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13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018

作者： Zheng, Zengwei Zhou, Yanzhen Sun, Lin Hangzhou Key Laboratory for IoT Technology and Application Zhejiang University City College Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9783030150921

With the rapid development of mobile payment technology in China, people can use smartphone with some mobile payment apps (such as Alipay, WeChat pay and Apple pay etc.) to pay bills instead of paying cash. Some commercial platforms accumulated large transaction date from users’ smartphones. In the repeat consumption activities, the final few (or recency) consumption has a great impact on current consumption than long-ago consumption. But traditional HMM can’t deal with this recency effect in our repeat consumption case. This paper proposes a modified HMM method based on recency effect to predict the users’ repeat consumption behavior. We introduce a factor to represent the different recency effect of different time distance. An empirical study on real-world data sets shows encouraging results on our approach, especially on the consumer group which has the most uncertain consumption behavior. © 2019, Springer Nature Switzerland AG.

关键词： Hidden Markov models

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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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Inferring Circadian Gene Regulatory Relationships from Gene Expression Data with a Hybrid Framework

SSRN

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SSRN 2022年

作者： Hu, Shuwen Jing, Yi Li, Tao Wang, You-Gan Liu, Zhenyu Gao, Jing Tian, Yu-Chu School of Computer Science Queensland University of Technology Brisbane4001 Australia Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agricultural and Animal Husbandry Hohhot010018 China School of Life Sciences Inner Mongolia Agricultural University Hohhot010018 China Institute for Learning Sciences & Teacher Education Brisbane Australia Australian Catholic University Brisbane4000 Australia School of Computer and Information Engineering Inner Mongolia Agriculture University Hohhot010018 China

The central biological clock controls countless aspects of mammalian physiology, such as sleep, metabolism, and immune system regulation. Gene regulatory relationships play an important role in understanding the mechanisms that underlie a variety of biological cellular processes. In principle, it is feasible to infer circadian gene regulatory relationships from time-series gene expression data. But the correlation-based inference of the relationships between genes does not give sufficient information about causation. Moreover, gene expression data usually have low-sampling observations and high dimensions, leading to difficulties in gene data analysis. In this paper, a new hybrid framework was proposed to infer circadian gene regulatory relationships from gene expression data of rats. For practical clustering computation of the high-dimensional problem, the framework hybridizes the fuzzy C-means clustering algorithm and dynamic time warping (DTW) distance. After a cluster of genes is identified for the target gene, Wilcoxon signed-rank test was conducted to obtain the number of significant genes in the specific cluster. Finally, a dynamic vector autoregressive method was applied to the selected significant gene expression profiles to identify a directed causal regulatory relationship from partial correlation. Circadian gene regulatory relationships are inferred from the gene expression data of rats based on the Aanat target gene. The results show that genes Pde10a, Atp7b, Prok2, Per1, Rhobtb3 and Dclk1 stand out, which have been known to be essential for the regulation of circadian behaviour. The potential relationships between genes Tspan15, Eprs, Eml5 and Fsbp with a circadian rhythm need further experimental research. © 2022, The Authors. All rights reserved.

关键词： Rats

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A Multiple Factor Bike Usage Prediction Model in Bike-Sharing System 13th

A Multiple Factor Bike Usage Prediction Model in Bike-Sharin...

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13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018

ISBN: (纸本)9783030150921

Bike-sharing is becoming popular in the world, providing a convenient service for citizens. The system has to redistribute bikes among different stations frequently to solve the imbalance of spatial distribution. Real-time monitoring doesn’t solve this problem well, since it takes too much time to redistribute the bike and affects the user experience. In this paper, we first analyze the influence of factors such as time, weather, the location of stations. Then we cluster neighboring stations with similar usage pattern, and propose a lagged variable to simulate the effect of weather conditions in usage number. Finally, a multiple factor regression model with ARMA error (MFR-ARMA) is proposed to predict the check-out/in number of bikes in each cluster in a period of time. Evaluation dataset is from New York Bike Sharing System. The prediction results of the model are compared with four baseline methods. The experiments show a lower RMSLE and ER for check-out/in number prediction in our model. © 2019, Springer Nature Switzerland AG.

关键词： Forecasting

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Prototypes as Anchors: Tackling Unseen Noise for online continual learning

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷

作者： Shao-Yuan Li Yu-Xiang Zheng Sheng-Jun Huang Songcan Chen Kangkan Wang MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China State Key Lab. for Novel Software Technology Nanjing University Nanjing 211106 PR China Joint Laboratory of Spatial Intelligent Perception and Large Model Application Nanjing University of Aeronautics and Astronautics Nanjing 211106 PR China. Electronic address: lisy@***. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China. Electronic address: zhengyx@***. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China. Electronic address: huangsj@***. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China. Electronic address: s.chen@***. School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China. Electronic address: wangkangkan@***.

