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arXiv 2021年

作者： Fu, Yao Zhou, Yipeng Wu, Di Yu, Shui Wen, Yonggang Li, Chao Department of Computer Science Sun Yatsen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Department of Computing FSE Macquarie University 2122 Australia School of Computer Science University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore Co. Ltd China

In spite that Federated Learning (FL) is well known for its privacy protection when training machine learning models among distributed clients collaboratively, recent studies have pointed out that the naive FL is susceptible to gradient leakage attacks. In the meanwhile, Differential Privacy (DP) emerges as a promising countermeasure to defend against gradient leakage attacks. However, the adoption of DP by clients in FL may significantly jeopardize the model accuracy. It is still an open problem to understand the practicality of DP from a theoretic perspective. In this paper, we make the first attempt to understand the practicality of DP in FL through tuning the number of conducted iterations. Based on the FedAvg algorithm, we formally derive the convergence rate with DP noises in FL. Then, we theoretically derive: 1) the conditions for the DP based FedAvg to converge as the number of global iterations (GI) approaches infinity;2) the method to set the number of local iterations (LI) to minimize the negative influence of DP noises. By further substituting the Laplace and Gaussian mechanisms into the derived convergence rate respectively, we show that: 3) The DP based FedAvg with the Laplace mechanism cannot converge, but the divergence rate can be effectively prohibited by setting the number of LIs with our method;4) The learning error of the DP based FedAvg with the Gaussian mechanism can converge to a constant number finally if we use a fixed number of LIs per GI. To verify our theoretical findings, we conduct extensive experiments using two real-world datasets. The results not only validate our analysis results, but also provide useful guidelines on how to optimize model accuracy when incorporating DP into FL. Copyright © 2021, The Authors. All rights reserved.

关键词： Laplace transforms

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Advances in the Application of Radiomics in Colorectal Cancer

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Chinese Journal of Biomedical engineering(English Edition) 2021年第1期30卷 1-12页

作者： ZHOU Xue-zhi WEI Wei LIU Zhen-yu WANG Shuo TIAN Jie Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'an 710126China School of Electronics and Information Xi'an Polytechnic UniversityXi'an 710048China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100191China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China

Medical images contain a great deal of information that cannot be recognized by the human *** may not only fully express the heterogeneity of the tumor,but also reflect important information such as prognosis information of *** the development of image processing and artificial intelligence technology,an emerging field called"radiomics"has become a research hotspot,aiming to assist doctors to make decisions or to solve the thorny problems in clinical practice by advanced image analysis *** this paper,taking applications of radiomics in colorectal cancer as an example,we introduced the basic principle and technology of radiomics followed by studies focusing on different clinical questions during treatment cycle,including assessment and prediction of the pathological response status after neoadjuvant chemoradiotherapy,the decision of operative plan and the survival analysis after *** believe that advances in medical image analysis will promote the development of precision medicine.

关键词： radiomics colorectal cancer precision medicine artificial intelligence

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A short-term output power prediction model of wind power based on deep learning of grouped time series

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European Journal of Electrical engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International Information and engineering Technology Association. All rights reserved.

关键词： Wind power

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How likely is a random graph shift-enabled?

arXiv

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arXiv 2021年

作者： Chen, Liyan Cheng, Samuel Stankovic, Vladimir Stankovic, Lina Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhejiang Zhoushan316022 China Department of Computer Science and Technology Tongji University Shanghai201804 China School of Electrical and Computer Engineering University of Oklahoma OK74105 United States Department of Electronic and Electrical Engineering University of Strathclyde GlasgowG1 1XW United Kingdom

The shift-enabled property of an underlying graph is essential in designing distributed filters. This article discusses when a random graph is shift-enabled. In particular, popular graph models Erdős–Rényi (ER), Watts–Strogatz (WS), Barabási–Albert (BA) random graph are used, weighted and unweighted, as well as signed graphs. Our results show that the considered unweighted connected random graphs are shift-enabled with high probability when the number of edges is moderately high. However, very dense graphs, as well as fully connected graphs, are not shift-enabled. Interestingly, this behaviour is not observed for weighted connected graphs, which are always shift-enabled unless the number of edges in the graph is very low. © 2021, CC BY.

