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21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781713820697

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020 ISCA

关键词： Extraction

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Machine Reading Comprehension of High-Tech Industry Policies: A New Dataset and Chinese Pre-Trained Language Model

Machine Reading Comprehension of High-Tech Industry Policies...

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Telecommunications, Optics and computer Science (TOCS), IEEE Conference on

作者： Changchang Zeng Shaobo Li Bin Chen Chengdu Institute of Computer Application in Chinese Academy of Sciences University of Chinese Academy of Sciences Chengdu China State Key Laboratory of Public Big Data Guizhou University Guiyang China Chongqing Research Institute of HIT Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665424998

Machine reading comprehension (MRC) is a challenging research hotspot in the field of Artificial Intelligence (AI). It can be applied to many scenarios, such as intelligent question answering, intelligent document retrieval, and so on. In this article, we focus on the machine reading comprehension of high-tech industry policy texts in China. First, we create a cloze style machine reading comprehension dataset of Chinese high-tech industrial policies. Next, we propose a new pre-training objective named multi-segment ordering discriminator, and we also use domain-specific dictionary to improve the MLM pre-training process. Finally, on our dataset, we trained a new pre-trained language model for machine reading comprehension of Chinese industrial policies. Experiment results show that our pre-trained language model surpasses existing models such as BERT and RoBERTa in the new dataset.

关键词： Industries Vocabulary Computational modeling Bit error rate Food industry Petrochemicals Optics

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A Recency Effect Hidden Markov Model for Repeat Consumption Behavior Prediction 13th

A Recency Effect Hidden Markov Model for Repeat Consumption ...

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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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Deep Learning Based Search and Recommendation of Literature in Wanfang Knowledge Base

Deep Learning Based Search and Recommendation of Literature ...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Tongtong Li Qiang Lin Xiaoyan Chen Yanru Guo Shaofang Zhao School of Mathematics and Computer Science Northwest Minzu University Lanzhou Gansu China Dynamic Flow Data Computing and Application Laboratory Northwest Minzu University Lanzhou Gansu China Key Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Lanzhou Gansu China

Classifying Chinese text is a hot but difficult topic in natural language processing field. In this work, we aim to quickly recommend the required documents for users via searching keywords in a massive literature database. We use "Wanfang Data Knowledge Service Platform Journal Document User Behavior Log Data" as the object to research and build a text classifier based on deep learning techniques. It is proposed to perform data cleaning and data extraction for user browsing behavior logs and user download behavior logs. Firstly, Chinese word segmentation and keyword extraction are carried out on the document title information, and the keyword data set and document keyword data set based on the document name are constructed. Finally, using the traditional deep learning model and convolutional neural network model to build a text classifier for training and classification, as a model for users to search for keywords recommended for literature. Experimental results show that the constructed model is able to effectively classify and recommend documents with the users' search keywords, and extract keywords from document names in the construction model. The proposed model TEXTCNN V2 obtains a value for accuracy, precision and F-1 score is 0.9132, 0.9220 and 0.9154, respectively.

关键词： Deep learning Training Computational modeling Text categorization Search problems Data models Convolutional neural networks

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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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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...

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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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