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Spatiotemporal estimation and local driver analysis of fractional vegetation coverage in the Three-North region, China

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Advances in Space research 2025年

作者： Yi Long Hua Sun Fugen Jiang Song Chen Jie Tang Research Center of Forestry Remote Sensing and Information Engineering Central South University of Forestry and Technology Changsha China Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province Changsha China Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area Changsha China

Understanding the spatio-temporal dynamics of fractional vegetation coverage (FVC) and its driving factors is imperative for advancing ecological management and fostering sustainable development. Towards the Three-North region of China, five distinct nonnegative matrix factorization models were employed to estimate FVC. The quadratic optimization derived from improved constrained nonnegative matrix factorization, IC-NMF 2 , was implemented to refine FVC estimation from 2000 to 2022. The spatial evolution pattern and change trend analysis were performed. The global and local drivers of FVC were analyzed using optimal parameter-based geographic detector (OPGD) and geographically weighted random forest (GWRF) model. The findings indicated that the IC-NMF 2 model outperformed other models in estimation accuracy and stability. FVC in the Three-North region had notable directional and regional characteristics, with a predominant tendency of either improvement or relative stability. However, localized degradation was observed, particularly in the Tianshan Mountains and the Beijing-Tianjin-Hebei region. Both OPGD and GWRF analyses consistently identified average relative humidity and accumulated precipitation as the primary global drivers of FVC, which showed a bi-enhance interactive effect. Average relative humidity was the first local influencing factor of FVC in most regions, showing strong spatial alignment with FVC distributions. By integrating local factor importance with trend analysis, more precise recommendations for ecological protection and management for sub-regions can be formulated. This study demonstrates great potential for application in other regions, especially in monitoring vegetation ecosystems and their dynamic responses to global climate changes in desert areas.

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Fusion of Visible and Infrared Images Based on IHS Transformation and Regional Variance Matching Degree 6

Fusion of Visible and Infrared Images Based on IHS Transform...

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6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2018

作者： Zhou, Chengquan Zhao, Jinling Pan, Zhenggao Hong, Qi Huang, Linsheng National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data Anhui University Hefei230601 China Beijing Research Center for Information Technology in Agriculture Beijing100097 China Information Engineering Institute Suzhou University Suzhou234000 China

It is usually necessary to identify and extract specific characteristics by deriving an informative fused image from multiple images. An effective fusion algorithm was proposed by combining the intensity-hue-saturation (IHS) transformation and the regional variance matching degree (RVMD) in our study. Visible and thermal infrared images of wheat were used as the original data sources. After finishing the IHS transformation, a fusion rule was designed to produce the new component I. More specifically, the high frequency fusion rule was generated by the RVMD with a threshold of 0.5 and a 3 x 3 moving window, and the weighted average was used as the low frequency fusion rule. Experimental results show that the proposed algorithm can avoid producing color distortion in comparison with the IHS transformation, and additionally, it can also enhance the edge contrast and produce more obvious texture resolution. In addition, three quantitative indicators including entropy, standard deviation and average gradient were used to validate the proposed algorithm. The analysis results show that the values of three indicators are respectively 7.82, 63.93, 10.06, which are better than the results derived from the IHS transformation and regional variance fusion. © Published under licence by IOP Publishing Ltd.

关键词： Textures

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Novel Hemorrhagic Risk Score in Elderly Patients with Coronary Artery Disease and Gastrointestinal Malignant Tumor Comorbidity:A 10-year Clinical Inpatient data Analysis from 2 Medical centers

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Cardiology Discovery 2021年第3期1卷 163-172页

作者： Nandi Bao Wanling Wang Huitao Wu Yabin Wang Hebin Che Wenwen Meng Jiaxin Miao Dong Han Fan Yin National Clinical Research Center for Geriatric Diseases The Second Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China Medical School of Chinese People's Liberation Army General Hospital Beijing 100853China National Engineering Laboratory for Medical Big Data Application Technology The First Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China Department of Oncology The Second Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China

