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arXiv 2019年

作者： Zhang, Long Zhang, Zhe Wu, Fengcheng Wang, Danqing Gogna, Rahul Hou, Shaocong Watanabe, Kenji Taniguchi, Takashi Kulkarni, Krishnamurthy Kuo, Thomas Forrest, Stephen R. Deng, Hui Physics Department University of Michigan 450 Church Street Ann ArborMI48109-2122 United States State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Condensed Matter Theory Center Joint Quantum Institute Department of Physics University of Maryland College ParkMD20742 United States Applied Physics Program University of Michigan 450 Church Street Ann ArborMI48109-1040 United States Department of Electrical Engineering and Computer Science University of Michigan 450 Church Street Ann ArborMI48109-1040 United States National Institute for Materials Science Tsukuba Japan

Moiré superlattices formed in van der Waals bilayers have enabled the creation and manipulation of new quantum states, as is exemplified by the discovery of superconducting and correlated insulating states in twisted bilayer graphene near the magic angle. Twisted bilayer semiconductors may lead to tunable exciton lattices and topological states, yet signatures of moiré excitons have been reported only in closely angularly-aligned bilayers. Here we report tuning of moiré lattice in WS2 /MoSe2 bilayers over a wide range of twist angles, leading to the continuous tuning of moiré - lattice induced interlayer excitons and their hybridization with optically bright intralayer excitons. A pronounced revival of the hybrid excitons takes place near commensurate twist angles, 21.8◦ and 38.2◦, due to interlayer tunneling between states connected by a moiré reciprocal lattice vector. From the angle dependence, we obtain the effective mass of the interlayer excitons and the electron inter-layer tunneling strength. These findings pave the way for understanding and engineering rich moiré -lattice induced phenomena in angle-twisted semiconductor van dar Waals heterostructures. Copyright © 2019, The Authors. All rights reserved.

关键词： Excitons

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Beyond trees: Classification with sparse pairwise dependencies

arXiv

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arXiv 2018年

作者： Tenzer, Yaniv Moscovich, Amit Dorn, Mary Frances Nadler, Boaz Spiegelman, Clifford Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Los Alamos National Laboratory P.O. Box 1663 MS F600 Los AlamosNM87545 United States Department of Statistics Texas A&M University College StationTX77843 United States

Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, however, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise dependencies for accurate classification, here we propose to approximate the optimal decision boundary by a sparse linear combination of the univariate and bivariate log-transformed densities. Our proposed approach is semi-parametric in nature: we non-parametrically estimate the univariate and bivariate densities, remove pairs of variables that are nearly independent using the Hilbert-Schmidt independence criteria, and finally construct a linear SVM on the retained log-transformed densities. We demonstrate using both synthetic and real data that our resulting classifier, denoted SLB (Sparse Log-Bivariate density), is competitive with popular classification *** Codes 62H30 Copyright © 2018, The Authors. All rights reserved.

关键词： Binary trees

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Learning binary latent variable models: A tensor eigenpair approach

arXiv

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arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Doctrine based multi-resolution HLA distributed simulation 17

Doctrine based multi-resolution HLA distributed simulation

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32nd Annual ACM Symposium on applied Computing, SAC 2017

作者： Paul, Ricardo L. Nunes, Raul C. Oliveira, Vanderlan D. Kunde, Diogo Computer Science Graduate Program Fed. Univ. of Santa Maria Santa Maria RS Brazil Applied Computing Dpt. Fed. Univ. of Santa Maria Santa Maria RS Brazil

ISBN: (纸本)9781450344869

In distributed simulation, the purpose of multi-resolution methods is to allow simulation integration getting consis- tent views of different resolutions. In these methods, chal- lenges in the treatment of aggregation and disaggregation can be found, such as synchronization of events and objects, and data reliability when occurs a change of resolu- tion. Doctrines are the basis for operations involving different resources (human and material) with different roles to succeed in operation, and multi-resolution in distributed simulation can take advantage of this factor to ensure the reliability of aggregation and disaggregation processes. The aim of this paper is to propose a new strategy for treatment of multi-resolution, which builds converting rules from doctrine dynamic rules. The multi-resolution converter uses dynamic rules to describe aspects of the doctrine in the ag- gregation and disaggregation processes, increasing the reliability of multi-resolution distributed simulations. This strat- egy was implemented on a High Level Architecture (HLA) federation with virtual and constructive Commercial Off- The-Shelf (COTS) simulators. The experiments showed the exibility of this strategy on managing different doctrines. © 2017 ACM.

