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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Ni Li Manman Peng Buwen Cao Kenli Li Keqin Li College of Computer Science and Electric Engineering Hunan University Hunan Changsha 410082 China College of Information and Electronic Engineering Hunan Yiyang 413000 China Engineering Research Center of DongTing Lake regional ecological environment intelligent monitoring and disaster prevention and Mitigation Hunan Yiyang 413000 China Key Laboratory of city computing and IoT Hunan City University Hunan Yiyang 413000 China Department of Computer science State University of New York New Paltz NY 12561 USA

Spectral clustering is one of the most popular clustering approaches. Despite its good performance and strong theoretical supports, it is limited to high complexity of the graph Laplacian similarity matrix construction and eigen-decomposition problems. Recently, deep learning has been successfully adopted in graph representation. In the paper, we jointly learn the manifold graph construction and non-linear low-dimension mapping of the graph. In addition, we theoretically proved that our model according with spectral clustering theory. Meanwhile, we use the proposed non-linear coders as the building blocks to formulate a deep structure to further refine features of layer wise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both clustering accuracy and normalized mutual information( NMI )

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Using bursty announcements for detecting BGP routing anomalies

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arXiv 2019年

作者： Moriano, Pablo Hill, Raquel Jean Camp, L. Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37830 United States Computer and Information Sciences Department Spelman College AtlantaGA30314 United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Despite the robust structure of the Internet, it is still susceptible to disruptive routing updates that prevent network traffic from reaching its destination. Our research shows that BGP announcements that are associated with disruptive updates tend to occur in groups of relatively high frequency, followed by periods of infrequent activity. We hypothesize that we may use these bursty characteristics to detect anomalous routing incidents. In this work, we use manually verified ground truth metadata and volume of announcements as a baseline measure, and propose a burstiness measure that detects prior anomalous incidents with high recall and better precision than the volume baseline. We quantify the burstiness of inter-arrival times around the date and times of four large-scale incidents: the Indosat hijacking event in April 2014, the Telecom Malaysia leak in June 2015, the Bharti Airtel Ltd. hijack in November 2015, and the MainOne leak in November 2018;and three smaller scale incidents that led to traffic interception: the Belarusian traffic direction in February 2013, the Icelandic traffic direction in July 2013, and the Russian telecom that hijacked financial services in April 2017. Our method leverages the burstiness of disruptive update messages to detect these incidents. We describe limitations, open challenges, and how this method can be used for routing anomaly detection. Copyright © 2019, The Authors. All rights reserved.

关键词： Border Gateway Protocol

How to Look at Data: Environmental Practitioners' Lens through Two Case Studies 6

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How to Look at Data: Environmental Practitioners' Lens throu...

6th Workshop on Visualisation in Environmental sciences, EnvirVis 2018 at EuroVis 2018

作者： Ling, Meng Johnson, Jeffrey A. Feng, Zhiquan Chen, Jian Columbus OH United States NewFields HoustonTX United States School of Information Science and Engineering University of Jinan China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing China Department of Computer Science and Engineering The Ohio State University United States

ISBN: (纸本)9783038680635

Exploratory data visualization, an idea proposed by Tukey in 1977, is meant to output various types of visualization in order to make the data more understandable. While visualization researchers have generated many novel solutions to critical and complex environmental problems, to everyday environmental consultants, some practical considerations have to be made in the visualization analysis to help stakeholders generate and test hypotheses that would not be possible otherwise. We present two environmental case studies of using visualization to communicate key findings: constructing stratigraphic units (layered) and generating groundwater contaminant plumes (volumetric). These real-world cases show that many times visualization alone may not give us correct answers;often what works is the combination of visualization, domain experts' knowledge, and interpretation of the visualization solutions. The lack of any of them may lead to faulty conclusions. The first case study illustrates together how domain experts, visualization, and contamination conditions assisted in interpreting limited and ambiguous lithologic data. The second case study emphasizes conceptual and technical understanding and discusses some common factors affecting 3D interpolation, which again suggests that we must incorporate domain experts' knowledge as well as analytics into visualization for defensible decision making. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： Visualization

Performance Analysis of ZCC-Optical-CDMA over SMF for Fiber-To-The-Home Access Network

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Journal of Physics: Conference Series 2020年第2期1529卷

