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38th International Cosmic Ray Conference, ICRC 2023

作者： You, Xiaohao Hu, Shicong Xi, Shaoqiang 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, Z. 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. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jiang 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. Institute of High Energy Physics 19B Yuquan Road Shijingshan District Beijing China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center 1500 Kezhi Road Tianfu District Sichuan Chengdu610000 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 University of Science and Technology of China Anhui Hefei230026 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 Departement de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genéve

Searching for ultra-high energy (PeV) cosmic ray accelerating source is the core problem in the study of cosmic ray origin. One of the most direct methods is to look for the ultra-high energy gamma-ray radiation generated by the interaction between cosmic rays and interstellar gas near the accelerator. Twelve ultra-high energy gamma ray sources have been observed on the galactic disk by the Large High Altitude Air Shower Observatory (LHAASO). Here, we report the result for LHAASO J1825-1326 region using the Water Cherencov detector array (WCDA) and kilometer array (KM2A) data. The γ-ray emission above 1 TeV observed by LHAASO from the region of LHAASO J1825-1326 is well described by four components including galactic diffuse emission, 1LHAASO J1825-1418 and 1LHAASO J1825-1256u, the likely counterparts to known γ-ray sources, HAWC J1825-138 and HAWC J1826-128, respectively, and LHAASO J1825-1256u in the middle. At present, the origin and radiation mechanism of 1LHAASO J1825-1337u can not be fully confirmed. While CRs accelerated to energies of several PeV colliding with the ambient gas likely produce the observed radiation. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Binary semantic Similarity Comparison based on software gene

Binary semantic Similarity Comparison based on software gene

引用

作者： Wenjie Sun Yihang Chen Zheng Shan Fudong liu Qiuna Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Materials branch of State Grid Henan Electric Power Company

The similarity of binary code is widely used in code copyright protection, vulnerability mining, malicious code analysis and etc. In this paper, we proposed a method for measuring/evaluating the similarity of two binary files based on software genes. Some of Natural language processing methods were introduced into program semantics analysis, including word2vec and doc2vec models to generate assembly instruction embeddings and gene semantic embeddings. Then the longest common subsequence method was applied to evaluate the software similarity. Experiments show that our method can effectively evaluate the similarity of binary files.

关键词： Binary semantic Similarity Comparison based on software gene

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Variational Quantum Algorithm and Its Application on Non-Linear Equations

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Journal of Physics: Conference Series 2021年第1期1883卷

作者： Yihang Yang Zheng Shan Bo Zhao Le Xu School of Information Engineering Zhengzhou University Zhengzhou Henan 450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan 450000 China

Quantum algorithms of factoring problem have been paid more and more attention since Shor's algorithm was proposed. Combining the current quantum computer hardware level and integer factorization quantum algorithms, this paper proposes a 'classical + quantum' hybrid solution. This scheme first adopts classical methods and corresponding rules in the preprocessing step to simplify the nonlinear equations, which is used to reduce the number of qubits needed for the cost Hamiltonian. Then the variational quantum algorithm is used to find the approximate ground state of the cost Hamiltonian, which encodes the solution of the nonlinear equations. The program was verified on the IBM QX4 quantum machine, and the results showed that this hybrid solution can effectively reduce the quantum resources required to solve the nonlinear equations.

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Research on Quantum Programming Framework Based on NISQ Function

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Journal of Physics: Conference Series 2021年第1期1827卷

作者： Xiaonan Liu Le Xu Chunchao Hu Lei Jin State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan 450000 China School of Information Engineering Zhengzhou University Zhengzhou Henan 450000 China

In the past two decades, the field of quantum computing has developed rapidly, is gradually able to solve some difficult classical problems. With the development of the facilities for Noisy Intermediate-Scale Quantum(NISQ), the NISQ technology addresses the existing Quantum programming languages or frameworks, suggesting that they cannot fully meet the requirements of Quantum computers. In recent years, some quantum programming languages or frameworks have emerged which claim to support the classical computing algorithms of heterogeneous quantum. However, the code is too cumbersome and redundant, and it is difficult to map the code to heterogeneous quantum classical computation while meeting the requirements of timing. Therefore, a modular programming framework of heterogeneous quantum classical computing algorithm with NISQ function is proposed. The framework optimizes the quantum code and retains the quantum classical interactions that satisfy the temporal constraints.