关键词： Reliability

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Commentaries on Viewpoint: Using V̇o2max as a marker of training status in athletes - can we do better?

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Journal of applied physiology (Bethesda, Md. : 1985) 2022年第1期133卷 148-164页

作者： Pedro L Valenzuela Manuel Mateo-March Xabier Muriel Mikel Zabala Alejandro Lucia David Barranco-Gil Grégoire P Millet Franck Brocherie Johannes Burtscher Martin Burtscher Benjamin J Ryan Rachel A Gioscia-Ryan Stephane Perrey Víctor Rodrigo-Carranza Fernando González-Mohíno José María González-Ravé Jordan Santos-Concejero Benedito S Denadai Camila C Greco Arturo Casado Carl Foster Raffaele Mazzolari Giulia Nicole Baldrighi Elisa Pastorio Davide Malatesta Aurélien Patoz Fabio Borrani Stephen J Ives Justin A DeBlauw Ricardo Dantas de Lucas Fernando Klitzke Borszcz Eduardo Marcel Fernandes Nascimento Luiz Guilherme Antonacci Guglielmo Tiago Turnes Richard T Jaspers Stephan van der Zwaard Romuald Lepers Julien Louis Anderson Meireles Hiago L. R. de Souza Géssyca T de Oliveira Marcelo P dos Santos Rhaí A Arriel Moacir Marocolo B Hunter S Meyler D Muniz-Pumares Renato M Ferreira Abigail S Sogard Stephen J Carter Timothy D Mickleborough Guilherme Pereira Saborosa Raphael Dinalli de Oliveira Freitas Paula Souza Alves dos Santos João Pedro de Souza Ferreira Francisco de Assis Manoel Sandro Fernandes da Silva Christoph Triska Bettina Karsten Dajo Sanders Elliot S Lipksi David J Spindler Matthijs K. C. Hesselink Rodrigo Zacca Márcio Fagundes Goethel David Bruce Pyne Brayden M Wood Peyton E Allen Jaden L Gabelhausen Alexandra M Keller Mast T Lige Alicia S Oumsang Greg L Smart Hunter L Paris Arthur H Dewolf Guillaume Toffoli Borja Martinez-Gonzalez Samuele M Marcora Daniela Terson de Paleville Ricardo J Fernandes Susana M Soares J. Arturo Abraldes Guilherme Matta Arthur Henrique Bossi D G McCarthy W Bostad J Gibala Mary Vagula Grupo de Investigación en Actividad física y Salud (PaHerg) Instituto de Investigación Hospital 12 de Octubre (imas12) Madrid Spain Faculty of Sport Sciences Universidad Europea de Madrid Madrid Spain Sport Science Department. Universidad Miguel Hernández Elche Spain Human Performance and Sports Science Laboratory Faculty of Sport Sciences University of Murcia Murcia Spain Department of Physical Education & Sport Faculty of Sport Sciences University of Granada Granada Spain Institute of Sport Sciences University of Lausanne Lausanne Switzerland Laboratory Sport Expertise and Performance (EA 7370) French Institute of Sport (INSEP) Paris France Department of Sport Science University of Innsbruck Innsbruck Austria Thermal and Mountain Medicine Division US Army Research Institute of Environmental Medicine Natick Massachusetts Department of Anesthesiology University of Michigan Ann Arbor Michigan EuroMov Digital Health in Motion University of Montpellier Montpellier France Sport Training Lab University of Castilla-La Mancha Toledo Spain Facultad de Ciencias de la Vida y de la Naturaleza Universidad Nebrija Madrid Spain Department of Physical Education and Sport University of the Basque Country UPV/EHU Vitoria-Gasteiz Spain Human Performance Laboratory São Paulo State University Rio Claro Brazil Center for Sport Studies Rey Juan Carlos University Madrid Spain University of Wisconsin-La Crosse La Crosse Wisconsin Department of Physical Education and Sport University of the Basque Country (UPV/EHU) Vitoria-Gasteiz Spain Department of Molecular Medicine University of Pavia Pavia Italy Department of Brain and Behavioural Sciences − Medical and Genomic Statistics Unit University of Pavia Pavia Italy Department of Biomedical Sciences for Health University of Milan Milan Italy Institute of Sport Sciences of University of Lausanne (ISSUL) University of Lausanne Lausanne Switzerland Health and Human Physiological Sciences Skidmore College Saratoga Springs New

