Rao Wang, Shuangquan Wang, Huibing Haoning Wu, Timothy Wu, Haoning Yang, Biao Yang, Fuzhi Yoo, Jaejun Zhao, Tongtong Zhou, Yuanbo Zhuo, Haijie Zong, Ziyao Zou, Xueyi Wang, Li-Wen Cani, Marie-Paule Siu, Wan-Chi Yang, Huan Fu, Jianlong Shi, Yukai Chen, Junyang Lee, Kanghyu Park, Jaihyun Lee, Junyeop Min, Jeongki Lee, Bokyeung Ko, Hanseok Yoo, Jaejun Sohn, Kyung-Ah Micheloni, Christian Hu, Fengshuo Wang, Yanhong Lu, Yunhua Peng, Jinjia Wang, Huibing Zhuo, Haijie Lee, Junyeop Min, Jeongki Lee, Bokyeung Park, Jaihyun Ko, Hanseok Tai, Lab Ying Jilin Li, Youtu Lab Liu, Shuai Yang, Biao Liu, Xing Chen, Shuaijun Zhao, Lei Wang, Zhan Lin, Yuxuan Jia, Xu Gao, Qinquan Deng, Wei Kheradmand, Amin El-Khamy, Mostafa Wang, Shuangquan Bai, Dongwoon Lee, Jungwon Patel, Heena Chudasama, Vishal Upla, Kishor Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Meng, Zibo Ho, Chiu Man Computer Vision Lab ETH Zurich Switzerland LIX - Computer science laboratory at the cole polytechnique Palaiseau France Center of Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong Shanghai Jiao Tong University China Microsoft Research Beijing China Guangdong University of Technology China Intelligent Signal Processing Laboratory Korea University Korea Republic of Ajou University Korea Republic of EPFL University of Udine Italy BOE Technology Group Co. Ltd Dalian Maritime University China Guangdong OPPO Mobile Telecommunications Corp. Ltd Department of Video Information Processing Korea University Korea Republic of School of Electrical Engineering Korea University Korea Republic of Tencent Youtu Lab Yun Cao Tencent Youtu Tencent Youtu Lab Chengjie Wang Tencent Tencent Youtu Lab Feiyue Huang Tencent Youtu Lab Peking University China North China University of Technology China Huawei Technologies Co. Ltd China Fuzhou University Tong Tong Fuzhou University Imperial Vision Technology China Fuzhou University Imperial Vision Technology China Imperial Vision Technology SOC R&D Samsung Semiconductor Inc. United States Ulsan national institute of science and technology Korea Republic of Sardar Vallabhbhai National Institute Of Technology Surat India Norwegian University of Science and Technology Gjøvik Norway InnoPeak Technology

This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches w.r.t. a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Surrogate-assisted parallel tempering for Bayesian neural learning

arXiv

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arXiv 2018年

作者： Chandra, Rohitash Jain, Konark Kapoor, Arpit Centre for Translational Data Science School of Geosciences University of Sydney SydneyNSW2006 Australia School of Electrical and Electronics Engineering Indian Institute of Technology Guwahati Assam India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu India

Parallel tempering addresses some of the drawbacks of canonical Markov Chain Monte-Carlo methods for Bayesian neural learning with the ability to utilize high performance computing. However, certain challenges remain given the large range of network parameters and big data. Surrogate-assisted optimization considers the estimation of an objective function for models given computational inefficiency or difficulty to obtain clear results. We address the inefficiency of parallel tempering for large-scale problems by combining parallel computing features with surrogate assisted estimation of likelihood function that describes the plausibility of a model parameter value, given specific observed data. In this paper, we present surrogate-assisted parallel tempering for Bayesian neural learning where the surrogates are used to estimate the likelihood. The estimation via the surrogate becomes useful rather than evaluating computationally expensive models that feature large number of parameters and datasets. Our results demonstrate that the methodology significantly lowers the computational cost while maintaining quality in decision making using Bayesian neural learning. The method has applications for a Bayesian inversion and uncertainty quantification for a broad range of numerical models. Copyright © 2018, The Authors. All rights reserved.

