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4th Asia-Pacific Artificial Intelligence and big data Forum, AIBDF 2024

作者： Zhao, Feng Fan, Wenxuan Han, Zhongwei Chen, Hongyu School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong Yantai China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China Information Engineering College Yantai Institute of Technology Shandong Yantai China Shandong Technology and Business University Shandong Yantai China

ISBN: (纸本)9798400710865

Functional connectivity networks (FCNs), particularly those utilizing the phase lag index (PLI) method, have been instrumental in elucidating the pathological features of Major Depressive Disorder (MDD) by assessing the synchrony and lag of electroencephalogram (EEG) signal waveforms. Currently, the more prevalent methods are traditional FCNs that quantify phase synchronization as scalar values and dynamic FCNs (D-FCNs) that employ sliding window approaches to capture the instantaneous variations in functional connectivity. However, these two methods merely capture the low-level functional connections between paired regions of interest (ROIs) from both the static and dynamic perspectives, and fail to reflect the high-level functional connections involving multiple ROIs. Moreover, the feature dimension of the dynamic FCN is excessively high, and there exists a phase mismatch issue between different samples, which renders it impossible to be directly applied to the classification task, and no better solution is available at present. To mitigate these issues, we proposed a comprehensive framework that includes segmenting EEG sequences based on PLI to construct dynamic FCNs;employing seventh-order central moments for feature extraction to form low-order FCNs (Lo-FCNs);and establishing high-order FCNs (Ho-FCNs) based on Pearson correlation theory derived from Lo-FCNs. We conducted classification experiments across four distinct EEG frequency bands using our proposed framework while analyzing relevant features through graph theory and brain topological mapping techniques. The experiments showed that the proposed framework has a high classification accuracy rate in the classification experiments of MDD and NC, and proved the importance of high-order features in classification experiments, indicating that they may serve as physiological markers for MDD diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第06期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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High-precision inversion of vegetation parameters in the AI era: Integrating hyperspectral remote sensing and deep learning

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Innovation 2025年第6期6卷

作者： Hu, Jinkang Peng, Dailiang Chen, Jing M. Huete, Alfredo R. Yu, Le Lou, Zihang Cheng, Enhui Yang, Xuan Zhang, Bing Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China College of Resource and Environment University of Chinese Academy of Sciences Beijing100094 China School of Geographical Sciences Fujian Normal University Fuzhou350117 China Department of Geography and Planning University of Toronto TorontoONM5S 3G3 Canada School of Life Sciences University of Technology Sydney UltimoNSW2007 Australia Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Institute of Agricultural Remote Sensing and Information Technology Application College of Environmental and Resource Sciences Zhejiang University Hangzhou310058 China China Remote Sensing Satellite Ground Station Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

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Application of machine learning in ocean data

Application of machine learning in ocean data

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作者： Lou, Ranran Lv, Zhihan Dang, Shuping Su, Tianyun Li, Xinfang Qingdao China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and Technology Qingdao China Marine Data and Information Center The First Institute of Oceanography MNR Qingdao China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Qingdao China Thuwal Saudi Arabia

In recent years, machine learning has become a hot research method in various fields and has been applied to every aspect of our life, providing an intelligent solution to problems that could not be solved or difficult to be solved before. Machine learning is driven by data. It learns from a part of the input data and builds a model. The model is used to predict and analyze another part of the data to get the results people want. With the continuous advancement of ocean observation technology, the amount of ocean data and data dimensions are rising sharply. The use of traditional data analysis methods to analyze massive amounts of data has revealed many shortcomings. The development of machine learning has solved these shortcomings. Nowadays, the use of machine learning technology to analyze and apply ocean data becomes the focus of scientific research. This method has important practical and long-term significance for protecting the ocean environment, predicting ocean elements, exploring the unknown, and responding to extreme weather. This paper focuses on the analysis of the state of the art and specific practices of machine learning in ocean data, review the application examples of machine learning in various fields such as ocean sound source identification and positioning, ocean element prediction, ocean biodiversity monitoring, and deep-sea resource monitoring. We also point out some constraints that still exist in the research and put forward the future development direction and application prospects. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Biodiversity

