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arXiv 2024年

作者： Wu, Qianliang Jiang, Haobo Luo, Lei Li, Jun Ding, Yaqing Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic National University of Singapore Singapore

Establishing reliable correspondences is essential for 3D and 2D-3D registration tasks. Existing methods commonly leverage geometric or semantic point features to generate potential correspondences. However, these features may face challenges such as large deformation, scale inconsistency, and ambiguous matching problems (e.g., symmetry). Additionally, many previous methods, which rely on single-pass prediction, may struggle with local minima in complex scenarios. To mitigate these challenges, we introduce a diffusion matching model for robust correspondence construction. Our approach treats correspondence estimation as a denoising diffusion process within the doubly stochastic matrix space, which gradually denoises (refines) a doubly stochastic matching matrix to the ground-truth one for high-quality correspondence estimation. It involves a forward diffusion process that gradually introduces Gaussian noise into the ground truth matching matrix and a reverse denoising process that iteratively refines the noisy one. In particular, we deploy a lightweight denoising strategy during the inference phase. Specifically, once points/image features are extracted and fixed, we utilize them to conduct multiple-pass denoising predictions in the reverse sampling process. Evaluation of our method on both 3D and 2D-3D registration tasks confirms its effectiveness. The code is available at https://***/wuqianliang/Diff-Reg. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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THE COMMAND SUPPORT AT-SEA EXPERIMENT

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NAVAL ENGINEERS JOURNAL 1990年第3期102卷 25-36页

作者： BUSCHER, DJ SUNDAY, DM David J. Buscher:is an electrical engineer on the Principal Professional Staff of the Fleet Systems Department of The Johns Hopkins University Applied Physics Laboratory. He earned his BS degree in electrical engineering from Villanova University in 1967 an MSEE degree from the University of Maryland in 1972 and an MS degree in technical management from The Johns Hopkins University in 1985. He was the lead engineer on the Command Support At-Sea Experiment Project. He started his career in 1967 with the U.S. Army Harry Diamond Laboratory working on computer and communications projects. During this time he worked in Vietnam with the United States Army Vietnam Science Advisor Office. Since joining JHU/APL in 1978 he has worked on a variety of projects associated with computer graphics hospital patient information systems computer networks autonomous underwater vehicles and satellite communications. He is currently assistant group supervisor of the Computer Systems Group. Daniel M. Sunday:is a mathematician on the Principal Professional Staff of the Fleet Systems Department of The Johns Hopkins University Applied Physics Laboratory. He earned his Ph.D. in mathematics from the University of Minnesota in 1971 and did postdoctoral research on neural networks at the University of California at Berkeley. Since joining JHU/APL in 1980 Dr. Sunday has worked as a designer and developer of software systems with emphasis on medical imaging interactive graphics natural language interfaces and software standards. He was the network designer for the Headquarters Integrated Office System (HIOS) Network for the Army at the Pentagon. He is currently the lead software engineer on the Command Support At-Sea Experiment Project.

The Command Support At-Sea Experiment (CS@SE) provides experimental advanced graphics display systems consisting of large screen color displays and operator console color displays in the combat information center (CIC) of an Aegis cruiser and in the tactical flag command center (TFCC) of an aircraft carrier. CS@SE systems are designed to prototype potential command support capabilities in an at-sea environment to validate and refine requirements for planned production system upgrades. These systems use sophisticated color graphics techniques to provide real-time tactical displays that improve the availability of information to an operator by reducing clutter through the use of color, area fill, transparen overlays and intensity coding of track symbols. Interfaces wen developed with the Aegis Display System (ADS), Shipboarc Gridlock System with Auto-correlation (SGS/AC), Flag Dats Display System (FDDS) and Tomahawk Engagement Planning and Exercise Evaluation System (TEPEE) that provided the data for the presentation of a tactical display. Display elements included both real-time and over-the-horizon (OTH) surface track data, velocity leaders, tags, uncertainty ellipses, and history trails. The display also included filled land masses, country boundaries, commercial airways, cities, graphics overlays (i.e., operational areas), weapon system missile performance contours, and engagement plans. This paper describes the experiment, its installation and integration into the shipboard environments of an Aegis cruiser (USS Leyte Gulf ) and an aircraft carrier (USS America , its usage by the ships companies and embarked staffs, and the experiment result! and findings. Key conclusions of the experiment are: 1 Advanced graphics color displays can significantly enhance the ability of the warfighter to assimilate a complex tactical display. 2 Both ships reported a requirement for a correlated OTH and real-time track display with the ability to clearly differentiate the two types

关键词： computer Graphics

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Complementary Information Mutual Learning for Multimodality Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Shen, Chuyun Li, Wenhao Chen, Haoqing Wang, Xiaoling Zhu, Fengping Li, Yuxin Wang, Xiangfeng Jin, Bo School of Computer Science and Technology East China Normal University Shanghai200062 China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Huashan Hospital Fudan University Shanghai200040 China School of Computer Science and Technology East China Normal University Shanghai AI Laboratory Shanghai200062 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China