关键词： Large dataset

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Towards the Trust-Enhancements of Single Sign-On Services

Towards the Trust-Enhancements of Single Sign-On Services

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IEEE Conference on Dependable and Secure Computing

作者： Xuhua Bao Xiaokun Zhang Jingqiang Lin Dawei Chu Qiongxiao Wang Fengjun Li Legendsec Information Technology (Beijing) Inc. CHINA Joint Laboratory of Institute of Information Engineering Chinese Academy of Sciences and QiAnXin Group Academy of Opto-Electronics Chinese Academy of Sciences State Key Laboratory of Information Security Chinese Academy of Sciences Data Assurance and Communication Security Research Center Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Department of Electrical Engineering and Computer Science University of Kansas USA

Single sign-on (SSO) becomes popular as the identity management and authentication infrastructure in the Internet. A user receives an SSO ticket after being authenticated by the identity provider (IdP), and this IdP-issued ticket enables him to sign onto the relying party (RP). However, there are vulnerabilities (e.g., Golden SAML) that allow attackers to arbitrarily issue SSO tickets and then sign onto any RP on behalf of any user. Meanwhile, several incidents of certification authorities (CAs) also indicate that the trusted third party of security services is not so trustworthy as expected, and fraudulent TLS server certificates are signed by compromised or deceived CAs to launch TLS man-in-the-middle attacks. Various approaches are then proposed to tame the absolute authority of (compromised) CAs, to detect or prevent fraudulent TLS server certificates in the TLS handshakes. The trust model of SSO services is similar to that of certificate services. So this paper investigates the defense strategies of these trust-enhancements of certificate services, and attempts to apply these strategies to SSO to derive the trust-enhancements applicable in the SSO services. Our analysis derives (a) some security designs which have been commonly-used in the SSO services or non-SSO authentication services, and (b) two schemes effectively improving the trustworthiness of SSO services, which are not widely discussed or adopted.

关键词： Servers Public key Browsers Authentication Authorization Pins

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Discovery and confirmation of the shortest gamma ray burst from a collapsar

arXiv

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arXiv 2021年

作者： Ahumada, Tomás Singer, Leo P. Anand, Shreya Coughlin, Michael W. Kasliwal, Mansi M. Ryan, Geoffrey Andreoni, Igor Cenko, S. Bradley Fremling, Christoffer Kumar, Harsh Pang, Peter T.H. Burns, Eric Cunningham, Virginia Dichiara, Simone Dietrich, Tim Svinkin, Dmitry S. Almualla, Mouza Castro-Tirado, Alberto J. De, Kishalay Dunwoody, Rachel Gatkine, Pradip Hammerstein, Erica Iyyani, Shabnam Mangan, Joseph Perley, Dan Purkayastha, Sonalika Bellm, Eric Bhalerao, Varun Bolin, Bryce Bulla, Mattia Cannella, Christopher Chandra, Poonam Duev, Dmitry A. Frederiks, Dmitry Gal-Yam, Avishay Graham, Matthew Ho, Anna Y.Q. Hurley, Kevin Karambelkar, Viraj Kool, Erik C. Kulkarni, S.R. Mahabal, Ashish Masci, Frank McBreen, Sheila Pandey, Shashi B. Reusch, Simeon Ridnaia, Anna Rosnet, Philippe Rusholme, Benjamin Carracedo, Ana Sagués Smith, Roger Soumagnac, Maayane Stein, Robert Troja, Eleonora Tsvetkova, Anastasia Walters, Richard Valeev, Azamat F. Department of Astronomy University of Maryland College ParkMD20742 United States Astrophysics Science Division NASA Goddard Space Flight Center MC 661 GreenbeltMD20771 United States Center for Research and Exploration in Space Science and Technology NASA Goddard Space Flight Center GreenbeltMD20771 United States Joint Space-Science Institute University of Maryland College ParkMD20742 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Indian Institute of Technology Bombay Powai Mumbai400076 India LSSTC Data Science Fellow Nikhef Science Park 105 Amsterdam1098 XG Netherlands Department of Physics Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Louisiana State University Baton RougeLA70803 United States Institut für Physik und Astronomie Universität Potsdam Haus 28 Karl-Liebknecht-Str. 24/25 Potsdam14476 Germany Ioffe Institute Polytekhnicheskaya 26 St. Petersburg194021 - Russia American University of Sharjah Physics Department PO Box 26666 Sharjah United Arab Emirates Glorieta de la Astronomía s/n GranadaE-18008 Spain Departamento de Ingeniería de Sistemas y Automática Escuela de Ingenieros Industriales Universidad de Málaga Unidad Asociada al CSIC C. Dr. Ortiz Ramos sn Málaga29071 Spain School of Physics University College Dublin Dublin 4 Ireland Inter-University Centre for Astronomy and Astrophysics Pune411007 India Astrophysics Research Institute Liverpool John Moores University IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom National Centre for Radio Astrophysics Tata Institute of Fundamental Research Pune University Campus Ganeshkhind Pune411 007 India DIRAC Institute Department of Physics and Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States Nordita KTH Royal Institute of

