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arXiv 2024年

作者： Chen, Runhua Luo, Haoxiang Sun, Gang Yu, Hongfang Niyato, Dusit Dustdar, Schahram Glasgow College University of Electronic Science and Technology of China Chengdu611731 China James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Distributed Systems Group TU Wien Vienna1040 Austria ICREA Universitat Pompeu Fabra Barcelona08002 Spain

The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. Addressing blockchain's scalability challenges posed by the growing number of nodes and transactions calls for innovative solutions, among which sharding stands out as a pivotal approach to significantly enhance blockchain throughput. However, existing schemes still face challenges related to a) the impact of vehicle mobility on blockchain consensus, especially for cross-shard transaction;and b) the strict requirements of low latency consensus in a highly dynamic network. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we first develop a standard for evaluating the network stability of nodes, combined with the nodes' trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle cross-shard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in latency, throughput, consensus success rate, and node traffic load. © 2024, CC0.

关键词： Trees (mathematics)

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Time-weighted average of fine particulate matter exposure and cause-specific mortality in China: a nationwide analysis

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The Lancet Planetary Health 2020年第8期4卷 e343-e351页

作者： Dong, Zhaomin Wang, Hao Yin, Peng Wang, Lijun Chen, Renjie Fan, Wenhong Xu, Yilu Zhou, Maigeng School of Space and Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China National Center for Chronic and Noncommunicable Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Key Laboratory of Public Health Security School of Public Health Ministry of Education Fudan University Shanghai China College of Engineering Swansea University Bay Campus Swansea United Kingdom

Background: Most previous assessments of the hazardous effects attributable to fine particulate matter (PM2·5) exposure have used ambient PM2·5 as an exposure metric, resulting in substantial bias in effect ...

Background: Most previous assessments of the hazardous effects attributable to fine particulate matter (PM_2·5) exposure have used ambient PM_2·5 as an exposure metric, resulting in substantial bias in effect estimates. We did a study to examine the association between cause-specific mortality and the time-weighted average of PM_2·5 exposure after accounting for indoor exposure in 267 cities in China. Methods: We did a nationwide study, using Laser Egg air quality monitors in 36 cities to obtain data for indoor PM_2·5 concentrations from 18 484 anonymised households between Nov 1, 2015 and July 2, 2018. We developed and validated a nationwide indoor PM_2·5 prediction model for a further 302 cities by retrieving raw records of hourly concentrations from residents' air sensors;the model was used to predict indoor PM_2·5 during 2013 to 2018. Daily ambient PM_2·5 concentration data were estimated by averaging hourly ambient PM_2·5 concentrations obtained from China's National Urban Air Quality Real-time Publishing Platform. Daily numbers of deaths from all non-accidental causes were obtained from 324 cities from the Disease Surveillance Point System of China between Jan 1, 2013, to Dec 31, 2017, and calculated for 267 cities that had an average daily mortality above three, and data for PM_2·5 concentrations and meteorological information for at least 1 year between 2013 and 2017. We used distributed lag non-linear models to estimate city-specific associations between cause-specific mortality and reconstructed PM_2·5 exposure by considering indoor PM_2·5 exposure. We combined the city-specific effect estimates at the national level using a random effects meta-analysis. Findings: 13 972 records of daily indoor PM_2·5 concentrations for 36 cities, extracted from 47 459 183 raw records from the sensors were included for modelling indoor PM_2·5 le

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Correction to: MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

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Multimedia Tools and Applications 2021年第9期80卷 13019-13019页

作者： Rui Zhu Xiongfei Li Xiaoli Zhang Xiaowei Xu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Information Sciences University of Arkansas at Little Rock Little Rock USA

A Correction to this paper has been published: https://***/10.1007/s11042-021-10782-7

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Author Correction: The lactate receptor HCAR1 drives the recruitment of immunosuppressive PMN-MDSCs in colorectal cancer

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Nature immunology 2025年第4期26卷 635页

作者： Jiacheng He Xiaolei Chai Qiansen Zhang Yang Wang Yijie Wang Xinyu Yang Jingbo Wu Bo Feng Jing Sun Weiwei Rui Shuyin Ze Yuanyuan Fu Yumiao Zhao Ying Zhang Yao Zhang Meizhen Liu Chuang Liu Meifu She Xiangfei Hu Xueyun Ma Huaiyu Yang Dawei Li Senlin Zhao Guichao Li Zhen Zhang Zhonghui Tian Yanlin Ma Lingyan Cao Bo Yi Dali Li Ruth Nussinov Charis Eng Timothy A Chan Eytan Ruppin J Silvio Gutkind Feixiong Cheng Mingyao Liu Weiqiang Lu Shanghai Key Laboratory of Regulatory Biology Institute of Biomedical Sciences and School of Life Sciences East China Normal University Shanghai China. Department of Urology and Department of Pathology The Fifth People's Hospital of Shanghai Fudan University Shanghai China. Department of General Surgery and Department of Pathology Ruijin Hospital Shanghai Jiaotong University School of Medicine Shanghai China. Research Center for Complexity Sciences Hangzhou Normal University Hangzhou China. Department of Colorectal Surgery Department of Radiation Oncology Fudan University Shanghai Cancer Center Shanghai China. School of Life Sciences Fudan University Shanghai China. Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research Hainan Provincial Clinical Research Center for Thalassemia Department of Reproductive Medicine Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University) Ministry of Education The First Affiliated Hospital of Hainan Medical University Haikou China. Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine College of Stomatology Shanghai Jiao Tong University Shanghai China. Department of Gastrointestinal Surgery The Third Xiangya Hospital Central South University Changsha China. Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick Frederick MD USA. Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv Israel. Genomic Medicine Institute Lerner Research Institute Cleveland Clinic Cleveland OH USA. Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland OH USA. Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland OH USA. Center for Immunotherapy and Precision Immuno-Oncology Cleveland Clini

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Estimation of farmland evapotranspiration based on SEBAL model in Kai-Kong River Basin, Xinjiang

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IOP Conference Series: Earth and Environmental Science 2020年第1期502卷

作者： Cha Mingxing Wang Xiaoqin Li Yali Qiu Pengxun Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National & Local Joint Engineering Research Centre of Satellite Geospatial Information Technology Fuzhou University the Academy of Digital China (Fujian) Fuzhou 350108 China.

Evapotranspiration (ET) is an important reference for scientific management of farmland irrigation, accurate estimation of crop yields, and optimal allocation of water resources. The purpose of this paper is to estimate ET by taking the farmland of Kai-Kong River Basin in Xinjiang province as an example. The method was based on the SEBAL model and the Landsat data; it estimated the net radiation flux, soil heat flux using the land-surface parameters first, and the instantaneous evapotranspiration was then calculated using the energy residual method and the latent heat flux. The sinusoidal function method was used to extend the time scale of the instantaneous evapotranspiration to the daily evapotranspiration and the daily evapotranspiration were verified using the FAO56 Penman-Monteith formula. The compared results shown that the error of daily evapotranspiration between SEBAL and FAO56 Penman-Monteith is within 1mm, and except for the estimation accuracy of final growth stage is about 80%, the estimation accuracy of other growth stages are more than 80%. The SEBAL model, coupled with the Landsat remote sensing data, can offer an efficient and adequate alternative to estimate ET at catchment scale.

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Sensitivity Analysis of Vegetation Coverage to Soil Loss Rate in Red Soil Region of South China

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IOP Conference Series: Earth and Environmental Science 2020年第1期502卷

作者： Liu Yifeng Wang Xiaoqin Chen Miaojin Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National & Local Joint Engineering Research Centre of Satellite Geospatial Information Technology Fuzhou University the Academy of Digital China (Fujian) Fuzhou 350108 China.

