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Maximal coin-walker entanglement in a ballistic quantum walk

Physical Review A 2022年第4期105卷 042216-042216页

作者： Rong Zhang Ran Yang Jian Guo Chang-Wei Sun Jia-Chen Duan Heng Zhou Zhenda Xie Ping Xu Yan-Xiao Gong Shi-Ning Zhu National Laboratory of Solid State Microstructure School of Physics School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunication Nanjing 210023 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha 410073 China

We report that the position-inhomogeneous quantum walk (IQW) can be utilized to produce the maximal high-dimensional entanglement while maintaining the quadratic speedup spread of the wave function. Our calculations show that the maximal coin-walker entanglement can be generated in any odd steps or asymptotically in even steps, and the nearly maximal entanglement can be obtained in even steps after 2. We implement the IQW by a stable resource-saving time-bin optical network, in which a polarization Sagnac loop is employed to realize the precisely tunable phase shift. Our approach opens up an efficient way for high-dimensional entanglement engineering as well as promotes investigations on the role of coin-walker interactions in QW-based applications.

关键词： Entanglement manipulation Optical quantum information processing Quantum optics Quantum walks

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Security-as-a-Service with Cyberspace Mimic Defense Technologies in Cloud

Security-as-a-Service with Cyberspace Mimic Defense Technolo...

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2021国际计算机前沿大会

作者： Junchao Wang Jianmin Pang Jin Wei State Key Laboratory of Mathematical Engineering and Advanced Computing School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University

Users usually focus on the application-level requirements which are quite friendly and direct to them. However, there are no existing tools automating the application-level requirements to infrastructure provisioning and application deployment. Although some security issues have been solved during the development phase, the undiscovered vulnerabilities remain hidden threats to the application's security. Cyberspace mimic defense(CMD) technologies can help to enhance the application's security despite the existence of the vulnerability. In this paper,the concept of SECurity-as-a-Service(SECaaS) is proposed with CMD technologies in cloud environments. The experiment on it was implemented. It is found that the application's security is greatly improved to meet the user's security and performance requirements within budgets through SECaaS. The experimental results show that SECaaS can help the users to focus on application-level requirements(monetary costs,required security level, etc.) and automate the process of application orchestration.

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Mining defects of result-sensitive function based on information entropy 6th

Mining defects of result-sensitive function based on informa...

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Chen, Lin Yang, Chunfang Liu, Fenlin Gong, Daofu Yin, ZhongXu Zhengzhou Science and Technology Institute Zhengzhou450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811581007

Result-sensitive function is a typical type of security-sensitive function. The misuse of result-sensitive functions often leads to a lot kinds of software defects. Existing defect detection methods based on code mining for result-sensitive functions usually require a gived security rule or an inferred security rule as input. Based on the principle of consistency, we propose a defect detection method based on information entropy. Firstly, the feature vector about usage of function is extracted from every function instance. Then, the information entropy is introduced to measure the abnormal degree of the feature vector. The function instances with high degree of abnormality is regarded as dangerous instances. Experiments show that the proposed method can effectively detect dangerous instances of security defects without a gived security rule. © Springer Nature Singapore Pte Ltd 2020.

关键词： Defects

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A Scalable Attention Mechanism Based Neural Network for Text Classification 4

A Scalable Attention Mechanism Based Neural Network for Text...

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Zheng, Jianyun Pang, Jianmin Zhang, Xiaochuan Sun, Di Zhou, Xin Zhang, Kai Wang, Dong Li, Mingliang Wang, Jun State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China Xi'An Institute of Surveying and Mapping Xi'an710000 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. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Classification (of information)

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A Blockchain-Based CP-ABE Scheme with Partially Hidden Access Structures

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Security and Communication Networks 2021年第1期2021卷

作者： Ba, Yang Hu, Xuexian Chen, Yue Hao, Zenghang Li, Xuewei Yan, Xincheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China Kunming Audit Center Kunming650001 China State Key Laboratory of Space Medicine Fundamentals and Application Beijing100094 China

Data sharing has become a key technology to break down data silos in the big data era. Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in secure data-sharing schemes to realize flexible and fine-grained access control. However, in traditional CP-ABE schemes, the access structure is directly shared along with the ciphertext, potentially leading to users' private information leakage. Outsourcing data to a centralized third party can easily result in privacy leakage and single-point bottlenecks, and the lack of transparency in data storage and sharing casts doubts whether users' data are safe. To address these issues, we propose a blockchain-based CP-ABE scheme with partially hidden access structures (BCP-ABE-PHAS) to achieve fine-grained access control while ensuring user privacy. First, we propose an efficient CP-ABE scheme with partially hidden access structures, where the ciphertext size is constant. To assist data decryption, we design a garbled Bloom filter to help users quickly locate the position of wildcards in the access structure. Then, to improve storage efficiency and system scalability, we propose a data storage scheme that combines blockchain technology and the interplanetary file system, ensuring data integrity. Finally, we employ smart contracts for a transparent data storage and sharing process without third-party participation. Security analysis and performance evaluation show that the proposed BCP-ABE-PHAS scheme can preserve policy privacy with efficient storage and low computational overhead. © 2021 Yang Ba et al.

