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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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Uplink-downlink duality between multiple-access and broadcast channels with compressing relays

arXiv

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arXiv 2020年

作者： Liu, Liang Liu, Ya-Feng Patil, Pratik Yu, Wei the Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong the State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering the University of Toronto the Department of Statistics and Data Science and the Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S3G4 Canada

—Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers;the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear programming

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Potential numerical techniques and challenges for atmospheric modeling

Potential numerical techniques and challenges for atmospheri...

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作者： Li, J. Steppeler, J. Fang, F. Pain, C.C. Zhu, J. Peng, X. Dong, L. Li, Y. Tao, L. Leng, W. Wang, Y. Zheng, J. International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Hamburg Germany Applied Modelling and Computation Group Department of Earth Science and Engineering Imperial College London London United Kingdom International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Department of Atmospheric Sciences University of Chinese Academy of Sciences Beijing China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China State Key Laboratory of Scientific and Engineering Computing Chinese Academy of Sciences Beijing China School of Mathematics and Statistics Henan University Kaifeng China Center for Excellence in Regional Atmospheric Environment Institute of Urban Environment Chinese Academy of Sciences Xiamen China

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Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in China and the United states

arXiv

引用

arXiv 2020年

作者： Wang, Jingyuan Tang, Ke Feng, Kai Lin, Xin Lv, Weifeng Chen, Kun Wang, Fei School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Social Sciences Tsinghua University China State Key Laboratory of Software Development Environment Beihang University China Department of Statistics University of Connecticut United States Center for Population Health University of Connecticut Health Center United States Department of Population Health Sciences Weill Cornell Medical College Cornell University United States

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.;an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Polygenic risk score and lung adenocarcinoma risk among never-smokers by EGFR mutation status-a brief report

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Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2024年第4期20卷 521-530页

