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Added Benefit of the Early-Morning-Orbit Satellite Fengyun-3E on the Global Microwave Sounding of the Three-Orbit Constellation

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Advances in Atmospheric Sciences 2024年第1期41卷 39-52页

作者： Juan LI Zhengkun QIN Guiqing LIU Jing HUANG CMA Earth System Modeling and Prediction Centre Beijing 100081China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China joint center for data assimilation research and applications School of Atmospheric SciencesNanjing University of Information Science&TechnologyNanjing 210044China

The three-orbit constellation can comprehensively increase the spatial coverage of polar-orbiting satellites,but the polar-orbiting satellites currently in operation are only mid-morning-orbit and afternoon-orbit ***-3E(FY-3E)was launched successfully on 5 July 2021 in *** an early-morning-orbit satellite,FY-3E can help form a complete three-orbit observation system together with the mid-morning and afternoon satellites in the current mainstream operational *** this study,we investigate the added benefit of FY-3E microwave sounding observations to the midmorning-orbit Meteorological Operational satellite-B(Met Op-B)and afternoon-orbit Fengyun-3D(FY-3D)microwave observations in the Chinese Meteorological Administration global forecast system(CMA-GFS).The results show that the additional FY-3E microwave temperature sounder-3(MWTS-3)and microwave humidity sounder-2(MWHS-2)data can increase the global coverage of microwave temperature and humidity sounding data by 14.8% and 10.6%,*** enables the CMA-GFS to achieve nearly 100% global coverage of microwave-sounding observations at each analysis ***,after effective quality control and bias correction,the global biases and standard deviations of the differences between observations and model simulations are also *** on the Advanced Microwave Sounding Unit A and the Microwave Humidity Sounder onboard Met Op-B,and the MWTS-2 and MWHS-2 onboard FY-3D,adding the microwave sounding data of FY-3E can further reduce the errors of analysis results and improve the global prediction skills of CMA-GFS,especially for the southern-hemisphere forecasts within 96 hours,all of which are significant at the 95% confidence level.

关键词： data assimilation microwave temperature sounder MWTS-3

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Phenology of different types of vegetation and their response to climate change in the Qilian Mountains,China

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Journal of Mountain Science 2024年第2期21卷 511-525页

作者： ZHAO Kaixin LI Xuemei ZHANG Zhengrong LIU Xinyu Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhou 730070China National-Local joint Engineering research center of Technologies and applications for National Geographic State Monitoring Lanzhou 730070China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou 730070China

The Qilian Mountains(QM)possess a delicate vegetation ecosystem,amplifying the evident response of vegetation phenology to climate *** relationship between changes in vegetation growth and climate remains *** this end,we used MODIS NDVI data to extract the phenological parameters of the vegetation including meadow(MDW),grassland(GSD),and alpine vegetation(ALV))in the QM from 2002 to ***,we employed path analysis to reveal the direct and indirect impacts of seasonal climate change on vegetation ***,we decomposed the vegetation phenology in a time series using the trigonometric seasonality,Box-Cox transformation,ARMA errors,and Trend Seasonal components model(TBATS).The findings showed a distinct pattern in the vegetation phenology of the QM,characterized by a progressive shift towards an earlier start of the growing season(SOS),a delayed end of the growing season(EOS),and an extended length of the growing season(LOS).The growth cycle of MDW,GSD,and ALV in the QM species is clearly *** SOS for MDW and GSD occurred earlier,mainly between late April and August,while the SOS for ALVs occurred between mid-May and mid-August,a one-month delay compared to the other *** EOS in MDW and GSD were concentrated between late August and April and early September and early January,*** phenology exhibits distinct responses to seasonal temperature and precipitation *** advancement and delay of SOS were mainly influenced by the direct effect of spring temperatures and precipitation,which affected 19.59%and 22.17%of the study area,*** advancement and delay of EOS were mainly influenced by the direct effect of fall temperatures and precipitation,which affected 30.18%and 21.17%of the area,*** the contrary,the direct effects of temperature and precipitation in summer and winter on vegetation phenology seem less noticeable and were mainly influenced by indirect *** indirec