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Decadal variations in near-surface wind speed across the Northern Hemisphere on a centennial timescale and their possible causes

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Advances in Climate Change Research 2024年第6期15卷 1003-1012页

作者： Lin-Wei LI Jin-Lin ZHA Ting CHUNA Jian WU De-Ming ZHAO Wen-Xuan FAN Yan-Jun LYU Hui-Ping JIANG Key Laboratory of Atmospheric Environment and Processes in the Boundary Layer Over the Low-Latitude Plateau Region Department of Atmospheric ScienceYunnan UniversityKunming 650091China Key Laboratory of Regional Climate and Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Center for Energy&Environmental Policy Research Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China

The reduction in near-surface wind speed(NSWS),followed by its recent recovery,could be a feature of decadal ***,existing studies mainly analyse the characteristics and causes of NSWS changes on regional scales and over short *** decadal variations and underlying causes of global NSWS changes over long timescales remain *** this study,we investigated the characteristics of NSWS changes in the Northern Hemisphere(NH)on a centennial scale(1900–2010)and identify the potential causes of decadal variations by using an information flow *** results indicate that NSWS in the NH experienced a remarkable decadal variation,increasing during 1900–1945 and 1970–1980 and decreasing during 1945–1970 and 1980–***,regional differences in the distribution features and decadal variations of NSWS were *** and North America showed the highest data overlap compared to other regions,reaching 59.4%.The longest period of decadal variation in NSWS was found in North America(14 years),followed by Europe(11 years),with Asia showing the smallest period(10 years).When determining the effects of large-scale ocean–atmosphere circulation fields or internal climate models on NSWS changes,prioritising causality between the two variables and the study period is essential,rather than relying solely on simple correlation *** to the information flow technique,the decadal variations in NSWS over North America during 1950–2010 were linked with the Western Hemisphere Warm Pool,whereas the North Atlantic Oscillation influenced those in *** Tropical Northern Atlantic was the driver over *** study offers a scientific foundation for understanding NSWS characteristics on a centennial timescale and the primary causes of decadal variations in NSWS across the NH.

关键词： Near-surface wind speed Centennial timescale Information flow Regional difference Northern Hemisphere

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Low-rank matrix recovery with total generalized variation for defending adversarial examples

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Frontiers of Information technology & Electronic Engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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Observation-based sources evolution of non-methane hydrocarbons (NMHCs) in a megacity of China

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Journal of Environmental Sciences 2023年第2期124卷 794-805页

作者： Yarong Peng Hongli Wang Qian Wang Shengao Jing Jingyu An Yaqin Gao Cheng Huang Rusha Yan Haixia Dai Tiantao Cheng Qiang Zhang Meng Li Jianlin Hu Zhihao Shi Li Li Shengrong Lou Shikang Tao Qinyao Hu Jun Lu Changhong Chen Department of Environmental Science and Engineering Shanghai Key Laboratory of Atmospheric Particle Pollution and PreventionInstitute of Atmospheric SciencesFudan UniversityShanghai 200438China State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex Shanghai Academy of Environmental SciencesShanghai 200233China Department of Atmospheric and Oceanic Sciences Institute of Atmospheric SciencesFudan UniversityShanghai 200438China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingTsinghua UniversityBeijing 100084China Big Data Institute for Carbon Emission and Environmental Pollution Fudan UniversityShanghai 200438China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China

Both concentrations and emissions of many air pollutants have been decreasing due to implement of control measures in China,in contrast to the fact that an increase in emissions of non-methane hydrocarbons(NMHCs)has been *** study employed seven years continuous NMHCsmeasurements and the related activities data of Shanghai,a megacity in China,to explore evolution of emissions and effectiveness of air pollution control *** mixing ratio of NMHCs showed no statistical interannual changes,of which their compositions exhibited marked *** resulted in a decreasing trend of ozone formation potential by 3.8%/year(p<0.05,the same below),which should be beneficial to ozone pollution mitigation as its production in Shanghai is in the NMHCs-limited *** alkanes,aromatics and acetylene changed by+3.7%/year,-5.9%/year and-7.4%/year,respectively,and alkenes showed no apparent *** sources were apportioned by a positive matrix factorization ***,vehicular emissions(-5.9%/year)and petrochemical industry emissions(-7.1%/year)decreased significantly,but the decrease slowed down;significant reduction in solvent usage(-9.0%/year)appeared after 2010;however,emissions of natural gas(+12.6%/year)and fuel evaporation(with an increasing fraction)became more *** inconsistency between observations and inventories was found in interannual trend and speciation as well as source contributions,emphasizing the need for further validation in NMHCs emission *** study confirms the effectiveness of measures targeting mobile and centralized emissions from industrial sources and reveals a need focusing on fugitive emissions,which provided new insights into future air policies in polluted region.