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High repetition rate (≥ kHz) targets and optics from liquid microjets for the study and application of high intensity laser-plasma interactions

arXiv

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arXiv 2019年

作者： George, K.M. Morrison, J.T. Feister, S. Ngirmang, G. Smith, J.R. Klim, A.J. Snyder, J. Austin, D. Erbsen, W. Frische, K.D. Nees, J. Orban, C. Chowdhury, E.A. Roquemore, W.M. Innovative Scientific Solutions Inc. DaytonOH45459 United States Department of Physics and Astronomy University of California Los Angeles Los AngelesCA90095 United States Computer Science Program and Applied Physics Program California State University Channel Islands CamarilloCA93012 United States Department of Physics Ohio State University ColumbusOH43210 United States Department of Mathematical and Physical Sciences Miami University HamiltonOH45011 United States Center for Ultrafast Optical Sciences University of Michigan Ann ArborMI48109 United States Intense Energy Solutions LLC. Plain CityOH43064 United States Air Force Research Laboratory WPAFB Ohio45433 United States

High intensity laser-plasma interactions produce a wide array of energetic particles and beams with promising applications. Unfortunately, high repetition rate and high average power requirements for many applications are not satisfied by the lasers, optics, targets, and diagnostics currently employed. Here, we address the need for high repetition rate targets and optics through the use of liquids. A novel nozzle assembly is used to generate high-velocity, laminar-flowing liquid microjets which are compatible with a low-vacuum environment, generate little to no debris, and exhibit precise positional and dimensional tolerances. Jets, droplets, submicron thick sheets, and other exotic configurations are characterized with pump-probe shadowgraphy to evaluate their use as targets. To demonstrate a high repetition rate, consumable, liquid optical element, we present a plasma mirror created by a submicron thick liquid sheet. This plasma mirror provides etalon-like anti-reflection properties in the low-field of 0.1% and high reflectivity as a plasma, 69%, at a repetition rate of 1 kHz. Practical considerations of fluid compatibility, in-vacuum operation, and estimates of maximum repetition rate in excess of 10 kHz are addressed. The targets and optics presented here enable the use of relativistically intense lasers at high average power and make possible many long proposed applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Beam plasma interactions

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Manifold learning for organizing unstructured sets of process observations

arXiv

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arXiv 2018年

作者： Dietrich, Felix Kooshkbaghi, Mahdi Bollt, Erik M. Kevrekidis, Ioannis G. Department of Chemical and Biomolecular Engineering Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics Department of Electrical and Computer Engineering Clarkson Center for Complex Systems Science Clarkson University PotsdamNY13699-5815 United States

Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In this paper, we use manifold learning to organize unstructured ensembles of observations ("trials") of a system's response surface. We have no control over where every trial starts;and during each trial operating conditions are varied by turning "agnostic" knobs, which change system parameters in a systematic but unknown way. As one (or more) knobs "turn" we record (possibly partial) observations of the system response. We demonstrate how such partial and disorganized observation ensembles can be integrated into coherent response surfaces whose dimension and parametrization can be systematically recovered in a data-driven fashion. The approach can be justified through the Whitney and Takens embedding theorems, allowing reconstruction of manifolds/attractors through different types of observations. We demonstrate our approach by organizing unstructured observations of response surfaces, including the reconstruction of a cusp bifurcation surface for Hydrogen combustion in a Continuous Stirred Tank Reactor. Finally, we demonstrate how this observation-based reconstruction naturally leads to informative transport maps between input parameter space and output/state variable *** codes 37M20 Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

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Author Correction: Greengenes2 unifies microbial data in a single reference tree

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Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