关键词： Signal processing

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The role of proactive therapeutic drug monitoring in guiding infliximab therapeutic optimization in pediatric patients with Crohn's disease: A retrospective study

Pediatric Discovery

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Pediatric Discovery 2024年第4期2卷

作者： Junya Song Huihui Zhang Huihua Zhang Ximing Xu Xiaohua Liang Yongfang Liu Xiaomei Song Hong Guo Xue Zhan Jinlin Song Xiaoqin Zhou Department of Gastroenterology Children's Hospital of Chongqing Medical University (CHCQMU) Chongqing China National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders Chongqing Key Laboratory of Pediatrics Chongqing Key Laboratory of Child Health and Nutrition Chongqing China Big Data Center for Children's Medical Care Chongqing China Clinical Epidemiology and Biostatistics Department Children’s Hospital of Chongqing Medical University Chongqing China Department of Nutrition Children's Hospital of Chongqing Medical University Chongqing China Department of Gastroenterology Chongqing General Hospital Chongqing China College of Stomatology Chongqing Key Laboratory for Oral Diseases and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education Chongqing China

Therapeutic drug monitoring (TDM) plays an important role in guiding treatment plan adjustments and clinical outcomes in Crohn's disease. To evaluate the role of TDM-guided optimization of infliximab dosage in patients with pediatric Crohn's disease. Medical records of patients with pediatric Crohn's disease who were treated with infliximab and had proactive TDM from June 2020 to June 2022 at the Children's Hospital of Chongqing Medical University were included. Baseline influencing factors for infliximab trough concentration (TC) and clinical outcomes before and after the treatment change were analyzed to assess the value of adjusting treatment in the patients. Forty-six patients (male-to-female ratio = 1.55:1, age <18 years) were included. Univariate and multivariate analyses showed that hormone exposure (odds ratio: 0.042, 95% confidence interval: 0.002–0.924, p = 0.044), perianal lesions (5.813, 0.984–34.349, p = 0.052), simplified endoscopic score for Crohn's disease (1.656, 1.065–2.577, p = 0.025), and total protein (TP) (0.851, 0.749–0.967, p = 0.014) were correlated with infliximab TC. Shortening the treatment interval increased the infliximab TC (pre vs. post = 1.69 ± 0.8 vs. 12.03 ± 6.64, p = 0.001, n = 12) after 93.9 ± 37.47 days, decreased the pediatric Crohn's disease activity index and simplified endoscopic score for Crohn's disease, and increased the biochemical remission, clinical remission, endoscopic remission, and endoscopic response rates; however, there was no statistical significance. Hormone exposure, perianal lesions, simplified endoscopic score for Crohn's disease, and TP levels before the first infliximab use affected the infliximab TC. Shortening the treatment interval can improve infliximab TC levels and clinical outcomes.

关键词： Crohn's disease infliximab pediatric therapeutic drug monitoring

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On the degree of synchronization between air transport connectivity and COVID-19 cases at worldwide level

arXiv

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arXiv 2020年

作者： Sun, Xiaoqian Wandelt, Sebastian Zhang, Anming National Key Laboratory of CNS/ATM School of Electronic and Information Engineering Beihang University Beijing100191 China National Engineering Laboratory of Multi-Modal Transportation Big Data Beijing100191 China Sauder School of Business University of British Columbia VancouverBC Canada

The current outbreak of COVID-19 is an unprecedented event in air transportation. While it was known that air transportation plays a key role in the spread of a pandemic, it is probably the first time that global aviation contributed to the planet-wide spread of a pandemic, with over 500,000 reported casualties related to the infection. In this study, we perform an analysis of the role air transportation played and how the air transportation system was changed throughout the ongoing pandemic. The major decision makers were countries (governments) and airlines, who were involved mostly in the decision and implementation of travel bans. Here, we focus on the role of countries, by analyzing the degree of synchronization between the number of cases reported in specific countries and how/when these countries reacted concerning the air transportation operations. Our study also provides a comprehensive empirical analysis on the impact of the COVID-19 pandemic on aviation at a country-level. It is hoped that the study can lead to novel insights into the prevention and control of future waves or pandemics of other types. Copyright © 2020, The Authors. All rights reserved.

关键词： Synchronization

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Indoor Tracking by RFID Fusion with IMU data

Indoor Tracking by RFID Fusion with IMU Data

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作者： Wang, Fafa Su, Tingli Jin, Xuebo Zheng, Yangyang Kong, Jianlei Bai, Yuting School of Computer Information and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China

Indoor tracking and positioning technology have received significant attention. There are many techniques to track indoor targets, such as Wi-Fi technology, radio frequency identification (RFID), and inertial navigation technology. However, these technologies cannot accurately track a target with a single sensor due to the uncertainty of the sensors. This paper presents an indoor tracking method using RFID and inertial measurement units (IMU);the estimated trajectory is mainly determined by RFID, and the information about the trajectory based on IMU is added when the estimated trajectory is missing without RFID data. The results show that the fusion estimation algorithm has excellent performance in indoor tracking and is very effective when the target maneuvers, even where some measurements are lost. © 2018 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Trajectories

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Nonlinear Cross-Domain Feature Representation Learning Method Based on Dual Constraints

Nonlinear Cross-Domain Feature Representation Learning Metho...

