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Strengthening the three-dimensional comprehensive observation system of multi-layer interaction on the Tibetan Plateau to cope with the warming and wetting trend

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Atmospheric and Oceanic Science Letters 2022年第4期15卷 67-71页

作者： Yaoming Ma Binbin Wang Xuelong Chen Lei Zhong Zeyong Hu Weiqiang Ma Cunbo Han Maoshan Li Land–Atmosphere Interaction and its Climatic Effects Group State Key Laboratory of Tibetan Plateau Earth SystemResources and Environment(TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingChina College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina College of Atmospheric Science Lanzhou UniversityLanzhouChina National Observation and Research Station for Qomolongma Special Atmospheric Processes and Environmental Changes DingriChina Kathmandu Center of Research and Education Chinese Academy of SciencesBeijingChina China–Pakistan Joint Research Center on Earth Sciences Chinese Academy of SciencesIslamabadPakistan School of Earth and Space Sciences University of Science and Technology of ChinaHefeiChina CAS Center for Excellence in Comparative Planetology USTCHefeiChina Frontiers Science Center for Planetary Exploration and Emerging Technologies University of Science and Technology of ChinaHefeiChina Jiangsu Collaborative Innovation Center for Climate Change NanjingChina Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouChina School of Atmospheric Science Chengdu University of Information TechnologyChengduChina

Changes in the water cycle on the Tibetan Plateau(TP)have a significant impact on local agricultural production and livelihoods and its downstream *** the background of widely reported warming and wetting,the hydrological cycle has accelerated and the likelihood of extreme weather events and natural disasters occurring(i.e.,snowstorms,floods,landslides,mudslides,and ice avalanches)has also intensified,especially in the highelevation mountainous ***,an accurate estimation of the intensity and variation of each component of the water cycle is an urgent scientific question for the assessment of plateau environmental *** the transformation and movement of water between the atmosphere,biosphere and hydrosphere,the authors highlight the urgent need to strengthen the three-dimensional comprehensive observation system(including the eddy covariance system;planetary boundary layer tower;profile measurements of temperature,humidity,and wind by microwave radiometers,wind profiler,and radiosonde system;and cloud and precipitation radars)in the TP region and propose a practical implementation *** construction of such a three-dimensional observation system is expected to promote the study of environmental changes and natural hazards prevention.

关键词： Plateau warming and wetting Hydrological cycle Three-dimensional comprehensive observation system of multi-layer interaction Mountain-disaster response Tibetan Plateau

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Parametric Model Estimation for 3D Clothed Humans from Point Clouds

Parametric Model Estimation for 3D Clothed Humans from Point...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Kangkan Wang Huayu Zheng Guofeng Zhang Jian Yang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper presents a novel framework to estimate parametric model- s for 3D clothed humans from partial point clouds. It is a challenging problem due to factors such as arbitrary human shape and pose, large variations in clothing details, and significant missing data. Existing methods mainly focus on estimating the parametric model of undressed bodies or reconstructing the non-parametric 3D shapes from point clouds. In this paper, we propose a hierarchical regression framework to learn the parametric model of detailed human shapes from partial point clouds of a single depth frame. Benefiting from the favorable ability of deep neural networks to model nonlinearity, the proposed framework cascades several successive regression networks to estimate the parameters of detailed 3D human body models in a coarse-to-fine manner. Specifically, the first global regression network extracts global deep features of point clouds to obtain an initial estimation of the undressed human model. Based on the initial estimation, the local regression network then refines the undressed human model by using the local features of neighborhood points of human joints. Finally, the clothing details are inferred as an additive displacement on the refined undressed model using the vertex-level regression network. The experimental results demonstrate that the proposed hierarchical regression approach can accurately predict detailed human shapes from partial point clouds and outperform prior works in the recovery accuracy of 3D human models.

关键词： Deep learning Solid modeling Three-dimensional displays Shape Clothing Estimation Predictive models

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Toward PTNET Network Topology Analysis and Routing Algorithm Design 6th

Toward PTNET Network Topology Analysis and Routing Algorithm...

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6th International Conference on Data Science, ICDS 2019

作者： Han, Zhijie Zhang, Qingfang Du, Xiaoyu Guo, Kun He, Mingshu Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China School of Computer and Information Engineering Henan University KaifengHenan475004 China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789811528095

In recent years, data center network is used for transmission, storage and processing of big data, which plays an important role for applications in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server-centric data center network topology can ensure the scale of the data center network by recursively increasing the number of network nodes and links. relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server-centric data center network topology, which has more advantages in scalability, fault tolerance and so on. Multicast and broadcast in data center network have more application scenarios and use value. For example, the video conference online, multimedia remote education and other development are inseparable from the application and promotion of network multicast and broadcast. So it is necessary to research the routing algorithms of multicast and broadcast in the network. Based on the deep research of PTNet network, this paper analyzes and researches the network topology, multicast and broadcast routing algorithm. © Springer Nature Singapore Pte Ltd 2020.

