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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of Computer Science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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Ultra-high-amplitude Peregrine solitons induced by helicoidal spin-orbit coupling

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Physical Review Research 2024年第3期6卷 L032036-L032036页

作者： Cui-Cui Ding Qin Zhou B. A. Malomed Research Group of Nonlinear Optical Science and Technology Research Center of Nonlinear Science School of Mathematical and Physical Sciences State Key Laboratory of New Textile Materials and Advanced Processing Technologies Department of Physical Electronics School of Electrical Engineering Faculty of Engineering and the Center for Light-Matter University Instituto de Alta Investigación

In the framework of the model of a spatially nonuniform Bose-Einstein condensate with helicoidal spin-orbit (SO) coupling, we find abnormal Peregrine solitons (PSs) on top of flat and periodic backgrounds, with ultrahigh amplitudes. We explore the roles of the SO coupling strength and helicity pitch in the creation of these anomalously tall PSs and find that their amplitude, normalized to the background height, attains indefinitely large values. The investigation of the modulation instability (MI) in the same system demonstrates that these PSs exist in a range of relatively weak MI, maintaining the feasibility of their experimental observation.

关键词： Solitons

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Robust Zero Watermarking Algorithm for Medical Images Using Local Binary Pattern and Discrete Cosine Transform 8th

Robust Zero Watermarking Algorithm for Medical Images Using ...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Liu, Wenyi Li, Jingbing Shao, Chunyan Ma, Jixin Huang, Mengxing Bhatti, Uzair Aslam School of Information and Communication Engineering Hainan University Haikou570228 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan University Haikou570228 China

ISBN: (纸本)9783031067631

Thanks to the rapid development of the telemedicine, medical treatment has turned in great convenience. However, as the internet network can be addressed by any user, the medical images cannot avoid the defects of suffering from information leakage and require information encryption techniques. To protect the privacy of patients in medical images, a robust watermarking method, which combines medical images with watermark information, is proposed in this paper. First, the low-frequency feature vector of the medical image is extracted by combining local binary pattern (LBP) and transformation with discrete cosine transform (DCT). Second, hash sequence is implemented on to convert feature vector into binary sequences. At last, the medical image is embedded with hash sequence and watermark preprocessed by chaotic encryption technology using the zero-watermark technology. The experimental results show that the method can encrypt the medical images’ watermark effectively and has good robustness against geometric attacks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Local binary pattern

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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CATH: an effective method for detecting denial-of-service attacks in software defined networks

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Science China(Information Sciences) 2019年第3期62卷 75-89页

作者： Yi GUO Fu MIAO Liancheng ZHANG Yu WANG State Key Laboratory of Mathematical Engineering and Advanced Computing Institute for Network Science and Cyberspace Tsinghua University School of Computer Science Henan University of Engineering

Software defined networks(SDNs) are innovative network frameworks that have recently received wide attention. Their programming flexibility facilitates automatic network management and control, thus mitigating existing issues in the traditional network architecture. However, SDNs face several security risks,in particular denial-of-service(DoS) attacks, the most common and serious network attacks. To address such a threat, an SDN-DoS attack detection method is proposed based on fusing multiple flow features for describing the network catastrophe between the normal and the attack state. Several statistic attributes of SDN flow information are first chosen as detection features; subsequently, the cusp model is used to establish a catastrophe equilibrium surface for SDN states. After being trained, the cusp catastrophe model can be utilized to infer whether an SDN is under DoS attack. The experimental results demonstrate that the method can effectively and timely perceive SDN-DoS attacks, not only in simple networks but also in larger enterprise networks.

关键词： DoS attacks software defined network flow features cusp model equilibrium surface

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Deepstsnet: Reconstructing the Quantum state-Resolved Thermochemical Nonequilibrium Flowfield Using Deep Neural Operator Learning With Scarce Data

SSRN

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SSRN 2023年

作者： Lv, Jiaqi Hong, Qizhen Wang, Xiaoyong Mao, Zhiping Sun, Quanhua State Key Laboratory of High Temperature Gas Dynamics Institute of Mechanics Chinese Academy of Sciences Beijing100190 China School of Engineering Science University of Chinese Academy of Sciences Beijing100049 China School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Xiamen361005 China

The hypersonic flow is in a thermochemical nonequilibrium state due to the high-temperature caused by the strong shock compression. In a thermochemical nonequilibrium flow, the distribution of molecular internal energy levels strongly deviates from the equilibrium distribution (i.e., the Boltzmann distribution). It is intractable to directly obtain the microscopic nonequilibrium distribution from existed experimental measurements usually described by macroscopic field variables such as temperature or velocity. Motivated by the idea of deep multi-scale multi-physics neural network (DeepMMNet) proposed in [Mao et al, J. Comput. Phys., 2021], we develop in this paper a data assimilation framework called DeepStSNet to accurately reconstruct the quantum state-resolved thermochemical nonequilibrium flowfield by using sparse experimental measurements of vibrational temperature and pre-trained deep neural operator networks (DeepONets). In particular, we first construct several DeepONets to express the coupled dynamics between field variables in the thermochemical nonequilibrium flow and to approximate the state-to-state (StS) approach, which traces the variation of each vibrational level of molecule accurately. These proposed DeepONets are then trained by using the numerical simulation data, and would later be served as building blocks for the DeepStSNet. We demonstrate the effectiveness and accuracy of DeepONets with different test cases showing that the density and energy of vibrational groups as well as the temperature and velocity fields are predicted with high accuracy. We then extend the architectures of DeepMMNet by considering a simplified thermochemical nonequilibrium model, i.e., the 2T model, showing that the entire thermochemical nonequilibrium flowfield is well predicted by using scattered measurements of full or even partial field variables. We next consider a more accurate and complex thermochemical nonequilibrium model, i.e., the StS-CGM model, and develop a

