检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Zhuang, Tianming Qin, Zhiguang You, Li Deng, Erqiang Ding, Yi Cao, Mingsheng Guo, Yingkun Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China Department of Molecular Genetics Erasmus University Medical Center Rotterdam Netherlands Department of Radiology Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects Ministry of Education West China Second University Hospital Sichuan University Sichuan Chengdu610072 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

The electrocardiogram diagnosis played an important role in early arrhythmia prevention and cardiovascular disease detection. How to analysis and detect the electrocardiogram automatically becomes a challenging task in clinical practice. Convolutional neural network based approaches have been widely applied in the field of automatic arrhythmia detection and analysis. However, the existing methods fails to preserve the abundant and essential latent representatives from multiple dimensions. To address these problems, a dual spatial-channel aggregation module is proposed, which expects to concentrate on the prominent target information along spatial and channel dimension of electrocardiogram signals. Moreover, a multi-scale enhancement module with channel-wise gating mechanism is also proposed to enlarge the receptive resolution and further to improve the learning ability of long-range dependencies from the global information. Furthermore, these two proposed modules are integrated as a "plug-and-play" block that can be easily adopted as a drop-in replacement for any 3×3 convolution layers in convolutional neural networ based architectures. In order to evaluate the effectiveness and generalization of the proposed block, it is plugged in a series of convolutional neural network based frameworks and are evaluated on two benchmark datasets, MIT-BIH and PTB. The extensive experimental results show that the proposed "plug-and-play" block can significantly improve the arrhythmia classification performance. Moreover, compared to other state-of-the-art methods, the framework plugged with the proposed block can achieve a competitive classification performance, proving the feasibility and advancement of the proposed dual attention-based multiscale feature learning block. © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

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A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

A residual attention-based privacy-preserving biometrics mod...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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Low-latency federated learning over wireless channels with differential privacy

arXiv

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arXiv 2021年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Chen, Cailian Jin, Shi Han, Zhu Poor, H. Vincent School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO SydneyNSW2015 Australia The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. We solve this problem in the framework of multi-agent multiarmed bandit (MAMAB) to deal with the situation where there are multiple clients confornting different unknown transmission environments, e.g., channel fading and interferences. Specifically, we first transform the long-term constraints on both training performance and each client's DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., modified Hungarian algorithm and greedy matching with a better alternative (GMBA), in which the first one can achieve the optimal solution with a high complexity while the second one approaches a better tradeoff by enabling a verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Hard Sample Matters a Lot in Zero-Shot Quantization

arXiv

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arXiv 2023年

作者： Li, Huantong Wu, Xiangmiao Lv, Fanbing Liao, Daihai Li, Thomas H. Zhang, Yonggang Han, Bo Tan, Mingkui South China University of Technology China Changsha Hisense Intelligent System Research Institute Co. Ltd China Hong Kong Baptist University Hong Kong Key Laboratory of Big Data and Intelligent Robot Ministry of Education China PengCheng Laboratory China Information Technology R&D Innovation Center of Peking University China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models. Accordingly, quantized models obtained by these methods suffer from significant performance degradation on hard samples. To address this issue, we propose HArd sample Synthesizing and Training (HAST). Specifically, HAST pays more attention to hard samples when synthesizing samples and makes synthetic samples hard to fit when training quantized models. HAST aligns features extracted by full-precision and quantized models to ensure the similarity between features extracted by these two models. Extensive experiments show that HAST significantly outperforms existing ZSQ methods, achieving performance comparable to models that are quantized with real data. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Subjective cognitive decline domain improves accuracy of plasma Aβ_(42)/Aβ_(40)for preclinical Alzheimer’s disease diagnosis:The SILCODE study

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Chinese Medical Journal 2024年第9期137卷 1127-1129页

作者： Mingkai Zhang Xiaoni Wang Weina Zhao Yuxia Li Chao Ying Jiehui Jiang Yanning Cai Jie Lu Ying Han Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijing 100053China Department of Neurology Sir Run Run Shaw HospitalAffiliated with School of MedicineZhejiang UniversityHangzhouZhejiang 310016China Department of Neurology Hongqi Hospital Affiliated to Mudanjiang Medical UniversityMudanjiangHeilongjiang 157011China Heilongjiang Key Laboratory of Ischemic Stroke Prevention and Treatment MudanjiangHeilongjiang 157011China Department of Neurology Tangshan Central HospitalTangshanHebei 063000China Department of Neurobiology Xuanwu Hospital of Capital Medical UniversityKey Laboratory for Neurodegenerative Diseases of the Ministry of EducationBeijing 100053China Institute for Advanced Communication and Data Science Shanghai UniversityShanghai 200444China Department of Radiology and Nuclear Medicine Xuanwu HospitalCapital Medical UniversityBeijing 100053China Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Beijing 100053China Key Laboratory of Neurodegenerative Diseases Ministry of EducationBeijing 100053China School of Biomedical Engineering Hainan UniversityHaikouHainan 570228China Center of Alzheimer’s Disease Beijing Institute for Brain DisordersBeijing 100053China National Clinical Research Center for Geriatric Disorders Beijing 100053China Institute of Biomedical Engineering Shenzhen Bay LaboratoryShenzhenGuangdong 518132China

