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作者： Ouyang, Chen-Sen Chen, Yenming J. Tsai, Jinn-Tsong Chang, Yiu-Jen Huang, Tian-Hsiang Hwang, Kao-Shing Ho, Yuan-Chih Ho, Wen-Hsien Department of Information Engineering I-Shou University Kaohsiung Taiwan Department of Logistics Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Computer Science National Pingtung University Pingtung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Division of Cardiology Department of Internal Medicine Yuan's General Hospital Kaohsiung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan

Atrial fibrillation (AF) is a type of paroxysmal cardiac disease that presents no obvious symptoms during onset, and even the electrocardiograms (ECG) results of patients with AF appear normal under a premorbid status, rendering AF difficult to detect and diagnose. However, it can result in deterioration and increased risk of stroke if not detected and treated early. This study used the ECG database provided by the Physionet website (https://***), filtered data, and employed parameter-extraction methods to identify parameters that signify ECG features. A total of 31 parameters were obtained, consisting of P-wave morphology parameters and heart rate variability parameters, and the data were further examined by implementing a decision tree, of which the topmost node indicated a significant causal relationship. The experiment results verified that the P-wave morphology parameters significantly affected the ECG results of patients with AF. © 2021-IOS Press. All rights reserved.

关键词： Electrocardiograms

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Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors

arXiv

引用

arXiv 2025年

作者： Sun, Yuhao Zhang, Shixin Li, Wenzhuang Zhao, Jie Shan, Jianhua Shen, Zirong Chen, Zixi Sun, Fuchun Guo, Di Fang, Bin School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100083 China Beijing100083 China School of International Beijing University of Posts and Telecommunications Beijing100876 China School of Mechanical Engineering Anhui University of Technology China Zhili College Tsinghua University Beijing100084 China Biorobotics Institute The Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa56127 Italy Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

With the development of robotics technology, some tactile sensors, such as vision-based sensors, have been applied to contact-rich robotics tasks. However, the durability of vision-based tactile sensors significantly increases the cost of tactile information acquisition. Utilizing simulation to generate tactile data has emerged as a reliable approach to address this issue. While data-driven methods for tactile data generation lack robustness, finite element methods (FEM) based approaches require significant computational costs. To address these issues, we integrated a pinhole camera model into the low computational cost vision-based tactile simulator Tacchi that used the Material Point Method (MPM) as the simulated method, completing the simulation of marker motion images. We upgraded Tacchi and introduced Tacchi 2.0. This simulator can simulate tactile images, marked motion images, and joint images under different motion states like pressing, slipping, and rotating. Experimental results demonstrate the reliability of our method and its robustness across various vision-based tactile sensors. © 2025, CC BY.

关键词： Pinhole cameras

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Developing a medical artificial intelligence course for high school students

Developing a medical artificial intelligence course for high...

引用

International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Huang, Chao-Jung Wu, Tinghui Lu, Jui-Ting Lin, Beatrice Chang, Dawei Wang, Pochuan Wang, Mei-Chi Lee, Peijung Wang, Weichung National Taiwan University-Stanford Joint Program Office of AI in Biotechnology Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare Taipei Taiwan Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan Department of Mathematics Ecole Normale Supérieure de Lyon Lyon France Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Mathematics National Taiwan University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Artificial intelligence (ai) training courses often require prerequisites such as calculus or statistics. It is hence challenging to design and develop an introductory ai course for students of secondary education. This research intends to develop a medical ai course, provide high school students with an overview of deep learning applications in medical image analysis, and inspire them to pursue careers in the field of medical ai. We designed a 20-hour course, including lectures and two hands-on projects based on medical image classification. The proposed courses provided medical ai disciplines and built up their knowledge from basic to advanced levels. During the ten-day online courses, all the students were fully engaged and gave us positive feedback. The students endeavored to complete the experimental study in training, testing, and hypothesis of medical images application in the course. Their performance exceeded all expectations, for they did further analysis by tuning different hyperparameters. We designed a course evaluation form, which suggested that the students found it essential and expected to interact with the instructors. The results indicate that combining lectures with hands-on sessions would lead to evidently better achievement in terms of high school students' medical ai knowledge and positive attitudes while addressing real-world problems in the projects. Through this innovative education model, high school students regained their enthusiasm and were encouraged to cultivate their medical ai skills through self-learning while finishing the project. We conclude that this course could be successfully applied to interdisciplinary education in high school. © 2021 SPIE.

