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SSRN 2023年

作者： Gou, Lijun Yang, Jinrong Yu, Hangcheng Wang, Pan Li, Xiaoping Tuo, Shi Institute of Intelligent Computing Zhejiang Laboratory Zhejiang Hangzhou China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Hubei Wuhan China

Unsupervised domain adaptation is critical for object detection tasks, which attempt to reduce domain bias-induced performance degradation. Previous works try to align image-level and instance-level shifts to minimize the domain discrepancy. But they may align single-class features to mixed-class features in image-level domain adaptation, because each image in the object detection task may be more than one category. To achieve the same category feature alignment between single-class and mixed-class, we treat the feature with different mixed-category as a new class and propose a mixed-classes H-divergence for object detection. Then, Semantic Prediction Models (SPM) and Semantic Bridging Components (SBC) are proposed to improve single-class and mixed-class semantic information and accomplish semantic separation. Finally, the loss of pix domain discriminator is reweighed based on the SPM results to reduce sample imbalance. Extensive unsupervised domain adaption experiments on widely used datasets illustrate our model can make object detection robustly in domain bias settings. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

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Journal of Computer Science & technology 2020年第2期35卷 305-319页

作者： Fu-Zhen Zhuang Ying-Min Zhou Hao-Chao Ying Fu-Zheng Zhang Xiang Ao Xing Xie Qing He Hui Xiong Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Public Health Zhejiang University School of MedicineHangzhou 310027China Meituan-Dianping Group Beijing 100102China Microsoft Research Asia Beijing 100080China Department of Management Science and Information Systems Rutgers UniversityNew Jersey 07102U.S.A.

Transfer learning has attracted a large amount of interest and research in last decades, and some effort has been made to build more precise recommendation systems. Most previous transfer recommendation systems assume that the target domain shares the same/similar rating patterns with the auxiliary source domain, which is used to improve the recommendation performance. However, almost all existing transfer learning work does not consider the characteristics of sequential data. In this paper, we study the new cross-domain recommendation scenario by mining novelty-seeking trait. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. Previous work performed on only one single target domain may not fully characterize users' novelty-seeking trait well due to the data scarcity and sparsity, leading to the poor recommendation performance. Along this line, we propose a new cross-domain novelty-seeking trait mining model (CDNST for short) to improve the sequential recommendation performance by transferring the knowledge from auxiliary source domain. We conduct systematic experiments on three domain datasets crawled from Douban to demonstrate the effectiveness of our proposed model. Moreover, we analyze the directed influence of the temporal property at the source and target domains in detail.

关键词： sequential recommendation novelty-seeking trait transfer learning

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

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2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Xin Zheng, Yuanjie Niu, Yi Li, Chengiiang Shandong Normal University School of Information Science and Engineering Jinan250358 China Key Lab of Intelligent Computing and Information Security Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan250358 China Department of Electrical Engineering Information Technology Shandong University of Science and Technology Jinan250031 China

ISBN: (纸本)9781728194813

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy. © 2020 IEEE.

关键词： Electroencephalography

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Ultra-high extinction ratio optical pulse generation with a thin film lithium niobate modulator for distributed acoustic sensing

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Photonics Research 2024年第1期12卷 40-50页

作者： YUAN SHEN XIAOQIAN SHU LINGMEI MA SHAOLIANG YU GENGXIN CHEN LIU LIU RENYOU GE BIGENG CHEN YUNJIANG RAO Research Center for Optical Fiber Sensing Zhejiang LaboratoryHangzhou 311100China Research Center for Intelligent Optoelectronic Computing Zhejiang LaboratoryHangzhou 311100China International Research Center for Advanced Photonics College of Optical Science and EngineeringZhejiang UniversityHangzhou 310058China Fiber Optics Research Center(FORC) Key Laboratory of Optical Fiber Sensing and CommunicationsUniversity of Electronic Science and Technology of ChinaChengdu 611731China

We experimentally demonstrate ultra-high extinction ratio(ER)optical pulse modulation with an electro-optical modulator(EOM)on thin film lithium niobate(TFLN)and its application for fiber optic distributed acoustic sensing(DAS).An interface carrier effect leading to a relaxation-tail response of TFLN EOM is discovered,which can be well addressed by a small compensation component following the main driving *** ultrahigh ER>50 dB is achieved by canceling out the tailed response during pulse modulation using the EOM based on a cascaded Mach–Zehnder interferometer(MZI)*** modulated optical_(√)pulses are then utilized as a probe light for a DAS system,showing a sensitivity up to-62.9 dB·rad∕Hz~2(7 pε/Hz)for 2-km single-mode sensing *** crosstalk suppression of 24.9 dB along the fiber is also obtained when the ER is improved from 20 dB to 50 dB,clearly revealing its importance to the sensing performance.

