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Multiple phase transitions in networks of directed networks

Physical Review E 2019年第1期99卷 012312-012312页

作者： Xueming Liu Linqiang Pan H. Eugene Stanley Jianxi Gao Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Department of Physics Center for Polymer Studies Boston University Boston Massachusetts 02215 USA Department of Computer Science Rensselaer Polytechnic Institute Troy New York 12180 USA

The robustness in real-world complex systems with dependency connectivities differs from that in isolated networks. Although most complex network research has focused on interdependent undirected systems, many real-world networks—such as gene regulatory networks and traffic networks—are directed. We thus develop an analytical framework for examining the robustness of networks made up of directed networks of differing topologies. We use it to predict the phase transitions that occur during node failures and to generate the phase diagrams of a number of different systems, including treelike and random regular (RR) networks of directed Erdős-Rényi (ER) networks and scale-free networks. We find that the the phase transition and phase diagram of networks of directed networks differ from those of networks of undirected networks. For example, the RR networks of directed ER networks show a hybrid phase transition that does not occur in networks of undirected ER networks. In addition, system robustness is affected by network topology in networks of directed networks. As coupling strength q increases, treelike networks of directed ER networks change from a second-order phase transition to a first-order phase transition, and RR networks of directed ER networks change from a second-order phase transition to a hybrid phase transition, then to a first-order phase transition, and finally to a region of collapse. We also find that heterogenous network systems are more robust than homogeneous network systems. We note that there are multiple phase transitions and triple points in the phase diagram of RR networks of directed networks and this helps us understand how to increase network robustness when designing interdependent infrastructure systems.

关键词： Attacks on networks Cascades Continuous phase transition Discontinuous phase transition Network phase transitions Percolation phase transition Random attacks Robustness Topological phase transition

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Multi-wavelength study of the luminous GRB 210619B observed with Fermi and ASIM

arXiv

引用

arXiv 2022年

作者： Caballero-García, M.D. Gupta, Rahul Pandey, S.B. Oates, S.R. Marisaldi, M. Ramsli, A. Hu, Y.-D. Castro-Tirado, A.J. Sánchez-Ramírez, R. Connell, P.H. Christiansen, F. Kumar Ror, A. Aryan, A. Bai, J.-M. Castro-Tirado, M.A. Fan, Y.-F. Fernández-García, E. Kumar, A. Lindanger, A. Mezentsev, A. Navarro-González, J. Neubert, T. Østgaard, N. Pérez-García, I. Reglero, V. Sarria, D. Sun, T.R. Xiong, D.-R. Yang, J. Yang, Y.-H. Zhang, B.-B. Glorieta de la Astronomía s/n GranadaE-18008 Spain Manora Peak Nainital263002 India Department of Physics Deen Dayal Upadhyaya Gorakhpur University Gorakhpur273009 India School of Physics and Astronomy Institute for Gravitational Wave Astronomy University of Birmingham B15 2TT United Kingdom Birkeland Centre for Space Science Department of Physics and Technology University of Bergen Norway National Institute for Astrophysics Osservatorio di Astrofisica e Scienzia dello Spazio Bologna Italy Unidad Asociada al CSIC Departamento de Ingeniería de Sistemas y Automática Escuela de Ingenierías Universidad de Málaga Málaga Spain Image Processing Laboratory University of Valencia Valencia Paterna Spain National Space Institute Technical University of Denmark Kgs. Lyngby Denmark Yunnan Observatories Chinese Academy of Sciences Kunming650216 China School of Studies in Physics and Astrophysics Pandit Ravishankar Shukla University Chattisgarh Raipur492010 India School of Astronomy and Space Science Nanjing University Nanjing210093 China Ministry of Education Nanjing210093 China

We report on detailed multi-wavelength observations and analysis of the very bright and long GRB 210619B, detected by the Atmosphere-Space Interactions Monitor (ASIM) installed on the International Space Station (ISS) and the Gamma-ray Burst Monitor (GBM) on-board the Fermi mission. Our main goal is to understand the radiation mechanisms and jet composition of GRB 210619B. With a measured redshift of z = 1.937, we find that GRB 210619B falls within the 10 most luminous bursts observed by Fermi so far. The energy-resolved prompt emission light curve of GRB 210619B exhibits an extremely bright hard emission pulse followed by softer/longer emission pulses. The low-energy photon indices (αpt) values obtained using the time-resolved spectral analysis of the burst suggest a transition between the thermal (during harder pulse) to non-thermal (during softer pulse) outflow. We examine the correlation between spectral parameters and find that both peak energy and αpt exhibit the flux tracking pattern. The late time broadband photometric dataset can be explained within the framework of the external forward shock model with νm c x (where νm, νc, and νx are the synchrotron peak, cooling-break, and X-ray frequencies, respectively) spectral regime supporting a rarely observed hard electron energy index (p Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

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Development of an MEMS based biomimetic whisker sensor for tactile sensing

Development of an MEMS based biomimetic whisker sensor for t...