关键词： Students

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A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images

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BioMedical engineering OnLine 2025年第1期24卷 1-14页

作者： Zhang, Mudan Sun, Xinhuan Li, Wuchao Cao, Yin Liu, Chen Tu, Guilan Wang, Jian Wang, Rongpin Department of Radiology Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis Guizhou Provincial People’s Hospital Guiyang China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Department of pathology Guizhou Provincial People’s Hospital Guiyang China Department of Radiology Southwest Hospital Army Medical University (Third Military Medical University) Chongqing China Laboratory department Guizhou Provincial Center for Clinical Laboratory Guiyang China Medical college of Guizhou University Guiyang China

To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation. A hybrid multi-instance learning model is proposed based on the Transformer and the graph attention network, called TGMIL, to classify the differentiation of gastric adenocarcinoma. A total of 613 WSIs from patients with gastric adenocarcinoma were retrospectively collected from two different hospitals. According to the differentiation of gastric adenocarcinoma, the data were divided into four groups: normal group (n = 254), well differentiation group (n = 166), moderately differentiation group (n = 75), and poorly differentiation group (n = 118). The gold standard of differentiation classification was blindly established by two gastrointestinal pathologists. The WSIs were randomly split into a training dataset consisting of 494 images and a testing dataset consisting of 119 images. Within the training set, the WSI count of the normal, well, moderately, and poorly differential groups was 203, 131, 62, and 98 individuals, respectively. Within the test set, the corresponding WSI count was 51, 35, 13, and 20 individuals. The TGMIL model developed for the differential prediction task exhibited remarkable efficiency when considering sensitivity, specificity, and the area under the curve (AUC) values. We also conducted a comparative analysis to assess the efficiency of five other models, namely MIL, CLAM_SB, CLAM_MB, DSMIL, and TransMIL, in classifying the differentiation of gastric cancer. The TGMIL model achieved a sensitivity of 73.33% and a specificity of 91.11%, with an AUC value of 0.86. The hybrid multi-instance learning model TGMIL could accurately classify the differentiation of gastric adenocarcinoma using WSI without the need for labor-intensive and time-consuming manual annotations, w

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V³H: View Variation and View Heredity for Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2020年第3期1卷 233-247页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan 430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China The Department of Electrical and Computer Engineering University of Florida Gainesville 32611 FL United States

Real data often appear in the form of multiple incomplete views. Incomplete multiview clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V³H). Inspired by the variation and the heredity in genetics, V³H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V³H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V³H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V³H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multiview data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts. Impact Statement—Incomplete multiview clustering is a popular technology to cluster incomplete datasets from multiple sources. The technology is becoming more significant due to the absence of the expensive requirement of labeling these datasets. However, previous algorithms cannot fully learn the information of each view. Inspired by variation and heredity in genetics, our proposed algorithm V³H fully learns the information of each view. Compared with the state-of-the-art algorithms, V³H improves clustering

关键词： Incomplete multiview clustering view heredity view variation

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Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

arXiv

引用

arXiv 2023年

作者： Qiao, Yuanyuan Niu, Yong Han, Zhu Mao, Shiwen He, Ruisi Wang, Ning Zhong, Zhangdui ai, Bo The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 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 Seoul446-701 Korea Republic of The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable Intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this paper, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

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Exploratory study of highly susceptible to SARS-CoV-2 populations

