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Search for Gamma-Ray Spectral Lines from Dark Matter Annihilation up to 100 TeV toward the Galactic Center with MAGIC

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Physical Review Letters 2023年第6期130卷 061002-061002页

作者： H. Abe S. Abe V. A. Acciari T. Aniello S. Ansoldi L. A. Antonelli A. Arbet Engels C. Arcaro M. Artero K. Asano D. Baack A. Babić A. Baquero U. Barres de Almeida J. A. Barrio I. Batković J. Baxter J. Becerra González W. Bednarek E. Bernardini M. Bernardos A. Berti J. Besenrieder W. Bhattacharyya C. Bigongiari A. Biland O. Blanch G. Bonnoli Ž. Bošnjak I. Burelli G. Busetto R. Carosi M. Carretero-Castrillo G. Ceribella Y. Chai A. Chilingarian S. Cikota E. Colombo J. L. Contreras J. Cortina S. Covino G. D’Amico V. D’Elia P. Da Vela F. Dazzi A. De Angelis B. De Lotto A. Del Popolo M. Delfino J. Delgado C. Delgado Mendez D. Depaoli F. Di Pierro L. Di Venere E. Do Souto Espiñeira D. Dominis Prester A. Donini D. Dorner M. Doro D. Elsaesser G. Emery V. Fallah Ramazani L. Fariña A. Fattorini L. Font C. Fruck S. Fukami Y. Fukazawa R. J. García López M. Garczarczyk S. Gasparyan M. Gaug J. G. Giesbrecht Paiva N. Giglietto F. Giordano P. Gliwny N. Godinović J. G. Green D. Green D. Hadasch A. Hahn T. Hassan L. Heckmann J. Herrera D. Hrupec M. Hütten R. Imazawa T. Inada R. Iotov K. Ishio I. Jiménez Martínez J. Jormanainen D. Kerszberg Y. Kobayashi H. Kubo J. Kushida A. Lamastra D. Lelas F. Leone E. Lindfors L. Linhoff S. Lombardi F. Longo R. López-Coto M. López-Moya A. López-Oramas S. Loporchio A. Lorini E. Lyard B. Machado de Oliveira Fraga P. Majumdar M. Makariev G. Maneva N. Mang M. Manganaro S. Mangano K. Mannheim M. Mariotti M. Martínez A. Mas Aguilar D. Mazin S. Menchiari S. Mender S. Mićanović D. Miceli T. Miener J. M. Miranda R. Mirzoyan E. Molina H. A. Mondal A. Moralejo D. Morcuende V. Moreno T. Nakamori C. Nanci L. Nava V. Neustroev M. Nievas Rosillo C. Nigro K. Nilsson K. Nishijima T. Njoh Ekoume K. Noda S. Nozaki Y. Ohtani T. Oka J. Otero-Santos S. Paiano M. Palatiello D. Paneque R. Paoletti J. M. Paredes L. Pavletić M. Persic M. Pihet F. Podobnik P. G. Prada Moroni E. Prandini G. Principe C. Priyadarshi I. Puljak W. Rhode M. Ribó J. Rico C. Righi A. Rugliancich N. Sahakyan T. Saito S. Sakurai K. Satalecka F. G. Sat Japanese MAGIC Group: Institute for Cosmic Ray Research (ICRR) The University of Tokyo Kashiwa 277-8582 Chiba Japan Instituto de Astrofísica de Canarias and Departamento de Astrofísica Universidad de La Laguna E-38200 La Laguna Tenerife Spain National Institute for Astrophysics (INAF) I-00136 Rome Italy Università di Udine and INFN Trieste I-33100 Udine Italy Max-Planck-Institut für Physik D-80805 München Germany Università di Padova and INFN I-35131 Padova Italy Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology (BIST) E-08193 Bellaterra (Barcelona) Spain Technische Universität Dortmund D-44221 Dortmund Germany Croatian MAGIC Group: University of Zagreb Faculty of Electrical Engineering and Computing (FER) 10000 Zagreb Croatia IPARCOS Institute and EMFTEL Department Universidad Complutense de Madrid E-28040 Madrid Spain Centro Brasileiro de Pesquisas Físicas (CBPF) 22290-180 URCA Rio de Janeiro (RJ) Brazil University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics 90-236 Lodz Poland Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n 18008 Granada Spain Deutsches Elektronen-Synchrotron (DESY) D-15738 Zeuthen Germany ETH Zürich CH-8093 Zürich Switzerland Università di Pisa and INFN Pisa I-56126 Pisa Italy Universitat de Barcelona ICCUB IEEC-UB E-08028 Barcelona Spain Armenian MAGIC Group: A. Alikhanyan National Science Laboratory 0036 Yerevan Armenia Centro de Investigaciones Energéticas Medioambientales y Tecnológicas E-28040 Madrid Spain Department for Physics and Technology University of Bergen Norway INFN MAGIC Group: INFN Sezione di Catania and Dipartimento di Fisica e Astronomia University of Catania I-95123 Catania Italy INFN MAGIC Group: INFN Sezione di Torino and Università degli Studi di Torino I-10125 Torino Italy INFN MAGIC Group: INFN Sezione di Bari and Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari I-70125 Bari Italy

