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2018国际计算机前沿大会（原国际青年计算机大会）

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

Malware clustering analysis plays an important role in largescale 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. Graphbased method like VAMO is more robust but it needs to maintain a largesize 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 largesize 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.

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Universal Dynamical Scaling of Quasi-Two-Dimensional Vortices in a Strongly Interacting Fermionic Superfluid

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Physical Review Letters 2021年第18期126卷 185302-185302页

作者： Xiang-Pei Liu Xing-Can Yao Youjin Deng Xiao-Qiong Wang Yu-Xuan Wang Chun-Jiong Huang Xiaopeng Li Yu-Ao Chen Jian-Wei Pan Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics University of Science and Technology of China Hefei 230026 China Shanghai Branch CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Shanghai Research Center for Quantum Sciences Shanghai 201315 China MinJiang Collaborative Center for Theoretical Physics College of Physics and Electronic Information Engineering Minjiang University Fuzhou 350108 China State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computingand Department of Physics Fudan University Shanghai 200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China

Vortices play a leading role in many fascinating quantum phenomena. Here we generate a large number of vortices by thermally quenching a fermionic superfluid of Li6 atoms in an oblate optical trap and study their annihilation dynamics and spatial distribution. Over a wide interaction range from the attractive to the repulsive side across the Feshbach resonance, these quasi-two-dimensional vortices are observed to follow algebraic scaling laws both in time and space, having exponents consistent with the two-dimensional universality. We further simulate the classical XY model on the square lattice by a Glauber dynamics and find good agreement between the numerical and experimental behaviors. Our work provides a direct demonstration of the universal 2D vortex dynamics.

关键词： Fermi gases Vortices in superfluids Fermionic condensates Ultracold gases

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Loop-nest Auto-vectorization Method Based on Benefit Analysis

Loop-nest Auto-vectorization Method Based on Benefit Analysi...

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作者： Yao Jinyang Zhao Rongcai Wang Qi Tao Xiaohan State Key Laboratory of Mathematical Engineering and Advanced Computing

The SIMD(Single Instruction Multiple Data) extension component has been widely used in modern processors to improve the execution efficiency of the *** mainstream compilers(such as GCC,ICC,OPEN64,etc.) have supported the SIMD automatic *** current compilers can only vectorize the innermost layer of the loop,but this way often does not get the optimal performance *** at this kind of problem and combining with the processor architecture,a loopnest vectorization method based on the benefit analysis model is proposed and implemented in the GCC *** method can analyze different loop-nest vectorization schemes effectively and choose the most profitable one as the final optimization *** article first reviewed the researches about loop-nest vectorization,then introduces this new code generation method for loop-nest ***,we use several common application examples for *** experimental results show that the results of the benefit model are similar to the actual test results,and accordingly increase the efficiency for loop-nest vectorization.

关键词： Vectorization Loop-nest Benefit Analysis Cost Model

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CFTD from TQFTD+1 via Holographic Tensor Network, and Precision Discretisation of CFT2

arXiv

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arXiv 2022年

作者： Chen, Lin Zhang, Haochen Ji, Kaixin Shen, Ce Wang, Ruoshui Zeng, Xiangdong Hung, Ling-Yan State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China School of Physics and Optoelectronics South China University of Technology Guangzhou510641 China Department of Physics Shandong University Jinan250100 China International Centre for Theoretical Physics Asia-Pacific University of Chinese Academy of Sciences Beijing100190 China Cornell University IthacaNY14853 United States Institute for Advanced Study Tsinghua University Beijing100084 China Institute for Nanoelectronic devices and Quantum computing Fudan University Shanghai200433 China Yau Mathematical Sciences Center Tsinghua University Beijing100084 China

We show that the path-integral of conformal field theories in D dimensions (CFTD) can be constructed by solving for eigenstates of an RG operator following from the Turaev-Viro formulation of a topological field theory in D + 1 dimensions (TQFTD+1), explicitly realising the holographic sandwich relation between a symmetric theory and a TQFT. Generically, exact eigenstates corresponding to symmetric-TQFTD follow from Frobenius algebra in the TQFTD+1. For D = 2, we constructed eigenstates that produce 2D rational CFT path-integral exactly, which, curiously connects a continuous field theoretic path-integral with the Turaev-Viro state sum. We also devise and illustrate numerical methods for D = 2, 3 to search for CFTD as phase transition points between symmetric TQFTD. Finally since the RG operator is in fact an exact analytic holographic tensor network, we compute "bulk-boundary" correlator and compare with the AdS/CFT dictionary at D = 2. Promisingly, they are numerically compatible given our accuracy, although further works will be needed to explore the precise connection to the AdS/CFT correspondence. Copyright © 2022, The Authors. All rights reserved.