Vegetation is an important basic component of terrestrial ecosystem and one of the most sensitive factors affecting soil erosion. How to analyse the quantitative relationship between fractional vegetation cover (FVC) and soil erosion and determine the sensitive coverage of erosion is of great significance for guiding government departments to implement soil and water conservation policies. In this paper, through the variable-controlling mothed, the forest land, grassland and bare land in Changting County, a typical red soil region in southern China, were used as experimental areas to study the sensitivity of FVC change to soil erosion. The soil erosion amount was calculated by RUSLE model, and soil loss rate (rsl) were used to characterize soil erosion. The results show that when FVC changes from 0 to 0.8, the fitting curves rsl are in accordance with the logistic model distributions, with a high coefficient of determination R2, and the sensitivity of rsl is less sensitive in the range of 0~0.05, it is most sensitive when FVC reaches a certain level, and there is basically no sensitivity when it reaches 0.7 or above; the fitting curves of rsl at different slope levels are similar to their overall distribution, which are consistent with the logistic models, and R2 is above 0.99, the curves of rsl at each slope level are different in the same FVC interval, for areas with poor vegetation coverage (0~0.2) and high slope (>25°), the curve is less sensitive to terrain; rsl generally has higher sensitivity in the range of 0.2~0.6 coverage; in areas above 0.6, the sensitivity decreases to zero.

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Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events

arXiv

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arXiv 2024年

作者： Wu, Xin Huang, Zhanchao Wang, Li Chanussot, Jocelyn Tian, Jiaojiao The School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China The Key Laboratory of Spatial Data Mining and Information Sharing The Ministry of Education The Academy of Digital China Fuzhou University Fuzhou350108 China The National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Remote Sensing Technology Institute German Aerospace Center Weßling82205 Germany

In large-scale disaster events, the planning of optimal rescue routes depends on the object detection ability at the disaster scene, with one of the main challenges being the presence of dense and occluded objects. Existing methods, which are typically based on the RGB modality, struggle to distinguish targets with similar colors and textures in crowded environments and are unable to identify obscured objects. To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities. Based on these datasets, we propose a multimodal collaboration network for dense and occluded vehicle detection, MuDet for short. MuDet hierarchically enhances the completeness of discriminable information within and across modalities and differentiates between simple and complex samples. MuDet includes three main modules: Unimodal Feature Hierarchical Enhancement (Uni-Enh), Multimodal Cross Learning (Mul-Lea), and Hard-easy Discriminative (He-Dis) Pattern. Uni-Enh and Mul-Lea enhance the features within each modality and facilitate the cross-integration of features from two heterogeneous modalities. He-Dis effectively separates densely occluded vehicle targets with significant intra-class differences and minimal inter-class differences by defining and thresholding confidence values, thereby suppressing the complex background. Experimental results on two re-labeled multimodal benchmark datasets, the 4K-SAI-LCS dataset, and the ISPRS Potsdam dataset, demonstrate the robustness and generalization of the MuDet. The codes of this work are available openly at https://***/Shank2358/MuDet. © 2024, CC BY-NC-SA.

关键词： Remote sensing

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Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

arXiv

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arXiv 2023年

作者： Xiao, Yubin Wang, Di Li, Boyang Chen, Huanhuan Pang, Wei Wu, Xuan Li, Hao Xu, Dong Liang, Yanchun Zhou, You The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The School of Computer Science University of Science & Technology of China Hefei230026 China The School of Mathematical and Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom College of Computer National University of Defense Technology Hunan Changsha410073 China The Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia United States The School of Computer Science Zhuhai College of Science and Technology Zhuhai519000 China

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recently, neural networks have gained popularity in this research area because as shown in the literature, they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, non-autoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP encoded information as rewards and seamlessly integrates it into reinforcement learning strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSPs demonstrate that NAR4TSP outperforms five state-of-the-art models in terms of solution quality, inference speed, and generalization to unseen scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Traveling salesman problem

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Large-Scale Tensorized Multi-View Kernel Subspace Clustering

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ACM Transactions on Intelligent Systems and Technology 1000年

作者： Guang-Yu Zhang Dong Huang Chang-Dong Wang College of Mathematics and Informatics South China Agricultural University China School of Computer Science and Engineering Sun Yat-sen University Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Guangdong Key Laboratory of Big Data Analysis and Processing Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they typically recover anchor graph structure in the original linear space, restricting their feasibility for nonlinear scenarios. Second, they usually overlook the potential benefits of jointly capturing the inter-view and intra-view information for enhancing the anchor representation learning. Third, these approaches mostly perform anchor-based subspace learning by a specific matrix norm, neglecting the latent high-order correlation across different views. To overcome these limitations, this paper presents an efficient and effective approach termed Large-scale Tensorized Multi-view Kernel Subspace Clustering (LTKMSC). Different from the existing AMSC approaches, our LTKMSC approach exploits both inter-view and intra-view awareness for anchor-based representation building. Concretely, the low-rank tensor learning is leveraged to capture the high-order correlation (i.e., the inter-view complementary information) among distinct views, upon which the \(l_{1,2}\) norm is imposed to explore the intra-view anchor graph structure in each view. Moreover, the kernel learning technique is leveraged to explore the nonlinear anchor-sample relationships embedded in multiple views. With the unified objective function formulated, an efficient optimization algorithm that enjoys low computational complexity is further designed. Extensive experiments on a variety of multi-view datasets have confirmed the efficiency and effectiveness of our approach when compared with the other competitive approaches.

关键词： data clustering Multi-view clustering Large-scale clustering Tensorized kernel subspace clustering Inter-view and intra-view awareness

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A Multi-Perspective Exploration of the Salinization Mechanisms of Groundwater in the Guanzhong Basin, China

SSRN

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SSRN 2024年

作者： Mu, Dawei Li, Peiyue De Baets, Bernard Li, Dongfeng Li, Zhaoyu He, Song School of Water and Environment Chang’an University No.126 Yanta Road Shaanxi Xi’an710054 China Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region The Ministry of Education Chang’an University No.126 Yanta Road Shaanxi Xi’an710054 China Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions The Ministry of Water Resources Chang’an University No.126 Yanta Road Shaanxi Xi’an710054 China KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Coupure Links 653 Ghent9000 Belgium PowerChina Sinohydro Bureau 3 Co. Ltd. No. 4069 Expo Avenue Chanba Ecological District Shaanxi Xi’an710024 China PowerChina Northwest Engineering Corporation Limited No. 18 Zhangbadong Road Shaanxi Xi’an710065 China

To determine the mechanism responsible for the saline groundwater enrichment in different regions of the Guanzhong Basin, China, and to reveal the factors influencing salinization, this study employed multivariate statistical methods, hydrochemical analysis, isotope studies, and hydrochemical modeling. The results indicate significant regional variations in total dissolved solids (TDS), with concentrations exceeding 1000 mg/L predominantly occurring north of the Weihe River and east of the Jinghe River. The correlations of groundwater chloride (Cl) with Cl/Br molar ratio and stable isotopes show that groundwater salinity in the Guanzhong Basin is mainly controlled by mineral dissolution, evaporation, and concentration. In addition, human activities, such as vertical irrigation recharge and excessive fertilizer use, exacerbate local salinity levels. Irrigation activities worsen the shallow groundwater salt enrichment in the runoff zone of the Central Basin, revealed by the high salinity (> 3000 mg/L), high Cl/Br ratios (> 2000), moderate δ2H (-57.5 to -67.5‰) and moderate δ18O (-8.1 to -8.9‰). High salinity (> 1000 mg/L), high nitrate concentration (> 100 mg/L), and moderate Cl (100 to 500 mg/L) indicate the impact of excessive fertilizer use. It is worth noting that intensive groundwater withdrawal disrupts the dynamic balance within the aquifer, causing shallow high-saline groundwater to percolate downward, thereby increasing the risk of deep groundwater pollution. The research enhances the understanding of groundwater salinity transport and provides insights into the effects of groundwater salinization in the irrigation area. © 2024, The Authors. All rights reserved.

关键词： Groundwater pollution

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