引用

IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Zihou Wei Qing Shi Chang Li Shurui Yan Guanglu Jia Zhigang Zeng Qiang Huang Toshio Fukuda Key Laboratory of Biomimetic Robots and Systems Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education Beijing China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

It has been demonstrated that biological whisker sensing system has excellent capacity to achieve the pattern recognition, and whisker sensor based robot tactile perception has caused widespread concern. However, most of current existed whisker sensors are too large in size, and are difficult to be integrated into a small robot. In this paper, we proposed a MEMS based biomimetic whisker sensor inspired by rats, which integrates four sensing units on a square silicon wafer with a side length of 6.8mm and makes it possible to be installed on a small robot with similar size to a real rat. Our sensor measure the bending moment at the root of the whisker based on the piezoresistive effect. We have explored the range of functions that whisker arrays can serve on and it is shown that our whisker sensor can determine obstacle distance, and is also capable of gathering surface information including shape and texture.

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Refrigerant capacity detection of dehumidifier based on time series and neural networks 1

引用

13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Peng, Gang Yang, Zuhuang Wang, Min School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan China Shenzhen Institute Huazhong University of Science and Technology Shenzhen518060 China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

Refrigerant leakage is common in the use of dehumidifier, leading to decrease of dehumidification ability. To detect refrigerant capacity effectively and maintain it timely, a neural network with deep learning skills was proposed based on time series. As input of networks, the time series consists of the operating parameters of the dehumidifier at multiple time points. To determine the refrigerant capacity, the proposed method combines the outputs on the neural networks of all the time series examples in a single run of dehumidifier. As the result suggests, the proposed method is capable of detecting the refrigerant capacity with a low missing rate and high accuracy, which improves the maintenance mechanism of the dehumidifier and is of great significance to ensuring a comfortable air environment for user. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Time series

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Robust pulmonary segmentation for chest radiography, combining enhancement, adaptive morphology and innovative active contours

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Research on Biomedical Engineering 2018年第3期34卷 234-245页

作者： Vital, Daniel Aparecido Sais, Barbara Teixeira Moraes, Matheus Cardoso Laboratory of Image and Signal Processing Institute of Science and Technology Federal University of São Paulo São José dos CamposSP Brazil

Introduction: Statistical data reveal that approximately 140 million radiological exams are performed annually in Brazil. These exams are designed to detect and to analyze fractures, caused by different types of trauma;as well as, to diagnose pathologies such as pulmonary diseases. For better visualization of those lesions or abnormalities, methods of image segmentation can be implemented. Such methods lead to the separation of the region of interest, which allows extracting the characteristics and anomalies of the desired tissue. However, the methods developed by researchers in this area still have restrictions. Consequently, we present an automatic pulmonary segmentation approach that overcomes these constraints. Methods: This method is composed of a combination of Discrete Wavelet Packet Frame (DWPF), morphological operations and Gradient Vector Flow (GVF). The methodology is divided into four steps: Pre-processing - the original image is enhanced by discrete wavelet;processing - where occurs a combination of the Otsu threshold with a series of morphological operations in order to identify the pulmonary object;Post-processing - an innovative form of using GVF improves the binary information of pulmonary tissue, and;Evaluation – the segmented images were evaluated for accuracy of detection the pulmonary region and border. Results: The evaluation was carried out by segmenting 247 digital X-ray challenging images of the thorax human. The results show high for values of Overlap (97,63% ± 3.34%), and Average Contour Distance (0.69mm ± 0.95mm). Conclusion: The results allow verifying that the proposed technique is robust and more accurate than other methods of lung segmentation, besides being a fully automatic method of lung segmentation. © 2018, Brazilian Society of Biomedical Engineering. All Rights Reserved.

关键词： image segmentation

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Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan images

arXiv

引用

arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Dynamic HypE for solving single objective optimisation problems

引用

International Journal of Innovative Computing and Applications 2019年第1期10卷 51-58页

作者： Zhang, Qing Zhao, Fei Zeng, Sanyou School of Computer Science Huanggang Normal University Huanggang Hubei438000 China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research Institute Chengdu Sichuan610041 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan Hubei430074 China

One difficulty in solving optimisation problems is the handling many local optima. The usual approaches to handle the difficulty are to introduce the niche-count into evolutionary algorithms (EAs) to increase population diversity. In this paper, we introduce the niche-count into the problems, not into the EAs. We construct a dynamic multi-objective optimisation problem (DMOP) for the single optimisation problem (SOP) and ensure both the DMOP and the SOP are equivalent to each other. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the local optima difficulty during the search process. A dynamic version of a multi-objective evolutionary algorithm (DMOEA), specifically, HypE-DE, is used to solve the DMOP;consequently the SOP is solved. Experimental results show that the performance of the proposed method is significantly better than the state-of-the-art competitors on a set of test problems. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Evolutionary algorithms

