检索结果-内蒙古大学图书馆

Multi-Variant Execution Research of Software Diversity

学校读者我要写书评

暂无评论

Multi-Variant Execution Research of Software Diversity

作者： Zhenwu Liu Zheng Zhang Jiexin Zhang Hao Liu State key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University

As more and more software products are threatened by malicious reverse analysis,along with software products are pirated, tampered with and so on, it is of great significance to study software security protection technology in depth. As a software security protection technology, software diversification introduces uncertainty into the target program and provides probabilistic protection for the target program. Multi-Variant Execution(MVE) are fine-grained implementation of software diversification that produces functionally identical variants at the system call level. This article first introduces the related concepts of multivariant execution. Secondly, it expounds the key technologies of multi-variant execution implementation-variant generation, variant monitor, input/output and synchronization,monitor-variant communication. Security of monitors and different variant communication technologies are analyzed and compared, their advantages and defects are pointed out respectively. Several implementation methods of multi-variant execution design are summarized. Finally, the reasons for the false negatives and false positives of attack events are analyzed, and the prospects and challenges of using multi-variant execution techniques to implement security systems are summarized.

关键词： Multi-Variant Execution Research of Software Diversity

Angle-Resolved Magneto-Chiral Anisotropy in a Non-Centrosymmetric Atomic Layer Superlattice

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Cheng, Long Bao, Mingrui Zhang, Jingxian Zhang, Xue Yang, Qun Li, Qiang Cao, Hui Qiu, Dawei Liu, Jia Ye, Fei Wang, Qing Liang, Genhao Li, Hui Cheng, Guanglei Zhou, Hua Zuo, Jian-Min Zhou, Xiaodong Shen, Jian Zhu, Zhifeng Mu, Sai Wang, Wenbo Zhai, Xiaofang School of Physical Science and Technology ShanghaiTech University Pudong District Shanghai201210 China CAS Key Laboratory of Microscale Magnetic Resonance School of Physical Sciences University of Science and Technology of China Hefei230026 China School of Information Science and Technology ShanghaiTech University Pudong District Shanghai201210 China State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Materials Science Division Argonne National Laboratory LemontIL60439 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China X-Ray Science Division Advanced Photon Source Argonne National Laboratory LemontIL60439 United States Department of Materials Science and Engineering Materials Research Laboratory University of Illinois Urbana–Champaign UrbanaIL61801 United States Center for Experimental Nanoscale Physics Department of Physics and Astronomy University of South Carolina ColumbiaSC29208 United States ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China

Chirality in solid-state materials has sparked significant interest due to potential applications of topologically-protected chiral states in next-generation information technology. The electrical magneto-chiral effect (eMChE), arising from relativistic spin-orbit interactions, shows great promise for developing chiral materials and devices for electronic integration. Here we demonstrate an angle-resolved eMChE in an A-B-C-C type atomic-layer superlattice lacking time and space inversion symmetry. We observe non-superimposable enantiomers of left-handed and right-handed tilted uniaxial magnetic anisotropy as the sample rotates under static fields, with the tilting angle reaching a striking 45°. Magnetic force microscopy and atomistic simulations correlate the tilt to the emergence and evolution of chiral spin textures. The Dzyaloshinskii-Moriya interaction ‘lock’ effect in competition with Zeeman effect is demonstrated to be responsible for the angle-resolved eMChE. Our findings open up a new horizon for engineering angle-resolved magneto-chiral anisotropy, shedding light on the development of novel angle-resolved sensing or writing techniques in chiral spintronics. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic anisotropy

Multi-label Classification with High-rank and High-order Label Correlations

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

DPA-2: a large atomic model as a multi-task learner

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

Mechanism of friction-induced chemical reaction high-efficient polishing single crystal 4H-SiC wafer using pure iron

学校读者我要写书评

暂无评论

Tribology International 2024年 193卷

作者： Wu, Min Huang, Hui Wu, Yueqin Xu, Zhiteng Li, Tukun Macleod, Iain Wu, Xiaolei National and Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Material Products Huaqiao University Xiamen 361021 China Institute of Manufacturing Engineering Huaqiao University Xiamen 361021 China State Key Laboratory of High-Performance Tools Zhengzhou 450001 China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH United Kingdom IMA Ltd 29 Clay Lane Cheshire WA15 8PJ United Kingdom Zhengzhou Research Institute for Abrasives & Grinding Co. Ltd Zhengzhou 450001 China

