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Artificial intelligence-assisted niacin skin flush screening in early psychosis identification and prediction

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General Psychiatry 2022年第2期35卷 76-78页

作者： Tao Chen Haichun Liu Renfang Tian Ranpiao Gan Wenzuo Xu Tianhong Zhang Jijun Wang Department of Economics University of WaterlooWaterlooOntarioCanada Depatment of Statistics and Actuarial Science(Cross Appointed) University of Waterloo WaterlooOntarioCanada Big Data Research Lab Universty of WaterlooWaterlooOntarioCanada Shanghai Niacin Tech Co. LtdShanghal China Labor and Worklife Program Harvard UniversityCambridgeMassachusettsUSA Department of Automation Shanghai Jao Tong UniversityShanghaiChina Ningbo Artificial Intelligent Institute Shanghai Jiao Tong UniversityNingboChina School of Management Economicsand MathematicsKing's University CollegeLondon0ntarioCanada Shanghai Intelligent Psychological Evaluaton and Intervention Engineering Technology Research Center Shanghai Key Laboratory of Psychotic DisordersShanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)ShanghaiChina CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghaiChina Institute of Psychology and Behavioral Science Shanghai Jiao Tong UniversiyShanghaiChina

Schizophrenia is a devastating mental disorder affecting 20 million people *** diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the *** on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.

关键词： diagnosis prognosis prediction

Applications of Game Theory in Vehicular Networks: A Survey

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

Unsupervised vehicle re-identification with progressive adaptation

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arXiv 2020年

作者： Peng, Jinjia Wang, Yang Wang, Huibing Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University LiaoningDalian China Pengcheng Laboratory ShenzhenGuangdong China Key Laboratory of Knowledge Engineering with Big Data Ministry of education Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes;worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as "pseudo target samples". These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicles

A Review of the Evolutionary Algorithm Based VRP Problem

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A Review of the Evolutionary Algorithm Based VRP Problem

Chinese Control Conference (CCC)

作者： Lili Pan Bing Hu Zhe Sun Lei Xu Guangfu Gong Xiangpeng Xie Zhixin Sun Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing University of Posts and Telecommunications Nanjing P. R. China Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing P. R. China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P. R. China Anhui Yougu Express Intelligent Technology Co. LTD Nanling Anhui P. R. China

The problem of vehicle routing planning (VRP) has far-reaching influence, and is widely used in vehicle scheduling, industrial production, transportation, logistics and distribution. The study of VRP and its associated derivations has grown significantly in recent years. So, starting with the fundamental VRP, this paper categorizes the VRP based on its traits and real-world applications. It then in-depth analyses the capacity constrained VRP, time window VRP, batched delivery VRP, dynamic VRP, and stochastic demand VRP, and provides an update on each type of VRP's research. Additionally, a summary of the VRP problem-solving methods is provided. The study of evolutionary algorithms is the main topic of this paper, and the fundamental solution method is provided in accordance with the development of the algorithms.

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MDPL-net: Multi-layer Dictionary Learning Network with Added Skip Dense Connections

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MDPL-net: Multi-layer Dictionary Learning Network with Added...

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Lin Wu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Dictionary learning (DL) is powerful for representation learning, while it fails to capture the deep hierarchical information hidden in data. In this paper, we propose a new generalized end-to-end mulita-layer representation learning architecture referred to as Multi-layer Dictionary Pair Learning Network (MDPL-net) for the deep sparse and hierarchical representation of images. To enable MDPL-net to conduct accurate classification, MDPL-net clearly integrates the skip connection end-to-end network and multi-layer deep sparse dictionary learning into a unified architecture. The representation learning module has several hidden DL blocks, where each hidden DL block has a dictionary pair learning (DPL) layer, a batch-norm layer and an activation function layer, and the DL blocks are connected in a feed-forward manner. To further improve the information flow and maintain the privileged features between different DL blocks, a novel skip dense connectivity pattern is deployed between hidden DL blocks, which can obtain more stable and discriminative features. The DPL layer jointly formulates the discriminative synthesis dictionary and analysis dictionary by minimizing reconstruction error within each batch over the feature maps from front layers. Extensive results on benchmark databases demonstrate the effectiveness of MDPL-net for discriminative representation and robust image classification.

