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Extracting hidden messages of MLSB steganography based on optimal stego subset

Science China(Information Sciences) 2018年第11期61卷 241-243页

作者： Chunfang YANG Xiangyang LUO Jicang LU Fenlin LIU Zhengzhou Information Science and Technology Institute State Key Laboratory of Mathematical Engineering and Advanced Computing

Dear editor,In recent years, steganalysis researchers have tried their best to extract the hidden messages. For example, under the condition of a known embedding position generator, Liu et al.[1] proposed a collision attack algorithm to recover the stego key of least significant bit (LSB) steganography. Fridrich et al.[2] proposed a chi-squared-test-based method to recover the stego key of LSB steganography for the case of an unknown carrier. Under the condition of multiple stego images embedded into the same positions, Ker [3] first proposed locating the

关键词： LSB

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

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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

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Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

arXiv

引用

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

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Searching for neutrino emission from the Cygnus region 38

Searching for neutrino emission from the Cygnus region

引用

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

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

引用

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：

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Mechanism of Friction-Induced Chemical Reaction High-Efficient Polishing Single Crystal 4h-Sic Wafer Using Pure Iron

SSRN

引用

SSRN 2023年

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 Xiamen361021 China Institute of Manufacturing Engineering Huaqiao University Xiamen361021 China State Key Laboratory of High-Performance Tools Zhengzhou450001 China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom IMA Ltd 29 Clay Lane CheshireWA15 8PJ United Kingdom Zhengzhou Research Institute for Abrasives & Grinding Co. Ltd Zhengzhou450001 China

This paper proposes a novel method for friction-induced chemical reaction machining of single-crystal 4H-SiC wafer using a 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 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 reaction 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. © 2023, The Authors. All rights reserved.

关键词： Silicon carbide

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China 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 Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Searching for neutrino signals correlated with LHAASO diffuse Galactic emission 38

Searching for neutrino signals correlated with LHAASO diffus...

引用

38th International Cosmic Ray Conference, ICRC 2023

The diffuse Galactic γ-ray emission is produced from the interaction of cosmic rays and interstellar medium or radiation fields in the Galaxy, where neutrino production is also expected. Recently, the Large High Altitude Air Shower Observatory (LHAASO) reported the measurements of the diffuse γ-ray from the Galactic plane with energies above 10 TeV. In this study, we construct the neutrino emission template based on LHAASO’s observation and search for diffuse neutrinos accompanying the LHAASO diffuse Galactic γ-ray emission using ten-year IceCube track events. No significant signals are found. We set 90% confidence level upper limits, resulting in the neutrino flux of dν/dEν = 1.27 × 10−14(Eν/25TeV)−2.99TeV−1cm−2s−1 for the diffuse γ-ray flux map with a 1.5σ significance cut. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Influence of the dissipative topological edge state on quantized transport in MnBi2Te4

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Physical Review B 2022年第16期105卷 165411-165411页

作者： Weiyan Lin Yang Feng Yongchao Wang Zichen Lian Hao Li Yang Wu Chang Liu Yihua Wang Jinsong Zhang Yayu Wang Xiaodong Zhou Jian Shen State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai China Department of Physics Fudan University Shanghai China Beijing Academy of Quantum Information Science Beijing China Beijing Innovation Center for Future Chips Tsinghua University Beijing China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing China School of Materials Science and Engineering Tsinghua University Beijing China Tsinghua-Foxconn Nanotechnology Research Center Department of Physics Tsinghua University Beijing China Department of Mechanical Engineering Tsinghua University Beijing China Shanghai Research Center for Quantum Sciences Shanghai China Frontier Science Center for Quantum Information Beijing China Shanghai Qi Zhi Institute Shanghai China Zhangjiang Fudan International Innovation Center Fudan University Shanghai China Collaborative Innovation Center of Advanced Microstructures Nanjing China

The beauty of the quantum Hall (QH) effect is the metrological precision of Hall resistance quantization that originates from the topological edge states. Understanding the factors that lead to quantization breakdown not only provides important insights on the nature of the topological protection of these edge states, but is beneficial for device applications involving such quantized transport. In this work, we combine conventional transport and real space conductivity mapping to investigate whether the quantization breakdown is tied to the disappearance of edge state in the hotly studied MnBi2Te4 system. Our experimental results unambiguously show that topological edge state does exist when quantization breakdown occurs. Such edge state is dissipative in nature and could lead to a quantization breakdown due to its diffusive character causing overlapping with bulk and other edge states in real devices. Our findings bring attention to issues that are generally inaccessible in the transport study of QH, but can play important roles in practical measurements and device applications.

关键词： Quantum anomalous Hall effect Topological insulators Scanning probe microscopy

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A biologically plausible audio-visual integration model for continual learning

arXiv

引用

arXiv 2020年

作者： Chen, Wenjie Du, Fengtong Wang, Ye Cao, Lihong State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

The problem of catastrophic forgetting has a history of more than 30 years and has not been completely solved yet. Since the human brain has natural ability to perform continual lifelong learning, learning from the brain may provide solutions to this problem. In this paper, we propose a novel biologically plausible audio-visual integration model (AVIM) based on the assumption that the integration of audio and visual perceptual information in the medial temporal lobe during learning is crucial to form concepts and make continual learning possible. Specifically, we use multi-compartment Hodgkin-Huxley neurons to build the model and adopt the calcium-based synaptic tagging and capture as the model’s learning rule. Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain. We then test our model under this new paradigm. Our experimental results show that the proposed AVIM can achieve state-of-the-art continual learning performance compared with other advanced methods such as OWM, iCaRL and GEM. Moreover, it can generate stable representations of objects during learning. These results support our assumption that concept formation is essential for continuous lifelong learning and suggest the proposed AVIM is a possible concept formation mechanism. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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