Linelike features in TeV γ rays constitute a “smoking gun” for TeV-scale particle dark matter and new physics. Probing the Galactic Center region with ground-based Cherenkov telescopes enables the search for TeV spectral features in immediate association with a dense dark matter reservoir at a sensitivity out of reach for satellite γ-ray detectors, and direct detection and collider experiments. We report on 223 hours of observations of the Galactic Center region with the MAGIC stereoscopic telescope system reaching γ-ray energies up to 100 TeV. We improved the sensitivity to spectral lines at high energies using large-zenith-angle observations and a novel background modeling method within a maximum-likelihood analysis in the energy domain. No linelike spectral feature is found in our analysis. Therefore, we constrain the cross section for dark matter annihilation into two photons to ⟨σv⟩≲5×10−28 cm3 s−1 at 1 TeV and ⟨σv⟩≲1×10−25 cm3 s−1 at 100 TeV, achieving the best limits to date for a dark matter mass above 20 TeV and a cuspy dark matter profile at the Galactic Center. Finally, we use the derived limits for both cuspy and cored dark matter profiles to constrain supersymmetric wino models.

关键词： Dark matter Gamma ray astronomy Particle dark matter Milky Way Cosmic ray & astroparticle detectors Cosmic rays & astroparticles Electromagnetic radiation astronomy Telescopes

Optimal verification and fidelity estimation of maximally entangled states

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Physical Review A 2019年第5期99卷 052346-052346页

作者： Huangjun Zhu Masahito Hayashi Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 China State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China Graduate School of Mathematics Nagoya University Nagoya 464-8602 Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore

We study the verification of maximally entangled states by virtue of the simplest measurement settings: local projective measurements without adaption. We show that optimal protocols are in one-to-one correspondence with complex projective 2-designs constructed from orthonormal bases. Optimal protocols with minimal measurement settings are in one-to-one correspondence with complete sets of mutually unbiased bases. Based on this observation, optimal protocols are constructed explicitly for any local dimension, which can also be applied to estimating the fidelity with the target state and to detecting entanglement. In addition, we show that incomplete sets of mutually unbiased bases are optimal for verifying maximally entangled states when the number of measurement settings is restricted. Moreover, we construct optimal protocols for the adversarial scenario in which state preparation is not trusted. The number of tests has the same scaling behavior as the counterpart for the nonadversarial scenario; the overhead is no more than three times. We also show that the entanglement of the maximally entangled state can be certified with any given significance level using only one test as long as the local dimension is large enough.