Objective:Older patients with comorbidity,such as coronary heart disease(CHD)and malignant gastrointestinal tumors,are at a high risk of bleeding ***,risk prediction models based on risk factor assessment remain *** study aimed to establish an individualized bleeding risk assessment system based on the analysis of 10-year inpatient clinical big ***:Total clinical data of 56,819 patients with CHD and 25,988 patients with malignant digestive tract tumors(admitted from January 2008 to December 2017)were retrospectively collected at the First and Second Medical centers of Chinese People's Liberation Army General *** them,1307 patients with CHD and malignant digestive tract tumors were screened as the derivation *** dependent variable was the occurrence of major clinical bleeding *** statistics and hypothesis tests of differences were performed for independent variables according to the occurrence of *** Tree,eXtreme Gradient Boosting(XGBoost),logistic regression,and random forest models were used for *** accuracy,sensitivity,specificity,and area under the receiver operating characteristic curve(AUC-ROC)were applied as the criteria for evaluating and verifying model *** evaluate this developed model,another cohort comprising 454 patients(admitted from January 2018 to December 2019)was prospectively enrolled as the validation cohort based on the same inclusion and exclusion ***:Among the 64 variables with<50%missing values,the recursive feature elimination method with a random forest model was used to screen the selected *** highest accuracy was obtained following the selection of 10 scalars,and the final model was constructed *** demonstrated the best performance *** AUC-ROC of this model was 0.981,with an accuracy,sensitivity,and specificity of 0.939,0.950,and 0.927,*** the validation cohort,the AUC-ROC,accurac

关键词： Coronary disease Gastrointestinal cancers Bleeding Multivariate analysis Risk factors Machine learning

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Model-assisted inference for covariate-specific treatment effects with high-dimensional data

arXiv

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arXiv 2021年

作者： Wu, Peng Tan, Zhiqiang Hu, Wenjie Zhou, Xiao-Hua Beijing International Center for Mathematical Research Peking University Beijing100871 China Department of Statistics Rutgers University 110 Frelinghuysen Road PiscatawayNJ08854 United States Department of Probability and Statistics Peking University Beijing100871 China Department of Biostatistics Beijing International Center for Mathematical Research National Engineering Laboratory of Big Data Analysis and Applied Technology Peking University Beijing100871 China

Covariate-specific treatment effects (CSTEs) represent heterogeneous treatment effects across subpopulations defined by certain selected covariates. In this article, we consider marginal structural models where CSTEs are linearly represented using a set of basis functions of the selected covariates. We develop a new approach in high-dimensional settings to obtain not only doubly robust point estimators of CSTEs, but also model-assisted confidence intervals, which are valid when a propensity score model is correctly specified but an outcome regression model may be misspecified. With a linear outcome model and subpopulations defined by discrete covariates, both point estimators and confidence intervals are doubly robust for CSTEs. In contrast, confidence intervals from existing high-dimensional methods are valid only when both the propensity score and outcome models are correctly specified. We establish asymptotic properties of the proposed point estimators and the associated confidence intervals. We present simulation studies and empirical applications which demonstrate the advantages of the proposed method compared with competing ones. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

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Context aware 3d unet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

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Sledge: Towards Efficient Live Migration of Docker Containers

Sledge: Towards Efficient Live Migration of Docker Container...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Bo Xu Song Wu Jiang Xiao Hai Jin Yingxi Zhang Guoqiang Shi Tingyu Lin Jia Rao Li Yi Jizhong Jiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China The University of Texas at Arlington Arlington TX USA Alibaba Cloud Computing Co. Ltd. Hangzhou China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.

关键词： Cloud computing Runtime Scalability Loading Quality of service Containers Maintenance engineering

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MorLog: Morphable Hardware Logging for Atomic Persistence in Non-Volatile Main Memory

MorLog: Morphable Hardware Logging for Atomic Persistence in...

