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38th International Cosmic Ray Conference, ICRC 2023

作者： Zhang, Yi He, Jiayin Zhang, Rui Zhao, Shiping Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. 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 della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. 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. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. 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. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. 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, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. 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, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 9 Jiangsu Nanjing210023 China University of Science and Technology of China Anhui Hefei230026 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China 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 Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China 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 Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Cor

We analyze gamma-ray emission from the Northern Fermi bubble region at the ultra-high-energy range, using the data collected by LHAASO-KM2A from December 2019 to September 2022. Employing an improved gamma/hadron separation method, the median energy of the gamma rays is above 25 TeV. We perform the "direct integral method" in the background estimation;however, no significant excess is observed. Consequently, we present the expected upper limits for gamma-ray emissions within the Fermi bubble region at this energy range. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation

Cross-domain Object Detection through Coarse-to-Fine Feature...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yangtao Zheng Di Huang Songtao Liu Yunhong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach.

关键词： Feature extraction Subspace constraints Object detection Detectors Task analysis Semantics Prototypes

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Self-embedding Image Watermarking based on Combined Decision Using Pre-offset and Post-offset Blocks

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Computers, Materials & Continua 2018年第11期57卷 243-260页

作者： Daofu Gong Yan Chen Haoyu Lu Zhenyu Li Yibing Han Zhengzhou Science and Technology Institute Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Department of Computer Science University of YorkYork YO105GHUK

To detect and recover random tampering areas,a combined-decision-based self-embedding watermarking scheme is proposed *** this scheme,the image is first partitioned into 2×2 size ***,the high 5 bits of a block’s average value is embedded into its offset *** tampering type of block is detected by comparing the watermarks of its pre-offset and post-offset *** theoretical analysis and experiments demonstrate that the proposed scheme not only has a lower ratio of false detection but also better performance with regard to avoiding random tampering.

关键词： Fragile watermarking self-embedding offset block tamper recovery random tampering

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SURE-based Stopping Strategy for Fine-tunable Supervised PET Image Denoising

SURE-based Stopping Strategy for Fine-tunable Supervised PET...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Jianan Cui Kuang Gong Ning Guo Scott Wollenweber Floris Jansen Huafeng Liu Quanzheng Li State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Center for Advanced Medical Computing and Analysis Massachusetts General Hospital/Harvard Medical School Boston MA USA Gordon Center for Medical Imaging Massachusetts General Hospital/Harvard Medical School Boston MA USA GE Healthcare Waukesha USA

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Previously, based on the unsupervised deep learning method, conditional deep image prior (CDIP), we showed the possibility of finetuning during the testing phase after supervised learning. However, one barrier for CDIP finetuning is that it needs early stopping before the network output converges to the noise which is hard to determine. In this work, we proposed to construct the finetuning loss based on Stein’s unbiased risk estimator (SURE), which can provide an unbiased estimation of the true mean square error (MSE). We demonstrated that using SURE-based finetuning loss, the network output can be close to the ground truth while not converging to the noise. Quantification results based on seven real patient datasets show that the proposed method is more stable during finetuning than CDIP.

关键词： Training Deep learning Supervised learning Estimation Mean square error methods Testing Image denoising

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Disorder-induced pronounced magnetoresistive hysteresis in Josephson junctions

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Physical Review B 2025年第13期111卷 134513-134513页

作者： Yu Wu Daiqiang Huang Huanyu Zhang Anita Guarino Rosalba Fittipaldi Chao Ma Wenjie Hu Chang Niu Zhen Wang Weichao Yu Yuriy Yerin Antonio Vecchione Yang Liu Mario Cuoco Hangwen Guo Jian Shen State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 People's Republic of China Department of Physics Fudan University Shanghai 200433 People's Republic of China Shanghai Aerospace Control Technology Institute Shanghai 201109 People's Republic of China International Center for Quantum Materials Peking University Beijing 100871 People's Republic of China CNR-SPIN c/o Universitá di Salerno I-84084 Fisciano (SA) Italy College of Materials Science and Engineering Hunan University Changsha 410082 People's Republic of China Department of Physics University of Science and Technology of China Hefei Anhui 230026 Peoples Republic of China CNR-SPIN via del Fosso del Cavaliere 100 00133 Roma Italy Hefei National Laboratory Hefei 230088 People's Republic of China Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201210 People's Republic of China Shanghai Research Center for Quantum Sciences Shanghai 201315 People's Republic of China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 People's Republic of China

The relation between superconductivity and time-reversal symmetry is one of the most fascinating problems in condensed-matter physics. Here we report an anomalous transport behavior in superconducting Josephson junctions induced by surface disorder. We fabricate vertical Josephson junctions using metallic superconductor (Al) and ion-bombarded Sr2RuO4. Below the superconducting transition temperature, we observe persistent magnetoresistive hysteresis behavior dependent on the disorder deposition time. Field- and temperature-dependent measurements suggest that the observed effects arise from disorder-induced anomalous flux in Sr2RuO4 which can be sensitively detected by superconducting Al. To account for the observed findings, we consider different physical scenarios such as interband surface pairing reconstruction, magnetic surface states, and vortex phases.