关键词： Quantum entanglement Quantum tomography

Laser-Seeding Attack in Quantum Key Distribution

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Physical Review Applied 2019年第6期12卷 064043-064043页

作者： Anqi Huang Álvaro Navarrete Shi-Hai Sun Poompong Chaiwongkhot Marcos Curty Vadim Makarov Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People’s Republic of China Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada N2L 3G1 EI Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo E-36310 Spain School of Physics and Astronomy Sun Yat-Sen University Zhuhai 519082 People’s Republic of China Department of Physics and Astronomy University of Waterloo Waterloo Ontario Canada N2L 3G1 Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia

Quantum key distribution (QKD) based on the laws of quantum physics allows the secure distribution of secret keys over an insecure channel. Unfortunately, imperfect implementations of QKD compromise its information-theoretical security. Measurement-device-independent quantum key distribution (MDI QKD) is a promising approach to remove all side channels from the measurement unit, which is regarded as the “Achilles’ heel” of QKD. An essential assumption in MDI QKD is, however, that the sources are trusted. Here we experimentally demonstrate that a practical source based on a semiconductor laser diode is vulnerable to a laser-seeding attack, in which light injected from the communication line into the laser results in an increase of the intensities of the prepared states. The unnoticed increase of intensity may compromise the security of QKD, as we show theoretically for the prepare-and-measure decoy-state BB84 and MDI QKD protocols. Our theoretical security analysis is general and can be applied to any vulnerability that increases the intensity of the emitted pulses. Moreover, a laser-seeding attack might be launched as well against decoy-state-based quantum cryptographic protocols beyond QKD.

关键词： Optical quantum information processing Optoelectronics Photonics Quantum algorithms Quantum communication Quantum cryptography Semiconductor quantum optics Photodiodes

Presenting unbinned differential cross section results

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arXiv 2021年

作者： Arratia, Miguel Butter, Anja Campanelli, Mario Croft, Vincent Gillberg, Dag Ghosh, Aishik Lohwasser, Kristin Malaescu, Bogdan Mikuni, Vinicius Nachman, Benjamin Rojo, Juan Thaler, Jesse Winterhalder, Ramon Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Institut für Theoretische Physik Universität Heidelberg Heidelberg Germany University College London LondonWC1E 6BT United Kingdom Department of Physics and Astronomy Tufts University BostonMA02155 United States Department of Physics Carleton University OttawaONK1S 5B6 Canada Department of Physics and Astronomy University of California IrvineCA92697 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States University of Sheffield SheffieldS10 2TN United Kingdom LPNHE Sorbonne Université Université de Paris CNRS/IN2P3 Paris France National Energy Research Scientific Computing Center BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Nikhef Theory Group Science Park 105 Amsterdam1098 XG Netherlands Department of Physics and Astronomy Vrije Universiteit Amsterdam AmsterdamNL-1081 HV Netherlands Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Université Catholique de Louvain Louvain-la-Neuve1348 Belgium

Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinned measurements can enable, improve, or at least simplify comparisons between experiments and with theoretical predictions. Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before. There is currently no community standard for publishing unbinned data. While there are also essentially no measurements of this type public, unbinned measurements are expected in the near future given recent methodological advances. The purpose of this paper is to propose a scheme for presenting and using unbinned results, which can hopefully form the basis for a community standard to allow for integration into analysis workflows. This is foreseen to be the start of an evolving community dialogue, in order to accommodate future developments in this field that is rapidly evolving. © 2021, CC BY.

关键词：

Laser Seeding Attack In Quantum Key Distribution

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arXiv 2019年

作者： Huang, Anqi Navarrete, Álvaro Shi-Hai, Sun Chaiwongkhot, Poompong Curty, Marcos Makarov, Vadim Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada EI Telecomunicación Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain School of Physics and Astronomy Sun Yat-Sen University Zhuhai519082 China Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Russian Quantum Center Skolkovo Moscow121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

Quantum key distribution (QKD) based on the laws of quantum physics allows the secure distribution of secret keys over an insecure channel. Unfortunately, imperfect implementations of QKD compromise its information-theoretical security. Measurement-device-independent quantum key distribution (MDI-QKD) is a promising approach to remove all side channels from the measurement unit, which is regarded as the "Achilles’ heel" of QKD. An essential assumption in MDI-QKD is however that the sources are trusted. Here we experimentally demonstrate that a practical source based on a semiconductor laser diode is vulnerable to a laser seeding attack, in which light injected from the communication line into the laser results in an increase of the intensities of the prepared states. The unnoticed increase of intensity may compromise the security of QKD, as we show theoretically for the prepare-and-measure decoy-state BB84 and MDI-QKD protocols. Our theoretical security analysis is general and can be applied to any vulnerability that increases the intensity of the emitted pulses. Moreover, a laser seeding attack might be launched as well against decoy-state based quantum cryptographic protocols beyond QKD. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