关键词： Characteristics Source apportionment Observation-based Interannual trend Shanghai

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A data driven time-dependent transmission rate for tracking an epidemic:a case study of 2019-nCoV

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Science Bulletin 2020年第6期65卷 425-427,M0003页

作者： Norden EHuang Fangli Qiao First Institute of Oceanography Ministry of Natural Resources of ChinaQingdao 266061China Key Laboratory of Marine Science and Numerical Modeling Ministry of Natural Resources of ChinaQingdao 266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Qingdao 266061China

Ever since the first report of the discovery of an unknown novel coronavirus that had caused pneumonia in Wuhan,China,had filed to WHO by China,on December 31,2019,the government had instituted drastic measures to contain the outbreak of the *** keep transparency,the China's National Health Commission reports confirmed,suspected,recovered and death case numbers daily,yet the epidemiological community is still calling for more ***,they want to have the critical"Basic Reproduction,Number"*** to Jones[1]and van den Driessche and Watmough[2],Ro,is defined as the expected number of secondary cases produced by a single(typical)infection in a completely susceptible population.

关键词：数据驱动 filed transparency

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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Significant greening of China’s mangrove forests during the last three decades

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Ecological Indicators 2025年 176卷

作者： Xinzhi Wang Rong Zhang Mingming Jia Zongming Wang Chuanpeng Zhao Xianxian Guo Huiying Li Zhaohui Xue Chao Zhou Xiangming Xiao State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun 130102 China College of Resources and Environment University of Chinese Academy of Sciences Beijing 100049 China International Research Center of Big Data for Sustainable Development Goals 100094 Beijing China National Marine Environmental Monitoring Center Ministry of Ecology Environment 116023 Dalian China School of Environmental and Municipal Engineering Qingdao University of Technology Qingdao 266520 China College of Geography and Remote Sensing Hohai University Nanjing 211100 China Department of Microbiology and Plant Biology Center for Earth Observation and Modeling University of Oklahoma Norman OK USA

In recent decades, China’s mangroves have experienced severe degradation, followed by significant initiatives aimed at conservation and recovery. These initiatives have brought about significant changes in the extent of mangrove areas and their health conditions. Previous research has primarily concentrated on the dynamics of mangrove areas. Few studies have addressed the health condition, limited by lower spatial resolution, short time spans, and saturation effects of traditional vegetation indices. To overcome these challenges, we leveraged 37,144 Landsat scenes from 1993 to 2022 to generate kernel normalized difference vegetation index (kNDVI) images, monitoring spatial and temporal progression of mangrove greenness by kNDVI dynamics. Additionally, we analyzed the driving forces of kNDVI changes and environmental factors. The results showed that: (1) China’s mangroves’ kNDVI values were increasing obviously during the last three decades, which indicated significant greening trends; (2) mangroves with greening tendencies were predominantly found in densely distributed regions, whereas degradation occurred in dispersed and isolated mangroves; (3) in general, mangrove greenness was positively correlated with sea surface salinity ( r = 0.540, p < 0.01) and air temperature ( r = 0.488, p < 0.01). This study considered the spatial–temporal distribution characteristics of China’s mangroves, analyzed changes in their greening trends using kNDVI. The results and discussion could offer scientific references for the protection, restoration, and management of China’s mangroves, potentially helping to address challenges posed by changes in terrestrial and marine environments.

关键词： Mangrove greening Health condition kNDVI Nature reserves Environmental drives China

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