To the Editor:Accumulating evidence has shown that the preclinical stage of Alzheimer’s disease(AD)(i.e.,asymptomatic amyloidosis)lasts for decades before the onset of cognitive symptoms,providing a large window for early *** pathology,the earliest pathological change associated with AD,can be detected in vivo with cerebrospinal fluid(CSF)analysis or positron emission tomography(PET),and its presence is necessary for the diagnosis of preclinical AD(pre-AD).However,both PET scans and CSF analyses are expensive,hampering their use in large-scale ***,blood-based biomarkers are desirable alternatives,as they are cost-effective and not invasive.

关键词： diagnosis clinical Alzheimer

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Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency

arXiv

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arXiv 2022年

作者： Li, Zihan Chen, Wentao Wei, Zhiqing Luo, Xingqi Su, Bing The School of Informatics Xiamen University Xiamen361005 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science & Technology Beijing Institute of Technology Beijing100081 China The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China

Supervised learning has been widely used for attack categorization, requiring high-quality data and labels. However, the data is often imbalanced and it is difficult to obtain sufficient annotations. Moreover, supervised models are subject to real-world deployment issues, such as defending against unseen artificial attacks. To tackle the challenges, we propose a semi-supervised fine-grained attack categorization framework consisting of an encoder and a two-branch structure and this framework can be generalized to different supervised models. The multilayer perceptron with residual connection is used as the encoder to extract features and reduce the complexity. The Recurrent Prototype Module (RPM) is proposed to train the encoder effectively in a semi-supervised manner. To alleviate the data imbalance problem, we introduce the Weight-Task Consistency (WTC) into the iterative process of RPM by assigning larger weights to classes with fewer samples in the loss function. In addition, to cope with new attacks in real-world deployment, we propose an Active Adaption Resampling (AAR) method, which can better discover the distribution of unseen sample data and adapt the parameters of encoder. Experimental results show that our model outperforms the state-of-the-art semi-supervised attack detection methods with a 3% improvement in classification accuracy and a 90% reduction in training time. Copyright © 2022, The Authors. All rights reserved.

关键词： Intrusion detection

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model

Elucidating Spatial Complex Structures from Mass Spectrometr...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Jiangnan Cui Junjie Xia Xuan Li Yan Wang Fufang Qiu Yaochen Xu Keren Xu Chunman Zuo School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Department of Neurosurgery Huashan Hospital Shanghai China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms.

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Event-triggering-based H_∞ load frequency control for multi-area cyber–physical power system under DoS attacks

Franklin Open

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Franklin Open 2023年 3卷

作者： Liu, Xingyue Shi, Kaibo Zhou, Kun Wen, Shiping Tang, Yiqian Tang, Lin School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 China

In this work, the event-triggering-based resilient H∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicle... 详细信息

In this work, the event-triggering-based resilient H_∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicles (EVs). In MA-CPPS, the sampled measurements are transmitted through the communication network, which may be subjected to the energy-limited DoS attack with a resilient index of maximum allowable continuous data losses. Meanwhile, a novel adaptive event-triggering mechanism (AETM) characterized by parameters disturbances and dynamic adjustment mechanism of triggering threshold can effectively reduce the occupation of network bandwidth and computational burden in practical operation of sampled-data LFC in MA-CPPS. Hence a sampled-data LFC model of considering energy-limited DoS attack and AETM is established firstly. Then based on a novel AETM transmission interval-dependent loop functional approach, a resilient H_∞ state-feedback controller is designed with guaranteed H_∞ performance and resilient index of energy-limited DoS attack. Finally, the effectiveness and superiority of our proposed scheme are verified by one-area and three-areas LFC CPPS. © 2023 The Authors

关键词： Adaptive event-triggering mechanism Cyber–physical power system Denial-of-service attacks Resilient H∞ load frequency control scheme Transmission interval-dependent loop functional approach

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A Survey on Applications of Reinforcement Learning in Spatial Resource Allocation

arXiv

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arXiv 2024年

作者： Zhang, Di Wang, Moyang Mango, Joseph Li, Xiang Xu, Xianrui Key Laboratory of Geographic Information Science Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China Department of Transportation and Geotechnical Engineering University of Dar es Salaam Dar es salaam Tanzania United Republic of Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration East China Normal University Shanghai200241 China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources East China Normal University Shanghai200241 China School of Economics and Management Shanghai University of Sport Shanghai200438 China

The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Therefore, this paper aims to summarize and review recent theoretical methods and applied research utilizing reinforcement learning to address spatial resource allocation problems. It provides a summary and comprehensive overview of its fundamental principles, related methodologies, and applied research. Additionally, it highlights several unresolved issues that urgently require attention in this direction for the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

TechRxiv

引用

TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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