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Annual International Symposium on Computer Architecture, ISCA

作者： Xueliang Wei Dan Feng Wei Tong Jingning Liu Liuqing Ye Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology (School of Computer Science and Technology Huazhong University of Science and Technology) Ministry of Education of China

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

Byte-addressable non-volatile memory (NVM) is emerging as an alternative for main memory. Non-volatile main memory (NVMM) systems are required to support atomic persistence and deal with the high overhead of programming NVM cells. To this end, recent studies propose hardware logging and data encoding designs for NVMM systems. However, prior hardware logging designs incur either extra ordering constraints or redundant log data. Moreover, existing data encoding designs are unaware of the characteristics of log data, resulting in writing unnecessary log *** this paper, we propose a morphable hardware logging design (MorLog) that only logs the data necessary for recovery and dynamically selects encoding methods with least write overhead. We observe that (1) only the oldest undo and the newest redo data in each transaction are necessary for recovery, and (2) the log data for clean bits are clean. The first motivates our morphable logging mechanism. This mechanism logs both undo and redo data for the first update to the data in a transaction, and then logs only redo data. Undo data are eagerly written to NVMM to ensure atomicity, while redo data are buffered in a volatile log buffer and L1 caches to write only the newest redo data to NVMM. The second motivates our selective log data encoding mechanism. This mechanism simultaneously encodes log data with different methods, and writes the encoded log data with the least write cost to NVMM. We devise a differential log data compression method to exploit the characteristics of log data. This method directly discards clean bits from log data and compresses remained dirty bits. Our evaluation shows that MorLog improves performance by 72.5%, reduces NVMM write traffic by 41.1%, and decreases NVMM write energy by 49.9% compared with the state-of-the-art design.

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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An enhanced combined model for NEV sales prediction utilizing complexity self-awareness and sentiment score correction

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Energy 2025年 330卷

作者： Dai, Wan Liu, Yuquan Huang, Xinyi Zou, Biyou Zhu, Jiaming School of Internet Anhui University Anhui Hefei230039 China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui Hefei230601 China Stony Brook Institute at Anhui University Anhui Hefei230039 China Science Island Branch of Graduate School University of Science and Technology of China Anhui Hefei230026 China

Accurately predicting new energy vehicle (NEV) sales not only contributes to promoting sustainable mobility but also provides support for resource allocation and infrastructure planning. To address the limitations of existing methods in integrating market sentiment and capturing multiscale data characteristics, this paper proposes a hybrid forecasting model that incorporates complexity self-awareness and sentiment score correction. First, a comprehensive time series dataset is constructed using historical sales data and the corresponding online reviews. Second, the market-oriented sentiment score self-correction (MSSC) algorithm is applied to dynamically optimize sentiment variables. Then, ensemble empirical mode decomposition (EEMD) is used to extract multifrequency components, which are evaluated and classified via the self-aware complexity-driven intelligent quantification (SCIQ) method for refined modelling. An empirical analysis based on BYD Tang NEV sales data demonstrates that the proposed model outperforms state-of-the-art benchmark models across four evaluation metrics. Moreover, after incorporating SCIQ and MSSC, the prediction accuracy improved by 30.04% and 22.34%, respectively, in terms of the RMSE, and by 2.04% and 1.39%, respectively, in terms of the first-order effectiveness. Furthermore, SHAP analysis is employed to interpret key influencing factors, enabling the formulation of consumer-oriented policy recommendations that support high-quality NEV industry development. © 2025 Elsevier Ltd

关键词： Sales

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Robust capturing chromosome conformation using the DLO Hi-C 2.0 method

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Journal of Genetics and Genomics 2020年第10期47卷 655-658页

作者： Zhihui Zhang Chengchao Wu Khaista Rahman Weize Xu Guoliang Li Da Lin Gang Cao State Key Laboratory of Agricultural Microbiology Huazhong Agricultural UniversityWuhan430070China College of Veterinary Medicine Huazhong Agricultural UniversityWuhan430070China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan430070China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data3D Genomics Research CenterHuazhong Agricultural UniversityWuhan430070China College of Informatics Huazhong Agricultural UniversityWuhan430070China College of Bio-Medicine and Health Huazhong Agricultural UniversityWuhan430070China

The human genome contains around 20,000 protein-coding genes and a vast of transcriptional regulatory elements(ENCODE Project Consortium,2012;Roadmap Epigenomics Consortium et al.,2015;Stunnenberg and Hirst,2016).It still remains elusive how these regulatory elements regulate their target genes across genomic distances of millions of bases.

关键词： transcriptional ). bases

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