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SSRN 2022年

作者： Gao, Pengli Hui, Hui Guo, Chanjuan Liu, Yu Su, Ya Huang, Xiazi Shang, Wenting Jiang, Jingying Tian, Jie Guo, Kunxiong CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Biological Science and Medicine Engineering School of Engineering Medicine Beihang University No. 37 Xueyuan Road Beijing100191 China Key Laboratory of Big Data-Based Precision Medicine Beihang University Ministry of Industry and Information Technology of the People’s Republic of China No. 37 Xueyuan Road Beijing100191 China School of Engineering Medicine Beihang University No. 37 Xueyuan Road Beijing100191 China Department of Hepatobiliary Surgery Zhujiang Hospital Southern Medical University Guangzhou China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine China

Real-time renal imaging is indispensable for monitoring kidney-related disease processes and evaluating the nephrotoxicity of nanoprobes. Owing to their small size, tunable photoluminescence properties, and low biotoxicity, carbon dots (CDs) are widely employed as nanoprobes for imaging-guided disease diagnosis. However, the preparation of near-infrared CDs is a stupendous challenging. Here, CDs with near-infrared fluorescence (NIR-CDs ) were prepared by one-pot pyrolysis and were comprehensively evaluated for real-time renal imaging. The NIR-CDs possessed super-small size (1.8 ± 0.2 nm) and high quantum yield (14.3% in aqueous solution) at 696 nm. NIR-CDs exhibited high-efficiency renal clearance at 24 h post-treatment (97.47-99.27% ID) via glomerular filtration. The irreversible clearance of NIR-CDs enables effective real-time monitoring of kidney function in the normal and acute kidney injury (AKI) mouse models. Consequently, NIR-CDs can be utilized for imaging analyses of impaired kidney function, pioneering a serviceable guideline for neoapplications of NIR-CDs. © 2022, The Authors. All rights reserved.

关键词： Nanoprobes

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Federated multi-view learning for private medical data integration and analysis

arXiv

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arXiv 2021年

作者： Che, Sicong Peng, Hao Sun, Lichao Chen, Yong He, Lifang College of Information and Electrical Engineering China Agricultural University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in medical field. Two critical challenges are identified: Firstly, medical data is naturally distributed across multiple local sites, making it difficult to collectively train machine learning models without data leakage. Secondly, in medical applications, data are often collected from different sources and views, resulting in heterogeneity and complexity that requires reconciliation. This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV). We experimented with real-world keyboard data collected from BiAffect study. The results demonstrated that the proposed FedMV approach can make full use of multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV methods perform better than their single-view and pairwise counterparts. Besides, the proposed model can be easily adapted to deal with multi-view sequential data in a federated environment, which has been modeled and experimentally studied. To the best of our knowledge, this framework is the first to consider both vertical and horizontal diversification in the multi-view setting as well as their sequential federated learning. Copyright © 2021, The Authors. All rights reserved.

关键词： data integration

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An Adaptive Control Method for Electro-hydrostatic Actuator Based on Virtual Decomposition Control

An Adaptive Control Method for Electro-hydrostatic Actuator ...

引用

Advanced Reliability and Maintenance Modeling (APARM), Asia-Pacific International Symposium on

作者： Youhao Shen Xingjian Wang Shaoping Wang Jouni Mattila School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Faculty of Engineering and Natural Sciences Tampere University Tampere Finland

ISBN: (数字)9781728171029

ISBN: (纸本)9781728171036

This paper presents an adaptive control method for Electro-hydrostatic Actuator (EHA) based on Virtual Decomposition Control (VDC) under higher-order model, nonlinearities and uncertainties. In the fifth-order dynamic model of EHA, its nonlinear factors make the superior control very difficult. This paper decomposes EHA into four subsystems according to energy conversion, designs the control law for each subsystem, and provides an adaptive control based on VDC. The proposed adaptive controller can solve the problems of multiple uncertainties and nonlinearities in the system and guarantee the stability of EHA. The stability of the subsystems and EHA system are rigorously proven. The simulation results show that the proposed method can effectively control the EHA position and improve the EHA control performance compared with the conventional control method.