关键词： Hysteresis

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An Image Steganography Algorithm Based on Quantization Index Modulation Resisting Scaling Attacks and Statistical Detection

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Computers, Materials & Continua 2018年第7期56卷 151-167页

作者： Yue Zhang Dengpan Ye Junjun Gan Zhenyu Li Qingfeng Cheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China Computer School of Wuhan University Wuhan 430072China Department of Computer Science University of YorkYork YO105GHUK

In view of the fact that the current adaptive steganography algorithms are difficult to resist scaling attacks and that a method resisting scaling attack is only for the nearest neighbor interpolation method,this paper proposes an image steganography algorithm based on quantization index modulation resisting both scaling attacks and statistical *** the spatial image,this paper uses the watermarking algorithm based on quantization index modulation to extract the embedded *** construct the embedding distortion function of the new embedded domain based on S-UNIWARD steganography,and use the minimum distortion coding to realize the embedding of the secret ***,according to the embedding modification amplitude of secret messages in the new embedded domain,the quantization index modulation algorithm is applied to realize the final embedding of secret messages in the original embedded *** experimental results show that the algorithm proposed is robust to the three common interpolation attacks including the nearest neighbor interpolation,the bilinear interpolation and the bicubic *** the average correct extraction rate of embedded messages increases from 50%to over 93% after 0.5 times-fold scaling attack using the bicubic interpolation method,compared with the classical steganography algorithm *** the algorithm proposed has higher detection resistance than the original watermarking algorithm based on quantization index modulation.

关键词： Image steganography anti-scaling attack anti-statistical detection quantization index modulation

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Author Correction: An optoelectrochemical synapse based on a single-component n-type mixed conductor

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Nature communications 2025年第1期16卷 5045页

作者： Yazhou Wang Wentao Shan Hanrui Li Yizhou Zhong Shofarul Wustoni Johana Uribe Tianrui Chang Valentina E Musteata Tania Cecilia Hidalgo Castillo Wan Yue Haifeng Ling Nazek El-Atab Sahika Inal Organic Bioelectronics Laboratory Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia. Smart Advanced Memory Devices and Applications (SAMA) Laboratory Computer Electrical Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia. Imaging and Characterization Core Lab King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia. School of Materials Science and Engineering State Key Laboratory of Optoelectronic Materials and Technologies Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices Sun Yat-sen University Guangzhou 510275 China. State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM) Nanjing University of Posts & Telecommunications Nanjing 210023 China. Organic Bioelectronics Laboratory Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia. sahika.inal@kaust.edu.sa.

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A Pipelining Loop Optimization Method for Dataflow Architecture

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Journal of Computer Science & Technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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Quingo: A programming framework for heterogeneous quantum-classical computing with NISQ features

arXiv

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arXiv 2020年

作者： Fu, Xiang Yu, Jintao Su, Xing Jiang, Hanru Wu, Hua Cheng, Fucheng Deng, Xi Zhang, Jinrong Jin, Lei Yang, Yihang Xu, Le Hu, Chunchao Huang, Anqi Huang, Guangyao Qiang, Xiaogang Deng, Mingtang Xu, Ping Xu, Weixia Liu, Wanwei Zhang, Yu Deng, Yuxin Wu, Junjie Feng, Yuan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Computer National University of Defense Technology Changsha410073 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China School of Information Engineering Zhengzhou University Zhengzhou450001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Computing Science College of Computer National University of Defense Technology Changsha410073 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Centre for Quantum Software and Information University of Technology Sydney Sydney2007 Australia

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Delay-distance correlation study for IP geolocation

arXiv

引用

arXiv 2019年

作者： Ding, Shichang Luo, Xiangyang Ye, Dengpan Liu, Fenlin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450001 China School of Computer Wuhan University Wuhan Hubei430072 China

Although many classical IP geolocation algorithms are suitable to rich-connected networks, their performances are seriously affected in poor-connected networks with weak delay-distance correlation. This paper tries to improve the performances of classical IP geolocation algorithms by finding rich-connected sub-networks inside poor-connected networks. First, a new delay-distance correlation model (RTD-Corr model) is proposed. It builds the relationship between delay-distance correlation and actual network factors such as the tortuosity of the network path, the ratio of propagation delay and the direct geographical distance. Second, based on the RTD-Corr model and actual network characteristics, this paper discusses about how to find rich-connected networks inside China Internet which is a typical actual poor-connected network. Then we find rich-connected sub-networks of China Internet through a large-scale network measurement which covers three major ISPs and thirty provinces. At last, based on the founded rich-connected sub-networks, we modify two classical IP geolocation algorithms and the experiments in China Internet show that their accuracy is significantly increased. Copyright © 2019, The Authors. All rights reserved.

关键词： Network security

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