关键词： Tensors

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Dual-responsive nanogels with high drug loading for enhanced tumor targeting and treatment

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Chinese Chemical Letters 2024年

作者： Haotian Shi Yuchao Luo Song Zhang Meijun Zhao Chaoyong Liu Qing Pei Helei Wang Qiong Dai Zhigang Xie Bin Xu Wenjing Tian State Key Laboratory of Supramolecular Structure and Materials College of Chemistry Jilin University Changchun 130012 China Beijing Advanced Innovation Center for Soft Matter Science and Engineering College of Life Science and Technology Beijing University of Chemical Technology Beijing 100029 China School of Mechanical Science and Aerospace Engineering Jilin University Changchun 130022 China Research Center for Life Science Computing Zhejiang Lab Hangzhou 311100 China State Key Laboratory of Polymer Physics and Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun 130022 China Gastric and Intestinal Department General Surgery Center First Hospital of Jilin University Changchun 130021 China

Nanomedicine holds considerable promise for advancing cancer therapy, however, effective delivery of drugs to solid tumors remains a challenge due to rapid systemic clearance and inefficient cellular uptake. Herein, we have developed a novel charge-reversible nanogel to deliver paclitaxel (PTX) dimers (DPP) with enhanced stability and targeting precision. The nanogels exhibit a dynamic charge-reversal mechanism responsive to the acidic tumor microenvironment (TME), optimizing the cellular uptake of prodrugs. In the high glutathione (GSH) conditions within cancer cells, the disulfide bonds in the DPP are cleaved, resulting in the intracellular release of active PTX and reduced drug toxicity to normal cells. In vivo pharmacokinetic studies revealed an extended plasma elimination half-life for the charge-reversible nanocarriers, and antitumor efficacy studies demonstrated superior tumor suppression with minimal systemic toxicity. This research underscores the potential of integrating charge-reversal and responsive release mechanisms into one nanocarrier system, balancing the long circulation and high tumor cell internalization capacity of the nanocarrier, and providing a promising strategy for targeted delivery of nanomedicine.

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Open set recognition of radar specific emitter based on adversarial reciprocal point learning

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Signal Processing 2026年 238卷

作者： Wenxu Zhang Lin An Wencheng Yang Zhongkai Zhao Feiran Liu College of Information and Communication Engineering Harbin Engineering University Harbin 150001 China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin 150001 China AVIC United Technology Center for Electromagnetic Spectrum Collaborative Detection and Intelligent Cognition Harbin Engineering University Harbin 150001 China Yantai Research Institute Harbin Engineering University Yantai 264001 China School of Mathematics Physics and Computing University of Southern Queensland Toowoomba QLD 4350 Australia Department of Electrical Engineering Wright State University Dayton OH 45435 USA

Radar specific emitter identification (SEI) is a key technology in electromagnetic spectrum control. Although the emergence of deep learning has promoted the development of SEI, there are still many shortcomings in the current research results. Most of the traditional deep learning algorithms are applicable to closed-set identification and can only be used when the database is complete. In addition, individual differences in radar signals are susceptible to noise interference, but traditional denoising methods are usually independent of the feature extraction process, making it difficult to ensure that certain individual information is not lost. Therefore, in this paper, we propose a new radar emitter open set recognition method called adversarial reciprocal point learning with adaptive denoising (ARPLAD). Firstly, we design a new feature extraction network for one-dimensional signals, which combines deep residual shrinkage network with efficient attention mechanism to autonomously denoise signals and focus on important parts of signal features. Secondly, we train the network using adversarial reciprocal point learning combined with center loss to extract discriminative features with compact intraclass distances and separable interclass distances, which can efficiently discriminate unknown signals and reduce the risk of open set identification. The experimental results show that ARPLAD exhibits excellent performance in different conditions, providing an effective solution for SEI in open electromagnetic environments.

关键词： Deep learning Radar specific emitter identification Open set recognition Adversarial reciprocal point learning

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Nucleon Transversity Distribution in the Continuum and Physical Mass Limit from Lattice QCD

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Physical Review Letters 2023年第26期131卷 261901-261901页

作者： Fei Yao Lisa Walter Jiunn-Wei Chen Jun Hua Xiangdong Ji Luchang Jin Sebastian Lahrtz Lingquan Ma Protick Mohanta Andreas Schäfer Hai-Tao Shu Yushan Su Peng Sun Xiaonu Xiong Yi-Bo Yang Jian-Hui Zhang Center of Advanced Quantum Studies Department of Physics Beijing Normal University Beijing 100875 China Institut für Theoretische Physik Universität Regensburg D-93040 Regensburg Germany Department of Physics Center for Theoretical Physics and Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei 10617 Taiwan Physics Division National Center for Theoretical Sciences Taipei 10617 Taiwan Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Department of Physics University of Maryland College Park Maryland 20742 USA Physics Department University of Connecticut Storrs Connecticut 06269-3046 USA RIKEN BNL Research Center Brookhaven National Laboratory Upton New York 11973 USA School of Physical Sciences National Institute of Science Education and Research HBNI Odisha 752050 India Institute of Modern Physics Chinese Academy of Sciences Lanzhou Gansu Province 730000 China Department of Physics and Institute of Theoretical Physics Nanjing Normal University Nanjing Jiangsu 210023 China School of Physics Central South University Changsha 418003 China CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China International Centre for Theoretical Physics Asia-Pacific Beijing/Hangzhou China University of Chinese Academy of Sciences School of Physical Sciences Beijing 100049 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China