引用

Life research 2022年第3期5卷 10-14页

作者： Xiaodong Jia Chengliang Yin Shanshan Lu Yan Chen Qingyan Liu Junfan Bai Lei Zhi Yinying Lu Comprehensive Liver Cancer Center The Fifth Medical Center of PLA General HospitalBeijingChina.0000-0001-8262-5749 National Engineering Laboratory for Medical Big Data Application Technology Chinese PLA General HospitalBeijingChina Faculty of Medicine Macao University of Science and TechnologyMacaoChina Medical Big Data Research Center Medical Innovation Research Division of PLA General HospitalBeijingChina Center for Synthetic and Systems Biology(CSSB) Tsinghua UniversityBeijingChina Peking University BeijingChina Department of Chronic Disease Luzhou Center for Disease Control and PreventionLuzhouChina

The coronavirus disease 2019(COVID-19),caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is threatening public *** human angiotensin-converting enzyme 2(ACE2)has a remarkably high affinity binding to *** study explored whether certain populations,including obese and cancer patients,are susceptible to *** expression pattern of ACE2 in normal and tumor tissues of cancer patients was compared by the search for network database and re-analysis of pubic *** level of ACE2 expression in normal adipose tissue was higher than that in normal lung tissue,which indicated the adipose tissue might be vulnerable to SARS-CoV-2;the levels of ACE2 expressed by adipocytes and adipose progenitor cells were similar between non-obese individuals and obese individuals(BMI>29),but obese individuals have more adiposes so as to increase the number of ACE2-expressing cells;the expression of ACE2 in tumor tissues posed by five different types of cancers increased significantly compared with that in adjacent ***,we proposed the following hypothesis:the obese individuals and five types of cancer patients might have a higher risk of SARS-CoV-2 infection,which might become the target population of SARS-CoV-2 infection in the future.

关键词： COVID-19 adipose tissue obesity cancer ACE2

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可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

引用

science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

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MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments (vol 18, e1010241, 2022)

引用

PLOS COMPUTATIONAL BIOLOGY 2022年第9期18卷 e1010241页

作者： Alinejad-Rokny, Hamid Modegh, Rassa Ghavami Rabiee, Hamid R. Sarbandi, Ehsan Ramezani Rezaie, Narges Tam, Kin Tung Forrest, Alistair R. R. Harry Perkins Institute of Medical Research QEII Medical Centre and Centre for Medical Research The University of Western Australia Perth Australia Bio Medical Machine Learning Lab (BML) The Graduate School of Biomedical Engineering UNSW Sydney Sydney Australia Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia Bioinformatics and Computational Biology Lab Department of Computer Engineering Sharif University of Technology Tehran Iran Center for Complex Biological Systems University of California Irvine Irvine California United States of America

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

关键词： Genomics Chromatin Genome-wide association studies Single nucleotide polymorphisms Histones Gene regulation Deoxyribonucleases Insulators

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Learning from noisy labels via dynamic loss thresholding

arXiv

引用

arXiv 2021年

作者： Yang, Hao Jin, Youzhi Li, Ziyin Wang, Deng-Bao Miao, Lei Geng, Xin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China AI Technology Application Department Huawei Technologies Collaborative Innovation Center of Wireless Communications Technology China

Numerous researches have proved that deep neural networks (DNNs) can fit everything in the end even given data with noisy labels, and result in poor generalization performance. However, recent studies suggest that DNNs tend to gradually memorize the data, moving from correct data to mislabeled data. Inspired by this finding, we propose a novel method named Dynamic Loss Thresholding (DLT). During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds. Specifically, DLT compares the loss value of each sample with the current loss threshold. Samples with smaller losses can be considered as clean samples with higher probability and vice versa. Then, DLT discards the potentially corrupted labels and further leverages supervised learning techniques. Experiments on CIFAR-10/100 and Clothing1M demonstrate substantial improvements over recent state-of-the-art methods. In addition, we investigate two real-world problems for the first time. Firstly, we propose a novel approach to estimate the noise rates of datasets based on the loss difference between the early and late training stages of DNNs. Secondly, we explore the effect of hard samples (which are difficult to be distinguished) on the process of learning from noisy labels. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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