Gamma-ray bursts (GRBs) are among the brightest and most energetic events in the universe. The duration and hardness distribution of GRBs has two clusters1, now understood to reflect (at least) two different progenitors2. Short-hard GRBs (SGRBs;T90 90 >2 s) have been attributed to the collapse of peculiar massive stars (collapsars)3. The discovery of SN 1998bw/GRB 9804254 marked the first association of a LGRB with a collapsar and AT 2017gfo5/GRB 170817A/GW1708176 marked the first association of a SGRB with a binary neutron star merger, producing also gravitational wave (GW). Here, we present the discovery of ZTF20abwysqy (AT2020scz), a fast-fading optical transient in the Fermi Satellite and the InterPlanetary Network (IPN) localization regions of GRB 200826A;X-ray and radio emission further confirm that this is the afterglow. Follow-up imaging (at rest-frame 16.5 days) reveals excess emission above the afterglow that cannot be explained as an underlying kilonova (KN), but is consistent with being the supernova (SN). Despite the GRB duration being short (rest-frame T90 of 0.65 s), our panchromatic follow-up data confirms a collapsar origin. GRB 200826A is the shortest LGRB found with an associated collapsar;it appears to sit on the brink between a successful and a failed collapsar. Our discovery is consistent with the hypothesis that most collapsars fail to produce ultra-relativistic jets. © 2021, CC BY-NC-ND.

关键词： Gamma rays

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Embedded whispering-gallery mode microsphere resonator in a tapered hollow annular core fiber

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Photonics Research 2018年第12期6卷 1124-1129页

作者： JIAWEI WANG XIAOBEI ZHANG MING YAN LEI YANG FENGYU HOU WEN SUN XIAOTONG ZHANG LIBO YUAN HAI XIAO TINGYUN WANG Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University The Key Laboratory of In-Fiber Integrated Optics Ministry of Education College of Science Harbin Engineering University School of Electronic Engineering and Automation Guilin University of Electronic Technology Department of Electrical and Computer Engineering Clemson University

We propose and demonstrate a tapered hollow annular core fiber (HACF) coupler for excitation of whisperinggallery modes (WGMs) of an embedded microsphere resonator. The coupler is simply fabricated by fusion splicing of a segment of HACF with the single-mode fiber (SMF), and then improved by tapering the splicing joint to reduce the cone-apex angle. Therefore, the coupling efficiency from the SMF to the HACF is enhanced to excite various WGMs via evanescent field coupling. Normal positive, negative symmetrical Lorentzian and asymmetric Fano line shapes can be obtained by varying the resonator size and location. Another interesting phenomenon is observed that a higher Q-factor mode in a lower Q-factor mode has a contrast as high as 58. Temperature sensing with good stability is also demonstrated. This embedded WGM microsphere resonator in the tapered HACF is expected to promote environmental adaptability in practical applications due to its simplicity and

关键词： Single mode fiber (SMF) cavity Embedded WGM microsphere resonator

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Dynamical Sampling with Additive Random Noise

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Sampling Theory, Signal processing, and data Analysis 2018年第2期17卷 153-182页

作者： Akram Aldroubi Longxiu Huang Ilya Krishtal Akos Ledeczi Roy R. Lederman Peter Volgyesi Department of Mathematics Vanderbilt University Nashville USA Department of Mathematics Northern Illinois University DeKalb USA Department of Electrical Engineering and Computer Science Vanderbilt University Nashville USA Department of Statistics and Data Science Yale University New Haven USA Institute for Software Integrated Systems Vanderbilt University Nashville USA

Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution.