关键词： Uncertainty Adaptation models Friction Hydraulic systems Brushless motors Backstepping Reliability

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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Chinese Control and Decision Conference, CCDC

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Engineering RMIT University Melbourne VIC Australia

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This brief explores the approximation properties of a unique basis expansion based on Pascal's triangle, which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time representation. The roles of certain parameters, such as sampling time interval or model order, and signal characteristics, i.e., its curvature, on the approximation are investigated. Approximate errors in one and multiple-step predictions are analyzed. Furthermore, time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Shape Electronic mail Analytical models Mathematical analysis data models Upper bound

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FedMood: Federated learning on mobile health data for mood detection

arXiv

引用

arXiv 2021年

作者： Xu, Xiaohang Peng, Hao Sun, Lichao Bhuiyan, Md Zakirul Alam Liu, Lianzhong He, Lifang School of Cyber Science and Technology Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100083 China Department of Computer Science University of Illinois at Chicago Chicago United States Department of Computer and Information Sciences Fordham University JMH 334 E Fordham Road BronxNY10458 United States Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research. Although researchers hope that artificial intelligence can contribute to the diagnosis and treatment of depression, the traditional centralized machine learning needs to aggregate patient data, and the data privacy of patients with mental illness needs to be strictly confidential, which hinders machine learning algorithms clinical application. To solve the problem of privacy of the medical history of patients with depression, we implement federated learning to analyze and diagnose depression. First, we propose a general multi-view federated learning framework using multi-source data,which can extend any traditional machine learning model to support federated learning across different institutions or parties. Secondly, we adopt late fusion methods to solve the problem of inconsistent time series of multi-view data. Finally, we compare the federated framework with other cooperative learning frameworks in performance and discuss the related results. Copyright © 2021, The Authors. All rights reserved.

关键词： data privacy

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Speckle-based optical cryptosystem and its application for human face recognition via deep learning

arXiv

引用

arXiv 2022年

作者： Zhao, Qi Li, Huanhao Yu, Zhipeng Woo, Chi Man Zhong, Tianting Cheng, Shengfu Zheng, Yuanjin Liu, Honglin Tian, Jie Lai, Puxiang Department of Biomedical Engineering Hong Kong Polytechnic University Hong Kong Shenzhen Research Institute Hong Kong Polytechnic University Shenzhen518057 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medical Science and Engineering Beihang University Beijing100191 China Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing100190 China Photonics Research Institute Hong Kong Polytechnic University Hong Kong

Face recognition has recently become ubiquitous in many scenes for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data that should be carefully protected. Software-based cryptosystems are widely adopted nowadays to encrypt face images, but the security level is limited by insufficient digital secret key length or computing power. Hardware-based optical cryptosystems can generate enormously longer secret keys and enable encryption at light speed, but most reported optical methods, such as double random phase encryption, are less compatible with other systems due to system complexity. In this study, a plain yet high-efficient speckle-based optical cryptosystem is proposed and implemented. A scattering ground glass is exploited to generate physical secret keys of gigabit length and encrypt face images via seemingly random optical speckles at light speed. Face images can then be decrypted from the random speckles by a well-trained decryption neural network, such that face recognition can be realized with up to 98% accuracy. The proposed cryptosystem has wide applicability, and it may open a new avenue for high-security complex information encryption and decryption by utilizing optical speckles. Copyright © 2022, The Authors. All rights reserved.

关键词： Speckle

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Evaluation of CdZnTe spectrometer performance in measuring energy spectra during interventional radiology procedure

引用

Nuclear science and Techniques 2019年第9期30卷 55-60页

作者： Lin Chai Lian Chen Cui-Ping Yang Dong-Dong Zhou Meng-Meng Yang Wei-Wei Qu Gao-Long Zhang Da-Qian Hei Shou-Ping Xu Xin-Jian Chen State Key Laboratory of Radiation Medicine and Protection School of Radiation Medicine and Protection Soochow University Suzhou 215123 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions Suzhou 215123 China School of Physics and Nuclear Energy Engineering Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing 100083 China College of Materials Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China Department of Radiation Oncology PLA General Hospital Beijing 100853 China