In the context of online class-incremental continual learning (CIL), adapting to label noise becomes paramount for model success in evolving domains. While some continual learning (CL) methods have begun to address noisy data streams, most assume that the noise strictly belongs to closed-set noise-i.e., they follow the assumption that noise in the current task originates classes within the same task. This assumption is clearly unrealistic in real-world scenarios. In this paper, we first formulate and analyze the concepts of closed-set and open-set noise, showing that both types can introduce unseen classes for the current training classifier. Then, to effectively handle noisy labels and unknown classes, we present an innovative replay-based method Prototypes as Anchors (PAA), which learns representative and discriminative prototypes for each class, and conducts a similarity-based denoising schema in the representation space to distinguish and eliminate the negative impact of unseen classes. By implementing a dual-classifier architecture, PAA conducts consistency checks between the classifiers to ensure robustness. Extensive experimental results on diverse datasets demonstrate a significant improvement in model performance and robustness compared to existing approaches, offering a promising avenue for continual learning in dynamic, real-world environments.

关键词： Continual learning Noisy label Open-set noise

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Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI

arXiv

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arXiv 2024年

作者： Zhou, Lei Zhang, Yuzhong Zhang, Jiadong Qian, Xuejun Gong, Chen Sun, Kun Ding, Zhongxiang Wang, Xing Li, Zhenhui Liu, Zaiyi Shen, Dinggang The School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China The School of Biomedical Engineering ShanghaiTech University China The School of Computer Science and Engineering Nanjing University of Science and Technology China Ruijin Hospital Shanghai Jiaotong University School of Medicine China Department of Radiology Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province China The Shanghai General Hospital Shanghai JiaoTong University China Department of Radiology The Third Affiliated Hospital of Kunming Medical University China Department of Radiology Guangdong Provincial Peo-ple's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice, particularly for identifying the presence of breast disease. However, accurate segmentation of breast tumor is a challenging task, often necessitating the development of complex networks. To strike an optimal tradeoff between computational costs and segmentation performance, we propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. Specifically, the hybrid network consists of a encoder-decoder architecture by stacking convolution and deconvolution layers. Effective 3D transformer layers are then implemented after the encoder subnetworks, to capture global dependencies between the bottleneck features. To improve the efficiency of hybrid network, two parallel encoder subnetworks are designed for the decoder and the transformer layers, respectively. To further enhance the discriminative capability of hybrid network, a prototype learning guided prediction module is proposed, where the category-specified prototypical features are calculated through online clustering. All learned prototypical features are finally combined with the features from decoder for tumor mask prediction. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network achieves superior performance than the state-of-the-art (SOTA) methods, while maintaining balance between segmentation accuracy and computation cost. Moreover, we demonstrate that automatically generated tumor masks can be effectively applied to identify HER2-positive subtype from HER2-negative subtype with the similar accuracy to the analysis based on manual tumor segmentation. The source code is available at https://***/ZhouLlab/PLHN. Copyright © 2024, The Authors. All rights reserved.

关键词： Multilayer neural networks

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STGCN: A Spatial-Temporal Aware Graph Learning Method for POI Recommendation

STGCN: A Spatial-Temporal Aware Graph Learning Method for PO...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Haoyu Han Mengdi Zhang Min Hou Fuzheng Zhang Zhongyuan Wang Enhong Chen Hongwei Wang Jianhui Ma Qi Liu Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Meituan-Dianping Group Stanford University

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Point-of-Interest (POI) recommendation helps users find their interested places to visit based on the time and user location. Unlike traditional recommendation tasks, POI recommendation is personalized, spatial-aware, and temporally dependent. Although many previous works have tried modeling spatial and temporal characteristics, most of them suffer from the following two limitations: For the spatial aspect, existing works only consider the user-POI distance or POI-POI distance. However, we find that a user prefers different regions at different times, which is known as user-region periodic pattern. For the temporal aspect, most works treat user and time as two independent factors. However, different users may prefer the same POI in different time periods, which is known as user-POI periodic pattern. To address the limitation of existing works, we propose a novel Spatial-Temporal aware Graph Convolutional Neural Network (STGCN) for POI recommendation. Specifically, we first design a user record multigraph to fuse all the context information into a unified graph. Then, we propose a time-based neighborhood sampling algorithm and take advantage of the flexible propagation mechanism of GCNs to learn the representations of each node at a specific time. Furthermore, multiple scoring functions are proposed to exploit user-region periodic pattern and user-POI periodic pattern, respectively. We also develop a time smoothing strategy to alleviate the data sparsity problem. Extensive experiments are conducted on two real-world datasets, and the experimental results demonstrate the effectiveness of our method.