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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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Constraints on Ultraheavy Dark Matter Properties from Dwarf Spheroidal Galaxies with LHAASO Observations

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Physical Review Letters 2024年第6期133卷 061001-061001页

作者： Zhen Cao F. Aharonian Q. An Axikegu Y. X. Bai Y. W. Bao D. Bastieri X. J. Bi Y. J. Bi J. T. Cai Q. Cao W. Y. Cao Zhe Cao J. Chang J. F. Chang 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. Chen N. Cheng Y. D. Cheng M. Y. Cui S. W. Cui X. H. Cui Y. D. Cui B. Z. Dai H. L. Dai Z. G. Dai Danzengluobu D. della Volpe X. Q. Dong K. K. Duan J. H. Fan Y. Z. Fan J. Fang K. Fang C. F. Feng L. Feng S. H. Feng X. T. Feng Y. L. Feng S. Gabici B. Gao C. D. Gao L. Q. Gao Q. Gao W. Gao W. K. Gao M. M. Ge L. S. Geng G. Giacinti G. H. Gong Q. B. Gou M. H. Gu F. L. Guo X. L. Guo Y. Q. Guo Y. Y. Guo Y. A. Han H. H. He H. N. He J. Y. He X. B. He Y. He M. Heller Y. K. Hor B. W. Hou C. Hou X. Hou H. B. Hu Q. Hu S. C. Hu D. H. Huang T. Q. Huang W. J. Huang X. T. Huang X. Y. Huang Y. Huang Z. C. Huang X. L. Ji H. Y. Jia K. Jia K. Jiang X. W. Jiang Z. J. Jiang M. Jin M. M. Kang T. Ke D. Kuleshov K. Kurinov 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 X. R. Li Xin Li Y. Z. Li Zhe Li Zhuo Li E. W. Liang Y. F. Liang S. J. Lin 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. Liu R. Lu Q. Luo H. K. Lv B. Q. Ma L. L. Ma X. H. Ma J. R. Mao Z. Min W. Mitthumsiri H. J. Mu Y. C. Nan A. Neronov Z. W. Ou B. Y. Pang P. Pattarakijwanich Z. Y. Pei M. Y. Qi Y. Q. Qi B. Q. Qiao J. J. Qin D. Ruffolo A. Sáiz D. Semikoz C. Y. Shao L. Shao O. Shchegolev X. D. Sheng F. W. Shu H. C. Song Yu. V. Stenkin V. Stepanov Y. Su Q. N. Sun X. N. Sun Z. B. Sun P. H. T. Tam Q. W. Tang Z. B. Tang W. W. Tian C. Wang C. B. Wang G. W. Wang H. G. Wang H. H. Wang J. C. Wang K. Wang L. P. Wang L. Y. Wang P. H. Wang R. Wang W. Wang X. G. Wang X. Y. Wang Y. Wang Y. D. Wang Y. J. Wang Z. H. Wang Z. X. Wang Zhen Wang Zheng Wang D. M. Wei J. J. Wei Y. J. Wei T. Wen C. Y. Wu H. R. Wu S. Wu X. F. Wu Y. S. Wu S. Q. Xi J. Xia J. J. Xia G. M. Xiang D. X. Xiao G. Xiao G. G. Xin Y. L. Xin Y. Xing Z. Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center 100049 Beijing China 610000 Chengdu Sichuan China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 69029 Heidelberg Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China 230026 Hefei Anhui China School of Physical Science and Technology and School of Information Science and Technology Southwest Jiaotong University 610031 Chengdu Sichuan China School of Astronomy and Space Science Nanjing University 210023 Nanjing Jiangsu China Center for Astrophysics Guangzhou University 510006 Guangzhou Guangdong China Hebei Normal University 050024 Shijiazhuang Hebei China Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy Purple Mountain Observatory Tsung-Dao Lee Institute and School of Physics and Astronomy Shanghai Jiao Tong University 200240 Shanghai China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education 850000 Lhasa Tibet China National Astronomical Observatories School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai) Sun Yat-sen University 519000 Zhuhai and 510275 Guangzhou Guangdong China School of Physics and Astronomy Yunnan University 650091 Kunming Yunnan China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet 1211 Geneva Switzerland Institute of Frontier and Interdisciplinary Science Shandong University 266237 Qingdao Shandong China APC Universit’e Paris Cit’e CNRS/IN2P3 CEA/IRFU Observatoire de Paris 119 75205 Paris France Department of Engineering Physics Tsinghua University 100084 Beijing China School of P