关键词： Vegetation phenology Time series decomposition Path Analysis Climate change

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Enhancing action discrimination via category-specific frame clustering for weakly-supervised temporal action localization

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Frontiers of Information Technology & Electronic Engineering 2024年第6期25卷 809-823页

作者： Huifen XIA Yongzhao ZHAN Honglin LIU Xiaopeng REN School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China Jiangsu Engineering research center of Big data Ubiquitous Perception and Intelligent Agricultural applications Zhenjiang 212013China Changzhou Vocational Institute of Mechatronic Technology Changzhou 213164China

Temporal action localization (TAL) is a task of detecting the start and end timestamps of action instances and classifying them in an untrimmed video. As the number of action categories per video increases, existing weakly-supervised TAL (W-TAL) methods with only video-level labels cannot provide sufficient supervision. Single-frame supervision has attracted the interest of researchers. Existing paradigms model single-frame annotations from the perspective of video snippet sequences, neglect action discrimination of annotated frames, and do not pay sufficient attention to their correlations in the same category. Considering a category, the annotated frames exhibit distinctive appearance characteristics or clear action ***, a novel method to enhance action discrimination via category-specific frame clustering for W-TAL is proposed. Specifically,the K-means clustering algorithm is employed to aggregate the annotated discriminative frames of the same category, which are regarded as exemplars to exhibit the characteristics of the action category. Then, the class activation scores are obtained by calculating the similarities between a frame and exemplars of various categories. Category-specific representation modeling can provide complimentary guidance to snippet sequence modeling in the mainline. As a result, a convex combination fusion mechanism is presented for annotated frames and snippet sequences to enhance the consistency properties of action discrimination,which can generate a robust class activation sequence for precise action classification and localization. Due to the supplementary guidance of action discriminative enhancement for video snippet sequences, our method outperforms existing single-frame annotation based methods. Experiments conducted on three datasets (THUMOS14, GTEA, and BEOID) show that our method achieves high localization performance compared with state-of-the-art methods.

关键词： Weakly supervised Temporal action localization Single-frame annotation Category-specific Action discrimination

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AI Fairness-From Machine Learning to Federated Learning

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1203-1215页

作者： Lalit Mohan Patnaik Wenfeng Wang Consciousness Studies Program School of HumanitiesNational Institute of Advanced StudiesBangalore560012India research Institute of Intelligent Engineering and data applications Shanghai Institute of TechnologyShanghai201418China research center of Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi830011China Applied Nonlinear Science Lab Anand International College of EngineeringJaipur391320India London Institute of Technology The ASE-London CTI of SCOLondonCR26EQUK Sino-Indian joint research center of AI and Robotics The IMT InstituteBhubaneswar752054India

This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also *** a reliable and quantitative evaluation of AI fairness,many associated concepts have been proposed,formulated and ***,the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure *** privacy worries induce the data unfairness and hence,the biases in the datasets for evaluating AI fairness are *** imbalance between algorithms’utility and humanization has further reinforced *** for federated learning systems,these constraints on precision AI fairness still *** solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly.

关键词： Formulation evaluation classification constraints imbalance biases

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Effect of Meteorological data assimilation on Regional Air Quality Forecasts over the Korean Peninsula

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Journal of Meteorological research 2024年第2期38卷 262-284页

作者： Yunjae CHO Hyun Mee KIM Eun-Gyeong YANG Yonghee LEE Jae-Bum LEE Soyoung HA Department of Atmospheric Sciences Yonsei UniversitySeoul 03722Republic of Korea Air Quality Forecasting center National Institute of Environmental ResearchIncheon 22689Republic of Korea National center for Atmospheric research BoulderCO 80305USA University of Maryland Baltimore County BaltimoreMD 21250USA Global Modeling and assimilation Office NASA Goddard Space Flight CenterGreenbeltMD 20771USA