关键词： Blockchain

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Rotation Transformation: A Method to Improve the Transferability of Adversarial Examples

Rotation Transformation: A Method to Improve the Transferabi...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Zheming Li Hengwei Zhang Junqiang Ma Bo Yang Chenwei Li Jingwen Li State Key Laboratory of Mathematical Engineering and Advanced Computing Key Laboratory of Information Security of Henan Provincial Zhengzhou China PLA Army Beijing China

ISBN: (纸本)9781665406314

Convolutional neural network models are fragile to adversarial examples. Adding disturbances that humans cannot observe in clean images can make the model classification error. Among the adversarial attack methods, white-box attacks have achieved a high attack success rate, but the "overfitting" between the adversarial examples and the model has led to a low success rate of black-box attacks. To this end, this paper introduces the data augmentation method into the adversarial examples generation process, establishes a probability model to perform random rotation transformation on clean images, improves the mobility of adversarial examples, and improves the success rate of adversarial examples under black-box setting. The experimental results on ImageNet show that the RO-MI-FGSM method we proposed has a stronger attack effect, achieving a black-box attack success rate up to 80.3%.

关键词： Solids Data models Convolutional neural networks Intelligent systems

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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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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation

arXiv

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arXiv 2024年

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical & Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we verify for the first time that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. Copyright © 2024, The Authors. All rights reserved.

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Enhanced Anomalous Hall Effect of Pt on an Antiferromagnetic Insulator with Fully Compensated Surface

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Chinese Physics Letters 2022年第10期39卷 60-65页

作者： Yu Bai Zhe Wang Na Lei Wisal Muhammad Lifeng Xiang Qiang Li Huilin Lai Yinyan Zhu Wenbing Wang Hangwen Guo Lifeng Yin Ruqian Wu Jian Shen State Key Laboratory of Surface Physics and Institute for Nanoelectronics Devices and Quantum Computing Fudan UniversityShanghai 200433China Shanghai Qi Zhi Institute Shanghai 200232China Department of Physics Fudan UniversityShanghai 200433China Zhangjiang Fudan International Innovation Center Fudan UniversityShanghai 201210China Shanghai Research Center for Quantum Sciences Shanghai 201315China Fert Beijing Institute BDBCSchool of Electronic and Information EngineeringBeihang UniversityBeijing 100191China Department of Physics and Astronomy University of CaliforniaIrvineCalifornia 92697USA Collaborative Innovation Center of Advanced Microstructures Nanjing 210093China

We report a significantly enhanced anomalous Hall effect(AHE)of Pt on antiferromagnetic insulator thin film(3-unit-cell La_(0.7)Sr_(0.3)MnO_(3),abbreviated as LSMO),which is one order of magnitude larger than that of Pt on other ferromagnetic(e.g.Y_(3)Fe_(5)O_(12))and antiferromagnetic(***_(2)O_(3))insulator thin *** experiments demonstrate that the antiferromagnetic La_(0.7)Sr_(0.3)MnO_(3)with fully compensated surface suppresses the positive anomalous Hall resistivity induced by the magnetic proximity effect and facilitates the negative anomalous Hall resistivity induced by the spin Hall *** changing the substrate’s temperature during Pt deposition,we observed that the diffusion of Mn atoms into Pt layer can further enhance the *** anomalous Hall resistivity increases with increasing temperature and persists even well above the Neel temperature(T_(N))of *** Monte Carlo simulations manifest that the unusual rise of anomalous Hall resistivity above T_(N)originates from the thermal induced magnetization in the antiferromagnetic insulator.

关键词： effect ferromagnetic resistivity

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Expert with Outlier Exposure for Continual Learning of New Diseases

Expert with Outlier Exposure for Continual Learning of New D...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhengjing Xu Kanghao Chen Wei-Shi Zheng Zhijun Tan Xiaobo Yang Ruixuan Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE China State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou China Department of Network Intelligence Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Current intelligent diagnosis systems struggle to continually learn to diagnose more and more diseases due to catastrophic forgetting of old knowledge when learning new knowledge. Although storing small old data for subsequent continual learning can effectively help alleviate the forgetting issue, the heavy data imbalance between old classes and to-be-learned new classes in classifier training often causes biased prediction towards the new classes just learned by the updated classifier. In this study, an outlier detection technique is novelly applied to train an additional expert classifier for new classes to help alleviate the class imbalance issue and discriminate the learned new classes from old classes during inference (instead of the training phase). Specially, the stored small data of old classes are considered as outliers during training the expert classifier, such that the output probability distributions from the expert classifier are expected to be obviously different between test data of the old classes and those of the new classes. Such difference between old classes and new classes can be used to fine-tune the original output from the updated classifier which is responsible for prediction of all learned (old and new) classes. During inference, a novel ensemble strategy is proposed to combine the predictions from the updated classifier, the expert classifier, and the previously learned old classifier. The proposed learning and inference framework can be easily combined with existing continual learning strategies. Empirical evaluations on three medical image datasets and one natural image dataset show that the proposed framework can effectively improve continual learning performance.

关键词： Training Biological system modeling Probability distribution Bioinformatics Anomaly detection Medical diagnostic imaging Diseases

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