检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey

arXiv

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arXiv 2025年

作者： Niu, Feifei Li, Chuanyi Liu, Kui Xia, Xin Lo, David State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing China Software Engineering Application Technology Lab Huawei Hangzhou China College of Computer Science and Technology Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model’s capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area. © 2025, CC BY.

关键词： Online searching

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Direct observation of topological surface state in the topological superconductor 2M-WS2

arXiv

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arXiv 2022年

作者： Cho, Soohyun Huh, Soonsang Fang, Yuqiang Hua, Chenqiang Bai, Hua Jiang, Zhicheng Liu, Zhengtai Liu, Jishan Chen, Zhenhua Fukushima, Yuto Harasawa, Ayumi Kawaguchi, Kaishu Shin, Shik Kondo, Takeshi Lu, Yunhao Mu, Gang Huang, Fuqiang Shen, Dawei State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Institute for Solid State Physics University of Tokyo Kashiwa277-8581 Japan Zhejiang Province Key Laboratory of Quantum Technology and Device Department of Physics Zhejiang University Hangzhou310027 China Shanghai Synchrotron Facility Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201204 China Trans-scale Quantum Science Institute University of Tokyo Tokyo113-0033 Japan State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Science Shanghai200050 China State Key Laboratory of Rare Earth Materials Chemistry and Applications College of Chemistry and Molecular Engineering Peking University Beijing100871 China

The quantum spin Hall (QSH) effect has attracted extensive research interest because of the potential applications in spintronics and quantum computing, which is attributable to two conducting edge channels with opposite spin polarization and the quantized electronic conduc- tance of 2e2/h. Recently, 2M-WS2, a new stable phase of transition metal dichalcogenides with a 2M structure showing an identical layer configuration to that of the monolayer 1T′ TMDs, was suggested to be a QSH insulator as well as a superconductor with critical transition temper- ature around 8 K. Here, high-resolution angle-resolved photoemission spectroscopy (ARPES) and spin-resolved ARPES are applied to investigate the electronic and spin structure of the topological surface states (TSS) in the superconducting 2M-WS2. The TSS exhibits charac- teristic spin-momentum-locking behavior, suggesting the existence of long-sought nontrivial Z2 topological states therein. We expect that 2M-WS2 with co-existing superconductivity and TSS might host the promising Majorana bound states. © 2022, CC BY-NC-ND.

关键词： Tungsten compounds

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Experimental Evidence of Topological Surface states in Mg3Bi2 Films Grown by Molecular Beam Epitaxy

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Chinese Physics Letters 2019年第11期36卷 65-69页

作者： Tong Zhou Xie-Gang Zhu Mingyu Tong Yun Zhang Xue-Bing Luo Xiangnan Xie Wei Feng Qiuyun Chen Shiyong Tan Zhen-Yu Wang Tian Jiang Yuhua Tang Xin-Chun Lai Xuejun Yang State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073 Science and Technology on Surface Physics and Chemistry Laboratory Jiangyou 621908 National Innovation Institute of Defense Technology Academy of Military Sciences PLA ChinaBeijing 100010 Institute of Materials China Academy of Engineering PhysicsMianyang 621700 College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073 Academy of Military Sciences PLA China Beijing 100010 Beijing Academy of Quantum Information Sciences Beijing 100084

Nodal line semimetal(NLS) is a new quantum state hosting one-dimensional closed loops formed by the crossing of two bands. The so-called type-Ⅱ NLS means that these two crossing bands have the same sign in their slopes along the radial direction of the loop, which requires that the crossing bands are either right-tilted or left-tilted at the same time. According to the theoretical prediction, Mg3Bi2 is an ideal candidate for studying the type-Ⅱ NLS by tuning its spin-orbit coupling(SOC). high-quality Mg3 Bi2 films are grown by molecular beam epitaxy(MBE). By in-situ angle resolved photoemission spectroscopy(ARPES), a pair of surface resonance bands around theГ point are clearly seen. This shows that Mg3Bi2 films grown by MBE are Mg(1)-terminated by comparing the ARPES spectra with the first principles calculations results. Moreover, the temperature dependent weak anti-localization effect in Mg3Bi2 films is observed under magneto-transport measurements, which shows clear two-dimensional(2 D) e-e scattering characteristics by fitting with the Hikami–Larkin–Nagaoka model. Therefore, by combining with ARPES, magneto-transport measurements and the first principles calculations, this work proves that Mg3Bi2 is a semimetal with topological surface states. This paves the way for Mg3Bi2 to be used as an ideal material platform to study the exotic features of type-Ⅱ nodal line semimetals and the topological phase transition by tuning its SOC.

