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作者： Xie, Kun Wang, Lele Wang, Xin Xie, Gaogang Wen, Jigang Zhang, Guangxing Cao, Jiannong Zhang, Dafang College of Computer Science and Electronics Engineering Hunan University Changsha410006 China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony BrookNY11794 United States Network Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as capacity planning and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a 2-D matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel sequential tensor completion algorithm, which can efficiently exploit the tensor decomposition result based on the previous traffic data to derive the tensor decomposition upon arriving of new data. Furthermore, to better capture the changes of data correlation over time, we propose a dynamic sequential tensor completion algorithm. To the best of our knowledge, we are the first to propose sequential tensor completion algorithms to significantly speed up the traffic data recovery process. This facilitates the modeling of Internet traffic with the tensor to well exploit the hidden structures of traffic data for more accurate missing data inference. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithms can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high. © 1993-2012 IEEE.

关键词： Tensors

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TOI-5205 b: A Short-period Jovian Planet Transiting a Mid-M Dwarf

arXiv

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arXiv 2022年

作者： Kanodia, Shubham Mahadevan, Suvrath Libby-Roberts, Jessica Stefansson, Gudmundur Cañas, Caleb I. Piette, Anjali A.A. Boss, Alan Teske, Johanna Chambers, John Zeimann, Greg Monson, Andrew Robertson, Paul Ninan, Joe P. Lin, Andrea S.J. Bender, Chad F. Cochran, William D. Diddams, Scott A. Gupta, Arvind F. Halverson, Samuel Hawley, Suzanne Kobulnicky, Henry A. Metcalf, Andrew J. Parker, Brock A. Powers, Luke Ramsey, Lawrence W. Roy, Arpita Schwab, Christian Swaby, Tera N. Terrien, Ryan C. Wisniewski, John Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Henry Norris Russell Fellow United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Hobby Eberly Telescope University of Texas Austin AustinTX78712 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Department of Physics & Astronomy University of California Irvine IrvineCA92697 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India McDonald Observatory Department of Astronomy The University of Texas Austin United States Center for Planetary Systems Habitability The University of Texas Austin United States Electrical Computer & Energy Engineering University of Colorado 425 UCB BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Time and Frequency Division National Institute of Standards and Technology 325 Broadway BoulderCO80305 United States Jet Propulsion Laboratory 4800 Oak Grove Drive PasadenaCA91109 United States Department of Astronomy University of Washington Box 351580 SeattleWA98195 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States Space Vehicles Directorate Air Force Research Laboratory 3550 Aberdeen Ave. SE AFB KirtlandNM87117 United States Space Telescope Sc

We present the discovery of TOI-5205 b, a transiting Jovian planet orbiting a solar metallicity M4V star, which was discovered using Transiting Exoplanet Survey Satellite photometry and then confirmed using a combination of precise radial velocities, ground-based photometry, spectra, and speckle imaging. TOI-5205 b has one of the highest mass ratios for M dwarf planets with a mass ratio of almost 0.3%, as it orbits a host star that is just 0.392 ± 0.015 M☉. Its planetary radius is 1.03 ± 0.03 RJ, while the mass is 1.08 ± 0.06 MJ. Additionally, the large size of the planet orbiting a small star results in a transit depth of ∼ 7%, making it one of the deepest transits of a confirmed exoplanet orbiting a main-sequence star. The large transit depth makes TOI-5205 b a compelling target to probe its atmospheric properties, as a means of tracing the potential formation pathways. While there have been radial-velocity-only discoveries of giant planets around mid-M dwarfs, this is the first transiting Jupiter with a mass measurement discovered around such a low-mass host star. The high mass of TOI-5205 b stretches conventional theories of planet formation and disk scaling relations that cannot easily recreate the conditions required to form such planets. © 2022, CC BY.

关键词： Photometry

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An efficient quantum blind digital signature scheme

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science China(Information sciences) 2017年第8期60卷 226-239页

作者： Hong LAI Mingxing LUO Josef PIEPRZYK Zhiguo QU Shudong LI Mehmet A.ORGUN College of Computer and Information Science Southwest University Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong University School of Electrical Engineering and Computer Science Queensland University of Technology Institute of Computer Science Polish Academy of Sciences School of Computer and Software Nanjing University of Information Science and Technology College of Mathematics and Information Science Shandong Technology and Business University School of Computer Science National University of Defense Technology Department of Computing Macquarie University Faculty of Information Technology Macau University of Science and Technology

Recently, many quantum digital signature(QDS) schemes have been proposed to authenticate the integration of a message. However, these quantum signature schemes just consider the situation for bit messages,and the signing-verifying of one-bit modality. So, their signature efficiency is very low. In this paper, we propose a scheme based on an application of Fibonacci-, Lucas-and Fibonacci-Lucas matrix coding to quantum digital signatures based on a recently proposed quantum key distribution(QKD) system. Our scheme can sign a large number of digital messages every time. Moreover, these special matrices provide a method to verify the integration of information received by the participants, to authenticate the identity of the participants, and to improve the efficiency for signing-verifying. Therefore, our signature scheme is more practical than the existing schemes.