In this Letter we try to search for signals generated by ultraheavy dark matter at the Large High Altitude Air Shower Observatory (LHAASO) data. We look for possible γ rays by dark matter annihilation or decay from 16 dwarf spheroidal galaxies in the field of view of the LHAASO. Dwarf spheroidal galaxies are among the most promising targets for indirect detection of dark matter that have low fluxes of astrophysical γ-ray background while having large amount of dark matter. By analyzing more than 700 days of observational data at LHAASO, no significant dark matter signal from 1 TeV to 1 EeV is detected. Accordingly we derive the most stringent constraints on the ultraheavy dark matter annihilation cross section up to EeV. The constraints on the lifetime of dark matter in decay mode are also derived.

关键词： Gamma ray astronomy Particle dark matter

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Higher-Order Quantum Genetic Algorithm and Its Application on Non-Linear Equations

引用

Journal of Physics: Conference Series 2021年第1期1827卷

作者： Lei Jin Zheng Shan Chunchao Hu Le Xu School of Information Engineering Zhengzhou University Zhengzhou Henan 450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan 450000 China

The high efficiency of quantum algorithms is caused by the quantum parallelism of the superposition principle and the quantum entangled, but traditional quantum genetic algorithms only use the quantum superposition principle. In order to further improve the performance of the algorithm, this paper proposes a new higher-order quantum genetic algorithm, which adds quantum entanglement properties on the basis of the principle of quantum superposition. Finally, the traditional quantum genetic algorithm and the higher-order quantum genetic algorithm are used to test a set of nonlinear equations many times. The results show that compared with the traditional quantum genetic algorithm, the evolutionary update operation of the higher-order quantum genetic algorithm does not involve multiple judgment conditions of the traditional quantum gate, requires less evolutionary algebra, and can converge quickly.

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MalCommunity: A graph-based evaluation model for malware family clustering 4th

MalCommunity: A graph-based evaluation model for malware fam...

引用

4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Chen, Yihang Liu, Fudong Shan, Zheng Liang, Guanghui State Key of Laboratory of Mathematical Engineering and Advanced Computing ZhengzhouHenan450001 China

ISBN: (纸本)9789811322020

Malware clustering analysis plays an important role in large-scale malware homology analysis. However, the generation approach of the ground truth data is usually ignored. The Labels from Anti-virus(AV) engines are most commonly used but some of them are inaccurate or inconsistent. To overcome the drawback, many researchers make ground truth data based on voting mechanism such as AVclass, but this method is difficult to evaluate different-granularity clustering results. Graph-based method like VAMO is more robust but it needs to maintain a large-size database. In this paper, we propose a novel evaluation model named MalCommunity based on the graph named Malware Relation Graph. Different from VAMO, the construction of the graph is free from a large-size database and just needs the AV label information of the samples in the test set. We introduce community detection algorithm Fast Newman to divide the sample set and use modularity parameter to measure the target clustering results. The experiment results indicate that our model has the ability of noise immunity of malware family classification inconsistency and granularity inconsistency from AV labels. Our model is also convenient to evaluate different-granularity clustering methods with different heights. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Graph theory