关键词： bands topological terminated

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Giant domain wall anomalous Hall effect in an antiferromagnet

arXiv

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arXiv 2023年

作者： Xia, Wei Bai, Bo Chen, Xuejiao Yang, Yichen Zhang, Yang Yuan, Jian Li, Qiang Yang, Kunya Liu, Xiangqi Shi, Yang Ma, Haiyang Yang, Huali He, Mingquan Li, Lei Xi, Chuanying Pi, Li Lv, Xiaodong Wang, Xia Liu, Xuerong Li, Shiyan Zhou, Xiaodong Liu, Jianpeng Chen, Yulin Shen, Jian Shen, Dawei Zhong, Zhicheng Wang, Wenbo Guo, Yanfeng School of Physical Science and Technology ShanghaiTech University Shanghai201210 China CAS Key Laboratory of Magnetic Materials Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Low Temperature Physics Lab College of Physics Center of Quantum Materials and Devices Chongqing University Chongqing401331 China Xi’an Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi’an710072 China Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory of the Chinese Academy of Sciences Anhui Hefei230031 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China College of Physics and Electronic Information Inner Mongolia Normal University 81 Zhaowuda Road Inner Mongolia Hohhot010022 China Analytical Instrumentation Center School of Physical Science and Technology ShanghaiTech University Shanghai201210 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Clarendon Laboratory Department of Physics University of Oxford OxfordOX1 3PU United Kingdom National Synchrotron Radiation Laboratory University of Science and Technology of China 42 South Hezuohua Road Anhui Hefei230029 China

Generally, the dissipationless Hall effect in solids requires time-reversal symmetry breaking (TRSB), where TRSB induced by external magnetic field results in ordinary Hall effect, while TRSB caused by spontaneous magnetization gives rise to anomalous Hall effect (AHE) which scales with the net magnetization. The AHE is therefore not expected in antiferromagnets with vanishing small magnetization. However, large AHE was recently observed in certain antiferromagnets with noncolinear spin structure and nonvanishing Berry curvature. Here, we report another origin of AHE in a layered antiferromagnet EuAl2Si2, namely the domain wall (DW) skew scattering with Weyl points near the Fermi level, in experiments for the first time. Interestingly, the DWs form a unique periodic stripe structure with controllable periodicity by external magnetic field, which decreases nearly monotonically from 975 nm at 0 T to 232 nm at 4 T. Electrons incident on DW with topological bound states experience strong asymmetric scattering, leading to a giant AHE, with the DW Hall conductivity (DWHC) at 2 K and 1.2 T reaching a record value of ∼1.51×104 S cm-1 among bulk systems and being two orders of magnitude larger than the intrinsic anomalous Hall conductivity. The observation not only sets a new paradigm for exploration of large anomalous Hall effect, but also provides potential applications in spintronic devices. © 2023, CC BY.

关键词： Hall effect

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Adversarial Relighting Against Face Recognition

arXiv

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arXiv 2021年

作者： Zhang, Qian Guo, Qing Gao, Ruijun Juefei-Xu, Felix Yu, Hongkai Feng, Wei School of Computer Science and Technology College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin300305 China State Administration of Cultural Heritage China Nanyang Technological University Singapore Alibaba Group United States Cleveland State University ClevelandOH United States

Deep face recognition (FR) has achieved significantly high accuracy on several challenging datasets and fosters successful real-world applications, even showing high robustness to the illumination variation that is usually regarded as a main threat to the FR system. However, in the real world, illumination variation caused by diverse lighting conditions cannot be fully covered by the limited face dataset. In this paper, we study the threat of lighting against FR from a new angle, i.e., adversarial attack, and identify a new task, i.e., adversarial relighting. Given a face image, adversarial relighting aims to produce a naturally relighted counterpart while fooling the state-of-the-art deep FR methods. To this end, we first propose the physical model-based adversarial relighting attack (ARA) denoted as albedo-quotient-based adversarial relighting attack (AQ-ARA). It generates natural adversarial light under the physical lighting model and guidance of FR systems and synthesizes adversarially relighted face images. Moreover, we propose the auto-predictive adversarial relighting attack (AP-ARA) by training an adversarial relighting network (ARNet) to automatically predict the adversarial light in a one-step manner according to different input faces, allowing efficiency-sensitive applications. More importantly, we propose to transfer the above digital attacks to physical ARA (Phy-ARA) through a precise relighting device, making the estimated adversarial lighting condition reproducible in the real world. We validate our methods on three state-of-the-art deep FR methods, i.e., FaceNet, ArcFace, and CosFace, on two public datasets. The extensive and insightful results demonstrate our work can generate realistic adversarial relighted face images fooling face recognition tasks easily, revealing the threat of specific light directions and strengths. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Optimizing Data Placement on Hierarchical Storage Architecture via Machine Learning 16th

Optimizing Data Placement on Hierarchical Storage Architectu...