关键词： blind quantum digital signature Fibonacci- Lucas-and Fibonacci-Lucas matrix coding digital messages signing-verifying modality

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Spatial sparse subspace clustering for compressive spectral imaging

arXiv

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arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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Plasmonic antenna coupling to hyperbolic phonon-polaritons for sensitive and fast mid-infrared photodetection with graphene

arXiv

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arXiv 2020年

作者： Castilla, Sebastián Vangelidis, Ioannis Pusapati, Varun-Varma Goldstein, Jordan Autore, Marta Slipchenko, Tetiana Rajendran, Khannan Kim, Seyoon Watanabe, Kenji Taniguchi, Takashi Martín-Moreno, Luis Englund, Dirk Tielrooij, Klaas-Jan Hillenbrand, Rainer Lidorikis, Elefterios Koppens, Frank H.L. ICFO - Institut de Ciències Fotòniques Barcelona Institute of Science and Technology CastelldefelsBarcelona08860 Spain Department of Materials Science and Engineering University of Ioannina Ioannina45110 Greece Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology CambridgeMA02139 United States CIC nanoGUNE E-20018 Donostia-San Sebastián Spain Instituto de Ciencia de Materiales de Aragón Departamento de Física de la Materia Condensada CSIC-Universidad de Zaragoza Zaragoza50009 Spain Advanced Materials Laboratory National Institute for Material Science Tsukuba305-0044 Japan Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain IKERBASQUE Basque Foundation for Science Bilbao48013 Spain Institute of Materials Science and Computing Ioannina45110 Greece ICREA - Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain

Integrating and manipulating the nano-optoelectronic properties of Van der Waals heterostructures can enable unprecedented platforms for photodetection and sensing. The main challenge of infrared photodetectors is to funnel the light into a small nanoscale active area and efficiently convert it into an electrical signal. Here, we overcome all of those challenges in one device, by efficient coupling of a plasmonic antenna to hyperbolic phonon-polaritons in hexagonal-BN to highly concentrate mid-infrared light into a graphene pn-junction. We balance the interplay of the absorption, electrical and thermal conductivity of graphene via the device geometry. This novel approach yields remarkable device performance featuring room temperature high sensitivity (NEP of 82 pW/√Hz) and fast rise time of 17 nanoseconds (setup-limited), among others, hence achieving a combination currently not present in the state-of-the-art graphene and commercial mid-infrared detectors. We also develop a multiphysics model that shows excellent quantitative agreement with our experimental results and reveals the different contributions to our photoresponse, thus paving the way for further improvement of these types of photodetectors even beyond mid-infrared range. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphene

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Merging Deep Learning, Chemistry, and Diffraction for High-Throughput Material Structure Prediction

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Microscopy and Microanalysis 2019年第S2期25卷 168-169页

作者： Matthew L. Gong Brandon D. Miller Ray R. Unocic Khallid Hattar Bryan Reed Dan Masiel Tolga Tasdizen Jeffery A. Aguiar Idaho National Laboratory Nuclear Materials Department Idaho Falls IdahoUSA. University of Utah Scientific Computing Imaging Institute Department of Electrical and Computer Engineering Salt Lake City UtahUSA. Oak Ridge National Laboratory Center for Nanophase Materials Science Oak Ridge TennesseeUSA. Sandia National Laboratories Center for Integrated Nanotechnologies Albuquerque New MexicoUSA. Integrated Dynamic Electron Solutions Pleasanton CaliforniaUSA.

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A chaotic artificial bee colony algorithm based on lévy search

A chaotic artificial bee colony algorithm based on lévy sea...

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作者： Lin, Shijie Dong, Chen Wang, Zhiqiang Guo, Wenzhong Chen, Zhenyi Ye, Yin College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Key Laboratory of Information Security of Network Systems Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Department of Electrical Engineering University of South Florida TampaFL33620 United States

A Lévy search strategy based chaotic artificial bee colony algorithm (LABC) is proposed in this paper. The chaotic sequence, global optimal mechanism and Lévy flight mechanism were introduced respectively into the initialization, the employed bee search and the onlooker bee search. The experiments show that the proposed algorithm performed better in convergence speed, global search ability and optimization accuracy than other improved ABC. Copyright © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Evolutionary algorithms

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Erratum: Towards a muon collider

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The European Physical Journal C 2024年第1期84卷 1-4页