作者： Batel Blechter Chao Agnes Hsiung Xiaoyu Wang Haoyu Zhang Wei Jie Seow Jianxin Shi Nilanjan Chatterjee Hee Nam Kim Maria Pik Wong Yun-Chul Hong Jason Yy Wong Juncheng Dai H Dean Hosgood Zhaoming Wang I-Shou Chang Jiyeon Choi Jiucun Wang Minsun Song Wei Hu Wei Zheng Jin Hee Kim Baosen Zhou Demetrius Albanes Min-Ho Shin Lap Ping Chung She-Juan An Hong Zheng Yasushi Yatabe Xu-Chao Zhang Young Tae Kim Xiao-Ou Shu Young-Chul Kim Roel C H Vermeulen Bryan A Bassig Jiang Chang James Chung Man Ho Bu-Tian Ji Michiaki Kubo Yataro Daigo Yukihide Momozawa Yoichiro Kamatani Takayuki Honda Hideo Kunitoh Shun-Ichi Watanabe Yohei Miyagi Haruhiko Nakayama Shingo Matsumoto Masahiro Tsuboi Koichi Goto Zhihua Yin Atsushi Takahashi Akiteru Goto Yoshihiro Minamiya Kimihiro Shimizu Kazumi Tanaka Tangchun Wu Fusheng Wei Jian Su Yeul Hong Kim In-Jae Oh Victor Ho Fun Lee Wu-Chou Su Yuh-Min Chen Gee-Chen Chang Kuan-Yu Chen Ming-Shyan Huang Hsien-Chih Lin Adeline Seow Jae Yong Park Sun-Seog Kweon Chien-Jen Chen Yu-Tang Gao Chen Wu Biyun Qian Daru Lu Jianjun Liu Hyo-Sung Jeon Chin-Fu Hsiao Jae Sook Sung Ying-Huang Tsai Yoo Jin Jung Huan Guo Zhibin Hu Tzu-Yu Chen Laurie Burdett Meredith Yeager Amy Hutchinson Sonja I Berndt Wei Wu Junwen Wang Jin Eun Choi Kyong Hwa Park Sook Whan Sung Li Liu Chang Hyun Kang Chung-Hsing Chen Jun Xu Peng Guan Wen Tan Chih-Liang Wang Alan Dart Loon Sihoe Ying Chen Yi Young Choi Jun Suk Kim Ho-Il Yoon Qiuyin Cai In Kyu Park Ping Xu Qincheng He Chih-Yi Chen Junjie Wu Wei-Yen Lim Kun-Chieh Chen John K C Chan Jihua Li Hongyan Chen Chong-Jen Yu Li Jin Joseph F Fraumeni Jie Liu Maria Teresa Landi Taiki Yamaji Yang Yang Belynda Hicks Kathleen Wyatt Shengchao A Li Hongxia Ma Bao Song Zhehai Wang Sensen Cheng Xuelian Li Yangwu Ren Motoki Iwasaki Junjie Zhu Gening Jiang Ke Fei Guoping Wu Li-Hsin Chien Fang-Yu Tsai Jinming Yu Victoria L Stevens Pan-Chyr Yang Dongxin Lin Kexin Chen Yi-Long Wu Keitaro Matsuo Nathaniel Rothman Kouya Shiraishi Hongbing Shen Stephen J Chanock Takashi Kohno Qing Lan Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA Saw Swee Hock School of Public Health National University of Singapore and National University Health System Singapore Singapore Department of Medicine Yong Loo Lin School of Medicine National University of Singapore and National University Health System Singapore Singapore. Department of Oncology School of Medicine Johns Hopkins University Baltimore MD USA Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Department of Preventive Medicine Chonnam National University Medical School Gwangju Republic of Korea. Department of Pathology Queen Mary Hospital Hong Kong Hong Kong. Department of Preventive Medicine Seoul National University College of Medicine Seoul Republic of Korea. Epidemiology and Community Health Branch National Heart Lung and Blood Institute Bethesda MD USA. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Epidemiology and Population Health Albert Einstein College of Medicine New York USA. Department of Computational Biology St. Jude Children's Research Hospital Memphis TN USA. National Institute of Cancer Research National Health Research Institutes Zhunan Taiwan. Ministry of Education Key Laboratory of Contemporary Anthropology School of Life Sciences Fudan University Shanghai China State Key Laboratory of Genetic Engineering School of Life Sciences Fudan University Shanghai China. Department of Statistics & Research Institute of Natural Sciences Sookmyung Women's University Seoul 04310 Korea. Division of Epidemiology Department of Medicine Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center Nashville TN USA. Department of Environmental Health