引用

16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Cheng, Peng Lu, Yutong Du, Yunfei Chen, Zhiguang Liu, Yang College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Changsha China National Supercomputer Center in Guangzhou School of Data and Computer Science Sun Yat-Sen University Guangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783030307080

As storage hierarchies are getting deeper on modern high-performance computing systems, intelligent data placement strategies that can choose the optimal storage tier dynamically is the key to realize the potential of hierarchical storage architecture. However, providing a general solution that can be applied in different storage architectures and diverse applications is challenging. In this paper, we propose adaptive storage learner (ASL), which explores the idea of using machine learning techniques to mine the relationship between data placement strategies and I/O performance under varied workflow characteristics and system status, and uses the learned model to choose the optimal storage tier intelligently. We implement a prototype and integrate it into an existing data management system. Empirical comparison based on real scientific workflows tests shows that ASL is capable of combining workflow characteristics and real-time system status to make optimal data placement decisions. © 2019, IFIP International Federation for Information Processing.

关键词： Real time systems

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Computational strategy of dynamic nanorod networks for dielectric elastomer nanocomposites towards the balance of high permittivity, low modulus, and high breakdown strength

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Polymer 2025年

作者： Han Qin Yancong Feng Tao Liu Baofeng Gao Ying Guo Jingshan Pan Ming Tian Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250000 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250000 China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays South China Academy of Advanced Optoelectronics South China Normal University Guangzhou 510006 China State Key Laboratory of Organic-Inorganic Composites College of Materials Science and Engineering Beijing University of Chemical Technology Beijing 100029 China

Dielectric elastomers exhibit immense potential in emerging fields such as soft robotics, flexible sensors, wearable electronic devices, and energy harvesting, but their widespread application is limited by the intricate paradoxes among the high permittivity, low modulus, and high breakdown strength. By regulating the aspect ratio, concentration, interaction strength, and crosslinking density, a dynamic nanorod network structure is constructed to harmonize comprehensive properties. The construction of the nanorod network significantly enhances permittivity due to the coupling effect of nanorods. Meanwhile, the low percolation threshold avoids high modulus, enabling large electro-induced strain. Under high strain, nanorod orientation shifts, leading to the destruction of the network, thereby avoiding the electric field concentration and achieving the enhancement of electromechanical stability. After the external field is withdrawn, entropic elasticity drives the recovery of elastomers, while the network reconstructs to restore the high dielectric constant. This strategy offers new insights into balancing high permittivity, low modulus, and high breakdown strength in practical applications.

关键词：

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An Enhanced Vision Transformer with Autoencoder for Noise Reduction in Knee X-Rays

SSRN

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SSRN 2025年

作者： Mahmood, Jabar Hamza Abdulnabi, Mohamed Shabbir Pasha, Malaika Azeem, Muhammad Ali, Aitizaz Department of Computer Science International Institute of Science Art and Technology Punjab Gujranwala Pakistan Faculty of Computing and Information Technology University of Sialkot Daska Road Punjab Sialkot51040 Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310007 China Asia Pacific University of Technology and Innovation Kuala Lumpur57000 Malaysia

Traditional methods for medical image denoising, such as Filtering and Convolutional NeuralNetworks, have shown limitations, particularly in preserving image integrity. Recent developmentsin Vision Transformers (ViTs) demonstrate encouraging denoising capabilities but come at the cost ofhigh resource consumption. To solve this, we present an efficient model, the Denoising AutoencoderVision Transformer (DAE-VIT). It is designed specifically for knee X-ray denoising. Unlike the largeSwin Transformer UNet (SUNET), which utilizes 99M parameters, DAE-VIT significantly reducesthe computational burden with only 5.8M parameters while maintaining superior performance. Weevaluated the model on a supervised dataset of knee images. The model gave promising results, withMean Squared Error (MSE) of 0.0036, Mean Absolute Error (MAE) of 0.022, Root Mean Square Error(RMSE) of 0.060, Normalized Cross-Correlation (NCC) of 0.962, Peak Signal-to-Noise Ratio (PSNR)of 32.177, and Structural Similarity Index (SSIM) of 0.872. These scores show the effectiveness ofDAE-VIT in noise reduction while preserving critical image details and make it a favorable alternativefor resource-efficient medical image denoising tasks. © 2025, The Authors. All rights reserved.

关键词： Salt and pepper noise

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Data quality control system and long-term performance monitor of LHAASO-KM2A

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Astroparticle Physics 2025年 164卷

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, D.X. Sun, Q.N. Sun, X.N. Sun, Z.B. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max–Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute & School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Ministry of Education Tibet Lhasa850000 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275 China Sun Yat-sen University Guangdong Zhuhai519000 Chi

The KM2A is the largest sub-array of the Large high Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic-ray and gamma-ray showers. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. For example, according to the observation of the Crab Nebula with KM2A at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be −0.003°±0.005° and 0.001°±0.006° in the R.A. and Dec directions, respectively. © 2024 Elsevier B.V.

关键词： Cosmology

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