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Square Kilometre Array science Data Challenge 3a: foreground removal for an EoR experiment

arXiv

引用

arXiv 2025年

作者： Bonaldi, A. Hartley, P. Braun, R. Purser, S. Acharya, A. Ahn, K. Aparicio Resco, M. Bait, O. Bianco, M. Chakraborty, A. Chapman, E. Chatterjee, S. Chege, K. Chen, X. Chen, Z. Conaboy, L. Cruz, M. Darriba, L. De Santis, M. Denzel, P. Diao, K. Feron, J. Finlay, C. Gehlot, B. Ghosh, S. Giri, S.K. Grumitt, R. Hong, S.E. Ito, T. Jiang, M. Jordan, C. Kim, S. Kim, M. Kim, J. Krishna, S.P. Kulkarni, A. López-Caniego, M. Labadie-García, I. Lee, H. Lee, D. Lee, N. Line, J. Liu, Y. Mao, Y. Mazumder, A. Mertens, F.G. Munshi, S. Nasirudin, A. Ni, S. Nistane, V. Norregaard, C. Null, D. Offringa, A. Oh, M. Oh, S.-H. Parkinson, D. Pritchard, J. Ruiz-Granda, M. Salvador López, V. Shan, H. Sharma, R. Trott, C. Yoshiura, S. Zhang, L. Zhang, X. Zheng, Q. Zhu, Z. Zuo, S. Akahori, T. Alberto, P. Allys, E. An, T. Anstey, D. Baek, J. Basavraj Brackenhoff, S. Browne, P. Ceccotti, E. Chen, H. Chen, T. Choudhuri, S. Choudhury, M. Coles, J. Cook, J. Cornu, D. Cunnington, S. Das, S. De Lera Acedo, E. Delouis, J.-M. Deng, F. Ding, J. Elahi, K.M.A. Fernandez, P. Fernández, C. Alcázar, A. Fernández Galluzzi, V. Gao, L.-Y. Garain, U. Garrido, J. Gendron-Marsolais, M.-L. Gessey-Jones, T. Ghorbel, H. Gong, Y. Guo, S. Hasegawa, K. Hayashi, T. Herranz, D. Holanda, V. Holloway, A.J. Hothi, I. Höfer, C. Jelić, V. Jiang, Y. Jiang, X. Kang, H. Kim, J.-Y. Koopmans, L.V. Lacroix, R. Lee, E. Leeney, S. Levrier, F. Li, Y. Liu, Y. Ma, Q. Meriot, R. Mesinger, A. Mevius, M. Minoda, T. Miville-Deschenes, M.-A. Moldon, J. Mondal, R. Murmu, C. Murray, S. Nirmala, S.R. Niu, Q. Nunhokee, C. O’Hara, O. Pal, S.K. Pal, S. Park, J. Parra, M. Patra, N.N. Pindor, B. Remazeilles, M. Rey, P. Rubino-Martin, J.A. Saha, S. Selvaraj, A. Semelin, B. Shah, R. Shao, Y. Shaw, A.K. Shi, F. Shimabukuro, H. Singh, G. Sohn, B.W. Stagni, M. Starck, J.-L. Sui, C. Swinbank, J.D. Sánchez, J. SKA Observatory Jodrell Bank Lower Whitington MacclesfieldSK11 9FT United Kingdom Max-Planck Institute for Astrophysics Karl-Schwarzschild-Straße 1 Garching85748 Germany Department of Earth Sciences Chosun University Gwangju61452 Korea Republic of Universidad Europea de Madrid Madrid28670 Spain Observatoire de Sauverny Versoix1290 Switzerland Department of Physics Trottier Space Institute McGill University 3600 rue University MontréalQCH3A 2T8 Canada School of Physics and Astronomy The University of Nottingham University Park NottinghamNG7 2RD United Kingdom Department of Physics and Astronomy University of the Western Cape Cape Town Bellville7535 South Africa Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Astronomy The University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Avda. de los Castros s/n SantanderE-39005 Spain Glorieta de la Astronomía s/n Granada18008 Spain Saint-Imier Switzerland Centre for Artificial Intelligence ZHAW Zurich University of Applied Sciences Technikumstrasse 71 Winterthur8400 Switzerland Department of Astronomy Tsinghua University Beijing100084 China Département de Physique Théorique Center for Astroparticle Physics Université de Geneve 24 quai Ernest Ansermet Geneve 41211 Switzerland Nordita KTH Royal Institute of Technology Stockholm University Hannes Alfv´ens väg 12 StockholmSE-106 91 Sweden Korea Astronomy and Space Science Institute Daejeon34055 Korea Republic of Astronomy Campus University of Science and Technology Daejeon34055 Korea Republic of Kumamoto University International Research Organization for Advanced Science and Technology 2-39-1 Kurokami Chuo-ku Kumamoto860-8555 Japan National Key Laboratory of Radar Signal Processing