关键词： Dictionaries databases Conferences Machine learning data mining Image reconstruction Image classification

Using artificial intelligence to document the hidden RNA virosphere

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四川生理科学杂志 2024年第10期46卷 2345-2345页

作者： Xin Hou National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases State Key Laboratory for Biocontrol School of Medicine Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen China Apsara Lab Alibaba Cloud Intelligence Alibaba Group Hangzhou China Wuhan Centers for Disease Control and Prevention Wuhan China Polar Research Institute of China Shanghai China Department of Nursing The Fifth Affiliated Hospital Sun Yat-sen University Zhuhai China School of Geospatial Engineering and Science Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Sun Yat-sen University Zhuhai China Key Laboratory of Comprehensive Observation of Polar Environment Ministry of Education Sun Yat-sen University Zhuhai China Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary Institute of Biodiversity Science and Institute of Eco-Chongming School of Life Sciences Fudan University Shanghai Shanghai China Centre for Virus Research Westmead Institute for Medical Research Westmead NSW Australia School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia College of Life Sciences Zhejiang University Hangzhou China School of Life Science Guangzhou University Guangzhou China Key Laboratory of Pathogen Infection Prevention and Control (MOE) State Key Laboratory of Respiratory Health and Multimorbidity National Institute of Pathogen Biology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China School of Public Health (Shenzhen) Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen China Guangzhou National Laboratory Guangzhou International Bio-Island Guangzhou China Department of Infectious Diseases and Public Health Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong SAR China Laboratory of Data Disco

Current metagenomic tools can fail to identify highly divergent RNA *** developed a deep learning algorithm,termed LucaProt,to discover highly divergent RNA-dependent RNA polymerase(RdRP)sequences in 10,487 metatranscriptomes generated from diverse global *** integrates both sequence and predicted structural information,enabling the accurate detection of RdRP *** this approach,we identified 161,979 potential RNA virus species and 180 RNA virus supergroups,including many previously poorly studied groups,as well as RNA virus genomes of exceptional length(up to 47,250 nucleotides)and genomic complexity.

关键词： artificial exceptional integrate

Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation

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arXiv 2020年

作者： Hao, Shijie Zhou, Yuan Guo, Yanrong Hong, Richang Cheng, Jun Wang, Meng 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 Hefei230009 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Chinese University of Hong Kong Hong Kong

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Citysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768×1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters. The code will be released at https://***/zhouyuan888888/SGCPNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

Space technology:A powerful tool for safeguarding world heritage

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The Innovation 2023年第3期4卷 27-28页

作者： Lei Luo Jie Liu Francesca Cigna Damian Evans Mario Hernandez Deodato Tapete Peter Shadie Athos Agapiou Abdelaziz Elfadaly Min Chen Lanwei Zhu Bihong Fu Ruixia Yang Shahina Tariq Mohamed Ouessar Rosa Lasaponara Xinyuan Wang Huadong Guo International Research Center of Big Data for Sustainable Development Goals(CBAS) Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research Institute(AIR)Chinese Academy of Sciences(CAS)Beijing 100094China International Centre on Space Technologies for Natural and Cultural Heritage(HIST)Under the Auspices of UNESCO Beijing 100094China Institute of Atmospheric Sciences and Climate(ISAC) National Research Council(CNR)00133 RomeItaly École Française d’Extreme-Orient 22 Avenue du Président Wilson75116 ParisFrance International Society for Digital Earth(ISDE) Mexico CityMexico Italian Space Agency(ASI) 00133 RomeItaly Blue Mountains World Heritage Institute 16 Dunmore LaneKatoombaNSW 2780Australia Earth Observation Cultural Heritage Research Lab Department of Civil Engineering and GeomaticsCyprus University of TechnologyPO Box 503296 Limassol 3036Cyprus National Authority for Remote Sensing and Space Sciences Cairo 1564Egypt Key Laboratory of Virtual Geographic Environment Ministry of Education of PRCNanjing Normal UniversityNanjing 210023China COMSATS University Islamabad(CUI) Islamabad 45550Pakistan Institut des Regions Arides(IRA)-Medenine Medenine 4119Tunisia Institute of Methodologies for Environmental Analysis(IMAA) CNRC.da Santa LojaTito Scalo85050 PotenzaItaly

WORLD HERITAGE AND SPACE TECHNOLOGY The Convention Concerning the Protection of the World Cultural and Natural Heritage(WHC),adopted by United Nations educational,Scientific and Cultural Organization(UNESCO)on November 16,1972,aims to ensure the identification,protection,conservation,presentation,and transmission to future generations of the world’s cultural and natural *** WHC works toward these goals by emphasizing the Outstanding Universal Value(OUV)of heritage sites and the unique contribution such places can make to conservation and human development agendas.1 As of the end of January 2023,theWHC has been signed by 194 state parties,covering 1,157 sites(including 900 cultural,218 natural,and 39 mixed properties),55 of which are considered to be in *** sites,totaling an area of more than 370 million hectares are designated as World Heritage(WH)sites(https://***/en/list/).WH sites have played a significant role in the sustainable development of society globally and helped effectively maintain and preserve the cultural diversity and global biodiversity of the Earth.

关键词： heritage maintain SPACE