The Weather research and Forecasting model coupled with Chemistry(WRF-Chem),a type of online coupled chemistry-meteorology model(CCMM),considers the interaction between air quality and meteorology to improve air quality *** data assimilation(DA)can be used to reduce uncertainty in meteorological field,which is one factor causing prediction uncertainty in the *** this study,WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean *** nesting model domains were configured over East Asia(outer domain)and the Korean Peninsula(inner domain).Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological *** the meteorological DA was performed in the outer domain or both the outer and inner domains,the root-mean-square error(RMSE),bias of the predicted particulate matter(PM)concentrations,and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner *** indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain,subsequently improving air quality and meteorological *** to the experiment without meteorological DA,the RMSE and bias of the meteorological and PM variables were smaller in the experiments with *** effect of meteorological DA on the improvement of PM predictions lasted for approximately 58-66 h,depending on the ***,the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.

关键词： meteorological data assimilation regional air quality forecast particulate matter concentration optimal model domain forecast error WRF-Chem

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A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

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Journal of Arid Land 2024年第2期16卷 195-219页

作者： LIU Xinyu LI Xuemei ZHANG Zhengrong ZHAO Kaixin LI Lanhai Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhou 730070China National-Local joint Engineering research center of Technologies and applications for National Geographic State Monitoring Lanzhou 730070China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou 730070China State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China research center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi 830011China

Climate warming profoundly affects hydrological changes,agricultural production,and human *** and semi-arid areas of China are currently displaying a marked trend of warming and *** Chinese Tianshan Mountains(CTM)have a high climate sensitivity,rendering the region particularly vulnerable to the effects of climate *** this study,we used monthly average temperature and monthly precipitation data from the CN05.1 gridded dataset(1961-2014)and 24 global climate models(GCMs)of the Coupled Model Intercomparison Project Phase 6(CMIP6)to assess the applicability of the CMIP6 GCMs in the CTM at the regional *** on this,we conducted a systematic review of the interannual trends,dry-wet transitions(based on the standardized precipitation index(SPI)),and spatial distribution patterns of climate change in the CTM during *** further projected future temperature and precipitation changes over three terms(near-term(2021-2040),mid-term(2041-2060),and long-term(2081-2100))relative to the historical period(1961-2014)under four shared socio-economic pathway(SSP)scenarios(i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).It was found that the CTM had experienced significant warming and wetting from 1961 to 2014,and will also experience warming in the future(2021-2100).Substantial warming in 1997 was captured by both the CN05.1 derived from interpolating meteorological station data and the multi-model ensemble(MME)from the CMIP6 *** MME simulation results indicated an apparent wetting in 2008,which occurred later than the wetting observed from the CN05.1 in *** GCMs generally underestimated spring temperature and overestimated both winter temperature and spring precipitation in the *** and wetting are more rapid in the northern part of the *** the end of the 21st century,all the four SSP scenarios project warmer and wetter conditions in the CTM with multiple dry-wet ***,the rise in precipitation fails to counterbal

关键词： climate change Coupled Model Intercomparison Project Phase 6(CMIP6) global climate models(GCMs) shared socio-economic pathway(SSP)scenarios standardized precipitation index(SPI) Chinese Tianshan Mountains

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Precise control of page cache for containers

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Frontiers of Computer Science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering research center for Big data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Advances of machine learning in materials science: Ideas and techniques

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Frontiers of physics 2024年第1期19卷 155-194页

作者： Sue Sin Chong Yi Sheng Ng Hui-Qiong Wang Jin-Cheng Zheng Department of New Energy Science and Engineering Xiamen University MalaysiaSepang 43900Malaysia Engineering research center of Micro-nano Optoelectronic Materials and Devices Ministry of EducationFujian Key Laboratory of Semiconductor Materials and ApplicationsCI Center for OSEDand Department of PhysicsXiamen UniversityXiamen 361005China

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties;ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software;The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.

关键词： machine learning materials science

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Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer Science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 data Intelligence System research center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 259-277页

作者： Ming Gao Weiwei Cai Yizhang Jiang Wenjun Hu Jian Yao Pengjiang Qian School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China The center for Intelligent Systems and applications research School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China School of Information Engineering Huzhou UniversityHuzhou313000China

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency ***,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this ***,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

关键词： Edge computing resource scheduling predictive models

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