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17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Wu, Hanqian Liu, Mumu Wang, Jingjing Xie, Jue Shen, Chenlin School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing China NLP Lab School of Computer Science and Technology Soochow University Suzhou China Southeast University-Monash University Joint Graduate School Suzhou China

ISBN: (纸本)9783030017156

In e-commerce websites, user-generated question-answering text pairs generally contain rich aspect information of products. In this paper, we address a new task, namely Question-answering (QA) aspect classification, which aims to automatically classify the aspect category of a given QA text pair. In particular, we build a high-quality annotated corpus with specifically designed annotation guidelines for QA aspect classification. On this basis, we propose a hierarchical attention network to address the specific challenges in this new task in three stages. Specifically, we firstly segment both question text and answer text into sentences, and then construct (sentence, sentence) units for each QA text pair. Second, we leverage a QA matching attention layer to encode these (sentence, sentence) units in order to capture the aspect matching information between the sentence inside question text and the sentence inside answer text. Finally, we leverage a self-matching attention layer to capture different importance degrees of different (sentence, sentence) units in each QA text pair. Experimental results demonstrate that our proposed hierarchical attention network outperforms some strong baselines for QA aspect classification. © Springer Nature Switzerland AG 2018.

关键词： Text processing

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On RNN Models for Solving Dynamic System of Linear Equations

On RNN Models for Solving Dynamic System of Linear Equations

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International Conference on Intelligent Control and information processing (ICICIP)

作者： Huiyan Lu Ruiqi Liu Xiujuan Du Haiqi Liu Mei Lin Long Jin Jiliang Zhang Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang China The Fifth Electronics Research Institute of MIIT Guangzhou China The Provincial key Laboratory of the Internet of Things Qinghai Normal University Xi'ning China School of Computer Science and Engineering South China University of Technology Guangzhou China School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Neural networks have a wide range of applications in dealing with various online computing problems. This paper mainly retrospects one of the latest recurrent neural network (RNN) models and supplies summarizes on it. Firstly, formulations on the RNN model for dealing with the dynamic underdetermined system of linear equations with double bound constraints on state variables and residual errors are presented. Secondly, simple structures of the RNN model, that is, the neuron-connection architecture of RNN model for handling with the perturbed dynamic underdetermined linear system, as well as the RNN model and the unfolding in time of the computation involved in its forward computation are analyzed. In addition, the whole flowchart on the presented method for establishing the RNN model is also given. Then, experiments on executing the tasks of the UR5 robot when the end-effector tracks a “four-leaf clover” path and a “tricuspid valve” path synthesized by the RNN model are conducted, which show the superiority and accuracy of the presented RNN model.

关键词： Mathematical model Computational modeling Analytical models computer architecture Linear systems Robots Task analysis

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A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction—Application in Fast Biological Spectroscopy

arXiv

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arXiv 2020年

作者： Wang, Zi Guo, Di Tu, Zhangren Huang, Yihui Zhou, Yirong Wang, Jian Feng, Liubin Lin, Donghai You, Yongfu Agback, Tatiana Orekhov, Vladislav Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China School of Computer and Information Engineering Xiamen University of Technology Xiamen China College of Chemistry and Chemical Engineering Key Laboratory for Chemical Biology of Fujian Province High-field NMR Center Xiamen University Xiamen China China Mobile Group Xiamen China Biomedical Intelligent Cloud R&D Center Xiamen University Xiamen China Department of Molecular Sciences Swedish University of Agricultural Sciences Uppsala Sweden Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg Sweden

The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partial sampled exponentials is highly expected in general signal processing and many applications. Deep learning has shown astonishing potential in this field but many existing problems, such as lack of robustness and explainability, greatly limit its applications. In this work, by combining merits of the sparse model-based optimization method and data-driven deep learning, we propose a deep learning architecture for spectra reconstruction from undersampled data, called MoDern. It follows the iterative reconstruction in solving a sparse model to build the neural network and we elaborately design a learnable soft-thresholding to adaptively eliminate the spectrum artifacts introduced by undersampling. Extensive results on both synthetic and biological data show that MoDern enables more robust, high-fidelity, and ultra-fast reconstruction than the state-of-the-art methods. Remarkably, MoDern has a small number of network parameters and is trained on solely synthetic data while generalizing well to biological data in various scenarios. Furthermore, we extend it to an open-access and easy-to-use cloud computing platform (XCloud-MoDern), contributing a promising strategy for further development of biological applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal reconstruction

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Optimal seam line detection in laplacian pyramid domain for image stitching

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Journal of computers (Taiwan) 2018年第1期29卷 209-219页

作者： Yan, Wu-Xia Liu, Chuan-Cai Hu, Jun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

Parallax handing is a challenging problem for image stitching. Optimal seam line method is employed in this paper to deal with the misalignment on panoramas caused by parallax problem. It's an important method for orthoimage stitching in remote sensing. However, failures occur when it used in common image stitching due to the parallax problem. In order to obtain a better panorama for common image, the proposed approach mainly employs optimal seam line and contains the following three steps. First, the parallax problem can be alleviated by geometric correction. Then, the optimal seam line is detected within the overlapping region by dynamic programming in Laplacian pyramid domain. And images were blended by using blending trick on each corresponding level of the pyramids. Finally, the convincing panorama can be obtained by reconstructing the panoramic pyramid. The experiments demonstrated that the proposed method can obtain the clear and seamless panoramas. © 2018 computer Society of the Republic of China. All rights reserved.

关键词： Remote sensing

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Fake comment detection based on time series and density peaks clustering 18th

Fake comment detection based on time series and density peak...

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18th International Conference on Algorithms and Architectures for Parallel processing - ICA3PP 2018 Collocated Workshops: ICA3PP 2018 Workshop on Intelligent Algorithms for Large-scale Complex Optimization Problems, IALCOP 2018, ICA3PP 2018 Workshop on Security and Privacy in Data processing, SPDP 2018

作者： Di, Ruitong Wang, Hong Fang, Youli Zhou, Ying School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China Institute of Biomedical Sciences Shandong Normal University Jinan250014 China

ISBN: (纸本)9783030052331

This paper proposes a fake comment recognition method based on time series and density peaks clustering. Firstly, an anomaly recognition model based on multi-dimensional time series is constructed. Secondly, according to the idea of multi-scale features, seven benchmark-scale and corresponding subdivision-scale features are extracted hierarchically, and further, 49 features are finally obtained. At last, an optimized detection model based on density peaks clustering is proposed for identifying the fake comments, so as to improve the anti-noise ability of our method. The effectiveness of our proposed method is verified by several experiments, with the AUC value reaching 92%. © Springer Nature Switzerland AG 2018.

关键词： Time series

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An improved Isomap method for manifold learning

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International Journal of Intelligent Computing and Cybernetics 2017年第1期10卷 30-40页

作者： Taiguo Qu Zixing Cai College of Computer Science and Technology Hengyang Normal UniversityHengyangChina Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangChina School of Information Science and Engineering Central South UniversityChangshaChina

Purpose-Isometric feature mapping(Isomap)is a very popular manifold learning method and is widely used in dimensionality reduction and data *** most time-consuming step in Isomap is to compute the shortest paths between all pairs of data points based on a neighbourhood *** classical Isomap(C-Isomap)is very slow,due to the use of Floyd’s algorithm to compute the shortest *** purpose of this paper is to speed up ***/methodology/approach-Through theoretical analysis,it is found that the neighbourhood graph in Isomap is *** this case,the Dijkstra’s algorithm with Fibonacci heap(Fib-Dij)is faster than Floyd’s *** this paper,an improved Isomap method based on Fib-Dij is *** using Fib-Dij to replace Floyd’s algorithm,an improved Isomap method is presented in this ***-Using the S-curve,the Swiss-roll,the Frey face database,the mixed national institute of standards and technology database of handwritten digits and a face image database,the performance of the proposed method is compared with C-Isomap,showing the consistency with C-Isomap and marked improvements in terms of the high *** also demonstrate that Fib-Dij reduces the computation time of the shortest paths from O(N3)to O(N2lgN).Research limitations/implications-Due to the limitations of the computer,the sizes of the data sets in this paper are all smaller than 3,***,researchers are encouraged to test the proposed algorithm on larger data ***/value-The new method based on Fib-Dij can greatly improve the speed of Isomap.