作者： Alaan Ghazi S. A. Aljunid Alaa Fareed Syed Zulkarnain Syed Idrus C. B. M Rashidi Marwa albayaty Aras Al-dawoodi Ahmed M. Fakhrudeen School of Human Development & Techno Communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Technical Engineering Alkitab University Iraq Internet Research Laboratory School of Computing University Utara Malaysia Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Networks Department College of Computer Science and Information Technology Kirkuk University Iraq

To provide a sustainable fiber-to-the-home (FTTH), several multiplexing techniques have been developed for this purpose. The correlation features are the main obstacle behind the network performance limitation, which imposes to a high level of multiple access interference. However, the development of multiplexing techniques helps to overcome these limitations, such as optical-code division multiple access (Optical-CDMA). Optical-CDMA is considered as one of the most powerful solutions for FTTH. This paper aims to enhance FTTH network performance by applying Zero cross-correlation code (ZCC) with Optical-CDMA with maximum link single-mode fiber. In the simulation, the system performance is demonstrated in terms of bit error rate, Q-Factor and eye diagram measurements.

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Quantum Simulation for High Energy Physics

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arXiv 2022年

作者： Bauer, Christian W. Davoudi, Zohreh Balantekin, A. Baha Bhattacharya, Tanmoy Carena, Marcela de Jong, Wibe A. Draper, Patrick El-Khadra, Aida Gemelke, Nate Hanada, Masanori Kharzeev, Dmitri Lamm, Henry Li, Ying-Ying Liu, Junyu Lukin, Mikhail Meurice, Yannick Monroe, Christopher Nachman, Benjamin Pagano, Guido Preskill, John Rinaldi, Enrico Roggero, Alessandro Santiago, David I. Savage, Martin J. Siddiqi, Irfan Siopsis, George Van Zanten, David Wiebe, Nathan Yamauchi, Yukari Yeter-Aydeniz, Kübra Zorzetti, Silvia Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Department of Physics University of Wisconsin MadisonWI53706 United States T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Enrico Fermi Institute University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois UrbanaIL61801 United States QuEra Computing Inc BostonMA02135 United States Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University NY11794-3800 United States Department of Physics Brookhaven National Laboratory UptonNY11973 United States Pritzker School of Molecular Engineering Chicago Quantum Exchange Kadanoff Center for Theoretical Physics University of Chicago IL60637 United States QBraid Co. Harper Court 5235 ChicagoIL60615 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Duke Quantum Center Duke University DurhamNC27701 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States IonQ Inc. College ParkMD20740 United States Physics and Astronomy Department Rice University HoustonTX77005 United States Institute for Quantum Information and Matter Californi

It is for the first time that quantum simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in Quantum information sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This community whitepaper is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond. © 2022, CC BY.

关键词： High energy physics

Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

A novel bi-hemispheric discrepancy model for eeg emotion recognition

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arXiv 2019年

作者： Li, Yang Zheng, Wenming Wang, Lei Zong, Yuan Qi, Lei Cui, Zhen Zhang, Tong Song, Tengfei Key Laboratory of Child Development and Learning Science Ministry of Education Department of Information Science and Engineering Southeast University Nanjing Jiangsu210096 China Key Laboratory of Child Development and Learning Science Ministry of Education School of Biological Sciences and Medical Engineering Southeast University Nanjing Jiangsu210096 China School of Computing and Information Technology University of Wollongong NSW2500 Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu210096 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu210096 China

The neuroscience study [1] has revealed the discrepancy of emotion expression between left and right hemispheres of human brain. Inspired by this study, in this paper, we propose a novel bi-hemispheric discrepancy model (BiHDM) to learn the asymmetric differences between two hemispheres for electroencephalograph (EEG) emotion recognition. Concretely, we first employ four directed recurrent neural networks (RNNs) based on two spatial orientations to traverse electrode signals on two separate brain regions, which enables the model to obtain the deep representations of all the EEG electrodes' signals while keeping the intrinsic spatial dependence. Then we design a pairwise subnetwork to capture the discrepancy information between two hemispheres and extract higher-level features for final classification. Besides, in order to reduce the domain shift between training and testing data, we use a domain discriminator that adversarially induces the overall feature learning module to generate emotion-related but domain-invariant feature, which can further promote EEG emotion recognition. We conduct experiments on three public EEG emotional datasets, and the experiments show that the new state-of-the-art results can be achieved. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

Common Limitations of Image Processing Metrics: A Picture Story

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

Adaptive morphological reconstruction for seeded image segmentation

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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