We report a state-of-the-art lattice QCD calculation of the isovector quark transversity distribution of the proton in the continuum and physical mass limit using large-momentum effective theory. The calculation is done at four lattice spacings a={0.098,0.085,0.064,0.049} fm and various pion masses ranging between 220 and 350 MeV, with proton momenta up to 2.8 GeV. The result is nonperturbatively renormalized in the hybrid scheme with self-renormalization, which treats the infrared physics at large correlation distance properly, and extrapolated to the continuum, physical mass, and infinite momentum limit. We also compare with recent global analyses for the nucleon isovector quark transversity distribution.

关键词： Lattice QCD Parton distribution functions

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Suppressing decoherence in quantum plasmonic systems by the spectral-hole-burning effect

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Physical Review A 2021年第5期103卷 053517-053517页

作者： Jia-Bin You Xiao Xiong Ping Bai Zhang-Kai Zhou Wan-Li Yang Ching Eng Png Leong Chuan Kwek Lin Wu Institute of High Performance Computing A*STAR (Agency for Science Technology and Research) 1 Fusionopolis Way No. 16-16 Connexis Singapore 138632 State Key Laboratory of Optoelectronic Materials and Technologies School of Physics Sun Yat-sen University Guangzhou 510275 China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Chinese Academy of Sciences Wuhan 430071 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore National Institute of Education and Institute of Advanced Studies Nanyang Technological University 1 Nanyang Walk Singapore 637616 School of Electrical and Electronic Engineering Block S2.1 50 Nanyang Avenue Singapore 639798

Quantum plasmonic systems suffer from significant decoherence due to the intrinsically large dissipative and radiative dampings. Based on our quantum simulations via a quantum tensor network algorithm, we numerically demonstrate the mitigation of this restrictive drawback by hybridizing a plasmonic nanocavity with an emitter ensemble with inhomogeneously broadened transition frequencies. By burning two narrow spectral holes in the spectral density of the emitter ensemble, the coherent time of Rabi oscillation for the hybrid system is increased tenfold. With the suppressed decoherence, we move one step further in bringing plasmonic systems into practical quantum applications.

关键词： Cavity quantum electrodynamics Plasmonics Quantum optics

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A Scalable Attention Mechanism Based Neural Network for Text Classification

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Journal of Physics: Conference Series 2020年第2期1486卷

作者： Jianyun Zheng Jianmin Pang Xiaochuan Zhang Di Sun Xin Zhou Kai Zhang Dong Wang MingLiang Li Jun Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450002 China Xi'an Institute of Surveying and Mapping Xi'an 710000 China

In general, deep learning based text classification methods are considered to be effective but tend to be relatively slow especially for model training. In this work, we present a powerful, so-called 'scalable attention mechanism', which performs better than conventional attention mechanism in terms of both effectiveness and the speed of model training. Based on the scalable attention mechanism, we propose a neural network for text classification. The experimental results on eight representative datasets show that our method can obtain similar accuracy to state-of-the-art methods with training in less than 4 minutes on an NVIDIA GTX 1080Ti GPU. To the best of our knowledge, our method is at least twice faster than all the published deep learning classifiers.

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Stringent Tests of Lorentz Invariance Violation from LHAASO Observations of GRB 221009A

arXiv

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arXiv 2024年

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, 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. Takata, J. Tam, P.H.T. 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 Chengdu610000 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 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 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 Zhuhai519000 China Guangdong Guangzhou510275 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao2

On October 9, 2022, the Large High Altitude Air Shower Observatory (LHAASO) reported the observation of the very early TeV afterglow of the brightest-of-all-time GRB 221009A, recording the highest photon statistics in the TeV band ever obtained from a Gamma-ray burst. We use this unique observation to place stringent constraints on an energy dependence of the speed of light in vacuum, a manifestation of Lorentz invariance violation (LIV) predicted by some quantum gravity (QG) theories. Our results show that the 95% confidence level lower limits on the QG energy scales are EQG,1 > 10 times of the Planck energy EPl for the linear, and EQG,2 > 6 × 10−8EPl for the quadratic LIV effects, respectively. Our limits on the quadratic LIV case improve previous best bounds by factors of 5–7. Copyright © 2024, The Authors. All rights reserved.

关键词： Light velocity

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