We present and analyse the results of the science data challenge 3a (SDC3a, https://***/challenges/ foregrounds), an EoR foreground-removal community-wide exercise organised by the Square Kilometre Array Observatory (SKAO). The challenge ran for 8 months, from March to October 2023. Participants were provided with realistic simulations of SKA-Low data between 106 MHz and 196 MHz, including foreground contamination from extragalactic as well as Galactic emission, instrumental and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned from all corruptions, and the corresponding confidence levels. Here we describe the approaches taken by the 17 teams that completed the challenge, and we assess their performance using different metrics. The challenge results provide a positive outlook on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations. The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some cases there are some significant outliers. The smallest residual overall is 4.2+−204.2 × 10−4 K2h−3cMpc3 across all considered scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error being typically underestimated, sometimes very significantly. The most accurate error bars account for 60 ± 20% of the true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and improve error estimation. © 2025, CC BY.

关键词： Cosmology

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AIROGS: ARTIFICIAL INTELLIGENCE FOR ROBUST GLAUCOMA SCREENING CHALLENGE

arXiv

引用

arXiv 2023年

作者： De Vente, Coen Vermeer, Koenraad A. Jaccard, Nicolas Wang, He Sun, Hongyi Khader, Firas Truhn, Daniel Aimyshev, Temirgali Zhanibekuly, Yerkebulan Le, Tien-Dung Galdran, Adrian Ballester, Miguel Ángel González Carneiro, Gustavo Devika, R.G. Hrishikesh, P.S. Puthussery, Densen Liu, Hong Yang, Zekang Kondo, Satoshi Kasai, Satoshi Wang, Edward Durvasula, Ashritha Heras, Jónathan Zapata, Miguel Ángel Araújo, Teresa Aresta, Guilherme Bogunović, Hrvoje Arikan, Mustafa Lee, Yeong Chan Cho, Hyun Bin Choi, Yoon Ho Qayyum, Abdul Razzak, Imran Van Ginneken, Bram Lemij, Hans G. Sánchez, Clara I. Group Informatics Institute Universiteit Van Amsterdam Noord-Holland Amsterdam Netherlands The Department of Biomedical Engineering and Physics Amsterdam Umc Locatie Amc Noord-Holland Amsterdam Netherlands Department of Radiology and Nuclear Medicine Radboudumc Gelderland Nijmegen Netherlands The Rotterdam Ophthalmic Institute Rotterdam Eye Hospital Rotterdam Netherlands Project Orbis International Inc. New York United States The Peking Union Medical College Hospital Beijing China The Xuanwu Hospital Capital Medical University Beijing100053 China The Tsinghua University Beijing China The Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Cmc Technologies Llp Nur-Sultan Kazakhstan Kbc Belgium Universitat Pompeu Fabra Barcelona Spain The Australian Institute for Machine Learning Aiml University of Adelaide Australia Icrea Barcelona Spain The Centre for Vision Speech and Signal Processing University of Surrey United Kingdom The College of Engineering Trivandrum India Founding Minds Software India The Institute of Computing Technology Chinese Academy of Sciences China The Muroran Institute of Technology Japan The Niigata University of Health and Welfare Japan The Schulich School of Medicine and Dentistry University of Western Ontario London Canada The Department of Mathematics and Computer Science University of la Rioja Spain Hospital Vall Hebron Passeig Roser 126 Sant Cugat del Vallès Barcelona08195 Spain UPRetina Barcelona Spain The Christian Doppler Laboratory for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria The Institute of Ophthalmology University College London London United Kingdom The Research Institute for Future Medicine Samsung Medical Center Seoul Korea Republic of Sungkyunkwan University Samsung Medical Center Seoul Korea Republic of The Department of Artificial Intelligence and Informatics Mayo Clinic Jacks

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in realworld scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper, and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Learning compact target-oriented feature representations for visual tracking

arXiv

引用

arXiv 2019年

作者： Li, Chenglong Huang, Yan Wang, Liang Tang, Jin Lin, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Center for Research on Intelligent Perception and Computing NLPR CASIA School of Data and Computer Science Sun Yat-Sen University

Many state-of-the-art trackers usually resort to the pre-trained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected. To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative correlation filter framework. The feature representations and the correlation filter are jointly learnt to enhance to each other via a fast solver which only has very slight computational burden on the tracking speed. Extensive experiments on three benchmark datasets demonstrate that this proposed framework clearly outperforms baseline trackers with a modest impact on the frame rate, and performs comparably against the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Target tracking

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