引用

IEEE International Conference on big knowledge (ICBK)

作者： Han Ding Yuhong Zhang Shuai Yang Yaojin Lin Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou China

Feature representation learning is a research focus in domain adaptation. Recently, due to the fast training speed, the marginalized Denoising Autoencoder (mDA) as a standing deep learning model has been widely utilized for feature representation learning. However, the training of mDA suffers from the lack of nonlinear relationship and does not explicitly consider the distribution discrepancy between domains. To address these problems, this paper proposes a novel method for feature representation learning, namely Nonlinear cross-domain Feature learning based Dual Constraints (NFDC), which consists of kernelization and dual constraints. Firstly, we introduce kernelization to effectively extract nonlinear relationship in feature representation learning. Secondly, we design dual constraints including Maximum Mean Discrepancy (MMD) and Manifold Regularization (MR) in order to minimize distribution discrepancy during the training process. Experimental results show that our approach is superior to several state-of-the-art methods in domain adaptation tasks.

关键词： Noise reduction Manifolds Training Adaptation models Kernel Deep learning Feature extraction

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Multi-Scale Hierarchical Context-Aware Survival Prediction Network based on whole slide images

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Biomedical Signal Processing and Control 2025年 110卷

作者： Jinmiao Song Yatong Hao Hui Zhai Shuang Zhao Tao Ning Shan Huang Xiaodong Duan School of Software Xinjiang University Urumqi 830046 Xinjiang China School of Computer Science and Engineering Dalian Minzu University Dalian 116650 Liaoning China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian 116650 Liaoning China Dalian Key Laboratory of Digital Technology for Minzu Culture Dalian Minzu University Dalian 116650 Liaoning China The First Affiliated Hospital Xinjiang Medical University Urumqi 830011 Xinjiang China

In high-resolution whole slide images (WSIs), multi-scale information is crucial for survival prediction. However, due to the ultra-large sizes of WSIs, existing methods have not fully utilized the multi-scale information at the gigapixel scale. Additionally, WSI-based survival prediction, as a patient-level multiple instance learning (MIL) task, is far more complex than WSI-level MIL which presents a significant challenge. To address these challenges, we propose a Multi-Scale Hierarchical Context-Aware Survival Prediction Network (MSASurv). This network progressively explores the tumor microenvironment, tumor-associated tissue structures, and patient-level tumor heterogeneity in WSIs. We validated our approach using five types of cancer from The Cancer Genome Atlas (TCGA), including 3,068 H&E-stained WSIs. Experimental results demonstrate that our proposed MSASurv algorithm outperforms previous weakly supervised methods by 3.3% to 16.8%. The code and models are publicly available at https://***/yatonghao/MSASurv .

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Coherent photoproduction of low-pT charmonium in peripheral heavy ion collisions within the color dipole model

arXiv

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arXiv 2021年

作者： Yu, Gongming Cai, Yanbing Fu, Yongping Yang, Haitao Gao, Quangui Hu, Qiang Hu, Liyuan Li, Wei Song, Yushou Fundamental Science on Nuclear Safety and Simulation Technology Laboratory Harbin Engineering University Harbin150000 China Guizhou Key Laboratory in Physics and Related Areas Guizhou Key Laboratory of Big Data Statistic Analysis Guizhou University of Finance and Economics Guiyang550025 China Department of Physics West Yunnan University Lincang677000 China Department of Physics Zhaotong University Zhaotong657000 China Department of Physics Yuxi Normal University Yuxi653100 China Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China

We calculate the centrality dependence for coherent photoproduction of very low-pT J/ψ at Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) energies within the impact parameter dependent saturated color dipole model. By using the large equivalent photon fluxes, we present the differential cross section of very low-pT J/ψ produced by coherent photonuclear in peripheral heavy-ion collisions. The numerical results demonstrate that our calculation are agree with J/ψ data in peripheral heavy ion collisions at Relativistic Heavy Ion Collider (RHIC) energies. © 2021, CC BY.

关键词： Colliding beam accelerators

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