关键词： Multiphysics

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Green Floating Blockchain-Empowered Co-Trust Security Mechanism with Energy Efficiency Against Attack Threat for 6G-IoV

Green Floating Blockchain-Empowered Co-Trust Security Mechan...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yibing Liu Lijun Huo Hansong Xu Qianqian Pan Jun Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China School of Engineering The University of Tokyo Tokyo Japan Graduate School of Information Production and Systems Waseda University Fukuoka Japan

The Internet of Vehicles (IoV) based on the 6-th Generation (6G) communication brings convenience, but also raises anxiety about information security. Researchers have developed static security schemes based on the blockchain, but it results in excessive resource occupation. And it is difficult to resist some attacks such as desynchronization, Denial of Service (DoS), and covert intrusion. In response to the above problems, this paper proposes the Floating Blockchain Consensus Security (FBCS) scheme. It models the attack risk of Road Side Unit (RSU) through the traffic flow prediction to present the credibility evaluation and constructs the floating blockchain based on the results. And the security capability is adjusted through the dynamic joining and exit mode of trusted nodes and untrusted nodes. FBCS also establishes a relationship between blockchain size and energy consumption. Once the system resources are found to be insufficient, it applies the cloud center-based supplementary certification mechanism to provide authentication to untrusted nodes to enhance the stability of the *** analysis and simulation experiments prove that the FBCS can afford data privacy, maintain moderate security capabilities, and adapt defense capabilities according to the attack environment, reduce resource occupation to save cost.

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Breaking Limitation of Convergence in Continuous Optimization: Predefined-Time Gradient Flows

Breaking Limitation of Convergence in Continuous Optimizatio...

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IEEE Conference on Decision and Control

作者： Renyongkang Zhang Ge Guo Zhenyu Gao Miao Yu Lu Zhang Zhengsong Wang Meng Han Yanyan Yin College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Western Australia Australia

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper investigates the continuous-time unconstrained optimization via dynamical gradient flow systems. For strongly convex objective functions, a gradient flow with predefined time convergence is proposed, which is suitable for relaxing the objective function to only satisfy the PolyakLojasiewicz ($\mathbf{P}-\mathrm{L}$) condition. A predefined-time convergent Newton’s flow is designed for the minimization of strictly convex functions. The proposed result is extended for strongly convex objective functions with time-varying parameters. In our schemes, the upper bound of convergence time can be used as a prior choice without involving system initialization and complex parameter calculations. The convergence of algorithms is rigorously proved by using the Lyapunov method. Numerical simulation illustrates the effectiveness of the proposed methods.

关键词： Upper bound Linear programming Numerical simulation Minimization Convex functions Optimization Convergence Lyapunov methods

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A differential privacy preserving algorithm for greedy decision tree

A differential privacy preserving algorithm for greedy decis...

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Big Data & Artificial Intelligence & Software engineering (ICBASE), International Conference on

作者： Shudan Yang Nan Li Daozhu Sun Qiming Du Wenfu Liu Strategic Support Force Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing College of Cyberspace Security Zhengzhou China

ISBN: (纸本)9781665427104

In recent years, the contradiction between data application and privacy protection has become increasingly prominent, and differential privacy is considered an effective technology to resolve this contradiction. Therefore, we propose a differential privacy protection algorithm for greedy decision trees. Firstly, the privacy budget is allocated according to the structural characteristics of the tree. Secondly, the algorithm meets ϵ-differential privacy by adding Laplacian perturbation to the leaf nodes and exponential mechanism perturbation to the intermediate nodes. Finally, The integration strategy is used to construct random forest to avoid the instability of single tree. Experimental results show that this algorithm achieves a better balance between data availability and invisibility compared with other algorithms.

关键词： Privacy Differential privacy Laplace equations Perturbation methods Stability analysis Classification algorithms Resource management

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Function Projective Synchronization of Chaotic PMSMs with Certain and Uncertain System Parameters

Function Projective Synchronization of Chaotic PMSMs with Ce...

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Chinese Automation Congress (CAC)

作者： Mufeng Wang Ke Liu Yangyang Geng Xin Che Jingpei Wang College of Control Science&Engineering Zhejiang University Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665426480

In recent years, driven by huge engineering demands, industrial Cyber-Physical System (iCPS) has developed rapidly. As an important energy conversion device in iCPS, stable operation of permanent magnet synchronous motor (PMSM) is very important to ensure the availability of the whole iCPS. In this paper, based on Lyapunov stability theory, the function projective synchronization (FPS) of chaotic PMSM systems with certain and uncertain system parameters are investigated, respectively. The proposed control method is proved by using explicit numerical theory and then applied in numerical simulations. From the results of simulations, effectiveness of the control method is obviously. Our study can solve the synchronization and motors coordination problem in industry manufacture.

关键词： Uncertain systems Production Cyber-physical systems Numerical simulation Synchronous motors Permanent magnet motors Motor coordination

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