We assessed the association between a genome-wide polygenic risk score (PRS) developed for lung adenocarcinoma (LUAD) risk and mutation on the epidermal growth factor receptor (EGFR) gene in 998 East Asian never-smoking female LUAD cases (518 EGFR-positive; 480 EGFR-negative) and 4,544 never-smoking controls using case-case and multinomial regression analyses. We found that the PRS was more strongly associated with EGFR-positive LUAD compared to EGFR-negative LUAD, where the association between the fourth quartile of the PRS and EGFR-positive LUAD (OR=8.63, 95% CI:5.67, 13.14) was significantly higher than the association between the fourth quartile of the PRS with EGFR-negative LUAD (OR=3.50, 95% CI: 2.44, 5.00) (p-heterogeneity=3.66x10). Our findings suggest that germline genetic susceptibility may be differentially associated with LUAD in never-smoking female East Asian patients depending on the cancer's mutation status, which may have important public health and clinical implications.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Prediction of solar energetic events impacting space weather conditions

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Advances in Space Research 2024年

作者： Georgoulis, Manolis K. Yardley, Stephanie L. Guerra, Jordan A. Murray, Sophie A. Ahmadzadeh, Azim Anastasiadis, Anastasios Angryk, Rafal Aydin, Berkay Banerjee, Dipankar Barnes, Graham Bemporad, Alessandro Benvenuto, Federico Bloomfield, D. Shaun Bobra, Monica Campi, Cristina Camporeale, Enrico DeForest, Craig E. Emslie, A. Gordon Falconer, David Feng, Li Gan, Weiqun Green, Lucie M. Guastavino, Sabrina Hapgood, Mike Kempton, Dustin Kitiashvili, Irina Kontogiannis, Ioannis Korsos, Marianna B. Leka, K.D. Massa, Paolo Massone, Anna Maria Nandy, Dibyendu Nindos, Alexander Papaioannou, Athanasios Park, Sung-Hong Patsourakos, Spiros Piana, Michele Rawafi, Nour E. Sadykov, Viacheslav M. Toriumi, Shin Vourlidas, Angelos Wang, Haimin L. Wang, Jason T. Whitman, Kathryn Yan, Yihua Zhukov, Andrei N. Research Center for Astronomy and Applied Mathematics of the Academy of Athens Athens11527 Greece Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of Meteorology University of Reading ReadingRG6 6ET United Kingdom Paseo Manuel de Lardizabal 4 San Sebastián20018 Spain CIRES University of Colorado BoulderCO80309 United States NOAA Space Weather Prediction Center BoulderCO80305 United States School of Cosmic Physics Dublin Institute for Advanced Studies Dunsink Observatory DublinD15 XR2R Ireland Computer Science Department Georgia State University AtlantaGA30303 United States National Observatory of Athens Penteli15236 Greece Uttarakhand Nainital263001 India NorthWest Research Associates BoulderCO80301 United States INAF Turin Astrophysical Observatory via Osservatorio 20 10025 Pino Torinese Torino Italy MIDA Dipartimento di Matematica Università di Genova via Dodecaneso 35 16146 Genova Italy State of California Office of Data and Innovation 401 I Street Ste 200 SacramentoCA95814 United States Southwest Research Institute BoulderCO80302 United States Department of Physics & Astronomy Western Kentucky University Bowling GreenKY42101 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States NASA/Marshall Space Flight Center HuntsvilleAL35812 United States Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Mullard Space Science Laboratory University College London Holmbury St. MaryRH5 6NT United Kingdom University of Chinese Academy of Sciences Nanjing211135 China Visiting Scientist RAL Space STFC Rutherford Appleton Laboratory Oxfordshire DidcotOX11 0QX United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States An der Sternwarte 16 Potsdam14482 Germany Dipartime

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015), we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big Data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big Data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the

关键词： Solar energy

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Passivity-based Stabilization for a Strict-feedback Nonlinear System under a Proper state-dependent Switching 36

Passivity-based Stabilization for a Strict-feedback Nonlinea...

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第36届中国控制会议

作者： Yaowei Sun Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University) College of Information Science and EngineeringNortheastern University College of Mathematics and Statistics Zhoukou Normal University

ISBN: (纸本)9781538629185

In this paper, the passivity-based stabilization problem of a strict-feedback nonlinear system under a proper statedependent switching law is investigated. Here, the feedback passification problem of each subsystem does not need to be solvable. First, by using the recursive backstepping technique, a sufficient condition under which the closed-loop switched system with designed state feedback controllers is strictly passive from the new input to a designed virtual output is given under a state-dependent switching law. Moreover, the asymptotical stability property of the switched system is achieved by redesigning the introduced new input based on passivity. Finally, a numeral example shows the effectiveness of the proposed method.

关键词： Switched nonlinear systems Passivity Recursive backstepping

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Building trust networks in the absence of trust relations

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Frontiers of Information Technology & Electronic engineering 2017年第10期18卷 1591-1600页

作者： Xin WANG Ying WANG Jian-hua GUO College of Computer Science and Technology Jilin University Changchun 130012 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun 130012 China School of Mathematics and Statistics Northeast Normal University Changchun 130024 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin 541004 China

User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media *** issues pose a great challenge for predicting trust relations and further building trust networks. In this study,we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework,b Trust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.

关键词： Trust network Sparse learning Homophily effect Interaction behaviors

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More diverse means better: Multimodal deep learning meets remote sensing imagery classification

arXiv

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arXiv 2020年

作者： Hong, Danfeng Gao, Lianru Yokoya, Naoto Yao, Jing Chanussot, Jocelyn Du, Qian Zhang, Bing Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab Grenoble38000 France Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Graduate School of Frontier Sciences University of Tokyo Chiba277-8561 Japan RIKEN Tokyo103-0027 Japan School of Mathematics and Statistics Xian Jiaotong University Xian710049 China Univ. Grenoble Alpes INRIA CNRS Grenoble INP LJK Grenoble38000 France Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China

This is the pre-acceptance version, to read the final version please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Classification and identification of the materials lying over or beneath the Earth’s surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS) and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML) – cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on "what", "where", and "how" to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS datasets. Furthermore, the codes and datasets will be available at: https://***/danfenghong/IEEE_TGRS_MDL-RS, Copyright © 2020, The Authors. All rights reserved.

关键词： Network architecture

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