Radiologists must utilize medical images of multiple modalities for tumor segmentation and diagnosis due to the limitations of medical imaging technology and the diversity of tumor signals. This has led to the development of multimodal learning in medical image segmentation. However, the redundancy among modalities creates challenges for existing subtraction-based joint learning methods, such as misjudging the importance of modalities, ignoring specific modal information, and increasing cognitive load. These thorny issues ultimately decrease segmentation accuracy and increase the risk of overfitting. This paper presents the complementary information mutual learning (CIML) framework, which can mathematically model and address the negative impact of inter-modal redundant information. CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering. CIML first decomposes the multimodal segmentation task into multiple subtasks based on expert prior knowledge, minimizing the information dependence between modalities. Furthermore, CIML introduces a scheme in which each modality can extract information from other modalities additively through message passing. To achieve non-redundancy of extracted information, the redundant filtering is transformed into complementary information learning inspired by the variational information bottleneck. The complementary information learning procedure can be efficiently solved by variational inference and cross-modal spatial attention. Numerical results from the verification task and standard benchmarks indicate that CIML efficiently removes redundant information between modalities, outperforming SOTA methods regarding validation accuracy and segmentation effect. To emphasize, message-passing-based redundancy filtering allows neural network visualization techniques to visualize the knowledge relationship among different modalitie

关键词： Redundancy

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Formation learning algorithms for mobile agents subject to 2-D dynamically changing topologies

Formation learning algorithms for mobile agents subject to 2...

引用

American Control Conference

作者： Deyuan Meng Yingmin Jia Junping Du Jun Zhang Wenling Li Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 P. R. China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 P. R. China School of Electronic and Information Engineering Beihang University (BUAA) Beijing 100191 P. R. China

ISBN: (纸本)9781479901777

In this paper, we consider a two-dimensional (2-D) formation problem for multi-agent systems subject to switching topologies that dynamically change along both a finite time axis and an infinite iteration axis. We present a distributed iterative learning control (ILC) algorithm via the nearest neighbor rules. By employing the 2-D approach, we develop both the asymptotic and exponentially fast convergence of our formation ILC, which can be guaranteed by conditions in terms of the spectral radius and the matrix norms, respectively.

关键词： Multi-agent systems ILC switching topologies formation learning algorithms learning algorithms Multi-agent systems International Labour Conference Mobile agents spectral radius

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Developing a computer science-specific learning taxonomy 07

Developing a computer science-specific learning taxonomy

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2007 Working Group Reports on ITiCSE on Innovation and Technology in computer Science Education, ITiCSE-WGR 2007

作者： Fuller, Ursula Johnson, Colin G. Ahoniemi, Tuukka Cukierman, Diana Hernán-Losada, Isidoro Jackova, Jana Lahtinen, Essi Lewis, Tracy L. Thompson, Donna McGee Riedesel, Charles Thompson, Errol Computing Laboratory University of Kent CanterburyCT2 7NF United Kingdom Institute of Software Systems Tampere University of Technology Tampere Finland School of Computing Science Simon Fraser University BurnabyBC Canada Department of Lenguajes Y Sistemas Informáticos Universidad Rey Juan Carlos Madrid Spain Faculty of Management Science and Informatics University of Zilina Zilina Slovakia Department of Information Technology Radford University RadfordVA24142 United States Department of Learning Commons Simon Fraser University BurnabyBC Canada Department of Computer Science and Engineering University of Nebraska Lincoln 259 Avery Hall LincolnNE68588-0115 United States Massey University Wellington New Zealand

ISBN: (纸本)9781450378420

Bloom's taxonomy of the cognitive domain and the SOLO taxonomy are being increasingly widely used in the design and assessment of courses, but there are some drawbacks to their use in computer science. This paper reviews the literature on educational taxonomies and their use in computer science education, identifies some of the problems that arise, proposes a new taxonomy and discusses how this can be used in application-oriented courses such as programming.

关键词： Taxonomies

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Corrections to “Percolation Theory-Based Exposure-Path Prevention for Wireless Sensor Networks Coverage in Internet of Things” [Oct 13 3625-3636]

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IEEE Sensors Journal 2014年第3期14卷 926-926页

作者： Liang Liu Xi Zhang Huadong Ma Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing China Department of Electrical and Computer Engineering Networking and Information Systems Laboratory College Station TX USA

In the above paper (ibid., vol. 13, no. 10, pp. 3625-3636, Oct. 2013), Fig. 15 is incorrect. The correct Fig. 15 is presented here.