Interventional radiology has been beneficial for patients for over 30 years of age with the combination of diagnostic and therapeutic methods. The radiation affecting occupationally exposed workers should be evaluated by means of the energy spectra and flux of X-rays in the treatment room. The present study aims to obtain the energy spectra of interventional procedures and study the capability of some detectors to evaluate the dose in interventional procedures. These measurements were taken by silicon-drift, CdTe, and CdZnTe detectors. The energy spectra were corrected by the energy-response curve of each detector. The energy-response curves of silicon-drift and CdTe detectors provided by the manufacturers specification were used. The energy response of the CdZnTe detector was measured by 133Ba and 152Eu γ sources. The experimental data were compared with the simulation results, and their perfect agreement provides a way to correct the energy or dose response, which can be used for the personal dosimeter developed by our group. Moreover, the measured energy spectra can be used in individual radiation protection. The present study shows that the CdZnTe detector is a good candidate detector in interventional procedures.

关键词： Interventional radiology procedures Energyresponse curve Energy spectrum Radiation protection

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SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yuan, Zhenlong Yang, Zhidong Cai, Yujun Wu, Kuangxin Liu, Mufan Zhang, Dapeng Jiang, Hao Li, Zhaoxin Wang, Zhaoqi Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China School of Electrical Engineering and Computer Science Australia Hunan Police Academy Information Technology Department Changsha410100 China Shanghai Jiao Tong University Cooperative MediaNet Innovation Center Shanghai200240 China Lanzhou University DSLAB School of Information Science and Engineering 730000 China Ministry of Agriculture and Rural Affairs Agricultural Information Institute Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big Data 100081 China

Recently, patch-deformation methods have exhibited significant effectiveness in multi-view stereo owing to the deformable and expandable patches in reconstructing textureless areas. However, existing approaches neglect to address the problem of deformation instability caused by easily overlooked edge-skipping, potentially leading to matching distortions, thus leaving room for further improvement. To fill this gap, we propose SED-MVS, which adopts panoptic segmentation and multi-trajectory diffusion strategy for segmentation-driven and edge-aligned patch deformation. Specifically, to prevent unanticipated edge-skipping, we first employ SAM2 for panoptic segmentation as depth-edge guidance to guide patch deformation, followed by multi-trajectory diffusion strategy to ensure patches are comprehensively aligned with depth edges. Moreover, to avoid potential inaccuracy of random initialization, we combine both sparse points from LoFTR and monocular depth map from DepthAnything V2 to restore reliable and realistic depth map for initialization and supervised guidance. Finally, we integrate the segmentation image with the monocular depth map to exploit inter-instance occlusion relationship, then further regard them as occlusion map to implement two distinct edge constraint, thereby facilitating occlusion-aware patch deformation. Extensive results on ETH3D, Tanks & Temples, BlendedMVS, Strecha and DL3DV-10K datasets validate the state-of-the-art performance and robust generalization capability of our proposed method. © 1991-2012 IEEE.

关键词： Image segmentation

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Cross-domain object detection through coarse-to-fine feature adaptation

arXiv

引用

arXiv 2020年

作者： Zheng, Yangtao Huang, Di Liu, Songtao Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing100191 China

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarseto-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Transfer learning from grid-structured data to graph-structured data: Application to diagnosis of depression 30th

Transfer learning from grid-structured data to graph-structu...

引用

30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020

作者： Yang, Jiawei Wang, Shaoping Wang, Xingjian Liu, Rui Wang, Yun Cui, Jian Zhou, Yuan Zhou, Jingjing Feng, Yuan Feng, Lei Wang, Gang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing100088 China CAS Key Laboratory of Behavioral Science Institute of Psychology & Magnetic Resonance Imaging Research Center Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Psychology University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811485930

Graphs provide a way to study the characteristics of structural and functional connections between different brain regions. Graph convolutional neural networks have the ability to extract intrinsic local characteristics of networks. However, lack of data is a common problem in medical field, holding back the application of deep learning. Although transfer learning is an effective method to improve performance, it is difficult to find natural graph-structured datasets to pre-train deep learning models. To address this problem, we proposed a novel transfer learning method. The method uses grid-structured source data to pre-train a model, and fine-tuned it with graph-structured data in the task of interest. At last, the method is applied to the diagnosis of depression with a total of 83 samples. By comparing the performance between a fine-tuned model and a fully trained model, we tested the effectiveness of the proposal method. And there is a significant improvement in the accuracy of the pre-trained model. © ESREL2020-PSAM15 *** by Research Publishing, Singapore.

关键词： Diagnosis

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