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Ultrahigh-pressure form of SiO2 glass with dense pyrite-type crystalline homology

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Physical Review B 2019年第4期99卷 045153-045153页

作者： M. Murakami S. Kohara N. Kitamura J. Akola H. Inoue A. Hirata Y. Hiraoka Y. Onodera I. Obayashi J. Kalikka N. Hirao T. Musso A. S. Foster Y. Idemoto O. Sakata Y. Ohishi Department of Earth Sciences ETH Zürich Zürich 8092 Switzerland Department of Earth and Planetary Materials Science Tohoku University Sendai 980-8578 Japan Research Center for Advanced Measurement and Characterization National Institute for Materials Science (NIMS) Hyogo 679-5148 Japan Center for Materials research by Information Integration (CMI2) Research and Services Division of Materials Data and Integrated System (MaDIS) NIMS Ibaraki 305-0047 Japan Research and Utilization Division Japan Synchrotron Radiation Research Institute Hyogo 679-5198 Japan School of Materials Science Japan Advanced Institute of Science and Technology Ishikawa 923-1211 Japan PRESTO Japan Science and Technology Agency Tokyo 102-0076 Japan Department of Pure and Applied Chemistry Faculty of Science and Technology Tokyo University of Science Chiba 278-8510 Japan Department of Physics Tampere University of Technology FI-33101 Tampere Finland Department of Physics Norwegian University of Science and Technology NO-7481 Trondheim Norway Institute of Industrial Science The University of Tokyo Tokyo 153-8505 Japan Graduate School of Fundamental Science and Engineering Waseda University Tokyo 169-8555 Japan Kagami Memorial Research Institute for Materials Science and Technology Waseda University Tokyo 169-0051 Japan Mathematics for Advanced Materials-OIL AIST Sendai 980-8577 Japan WPI Advanced Institute for Materials Research Tohoku University Sendai 980-8577 Japan Kyoto University Institute for Advanced Study Kyoto University Kyoto 606-8501 Japan Center for Advanced Intelligence Project RIKEN Tokyo 103-0027 Japan Institute for Integrated Radiation and Nuclear Science Kyoto University Osaka 590-0494 Japan Department of Applied Physics Aalto University FI-00076 Aalto Finland Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kanazawa 920-1192 Japan

High-pressure synthesis of denser glass has been a longstanding interest in condensed-matter physics and materials science because of its potentially broad industrial application. Nevertheless, understanding its nature under extreme pressures has yet to be clarified due to experimental and theoretical challenges. Here we reveal the formation of OSi4 tetraclusters associated with that of SiO7 polyhedra in SiO2 glass under ultrahigh pressures to 200 gigapascal confirmed both experimentally and theoretically. Persistent homology analyses with molecular dynamics simulations found increased packing fraction of atoms whose topological diagram at ultrahigh pressures is similar to a pyrite-type crystalline phase, although the formation of tetraclusters is prohibited in the crystalline phase. This critical difference would be caused by the potential structural tolerance in the glass for distortion of oxygen clusters. Furthermore, an expanded electronic band gap demonstrates that chemical bonds survive at ultrahigh pressure. This opens up the synthesis of topologically disordered dense oxide glasses.

关键词： Chemical bonding Density of states Electronic structure Phase transitions Pressure effects Specific phase transitions Structural properties Topological phase transition Topological phases of matter Density functional theory Molecular dynamics Wannier function methods X-ray diffraction X-ray powder diffraction

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Publisher Correction: Brain imaging with portable low-field MRI

Nature Reviews Bioengineering

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Nature Reviews Bioengineering 2023年 680页

作者： W. Taylor Kimberly Annabel J. Sorby-Adams Andrew G. Webb Ed X. Wu Rachel Beekman Kevin N. Sheth Ritvij Bowry Steven J. Schiff Adam de Havenon Francis X. Shen Gordon Sze Pamela Schaefer Juan Eugenio Iglesias Matthew S. Rosen Department of Neurology and the Center for Genomic Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Department of Radiology Leiden University Medical Center Leiden The Netherlands Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China Division of Neurocritical Care and Emergency Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine Yale Center for Brain & Mind Health New Haven CT USA Departments of Neurosurgery and Neurology McGovern Medical School University of Texas Health Neurosciences Houston TX USA Department of Neurosurgery Yale School of Medicine New Haven CT USA Division of Vascular Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Harvard Medical School Center for Bioethics Harvard law School Boston MA USA Department of Psychiatry Massachusetts General Hospital Boston MA USA Department of Radiology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Division of Neuroradiology Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Centre for Medical Image Computing University College London London UK Computer Science and AI Laboratory Massachusetts Institute of Technology Boston MA USA

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