Efficient verification of Dicke states

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arXiv 2019年

作者： Liu, Ye-Chao Yu, Xiao-Dong Shang, Jiangwei Zhu, Huangjun Zhang, Xiangdong Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement Ministry of Education and School of Physics Beijing Institute of Technology Beijing100081 China Naturwissenschaftlich-Technische Fakultät Universität Siegen Walter-Flex-Str. 3 SiegenD-57068 Germany State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China

Among various multipartite entangled states, Dicke states stand out because their entanglement is maximally persistent and robust under particle losses. Although much attention has been attracted for their potential applications in quantum information processing and foundational studies, the characterization of Dicke states remains as a challenging task in experiments. Here, we propose efficient and practical protocols for verifying arbitrary n-qubit Dicke states in both adaptive and nonadaptive ways. Our protocols require only two distinct settings based on Pauli measurements besides permutations of the qubits. To achieve infidelity ∊ and confidence level 1 − δ, the total number of tests required is only O(n∊−1 ln δ−1). This performance is exponentially more efficient than all previous protocols based on local measurements, including quantum state tomography and direct fidelity estimation, and is comparable to the best global strategy. Our protocols are readily applicable with current experimental techniques and are able to verify Dicke states of hundreds of qubits. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum entanglement

Front Cover: Automated Bonding Analysis with Crystal Orbital Hamilton Populations (ChemPlusChem 11/2022)

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ChemPlusChem 2022年第11期87卷

作者： Dr. Janine George Dr. Guido Petretto Aakash Naik Dr. Marco Esters Dr. Adam J. Jackson Dr. Ryky Nelson Prof. Dr. Richard Dronskowski Prof. Dr. Gian-Marco Rignanese Prof. Dr. Geoffroy Hautier Federal Institute for Materials Research and Testing Department Materials Chemistry Unter den Eichen 87 12205 Berlin Germany Friedrich Schiller University Jena Institute of Condensed Matter Theory and Solid-State Optics Max-Wien-Platz 1 07743 Jena Germany Université Catholique de Louvain Institute of Condensed Matter and Nanosciences Chemin des Étoiles 8 1348 Louvain-la-Neuve Belgium former address: Department of Chemistry University of Oregon Eugene OR 97403 USA Department of Mechanical Engineering and Materials Science and Center for Autonomous Materials Design Duke University Durham NC 27708 USA Scientific Computing Department Science & Technology Facilities Council Rutherford Appleton Laboratory Didcot 0X11 0QX UK RWTH Aachen University Institute of Inorganic Chemistry 52056 Aachen Germany RWTH Aachen University Jülich-Aachen Research Alliance (JARA-CSD) 52056 Aachen Germany Shenzhen Polytechnic Hoffmann Institute of Advanced Materials 7098 Liuxian Blvd Nanshan District Shenzhen 518055 P. R. China Dartmouth College Thayer School of Engineering Hanover NH 03755 USA

A method based on MSVL for verification of the social network privacy policy 5th

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A method based on MSVL for verification of the social networ...

5th International Workshop on Structured Object-Oriented Formal Language and Method, SOFL+MSVL 2015

作者： Wang, Xiaobing Sun, Tao Institute of Computing Theory and Technology and ISN Laboratory Xidian University Xian710071 China

ISBN: (纸本)9783319312194

A lot of privacy issues exist in the social network for its data information is excessively public and spreads quickly. This paper presents a method of modeling the social networking system and verifying its privacy policy based on the temporal logic programming language MSVL, which is an executable subset of Projection Temporal Logic. First of all, select an appropriate social networking system and simplify it. Then, model the system with an MSVL program and describe its privacy policy with a Propositional Projection Temporal Logic formula. Next, execute the program with the formula in the modeling, simulation and verification platform of MSVL. Finally, we can improve or modify the verified privacy policy according to the results. © Springer International Publishing Switzerland 2016.

关键词： Temporal logic

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking