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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Testing viable f(R) models with the angular-diameter distance to compact quasar cores

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Physical Review D 2019年第10期99卷 103505-103505页

作者： Joseph Sultana Fulvio Melia Demosthenes Kazanas Department of Mathematics Faculty of Science University of Malta Msida MSD2080 Malta Department of Physics The Applied Math Program and Department of Astronomy The University of Arizona Tucson Arizona 85721 USA Astrophysics Science Division NASA/Goddard Space Flight Center Greenbelt Maryland 20771 USA

We consider here some popular f(R) models generally viewed as possible alternatives to the existence of dark energy in General Relativity. For each of these, we compute the redshift zmax at which the angular diameter distance dA(z) is expected to reach its maximum value. This turning point in dA(z) was recently measured in a model-independent way using compact quasar cores and was found to occur at zmax=1.70±0.20. We compare the predictions of zmax for the f(R) models with this observed value to test their viability at a deeper level than has been attempted thus far, thereby quantifying an important observational difference between such modified gravity scenarios and standard Lambda Cold Dark Matter (ΛCDM) cosmology. Our results show that, while the most popular f(R) models today are consistent with this measurement to within 1σ, the turning point zmax will allow us to prioritize these alternative gravity theories as the measurement precision continues to improve, particularly with regard to how well they mitigate the tension between the predictions of ΛCDM and the observations. For example, while the Hu-Sawicki version of f(R) increases this tension, the Starobinky model reduces it.

关键词： Alternative gravity theories Cosmological parameters Relativistic aspects of cosmology

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Quantum circuits for exact unitary t-designs and applications to higher-order randomized benchmarking

arXiv

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arXiv 2021年

作者： Nakata, Yoshifumi Zhao, Da Okuda, Takayuki Bannai, Eiichi Suzuki, Yasunari Tamiya, Shiro Heya, Kentaro Yan, Zhiguang Zuo, Kun Tamate, Shuhei Tabuchi, Yutaka Nakamura, Yasunobu Photon Science Center Graduate School of Engineering University of Tokyo Bunkyo-ku Tokyo113–8656 Japan JST PRESTO 4–1–8 Honcho Kawaguchi Saitama332–0012 Japan School of Mathematical Sciences Shanghai Jiao Tong University Shanghai400240 China Department of Mathematics Hiroshima University 1-3-1 Kagamiyama Higashihiroshima739-8562 Japan Fukuoka819-0385 Japan Mathematics Division National Center for Theoretical Sciences National Taiwan University Taipei10617 Taiwan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan Department of Applied Physics Graduate School of Engineering University of Tokyo 7-3-1 Hongo Bynkyo-ku Tokyo113-8656 Japan University of Tokyo Meguro-ku Tokyo153-8904 Japan Wako Saitama351-0198 Japan

A unitary t-design is a powerful tool in quantum information science and fundamental physics. Despite its usefulness, only approximate implementations were known for general t. In this paper, we provide for the first time quantum circuits that generate exact unitary t-designs for any t on an arbitrary number of qubits. Our construction is inductive and is of practical use in small systems. We then introduce a t-th order generalization of randomized benchmarking (t-RB) as an application of exact 2t-designs. We particularly study the 2-RB in detail and show that it reveals self-adjointness of quantum noise, a new metric related to the feasibility of quantum error correction (QEC). We numerically demonstrate that the 2-RB in one- and two-qubit systems is feasible, and experimentally characterize background noise of a superconducting qubit by the 2-RB. It is shown from the experiment that interactions with adjacent qubits induce the noise that may result in an obstacle toward a realization of QEC. © 2021, CC BY.

关键词： Benchmarking

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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TiEMPO: Open-source time-dependent end-to-end model for simulating ground-based submillimeter astronomical observations

arXiv

引用

arXiv 2021年

作者： Huijten, Esmee Roelvink, Yannick Brackenhoff, Stefanie A. Taniguchi, Akio Bakx, Tom J.L.C. Marthi, Kaushal B. Zaalberg, Stan Baselmans, Jochem J.A. Chin, Kah Wuy Huiting, Robert Karatsu, Kenichi Laguna, Alejandro Pascual Tamura, Yoichi Takekoshi, Tatsuya Yates, Stephen J.C. van Hoven, Maarten Endo, Akira Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Kavli Institute of NanoScience Faculty of Applied Sciences Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands Division of Particle and Astrophysical Science Graduate School of Science Nagoya University Aichi464-8602 Japan Kapteyn Astronomical Institute University of Groningen P.O. Box 800 Groningen9700 AV Netherlands SRON—Netherlands Institute for Space Research Sorbonnelaan 2 Utrecht3584 CA Netherlands National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Department of Astronomy School of Science University of Tokyo Bunkyo Tokyo113-0033 Japan Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan SRON—Netherlands Institute for Space Research Landleven 12 Groningen9747 AD Netherlands

The next technological breakthrough in millimeter-submillimeter astronomy is 3D imaging spectrometry with wide instantaneous spectral bandwidths and wide fields of view. The total optimization of the focal-plane instrument, the telescope, the observing strategy, and the signal-processing software must enable efficient removal of foreground emission from the Earth’s atmosphere, which is time-dependent and highly nonlinear in frequency. Here we present TiEMPO: Time-dependent End-to-end Model for Post-process Optimization of the DESHIMA spectrometer. TiEMPO utilizes a dynamical model of the atmosphere and parametrized models of the astronomical source, the telescope, the instrument, and the detector. The output of TiEMPO is a timestream of sky brightness temperature and detected power, which can be analyzed by standard signal-processing software. We first compare TiEMPO simulations with an on-sky measurement by the wideband DESHIMA spectrometer, and find good agreement in the noise power spectral density and sensitivity. We then use TiEMPO to simulate the detection of the line emission spectrum of a high-redshift galaxy using the DESHIMA 2.0 spectrometer in development. The TiEMPO model is open source. Its modular and parametrized design enables users to adapt it to design and optimize the end-to-end performance of spectroscopic and photometric instruments on existing and future telescopes. Copyright © 2021, The Authors. All rights reserved.

关键词： Millimeter waves

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Natural orbitals in multiconfiguration calculations of hyperfine structure parameters

arXiv

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arXiv 2020年

作者： Schiffmann, Sacha Godefroid, Michel Ekman, Jörgen Jönsson, Per Fischer, Charlotte Froese Spectroscopy Quantum Chemistry and Atmospheric Remote Sensing Université Libre de Bruxelles CP160/09 Brussels1050 Belgium Division of Mathematical Physics Department of Physics Lund University LundSE-22100 Sweden Department of Materials Science and Applied Mathematics Malmö University MalmöSE-20506 Sweden Department of Computer Science University of British Columbia 2366 Main Mall VancouverBCV6T1Z4 Canada

We are reinvestigating the hyperfine structure of sodium using a fully relativistic multiconfiguration approach. In the fully relativistic approach, the computational strategy somewhat differs from the original nonrelativistic counterpart used in [1]. Numerical instabilities force us to use a layer-by-layer approach that has some broad unexpected effects. Core correlation is found to be significant and therefore requires to be described in an adequate orbital basis. The natural-orbital basis provides an interesting alternative to the orbital basis from the layer-by-layer approach, allowing us to overcome some deficits of the latter, giving rise to magnetic dipole hyperfine structure constant values in excellent agreement with observations. Effort is made to assess the reliability of the natural-orbital bases and to illustrate their efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Atomic physics

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MHD natural convection in a differentially heated cavity filled with a C u-water nanofluid using FVM

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Case Studies in Thermal Engineering 2025年 72卷

作者： Mohamed Sadek Kehil Mohamed Kherief Nacereddine Usman Farid Berrahil Abdelkader Filali Adil Darvesh Mohamed Kezzar Abuzar Ghaffari Mohamed Rafik Sari Taseer Muhammad Ibrahim Mahariq Mechanical Engineering Department Faculty of Technology 20 August 1955 University of Skikda BP 26 21000 Skikda Algeria Normal High School of Technology Education ENSET-azzaba Skikda Algeria School of Qilu Transportation Shandong University Jinan China Department of Computer Science National University of Sciences and Technology Balochistan Campus (NBC) Quetta 87300 Pakistan Department of Mechanical and Electro-Mechanical Engineering Science and Technology Institute University Center Abdelhafid Boussouf Mila Algeria Applied Energetics and Pollution Laboratory Department of Mechanical Engineering Faculty of Sciences Technology University of Frères Mentouri-Constantine 1 Constantine Algeria Laboratoire Mécanique et Système Energétique Avancé Ecole Nationale Polytechnique de Constantine BP75 A Nouvelle Ville Ali Mendjli Constantine Algeria Department of Mathematics and Statistics Hazara University Mansehra 21300 Pakistan Materials and Energy Engineering Laboratory (LMGE) Technology Department Faculty of Technology 20 August 1955 University of Skikda PO Box 26 21000 Skikda Algeria Department of Mathematics Division of Science and Technology University of Education Lahore 54770 Pakistan Materials and Plant Maintenance Research Laboratory (LR3MI) Mechanical Engineering Department Faculty of Engineering Badji Mokhtar University of Annaba (UBMA) PO Box 12 23052 Annaba Algeria Department of Mathematics College of Sciences King Khalid University Abha 61413 Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 602105 Tamil Nadu India Najjad Zeenni Faculty of Engineering Al Quds University Jerusalem Palestine University College Korea University Seoul 02481 South Korea Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Applied Science Research Center Applied Science Private University Amman Jordan

This investigation presents a numerical exploration of magnetohydrodynamic (MHD) natural convection within a differentially heated square cavity containing Cu-water nanofluid (NF) that has a Prandtl number of 6.2, considering variable thermophysical properties. The impacts of Rayleigh number between 10 3 and 10 7 , nanoparticle volume fraction between 0 and 9 %, and Hartmann number Ha between 0 and 100, on flow behavior and heat transport (HT) characteristics are examined. Results indicate that increasing the Rayleigh number enhances fluid motion and HT, while the inclusion of Cu nanoparticles further augments HT due to improved thermal conductivity. However, high concentrations of nanoparticles can reduce the efficiency of convective HT. The presence of a horizontal magnetic field, represented by the Hartmann number, was found to decelerate fluid motion due to the Lorentz force, diminishing the effectiveness of convection and shifting the dominant HT mode towards conduction.

关键词： FVM Natural convection Cu-water NF Square cavity Temperature-dependent thermophysical properties

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CPMap: Design of click-points map-based graphical password authentication

CPMap: Design of click-points map-based graphical password a...

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作者： Meng, Weizhi Fei, Fei Jiang, Lijun Liu, Zhe Su, Chunhua Han, Jinguang Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark Department of Computer Science City University of Hong Kong Kowloon Hong Kong Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Luxembourg Division of Computer Science University of Aizu Aizuwakamatsu Japan Department of Computer Science University of Surrey Guildford United Kingdom

ISBN: (纸本)9783319998275

As traditional textual passwords suffer from many known limitations, graphical passwords (GPs) are proposed as one promising alternative to complement the existing authentication systems. To obtain a large password space, map-based GPs (geographical passwords) have been developed that allow users to choose one or more places on a map for authentication. For example, PassMap requires users to choose two places as their credentials, and GeoPass enables users to click only one place for authentication. Some research studies have reported that choosing only one place as a password may be not secure enough, whereas selecting two places may decrease the system usability. In this work, we first conducted a study to learn how users would choose two places under PassMap, and found that users may choose two similar locations due to time consideration. Motivated by this observation, we then design CPMap, a click-p^oints map-based GP scheme that allows users to choose one place on a world map at first and then click a point or an object on an image relating to the previously selected location. To investigate the performance of CPMap, we conducted another user study with up to 50 participants. It is found that users could achieve promising results with our scheme in the aspects of both security and usability. © IFIP International Federation for Information Processing 2018.

关键词： Authentication

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Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)

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NATURE BIOTECHNOLOGY 2020年第1期38卷 108-108页

作者： Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Kramers Nodal Lines and Weyl Fermions in SmAlSi

arXiv

引用

arXiv 2022年

作者： Zhang, Yichen Gao, Yuxiang Gao, Xue-Jian Lei, Shiming Ni, Zhuoliang Oh, Ji Seop Huang, Jianwei Yue, Ziqin Zonno, Marta Gorovikov, Sergey Hashimoto, Makoto Lu, Donghui Denlinger, Jonathan D. Birgeneau, Robert J. Kono, Junichiro Wu, Liang Law, Kam Tuen Morosan, Emilia Yi, Ming Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Physics Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics University of California BerkeleyCA94720 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Canadian Light Source Inc. 44 Innovation Boulevard SaskatoonSKS7N 2V3 Canada Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Advanced Light Source Lawrence Berekeley National Laboratory BerkeleyCA94720 United States Materials Science Division Lawrence Berekeley National Laboratory BerkeleyCA94720 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States

Kramers nodal lines (KNLs) have recently been proposed theoretically as a special type of Weyl line degeneracy connecting time-reversal invariant momenta. KNLs are robust to spin orbit coupling and are inherent to all non-centrosymmetric achiral crystal structures, leading to unusual spin, magneto-electric, and optical properties. However, their existence in in real quantum materials has not been experimentally established. Here we gather the experimental evidence pointing at the presence of KNLs in SmAlSi, a non-centrosymmetric metal that develops incommensurate spin density wave order at low temperature. Using angle-resolved photoemission spectroscopy, density functional theory calculations, and magneto-transport methods, we provide evidence suggesting the presence of KNLs, together with observing Weyl fermions under the broken inversion symmetry in the paramagnetic phase of SmAlSi. We discuss the nesting possibilities regarding the emergent magnetic orders in SmAlSi. Our results provide a solid basis of experimental observations for exploring correlated topology in SmAlSi. © 2022, CC BY-NC-SA.

关键词： Temperature

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