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Bridged Semantic Alignment for Zero-shot 3D Medical Image Diagnosis

arXiv

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arXiv 2025年

作者： Lai, Haoran Jiang, Zihang Yao, Qingsong Wang, Rongsheng He, Zhiyang Tao, Xiaodong Wei, Wei Lv, Weifu Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Stanford University Palo AltoCA94305 United States Medical Business Department iFlytek Co.Ltd. Anhui Hefei230088 China The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230001 China Department of Radiology The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230001 China Suzhou Institute for Advance Research USTC Jiangsu Suzhou215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China Institute of Computing Technology CAS Beijing100190 China

3D medical images such as Computed tomography (CT) are widely used in clinical practice, offering a great potential for automatic diagnosis. Supervised learning-based approaches have achieved significant progress but rely heavily on extensive manual annotations, limited by the availability of training data and the diversity of abnormality types. Vision-language alignment (VLA) offers a promising alternative by enabling zero-shot learning without additional annotations. However, we empirically discover that the visual and textural embeddings after alignment endeavors from existing VLA methods form two well-separated clusters, presenting a wide gap to be bridged. To bridge this gap, we propose a Bridged Semantic Alignment (BrgSA) framework. First, we utilize a large language model to perform semantic summarization of reports, extracting high-level semantic information. Second, we design a Cross-Modal Knowledge Interaction (CMKI) module that leverages a cross-modal knowledge bank as a semantic bridge, facilitating interaction between the two modalities, narrowing the gap, and improving their alignment. To comprehensively evaluate our method, we construct a benchmark dataset that includes 15 underrepresented abnormalities as well as utilize two existing benchmark datasets. Experimental results demonstrate that BrgSA achieves state-of-the-art performances on both public benchmark datasets and our custom-labeled dataset, with significant improvements in zero-shot diagnosis of underrepresented abnormalities. © 2025, CC BY.

关键词： Zero-shot learning

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Practical Iterative Quantum Consensus Protocol with Sharding Construction

引用

IEEE Journal on Selected Areas in Communications 2025年

作者： Ying, Chenhao Du, Yuxuan Zhang, Weiting Jiang, Xikun Wang, Gang Jin, Haiming Li, Jie Luo, Yuan Tao, Dacheng Shanghai Jiao Tong University Department of Computer Science Shanghai China Zhejiang University Shanghai Jiao Tong University State Key Laboratory of Blockchain and Data Security Blockchain Advanced Research Center Jiangsu Wuxi214101 China Nanyang Technological University College of Computing&Data Science Singapore Beijing Jiaotong University School of Electronic and Information Engineering Beijing China University of Copenhagen Department of Computer Science Denmark Northeastern University School of Computer Science and Engineering Shenyang China Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3 China

With the development of quantum blockchain, the quantum consensus protocols have garnered increasing attention, which play a crucial role in driving the implementation of quantum blockchains. However, existing protocols, derived from the classical consensus algorithms, face practical application challenges due to current quantum technology limitations. The first challenge is the bottleneck in generating large-scale entangled quantum states. The second challenge arises from the generation of malicious quantum states. The final challenge involves privacy concerns. To address these challenges, we propose a practical iterative QUantum consensus protocol with sharding construction, namely, Q-Union. In fact, Q-Union employs an iterative consensus algorithm where participating nodes are divided into multiple smaller shards, with the consensus process occurring within the current shard, and new shards are involved only if consensus is not achieved. Leveraging Greenberger-Horne- Zeilinge states and Aharonov states, Q-Union harnesses the advantages of quantum mechanics to achieve anonymous consensus, protecting the private information of participating nodes. Additionally, by integrating state verification, Q-Union ensures the correctness of the consensus procedure in the presence of malicious nodes generating adversarial quantum states. Finally, it is proven that Q-Union can also defend against Byzantine attacks from adversarial nodes, maintaining the same security level as traditional non-sharded consensus protocols. Specifically, it consistently outputs the correct consensus when the fraction of adversaries among participating nodes is less than 1/2 with synchronous communication. Both the theoretical analysis and performance illustration demonstrate the superior performance of the proposed Q-Union compared to state-of-the-art protocols. © 1983-2012 IEEE.

关键词： Quantum entanglement

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