关键词： modulator pulse interferometer

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Non-invasive reconstruction of dynamic myocardial transmembrane potential with graph-based total variation constraints

Non-invasive reconstruction of dynamic myocardial transmembr...

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作者： Xie, Shuting Wang, Linwei Zhang, Heye Liu, Huafeng State Key Laboratory of Modern Optical Instrumentation Department of Optical Engineering Zhejiang University Hangzhou310027 China Computational Biomedicine Laboratory Golisano College of Computing and Information Sciences Rochester Institute of Technology RochesterNY14623 United States School of Biomedical Engineering Sun Yat-Sen University Shenzhen510006 China

Non-invasive reconstruction of electrophysiological activity in the heart is of great significance for clinical disease prevention and surgical treatment. The distribution of transmembrane potential (TMP) in three-dimensional myocardium can help us diagnose heart diseases such as myocardial ischemia and ectopic pacing. However, the problem of solving TMP is ill-posed, and appropriate constraints need to be added. The existing state-of-art method total variation minimisation only takes advantage of the local similarity in space, which has the problem of over-smoothing, and fails to take into account the relationship among frames in the dynamic TMP sequence. In this work, the authors introduce a novel regularisation method called graph-based total variation to make up for the above shortcomings. The graph structure takes the TMP value of a time sequence on each heart node as the criterion to establish the similarity relationship among the heart. Two sets of phantom experiments were set to verify the superiority of the proposed method over the traditional constraints: infarct scar reconstruction and activation wavefront reconstruction. In addition, experiments with ten real premature ventricular contractions patient data were used to demonstrate the accuracy of the authors’ method in clinical applications. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Heart

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Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

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Science China(Technological Sciences) 2023年第6期66卷 1815-1830页

作者： LIU Yue DING Lin YANG ZhengWei GE XianYuan LIU DaHui LIU Wei YU Tao AVDEEV Maxim SHI SiQi School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Institute of Functional Materials Central Iron&Steel Research InstituteBeijing 100081China Australian Nuclear Science and Technology Organisation Locked Bag 2001Kirrawee DC NSW 2232Australia School of Chemistry The University of SydneySydney 2006Australia Zhejiang Laboratory Hangzhou 311100China

Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity ***,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal ***,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic ***,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility *** this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in *** extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.

关键词： Nickel-based single crystal superalloys text mining named entity recognition credibility analysis domain knowledge

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An Improved PI Controller for Stiction Compensation of Control Valves in Process Industry

An Improved PI Controller for Stiction Compensation of Contr...

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第43届中国控制会议

作者： Dong Yang Xun Lang Jiande Wu Yumin Lang Lei Xie Hongye Su Department of Electronic Engineering School of Information Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University State Key Laboratory of Industrial Control Technology Zhejiang University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the process industry, limit cycles are mostly induced by stiction-related nonlinearity in control valves, severely deteriorating control loop performance. Such problem highlights the importance of compensating for oscillations due to valve stiction. To this end, this work introduces an improved PI controller(IPIC) for stiction compensation, which retains the advantages of the traditional PI controller with fewer adjustment parameters. The enhancement involves nonlinear variation of the integral gain based on instantaneous error. Additionally, a differential evolution algorithm is utilized for controller *** to state-of-the-art methods in setpoint tracking, disturbance rejection, and robustness to parameter uncertainties, the proposed approach exhibits superior performance.

关键词： Stiction Limit Cycle Nonlinear PI controller Differential Evolution Algorithm

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A Multiplayer Perceptron Algorithm Based on Global Electrical Heterogeneity and Cardiodynamicsgram for Non-Invasive Detection of Coronary Microvascular Dysfunction 6

A Multiplayer Perceptron Algorithm Based on Global Electrica...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Zhao, Xiaoye Gong, Yinglan Zhang, Jucheng Liu, Haipeng Huang, Tianhai Jiang, Jun Xia, Ling Mao, Jiandong School of Instrument Science and Opto-electronic Engineering Hefei University of Technology Hefei China School of Medical Technology North Minzu University Yinchuan China Institute of Wenzhou Zhejiang University Wenzhou China The Second Affiliated Hospital Zhejiang University School of Medicine Department of Clinical Engineering Hangzhou China Coventry University Centre for Intelligent Healthcare Coventry United Kingdom The Second Affiliated Hospital Zhejiang University School of Medicine Department of Cardiology Hangzhou China Zhejiang University School of Medicine Key Laboratory for Biomedical Engineering of Ministry of Education Hangzhou China Institute of Biomedical Engineering Zhejiang University Hangzhou China Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia Yinchuan China