This paper proposes a novel method for friction-induced chemical reaction machining of single-crystal 4H-SiC wafers using pure iron. Material Removal Rate (MRR) and surface quality of single-crystal 4H-SiC were studied, and the results demonstrate that both high surface quality and high MRR (Ra: 2.2 nm, MRR: 375 nm/min) were obtained. The mechanism of material removal during friction was investigated by examining the critical roles of friction speed. Solid-state chemical reactions between metal and SiC surfaces can be induced by friction, and the subsequent removal of reactants, resulting in a near-damage-free machined surface. A removal mechanism model of friction was established, which reveals that both the rates of solid-state reaction and reactant removal significantly impact the final removal rate of friction. © 2024 Elsevier Ltd

关键词： Friction-induced chemical reaction Pure iron Removal mechanism SiC

advanced Analysis of Arabidopsis thaliana Leaf Traits Using RGB Imaging and Machine Learning

学校读者我要写书评

暂无评论

Advanced Analysis of Arabidopsis thaliana Leaf Traits Using ...

International Symposium on Industrial Electronics (INDEL)

作者： Rijad Sarić Edhem Čustović Martin Trtílek Amila Akagić Mathew G. Lewsey James Whelan La Trobe Institute for Sustainable Agriculture & Food (LISAF) La Trobe University Melbourne VIC Australia Australian Research Council Research Hub for Medicinal Agriculture La Trobe University Melbourne VIC Australia School of Computing Engineering and Mathematical Sciences La Trobe University (LTU) Melbourne VIC Australia Scientific Instruments Australia (SIA) Melbourne VIC Australia Photon Systems Instruments (PSI) Plant Phenotyping Research Centre Drasov Brno Czech Republic Faculty of Electrical Engineering University of Sarajevo (UNSA) Sarajevo Bosnia and Herzegovina State Key Laboratory of Plant Environmental Resilience College of Life Science Zhejiang University (ZJU) P.R. China Provincial International Science and Technology Cooperation Base on Engineering Biology Zhejiang University P.R China

ISBN: (数字)9798350352320

ISBN: (纸本)9798350352337

Image-based high-throughput plant phenotyping utilises various imaging techniques to automatically and non-invasively understand the growth of different plant species. These innovative imaging infrastructures are implemented to monitor plant development over time in indoor or outdoor environments. However, understanding the relationship between genotype and phenotype interactions under different environments remains challenging. This research study demonstrates superior extraction of leaf morphological features of different Arabidopsis thaliana ecotypes by analysing leaf geometry using a sequence of RGB images. Upon successful extraction of anatomical features, leaf length and area are converted into physical coordinates. Furthermore, considering these leaf features as 1D signals, the Fourier Spectrum is analysed, and most descriptive features are selected using PCA. Finally, leaf shape classification is established by training and testing five distinct ML models. A thorough evaluation of selected models demonstrates superiority in classifying two common leaf shapes of Arabidopsis plants.

关键词： Training Shape Biological system modeling Imaging Machine learning Feature extraction Real-time systems Reliability Testing Principal component analysis

Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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

学校读者我要写书评

暂无评论

Polymer 2025年 333卷

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：

Searching for neutrino emission from the Cygnus region 38

学校读者我要写书评

暂无评论

Searching for neutrino emission from the Cygnus region

38th International Cosmic Ray Conference, ICRC 2023

作者： Li, Wenlian Huang, Tian-Qi Xu, Donglian He, Huihai 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. Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China Key Laboratory of Particle Astrophysics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Key Laboratory of Particle Astrophyics 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 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 School of Physics and Astronomy Sun Yat-sen University Zhuhai519000 China School of Physics Sun Ya

The Cygnus region, which contains massive molecular and atomic clouds and young stars, is a promising Galactic neutrino source candidate. Cosmic rays transport in the region can produce neutrinos and γ-rays. Using ten years of track events detected by the IceCube Neutrino Observatory, we conduct searches for neutrino signals from the Cygnus region with a template method. Four templates, including the CO template, HI template, uniform template, and 2D Gaussian template, are used in this study. No significant emissions are found. We set upper limits for each template, with the resulting upper limit for the CO template being dν/dEν = 6.25 × 10−17(Eν/100TeV)−3TeV−1cm−2s−1 © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology