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arXiv 2018年

作者： Zhen, Liangli Li, Miqing Cheng, Ran Peng, Dezhong Yao, Xin 138632 Singapore School of Computer Science University of Birmingham BirminghamB152TT United Kingdom The Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Machine Intelligence Laboratory College of Computer Science Sichuan University Chengdu610065 China The CERCIA School of Computer Science University of Birmingham United Kingdom

In multiobjective optimisation, a set of scalable test problems with a variety of features allow researchers to investigate and evaluate the abilities of different optimisation algorithms, and thus can help them to design and develop more effective and efficient approaches. Existing test problem suites mainly focus on situations where all the objectives are fully conflicting with each other. In such cases, an m-objective optimisation problem has an (m − 1)-dimensional Pareto front in the objective space. However, in some optimisation problems, there may be unexpected characteristics among objectives, e.g., redundancy. The redundancy of some objectives can lead to the multiobjective problem having a degenerate Pareto front, i.e., the dimension of the Pareto front of the m-objective problem be less than (m − 1). In this paper, we systematically study degenerate multiobjective problems. We abstract three general characteristics of degenerate problems, which are not formulated and systematically investigated in the literature. Based on these characteristics, we present a set of test problems to support the investigation of multiobjective optimisation algorithms under situations with redundant objectives. To the best of our knowledge, this work is the first one that explicitly formulates these three characteristics of degenerate problems, thus allowing the resulting test problems to be featured by their generality, in contrast to existing test problems designed for specific purposes (e.g., visualisation). The usefulness of the proposed test problems has been demonstrated by the extensive studies of ten representative multiobjective evolutionary algorithms on these problems. The results indicate that none of the tested algorithms is able to effectively solve these proposed problems, calling for the need for developing new approaches to address degenerate multiobjective problems. The discussion regarding why certain MOEAs struggle to solve the degenerate problems and poten

关键词： Redundancy

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Design of gear reducer based on FOA optimization algorithm 1st

Design of gear reducer based on FOA optimization algorithm

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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Lin, Xiaojia Zhang, Fuquan Xu, Lin Department of Computer Science Fujian Business University Fuzhou350012 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang Univeristy Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China Innovative Information Industry Research Institute Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783319707297

In order to optimize the design of gear reducer, gear reducer optimal design to improve reliability and security, slow convergence and local optimum for FOA algorithm is proposed based on the improved type FOA gear reducer optimization design model. To avoid falling into local optimum Drosophila optimization algorithms, improved FOA algorithm by introducing a correction factor. Superior to the gear reducer seven variables optimization design model for the study, to ensure the safety and reliability of the premise, the FOA has the advantage of improved convergence speed and avoid local optimization problems, in order to verify the proposed method and reliability. © Springer International Publishing AG 2018.

关键词： Reliability

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Duplicate checking Strategies for Selecting Topics for Graduation Thesis Based on Maximum Public Sequence

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IOP Conference Series: Earth and Environmental science 2019年第3期267卷

作者： Guorui Yu Li Huang Jiewen Sun Shangchun Liao School of Computer Science and Technology Wuhan University of Science and Technology phone:*** Intelligent Information Processing and Hubei Real-time Industrial Systems Laboratory. Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science and Technology Ministry of Education.

The system is based on the undergraduate thesis management system as the practice platform, focusing on the realization of the thesis topic check function. The current check-up detection system is based on the entire contents of the query, and does not meet the requirements for an undergraduate thesis query. According to the characteristics of the thesis, the system first divides the keywords into the topic, selects the irrelevant parts, then removes the irrelevant words, and finally checks the keywords in the topic, and realizes the efficient and accurate check for inspection.

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Learning from large-scale noisy web data with ubiquitous reweighting for image classification

arXiv

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arXiv 2018年

作者： Li, Jia Song, Yafei Zhu, Jianfeng Cheng, Lele Su, Ying Ye, Lin Yuan, Pengcheng Han, Shumin State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Shenzhen Cyberspace Laboratory Shenzhen China National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science Peking University Beijing100871 China Computer Vision Technology Department of Baidu Beijing100871 China

Many advances of deep learning techniques originate from the efforts of addressing the image classification task on large-scale datasets. However, the construction of such clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that learns an image classification model from large-scale noisy data. By observing the web data, we find that there are five key challenges, i.e., imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. To alleviate these challenges, we assume that every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags and the labels. In this manner, the influence of bias and noise in the web data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the WebVision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks the first place in the image classification task. [***] 3 Apr 2019. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification

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A sub-Neptune transiting the young field star HD 18599 at 40 pc

arXiv

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arXiv 2022年

作者： de Leon, Jerome P. Livingston, John H. Jenkins, James S. Vines, Jose I. Wittenmyer, Robert A. Clark, Jake T. Winn, Joshual I.M. Addison, Brett Ballard, Sarah Bayliss, Daniel Beichman, Charles Benneke, Björn Berardo, David Anthony Bowler, Brendan P. Brown, Tim Bryant, Edward M. Christiansen, Jessie Ciardi, David Collins, Karen A. Collins, Kevin Crossfield, Ian Deming, Drake Dragomir, Diana Dressing, Courtney D. Fukui, Akihiko Gan, Tianjun Giacalone, Steven Gill, Samuel Alvarez, Erica Gonzàlez Gorjian, V. González Alvarez, E. Hesse, Katharine Horner, Jonathan Howell, Steve B. Jenkins, Jon M. Kane, Stephen R. Kendall, Alicia Kielkopf, John F. Kreidberg, Laura Latham, David W. Liu, Huigen Lund, Michael B. Matson, Rachel Matthews, Elisabeth Mengel, Mathew W. Morales, Farisa Mori, Mayuko Narita, Norio Nishiumi, Taku Okumura, Jack Plavchan, Peter Quinn, Sam Rabus, Markus Ricker, George Rudat, Alexander Schlieder, Joshua Schwarz, Richard P. Seager, Sara Shporer, Avi Smith, Alexis M.S. Sphorer, Avi Stassun, Keivan Tamura, Motohide Tan, Thiam G. Tinney, C.G. Vanderspek, Roland Gorjian, Varoujan Werner, Michael W. West, Richard G. Wright, Duncan Zhang, Hui Zhou, George Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Astrobiology Center 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomical Science The Graduated University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Núcleo de Astronomía Facultad de Ingeniería y Ciencias Universidad Diego Portales Av. Ejército 441 Santiago Chile Casilla 36-D Santiago Chile Universidad de Chile Camino el Observatorio Las Condes Santiago1515 Chile University of Southern Queensland Centre for Astrophysics West Street ToowoombaQLD4350 Australia Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Department of Astronomy University of Florida 211 Bryant Space Science Center GainesvilleFL32611 United States Department of Physics University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom NASA Exoplanet Science Institute California Institute of Technology PasadenaCA91106 United States Department of Physics Institute for Research on Exoplanets Université de Montréal MontréalQC Canada Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Astronomy The University of Texas AustinTX78712 United States CASA University of Colorado BoulderCO80309 United States Las Cumbres Observatory GoletaCA93117 United States Mullard Space Science Laboratory University College London Holmbury St Mary Dorking SurreyRH5 6NT United Kingdom Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States George Mason University 4400 University Drive MS 3F3 FairfaxVA22030 United States Department of Physics and Astronomy University of Kansas 1082 Malott 1251 Wescoe Hall Dr LawrenceKS66045 United

关键词： Stars

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

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science China(Information sciences) 2017年第11期60卷 43-64页

作者： Jibing GONG Xiaoxia GAO Hong CHENG Jihui LIU Yanqing SONG Mantang ZHANG Yi ZHAO School of Information Science and Engineering Yanshan University The Key Lab for Computer Virtual Technology and System Integration Yanshan University State Key Lab of Mathematical Engineering and Advanced Computing Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