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The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

arXiv

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arXiv 2023年

作者： Adewole, Maruf Rudie, Jeffrey D. Gbdamosi, Anu Toyobo, Oluyemisi Raymond, Confidence Zhang, Dong Omidiji, Olubukola Akinola, Rachel Suwaid, Mohammad Abba Emegoakor, Adaobi Ojo, Nancy Aguh, Kenneth Kalaiwo, Chinasa Babatunde, Gabriel Ogunleye, Afolabi Gbadamosi, Yewande Iorpagher, Kator Calabrese, Evan Aboian, Mariam Linguraru, Marius Albrecht, Jake Wiestler, Benedikt Kofler, Florian Janas, Anastasia LaBella, Dominic Kzerooni, Anahita Fathi Li, Hongwei Bran Iglesias, Juan Eugenio Farahani, Keyvan Eddy, James Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Familiar, Ariana Van Leemput, Koen Bukas, Christina Piraud, Maire Conte, Gian-Marco Johansson, Elaine Meier, Zeke Menze, Bjoern H. Baid, Ujjwal Bakas, Spyridon Dako, Farouk Fatade, Abiodun Anazodo, Udunna C. Lagos Nigeria Department of Radiation Biology Radiotherapy and Radiodiagnosis University of Lagos Lagos Nigeria Department of Radiology University of California San Diego United States Crestview Radiology Limited Lagos Nigeria Lagos University Teaching Hospital Lagos Nigeria Lagos State University Teaching Hospital Lagos Ikeja Nigeria NSIA-Kano Diagnostic Center Kano Nigeria Nnamdi Azikiwe University Teaching Hospital Anambra State Nnewi Nigeria Federal Medical Centre Ogun State Abeokuta Nigeria Federal Medical Centre Abia State Umuahia Nigeria National Hospital Abuja FCT Nigeria Benue State University Teaching Hospital Benue State Makurdi Nigeria Duke University Medical Center Department of Radiology United States University of California San FranciscoCA United States Yale University New HavenCT United States Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States Sage Bionetworks United States Department of Neuroradiology Technical University of Munich Munich Germany Helmholtz Research Center Munich Germany Duke University Medical Center Department of Radiation Oncology United States Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States University of Zurich Switzerland Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Mayo Clinic MN United States Precision FDA U.S. Food and Drug Administration Silver SpringMD United States Booz Allen Hamilton McLeanVA United States Department of Radiology Perelman School of Medicine U

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state- of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely. © 2023, CC BY.

关键词： Tumors

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The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

arXiv

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arXiv 2023年

作者： LaBella, Dominic Adewole, Maruf Alonso-Basanta, Michelle Altes, Talissa Anwar, Syed Muhammad Baid, Ujjwal Bergquist, Timothy Bhalerao, Radhika Chen, Sully Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Godfrey, Devon Hilal, Fathi Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kent, Collin Kirkpatrick, John Koer, Florian Van Leemput, Koen Li, Hongwei Bran Liu, Xinyang Mahtabfar, Aria McBurney-Lin, Shan McLean, Ryan Meier, Zeke Moawad, Ahmed W. Mongan, John Nedelec, Pierre Pajot, Maxence Piraud, Marie Rashid, Arif Reitman, Zachary Shinohara, Russell Takeshi Velichko, Yury Wang, Chunhao Warman, Pranav Wiggins, Walter Aboian, Mariam Albrecht, Jake Anazodo, Udunna Bakas, Spyridon Flanders, Adam Janas, Anastasia Khanna, Goldey Linguraru, Marius George Menze, Bjoern Nada, Ayman Rauschecker, Andreas M. Rudie, Jeff Tahon, Nourel Hoda Villanueva-Meyer, Javier Wiestler, Benedikt Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Lab Crestview Radiology Lagos Nigeria Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Missouri University ColumbiaMO United States Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Sage Bionetworks United States University of California San Francisco CA United States Duke University Medical Center School of Medicine United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM Central Institute for Translational Cancer Research Technical University of Munich Germany Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Helmholtz Ai Helmholtz Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar Technical University of Munich Germany Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Neuroradiology Technical University of Munich Munich Germany University of Zurich Switzerland Department of Neurosurgery Thomas Jefferson Univer

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring;yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma. Copyright © 2023, The Authors. All rights reserved.

关键词： Tumors

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