关键词： Learning systems Heating systems Smoothing methods Image color analysis Fuses Data mining Task analysis

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Identification of Cirrhosis Subtypes Through Heterogeneous Medical Information Network

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Gastroenterology & Hepatology Research 2022年第3期4卷 11-22页

作者： Jun Li Xin Dong Ya-Na Zhou Hui-Kun Wu Xiao-Dong Li Xue-Zhong Zhou Clinical College of Traditional Chinese Medicine Hubei University of Chinese MedicineHubei 430061China Hubei Key Laboratory of Liver and Kidney Research and Application of Traditional Chinese Medicine Hubei Provincial Hospital of Traditional Chinese MedicineHubei 430061China Affiliated Hospital of Hubei University of Traditional Chinese Medicine Hubei 430061China Hubei Academy of Traditional Chinese Medicine Hubei 430074China Institute of Medical Intelligence School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100063China

Background:Liver cirrhosis is a complex and heterogeneous disease,with a mortality rate of up to 57%,resulting in 1.03 million deaths per *** prevalence of liver cirrhosis is on the *** with liver cirrhosis have a variety of clinical phenotypes and are prone to various complications related to liver ***,there is an urgent need to improve the early prevention and clinical management of cirrhosis and its ***:We use a precise medical approach to analyze and characterize the complex manifestations of cirrhotic patient populations,and we propose a Heterogeneous Medical Record Network(HEMnet)that includes electronic medical records,molecular interaction networks,and domain *** train the network embedding vector on HEMnet to obtain the low-dimensional vector representation of each *** these vectors,we enriched the original medical record and identified six subtypes of ***:Subtype 1 is characterized by heart disease,and subtype 2 has the strongest association with metabolic-related *** 3 was characterized by Chronic gastritis *** 4 was characterized by Liver cirrhosis-related complications-serous *** 5 had the strongest association with hepatitis-cirrhosis-related complications diseases and gallbladder *** 6 was most strongly associated with Liver cirrhosis-related complications and hepatic *** assessing the human disease-gene association of each subtype,the rich phenotype and biological functions of each subtype at the gene level were matched to the disease comorbidities and clinical differences we identified through ***:Our approach demonstrates the utility of applying a precision medicine paradigm to cirrhosis and the prospect of extending this approach to other complexes,multifactorial diseases.

关键词： heterogeneous medical record network liver cirrhosis subtype characteristics

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Corrigendum to “Multi-classification of fundus diseases based on DSRA-CNN” [Biomed. Signal Process. Control 77 (2022) 103763]

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Biomedical Signal Processing and Control 2022年 77卷

作者： Xue-lian Yang San-li Yi School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650500 Yunnan China Key Laboratory of Computer Technology Application of Yunnan Province Kunming 650500 China

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Missed prediction of the neutron halo in 37Mg

arXiv

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arXiv 2023年

作者： Zhang, K.Y. Yang, S.Q. An, J.L. Zhang, S.S. Papakonstantinou, P. Mun, M.-H. Kim, Y. Yan, H. Institute of Nuclear Physics and Chemistry CAEP Sichuan Mianyang621900 China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Institute of Computer Application CAEP Sichuan Mianyang621900 China School of Physics Beihang University Beijing100191 China Rare Isotope Science Project Institute for Basic Science Daejeon34000 Korea Republic of Department of Physics Origin of Matter and Evolution of Galaxy Institute Soongsil University Seoul06978 Korea Republic of Center for Exotic Nuclear Studies Institute for Basic Science Daejeon34126 Korea Republic of Key Laboratory of Neutron Physics Institute of Nuclear Physics and Chemistry CAEP Sichuan Mianyang621900 China

Halo phenomena have long been an important frontier in both experimental and theoretical nuclear physics. 37Mg was identified as a halo nucleus in 2014 and remains the heaviest nuclear halo system to date. While the halo phenomenon in 37Mg was not predicted before the discovery, its description has been still challenging afterwards. In this Letter, we report a microscopic and self-consistent description of the neutron halo in 37Mg using the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) that was developed in 2010. The experimental neutron separation energies and empirical matter radii of neutron-rich magnesium isotopes as well as the deformed p-wave halo characteristics of 37Mg are well reproduced without any free parameters. In particular, the orbital occupied by the halo neutron in 37Mg, exhibiting p-wave components comparable to those suggested in experiments, remains consistent across various employed density functionals including PC-F1, PC-PK1, NL3*, and PK1. The DRHBc theory investigated only even-even magnesium isotopes in previous works and for that reason missed predicting 37Mg as a halo nucleus before 2014. Although the core and the halo of 37Mg are both prolate, higher-order shape decoupling on the hexadecapole and hexacontatetrapole levels is predicted. Copyright © 2023, The Authors. All rights reserved.

关键词： Neutrons

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