ISBN: (纸本)9798350353594

Coronary microvascular dysfunction (CMD) is a key etiology of myocardial ischemia. Currently, early and non- invasive detection of CMD is lack in clinical practice. We proposed a multiplayer perceptron (MLP) model based on global electrical heterogeneity (GEH)- and cardiodynamicsgram (CDG)-based features for non-invasive detection of CMD. 25 GEH parameters and CDG-based features were derived from vectocardiograms (VCGs) of 82 CMD patients and 107 healthy controls to train the support vector machine (SVM) and multiplayer perceptron (MLP) models. Seventeen features (14 GEH parameters, 3 CDG-based features) were finally selected, and the performance of the two models were compared. The proposed MLP model performs better than SVM and provides accuracy: 0.871, sensitivity: 0.925, specificity: 0.775, F1-score: 0.810, and AUC (area under curve): 0.849 in the testing dataset. The proposed MLP model could distinguish CMD from healthy controls, providing possibilities for a noninvasive, low-cost approach for early detection of CMD. © 2024 IEEE.

关键词： Noninvasive medical procedures

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Global analysis of protein and small-molecule substrates of ubiquitin-like proteins (UBLs)

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Molecular & cellular proteomics : MCP 2025年 100975页

作者： Guang-Can Shao Zhen-Lin Chen Shan Lu Qing-Cui Wu Yao Sheng Jing Wang Yan Ma Jian-Hua Sui Hao Chi Xiang-Bing Qi Si-Min He Li-Lin Du Meng-Qiu Dong National Institute of Biological Sciences Beijing 102206 Beijing China. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS 100190 Beijing China University of Chinese Academy of Sciences Beijing 100049 China. National Institute of Biological Sciences Beijing 102206 Beijing China Tsinghua Institute of Multidisciplinary Biomedical Research Tsinghua University 102206 Beijing China. University of Chinese Academy of Sciences Beijing 100049 China. Electronic address: smhe@***. Tsinghua Institute of Multidisciplinary Biomedical Research Tsinghua University 102206 Beijing China. Electronic address: dulilin@***. Tsinghua Institute of Multidisciplinary Biomedical Research Tsinghua University 102206 Beijing China. Electronic address: dongmengqiu@***.

Ubiquitin-like proteins (UBLs) constitute a family of evolutionarily conserved proteins that share similarities with ubiquitin in 3D structures and modification mechanisms. For most UBLs including Small-Ubiquitin-like Modifiers (SUMO), their modification sites on substrate proteins cannot be identified using the mass spectrometry-based method that has been successful for identifying ubiquitination sites, unless a UBL protein is mutated accordingly. To identify UBL modification sites without having to mutate UBL, we have developed a dedicated search engine pLink-UBL on the basis of pLink, a software tool for identification of cross-linked peptide pairs. pLink-UBL exhibited superior precision, sensitivity, and speed than "make-do" search engines such as MaxQuant, pFind, and pLink. For example, compared to MaxQuant, pLink-UBL increased the number of identified SUMOylation sites by 50 ∼ 300% from the same datasets. Additionally, we present a method for identifying small-molecule modifications of UBLs. This method involves antibody enrichment of a UBL C-terminal peptide following enrichment of a UBL protein, followed by LC-MS/MS analysis and a pFind 3 blind search to identify unexpected modifications. Using this method, we have discovered non-protein substrates of SUMO, of which spermidine is the major one for fission yeast SUMO Pmt3. Spermidine can be conjugated to the C-terminal carboxylate group of Pmt3 through its N or also likely, N amino group in the presence of SUMO E1, E2, and ATP. Pmt3-spermidine conjugation does not require E3 and can be reversed by SUMO isopeptidase Ulp1. SUMO-spermidine conjugation is present in mice and humans. Also, spermidine can be conjugated to ubiquitin in vitro by E1 and E2 in the presence of ATP. The above observations suggest that spermidine may be a common small molecule substrate of SUMO and possibly ubiquitin across eukaryotic species.

关键词： UBL antibody mass spectrometry pLink-UBL spermidine substrates

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