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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Quantum Entanglement Based on Two-Photon Joint Subtraction from Two-Mode Squeezed Vacuum States

Quantum Entanglement Based on Two-Photon Joint Subtraction f...

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OptoElectronics and Communications Conference, OECC

作者： Hongbin Song Heyu Huang Hao Liu Ai Zhou Guofeng Zhang Hidehiro Yonezawa The General Education Division and Guangdong Key Laboratory of Optoelectronic Materials and Chips and Shenzhen Key Lab of Semiconductor Lasers The Chinese University of Hong Kong Shenzhen Shenzhen China School of Materials Science and Engineering and National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan China National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan China Department of Applied Mathematics The Hong Kong Polytechnic University Hongkong China Optical Quantum Control Research Team RIKEN Center for Quantum Computing Saitama Japan

We present and analyze the quantum entanglement generated from two-photon joint subtraction from two-mode squeezed vacuum states (TMSV). Larger entanglement negativity than single-photon jointly subtracted TMSV, photo... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Protocols Correlation Quantum entanglement Simulation Elementary particle vacuum Quantum networks Photonics

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Machine learning framework for computing the most probable paths of stochastic dynamical systems

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Physical Review E 2021年第1期103卷 012124-012124页

作者： Yang Li Jinqiao Duan Xianbin Liu State Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics 29 Yudao Street Nanjing 210016 China Department of Applied Mathematics College of Computing Illinois Institute of Technology Chicago Illinois 60616 USA

The emergence of transition phenomena between metastable states induced by noise plays a fundamental role in a broad range of nonlinear systems. The computation of the most probable paths is a key issue to understanding the mechanism of transition behaviors. The shooting method is a common technique for this purpose to solve the Euler-Lagrange equation for the associated action functional, while losing its efficacy in high-dimensional systems. In the present work, we develop a machine learning framework to compute the most probable paths in the sense of Onsager-Machlup action functional theory. Specifically, we reformulate the boundary value problem of a Hamiltonian system and design a neural network to remedy the shortcomings of the shooting method. The successful applications of our algorithms to several prototypical examples demonstrate its efficacy and accuracy for stochastic systems with both (Gaussian) Brownian noise and (non-Gaussian) Lévy noise. This approach is effective in exploring the internal mechanisms of rare events triggered by random fluctuations in various scientific fields.

关键词： Diffusion Fluctuations & noise Neuronal networks

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Secrecy Performance Analysis of Multi-Functional RIS-Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Pei, Yingjie Ni, Wanli Xu, Jin Yue, Xinwei Tao, Xiaofeng Niyato, Dusit School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing102206 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive the secrecy outage probability (SOP) and secrecy throughput expressions of users in MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS;ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity;and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data

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Computers in Biology and Medicine 2024年 178卷 108704-108704页

作者： Taberna, Gaia Amaranta Samogin, Jessica Zhao, Mingqi Marino, Marco Guarnieri, Roberto Cuartas Morales, Ernesto Ganzetti, Marco Liu, Quanying Mantini, Dante Movement Control and Neuroplasticity Research Group KU Leuven Leuven3001 Belgium Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou730000 China Department of General Psychology University of Padova Padova35131 Italy Dirección Académica Universidad Nacional de Colombia Sede de La Paz La Paz202017 Colombia pRED Data & Analytics Roche Innovation Center Basel F. Hoffmann–La Roche Ltd Basel4070 Switzerland Department of Biomedical Engineering Southern University of Science and Technology Shenzhen518055 China KU Leuven Brain Institute KU Leuven Leuven3000 Belgium

Introduction: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets. Findings: Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. Conclusions: NET is freely available (https://***/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows. © 2024

关键词： Electroencephalography

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Reconfigurable Intelligent Surface Enhanced Wireless Localization: Phase Optimization for Malicious Interference Mitigation

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IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

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A Comprehensive Analysis in Investigating the Impact of Big Data Analytics in Wireless Networks in Failure Prediction, Prevention and Recovery 2

A Comprehensive Analysis in Investigating the Impact of Big ...

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2nd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Surya, S. Rajasekaran, P. Yuriy, Shvets Johar, Siti Sarawati Tripathi, Amrendra Magno Ventayen, Randy Joy Computer Science and Engineering School of Computing Kalasalingam University India Information Technology Dr. N.G.P Institute of Technology India Financial University under the Goverment of Russian Federation Institute of Control of Science Ras Moscow Russia Centre for General Studies and Co-curricular Universiti Tun Hussein Onn Malaysia Department of Social Science Parit Raja Johor Darul Takzim Batu Pahat86400 Malaysia School of Computer Science Upes Dehradun India Pangasinan State University Lingayen Campus Philippines

ISBN: (纸本)9781665437899

The current economic and competitive environment has enabled many businesses and industries to implement digital for enhancing the business operations and support in addressing the overall objectives. Dur to this there is a mounting avalanche information being shared between the individuals, companies and governments, the increase in the usage of mobile devices, internet, social media and other ecommerce services has supported in increasing the volume of data. This trend is expected to grow exponentially and hence there is a requirement to provide wireless network in an effective manner, the increased usage of data and information has resulted in facing various failures, like call drops for mobile users, low bandwidth for internet users etc. Hence, it is highly necessary to implement tools to understand the failure prediction, prevention and recovery on the network. Hence, this study is focused in investigating the impact of big data analysis in protecting the wireless networks from such failures and support in processing large volume of information in an effective manner, the big data analytics can also extract useful data will enable in addressing the concerned parties so as to make critical decisions. In this study, the researcher intends to conduct the survey using closed ended questionnaire from the individuals so as to measure the effectiveness of applying big data for failure prevention, protection and recovery in the wireless network. © 2022 IEEE.

关键词： Wireless networks

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Data protection and provenance in cloud of things environment: Research challenges

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International Journal of Information and Computer Security 2020年第4期12卷 416-435页

作者： Wang, Chundong Yang, Lei Guo, Hao Wan, Fujin Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin China Global Energy Internet Research Institute Beijing University of Technology Tianjin China Department of College of Computer and Control Engineering Nankai University Tianjin China

Internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks. © 2020 Inderscience Enterprises Ltd.

关键词： Data privacy

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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