关键词： Dijkstra’s algorithm Fibonacci heap Floyd’s algorithm Isomap Manifold learning Shortest path Paper type Research paper

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Integrating MOOCs with Traditional Classroom Instruction to Improve Teaching Effectiveness

Integrating MOOCs with Traditional Classroom Instruction to ...

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2017 2nd International Seminar on Education Innovation and Economic Management(SEIEM 2017)

作者： Xiaodong Wu Yuzhu Zeng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

Massive Open Online Course(MOOC) is a new teaching mode appeared in recent years and its emergence has constituted some challenge to the traditional teaching. However, MOOCs teaching mode has both advantages and disadvantages. In this paper, both the merits and the demerits of MOOC education are analyzed. We point out that the MOOC education mode and the traditional face-to-face classroom instruction should be combined together so as to improve the teaching effectiveness in modern education. Finally, how to blend the MOOC mode and the traditional classroom teaching and further utilize the network platform to improve the teaching efficiency in several learning phases are examined.

关键词： MOOCs face-to-face teaching traditional teaching network teaching platform

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Survey of key technologies on millimeter-wave CMOS integrated circuits

Survey of key technologies on millimeter-wave CMOS integrate...

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2017年第二届可靠性工程国际会议

作者： Fei Yu Lei Gao Lixiang Li Shuo Cai Wei Wang Chunhua Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation & School of Computer and Communication Engineering Changsha University of Science and Technology College of Information Science and Engineering Hunan University

In order to provide guidance for the development of high performance millimeter-wave complementary metal oxide semiconductor(MMW-CMOS) integrated circuits(IC), this paper provides a survey of key technologies on MMW-CMOS IC. Technical background of MMW wireless communications is described. Then the recent development of the critical technologies of the MMW-CMOS IC are introduced in detail and compared. A summarization is given, and the development prospects on MMWCMOS IC are also discussed.

关键词： Survey of key technologies on millimeter-wave CMOS integrated circuits

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A Delay-Aware Congestion Control Protocol for Wireless Sensor Networks

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Chinese Journal of Electronics 2017年第3期26卷 591-599页

作者： PEI Tingrui LEI Fangqing LI Zhetao ZHU Gengming PENG Xin Youngjune CHOI Hiroo SEKIYA The College of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Computer Science & Engineering Hunan University of Science and Technology College of Information and Communication Engineering Hunan Institute of Science and Technology Department of Information and Computer Engineering Ajou University Graduate School of Engineering Chiba University

In wireless sensor networks,congestion leads to buffer overflowing,and increases *** traditional solutions use rate adjustment to mitigate congestion,thus increasing the delay.A Delay-aware congestion control protocol(DACC)was presented to mitigate congestion and decrease *** order to improve the accuracy of the existing congestion detection model which is based on the buffer occupancy of a single node,DACC presents a new model considering both the real-time buffer occupancy and the average transmission time of *** uses the untapped bits in the IEEE 802.11 Distributed coordination function(DCF)frames header to carry congestion *** the congestion alleviation period,DACC presents a channel occupancy mechanism which is based on the real-time buffer occupancy for the purpose of decreasing delay and preventing packet *** results indicate that in terms of delay,packet delivery ratio,collision and buffer load,DACC has comparative advantages than those of 802.11 DCF,Priority-based congestion control protocol(PCCP)and Decoupling congestion control and fairness(DCCF).

关键词： Wireless sensor network Congestion control Delay Medium access control(MAC) protocol

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