关键词： Wireless sensor networks Internet Stochastic processes

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Development and application of MCAL tools for marine radar instruction

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computer Applications in engineering Education 1995年第4期3卷 259-259页

作者： Guirguis, S. Korany, E. Abdel-Bary, A. Institute of Graduate Studies and Research Alexandria University Alexandria Egypt Shawkat K. Guirguis:obtained the BSc and M.Sc. degrees in Computer Science and Automatic Control Faculty of Engineering Alexandria University in 1981 and 1984 respectively. In 1988 he obtained a PhD degree in Electronics and Communications Faculty of Engineering Cairo University co-supervised by the Imperial College of Science and Technology University of London where he spent two years as an academic visitor. His current research interests include software quality management multimedia automatic programming and decision support systems. He is currently a lecturer of Computer Science at the Institute of Graduate Studies & Research Alexandria University. Ezzat A. Korany:received the BS degree in Electrical Engineering (Electronics Section) from the University of Alexandria Egypt in 1971 the MS degree in Electrical Engineering (Computer Section) from Ain Shams University Cairo Egypt in 1977 and the PhD degree in Electrical Engineering (Digital Systems) from Florida Institute of Technology U.S.A. in 1982. He is currently an associate professor of Computer Science at the Institute of Graduate Studies & Research University of Alexandria. His research interests are in data communications and multimedia applications computer networks image processing and pattern recognition. Abdel-Latif A. Abdel-Bary:obtained the BEng degree in Electronics Technology from Arab Maritime Transport Academy in 1986. In 1995 he obtained the MSc degree in Information Technology from the Institute of Graduate Studies & Research Alexandria University. His current research interests include ICAL hypermedia and automatic programming.

In this article a multimedia computer-assisted learning (MCAL) system is presented. The major objective of this work was to investigate the potential of using such systems as tools for transferring instructional course information through various types of computer media as opposed to the classic CAL systems. The philosophy and techniques employed to design the system are investigated. Usage of the implemented system and its merits have been illustrated through its application to teach engineering students and technicians the theory and concepts of marine radar. System design, implementation, test, and revision phases are presented and discussed.

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Efficient Quantum State Tracking in Noisy Environments

arXiv

引用

arXiv 2022年

作者： Rambach, Markus Youssry, Akram Tomamichel, Marco Romero, Jacquiline Australian Research Council Centre of Excellence for Engineered Quantum Systems School of Mathematics and Physics University of Queensland QLD4072 Australia University of Technology Sydney Centre for Quantum Software and Information UltimoNSW2007 Australia Quantum Photonics Laboratory Centre for Quantum Computation and Communication Technology RMIT University MelbourneVIC3000 Australia Department of Electrical and Computer Engineering Centre for Quantum Technologies National University of Singapore Singapore119077 Singapore

Quantum state tomography, which aims to find the best description of a quantum state-the density matrix, is an essential building block in quantum computation and communication. Standard techniques for state tomography are incapable of tracking changing states and often perform poorly in the presence of environmental noise. Although there are different approaches to solve these problems theoretically, experimental demonstrations have so far been sparse. Our approach, matrix-exponentiated gradient tomography, is an online tomography method that allows for state tracking, updates the estimated density matrix dynamically from the very first measurements, is computationally efficient, and converges to a good estimate quickly even with noisy data. The algorithm is controlled via a single parameter, its learning rate, which determines the performance and can be tailored in simulations to the individual experiment. We present an experimental implementation of matrix-exponentiated gradient tomography on a qutrit system encoded in the transverse spatial mode of photons. We investigate the performance of our method on stationary and evolving states, as well as significant environmental noise, and find fidelities of around 95% in all cases. © 2022, CC BY.

关键词： Tomography

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Convex and scalable weakly labeled SVMs

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Yu-Feng Li Ivor W. Tsang James T. Kwok Zhi-Hua Zhou Google MPI for Intelligent Systems National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Engineering Nanyang Technological University Singapore Department of Computer Science and Engineering Hong Kong University of Science & Technology Hong Kong

In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete. This includes, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are completely unknown. Unlike supervised learning, learning with weak labels involves a difficult Mixed-Integer Programming (MIP) problem. Therefore, it can suffer from poor scalability and may also get stuck in local minimum. In this paper, we focus on SVMs and propose the WELLSVM via a novel label generation strategy. This leads to a convex relaxation of the original MIP, which is at least as tight as existing convex Semi-Definite Programming (SDP) relaxations. Moreover, the WELLSVM can be solved via a sequence of SVM subproblems that are much more scalable than previous convex SDP relaxations. Experiments on three weakly labeled learning tasks, namely, (i) semi-supervised learning; (ii) multi-instance learning for locating regions of interest in content-based information retrieval; and (iii) clustering, clearly demonstrate improved performance, and WELLSVM is also readily applicable on large data sets.

关键词： clustering convex relaxation cutting plane multi-instance learning semi-supervised learning weakly labeled data

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