作者： Accettura, Carlotta Adams, Dean Agarwal, Rohit Ahdida, Claudia Aimè, Chiara Amapane, Nicola Amorim, David Andreetto, Paolo Anulli, Fabio Appleby, Robert Apresyan, Artur Apyan, Aram Arsenyev, Sergey Asadi, Pouya Mahmoud, Mohammed Attia Azatov, Aleksandr Back, John Balconi, Lorenzo Bandiera, Laura Barlow, Roger Bartosik, Nazar Barzi, Emanuela Batsch, Fabian Bauce, Matteo Berg, J. Scott Bersani, Andrea Bertarelli, Alessandro Bertolin, Alessandro Black, Kevin Boattini, Fulvio Bogacz, Alex Bonesini, Maurizio Bordini, Bernardo Bottaro, Salvatore Bottura, Luca Braghieri, Alessandro Breschi, Marco Bruhwiler, Natalie Buffat, Xavier Buonincontri, Laura Burrows, Philip N. Burt, Graeme Buttazzo, Dario Caiffi, Barbara Calviani, Marco Calzaferri, Simone Calzolari, Daniele Capdevilla, Rodolfo Carli, Christian Casaburo, Fausto Casarsa, Massimo Castelli, Luca Catanesi, Maria Gabriella Cavallucci, Lorenzo Cavoto, Gianluca Celiberto, Francesco Giovanni Celona, Luigi Cerri, Alessandro Cesarini, Gianmario Cesarotti, Cari Chachamis, Grigorios Chance, Antoine Chen, Siyu Chien, Yang-Ting Chiesa, Mauro Colaleo, Anna Collamati, Francesco Collazuol, Gianmaria Costa, Marco Craig, Nathaniel Curatolo, Camilla Curtin, David Da Molin, Giacomo Dam, Magnus Damerau, Heiko Dasu, Sridhara de Blas, Jorge De Curtis, Stefania De Matteis, Ernesto De Rosa, Stefania Delahaye, Jean-Pierre Denisov, Dmitri Denizli, Haluk Densham, Christopher Dermisek, Radovan Di Luzio, Luca Di Meco, Elisa Di Micco, Biagio Dienes, Keith Diociaiuti, Eleonora Dorigo, Tommaso Dudarev, Alexey Edgecock, Robert Errico, Filippo Fabbrichesi, Marco Farinon, Stefania Ferrari, Anna Somoza, Jose Antonio Ferreira Filthaut, Frank Fiorina, Davide Fol, Elena Forslund, Matthew Franceschini, Roberto Ximenes, Rui Franqueira Gabrielli, Emidio Gallinaro, Michele Garosi, Francesco Giambastiani, Luca Gianelle, Alessio Gilardoni, Simone Giove, Dario Augusto Giraldin, Carlo Glioti, Alfredo Greco, Mario Greljo, Admir Groeber, Ramona Grojean, Christophe Grudiev, Alexej Gu, Jiayin Han, Chengcheng Han, T Organisation Européenne pour la Recherche Nucléaire (CERN) Geneva Switzerland STFC Rutherford Appleton Laboratory (RAL) Oxford UK Computer Science Department University of California (UC) Berkeley USA Dipartimento di Fisica Università di Pavia Pavia Italy INFN Sezione di Pavia Pavia Italy INFN Sezione di Torino Turin Italy Dipartimento di Fisica Università di Torino Turin Italy INFN Sezione di Padova Padua Italy INFN Sezione di Roma Rome Italy School of Physics and Astronomy University of Manchester Manchester UK Fermi National Accelerator Laboratory Batavia USA Department of Physics Brandeis University Waltham USA IRFU CEA University Paris-Saclay Gif-sur-Yvette France Center for Theoretical Physics Massachusetts Institute of Technology Cambridge USA Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt SISSA International School for Advanced Studies Trieste Italy INFN Sezione di Trieste Trieste Italy Department of Physics University of Warwick Coventry UK Dipartimento di Fisica Università di Milano Milan Italy INFN Laboratori Acceleratori e Superconduttività Applicata (LASA) Milan Italy INFN Sezione di Ferrara Ferrara Italy School of Computing and Engineering The University of Huddersfield Huddersfield UK Ohio State University Columbus USA Brookhaven National Laboratory Upton USA INFN Sezione di Genova Genoa Italy University of Wisconsin Wisconsin USA Center for Advanced Studies of Accelerators Jefferson Lab Newport News USA INFN Sezione di Milano Bicocca Milan Italy Dipartimento di Fisica Università di Milano Bicocca Milan Italy Scuola Normale Superiore Pisa Italy INFN Sezione di Pisa Pisa Italy Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione Università di Bologna Bologna Italy INFN Sezione di Bologna Bologna Italy Department of Physics University of California (UC) Berkeley USA Dipartimento di Fisica e Astronomia Università di Padova Padoa Italy John Adams Institute University of O

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image computing and computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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