Friend recommendation is a fundamental service in both social networks and practical applications,and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend++, a hybrid multi-individual recommendation model that integrates a weighted average method(WAM) into the random walk(RW) framework by seamlessly employing social ties, behavior context, and personal information. In Friend++, the first plus signifies recommending a new friend through network features, while the second plus stands for using node features. To verify our method, we conduct experiments on three social datasets crawled from the Sina microblog system(Weibo). Experimental results show that the proposed method significantly outperforms six baseline methods in terms of recall, precision, F1-measure, and MAP. As a final step, we describe a case study that demonstrates the scalability and universality of our method. Through discussion, we reach a meaningful conclusion: although common interests are more important than user activities in making recommendations, user interactions may be the most important factor in finding the most appropriate potential friends.

关键词： multi-individual friend recommendation architecture behavior context analysis Intimacy degree random walk framework social networks

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Adaptive morphological reconstruction for seeded image segmentation

arXiv

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arXiv 2019年

作者： Lei, Tao Jia, Xiaohong Liu, Tongliang Liu, Shigang Meng, Hongying Nandi, Asoke K. School of Electronical and Information Engineering Shaanxi University of Science and Technology Xi’an710021 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Electronical and Information Engineering Shaanxi University of Science and Technology Xi’an710021 China School of Computer Science University of Sydney Sydney Australia School of Computer Science Shaanxi Normal University Xi’an710119 China Department of Electronic and Computer Engineering Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom Department of Electronic and Computer Engineering Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Col- lege of Electronic and Information Engineering Tongji University Shanghai200092 China

—Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to filter seeds (regional minima) to reduce over-segmentation. However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. Firstly, AMR can adaptively filter useless seeds while preserving meaningful ones. Secondly, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that AMR is useful for improving algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time. Source code is available at https://***/SUST-reynole/AMR. Copyright © 2019, The Authors. All rights reserved.

关键词： Image segmentation

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computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

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Journal of Healthcare Engineering 2020年第1期2020卷

作者： Yilin Xie Zhuoyue Wu Xin Han Hongyu Wang Yifan Wu Lei Cui Jun Feng Zhaohui Zhu Zhongyuanlong Chen Department of Information Science and Technology Northwest University Xi’an Shaanxi 710127 *** School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi’an 710127 Shaanxi *** Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC Urumqi Xinjiang Uygur Autonomous Region 830049 China

The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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Walk-steered convolution for graph classification

arXiv

引用

arXiv 2018年

作者： Jiang, Jiatao Xu, Chunyan Cui, Zhen Zhang, Tong Zheng, Wenming Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of the Ministry of Education School of Computer Science and Engineering University of Science and Technology Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering University of Science and Technology Nanjing210094 China Key Laboratory of Child Development and Learning Science of the Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China

Graph classification is a fundamental but challenging issue for numerous real-world applications. Despite recent great progress in image/video classification, convolutional neural networks (CNNs) cannot yet cater to graphs well because of graphical non-Euclidean topology. In this work, we propose a walk-steered convolutional (WSC) network to assemble the essential success of standard convolutional neural networks as well as the powerful representation ability of random walk. Instead of deterministic neighbor searching used in previous graphical CNNs, we construct multi-scale walk fields (a.k.a. local receptive fields) with random walk paths to depict subgraph structures and advocate graph scalability. To express the internal variations of a walk field, Gaussian mixture models are introduced to encode principal components of walk paths therein. As an analogy to a standard convolution kernel on image, Gaussian models implicitly coordinate those unordered vertices/nodes and edges in a local receptive field after projecting to the gradient space of Gaussian parameters. We further stack graph coarsening upon Gaussian encoding by using dynamic clustering, such that high-level semantics of graph can be well learned like the conventional pooling on image. The experimental results on several public datasets demonstrate the superiority of our proposed WSC method over many state-of-the-arts for graph classification. Copyright © 2018, The Authors. All rights reserved.

关键词： Random processes

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