关键词： Network topology

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RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-based Semantic Segmentation

arXiv

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arXiv 2021年

作者： He, Shuang Lu, Xia Gu, Jason Tang, Haitong Yu, Qin Liu, Kaiyue Ding, Haozhou Chang, Chunqi Wang, Nizhuan Jiangsu Key Laboratory of Marine Bioresources and Environment Jiangsu Key Laboratory of Marine Biotechnology Co-Innovation Center of Jiangsu Marine Bio-Industry Technology Jiangsu Ocean University Lianyungang222005 China School of Geomatics and Marine Information Jiangsu Ocean University Lianyungang222005 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing211800 China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518055 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China

For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important. How to get the trade-off effectively is an open question, where current approaches of utilizing very deep models result in complex models with large memory consumption. In contrast to previous work that utilizes dilated convolutions or deep models, we propose a novel two-stream deep neural network for semantic segmentation of RSI (RSI-Net) to obtain improved performance through modeling and propagating spatial contextual structure effectively and a decoding scheme with image-level and graph-level combination. The first component explicitly models correlations between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions by using graph convolutional network, while densely connected atrous convolution network (DenseAtrousCNet) with multi-scale atrous convolution can expand the receptive fields and obtain image global information. Extensive experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets, where the comparison results demonstrate the superior performance of RSI-Net in terms of overall accuracy (91.83%, 93.31% and 93.67% on three datasets, respectively), F1 score (90.3%, 91.49% and 89.35% on three datasets, respectively) and kappa coefficient (89.46%, 90.46% and 90.37% on three datasets, respectively) when compared with six state-of-the-art RSI semantic segmentation methods. © 2021, CC BY-NC-ND.

关键词： Semantic Segmentation

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Cross-View Panorama Image Synthesis

arXiv

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arXiv 2022年

作者： Wu, Songsong Tang, Hao Jing, Xiao-Yuan Zhao, Haifeng Qian, Jianjun Sebe, Nicu Yan, Yan The School of Computer Science Guangdong University of Petrochemical Technology Maoming525000 China The Department of Information Technology and Electrical Engineering ETH Zurich Zurich8092 Switzerland The School of Computer Science Wuhan University Wuhan430072 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The School of Software Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Hoperun Software Co. Ltd. Nanjing210012 China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Trento Trento38123 Italy The Department of Computer Science Illinois Institute of Technology Chicago60616 United States

In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a topview aerial image, which is a challenging problem due to the large gap between the two image domains with different viewpoints. Instead of learning cross-view mapping in a feedforward pass, we propose a novel adversarial feedback GAN framework named PanoGAN with two key components: an adversarial feedback module and a dual branch discrimination strategy. First, the aerial image is fed into the generator to produce a target panorama image and its associated segmentation map in favor of model training with layout semantics. Second, the feature responses of the discriminator encoded by our adversarial feedback module are fed back to the generator to refine the intermediate representations, so that the generation performance is continually improved through an iterative generation process. Third, to pursue high-fidelity and semantic consistency of the generated panorama image, we propose a pixel-segmentation alignment mechanism under the dual branch discrimiantion strategy to facilitate cooperation between the generator and the discriminator. Extensive experimental results on two challenging cross-view image datasets show that PanoGAN enables highquality panorama image generation with more convincing details than state-of-the-art approaches. The source code and trained models are available at https://***/sswuai/PanoGAN. Copyright © 2022, The Authors. All rights reserved.

关键词： Feedback

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Research on evaluation index system for software vulnerability analysis methods 4

Research on evaluation index system for software vulnerabili...

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Li, Jin Huang, Min-Huan Lu, Shuai-Bing Li, Hu Chen, Jin-Fu National Key Laboratory of Science and Technology on Information System Security Beijing China School of Computer Science and Communication Engineering Jiangsu University Jiangsu China

ISBN: (纸本)9781728145280

Due to the diversity of existing software vulnerability analysis methods, a unified index system is required if we are to accurately and comprehensively evaluate the analytic effects in order to further improve the level of analysis. Accordingly, this paper first proposes the principles and process necessary to construct an evaluation index system for software vulnerability analysis. Moreover, by focusing on the problems identified in previous relevant research, an evaluation index system for software vulnerability analysis methods that contains four aspects-accuracy, coverage, value and performance-is proposed;this system is more comprehensive and complete than existing achievements. Finally, using the vulnerability analysis testing framework, the indexes of the three different versions of cppcheck are measured. The results show that the proposed evaluation index system can be used for the comparative evaluation of different vulnerability analysis methods. © 2019 IEEE.

关键词： Analysis method Evaluation index system Software vulnerability

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Dual Representation Learning for One-Step Clustering of Multi-View Data

arXiv

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arXiv 2022年

作者： Zhang, Wei Deng, Zhaohong Choi, Kup-Sze Wang, Jun Wang, Shitong The School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Media Design and Software Technology Wuxi214122 China The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China ZJLab Shanghai200433 China The Centre for Smart Health The Hong Kong Polytechnic University Hong Kong The Key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China

Multi-view data are commonly encountered in data mining applications. Effective extraction of information from multi-view data requires specific design of clustering methods to cater for data with multiple views, which is non-trivial and challenging. In this paper, we propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. The motivation originates from the rationale that multi-view data contain not only the consistent knowledge between views but also the unique knowledge of each view. Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improves the clustering performance. Results from extensive experiments conducted on benchmark multi-view datasets clearly demonstrate the superiority of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Cluster analysis

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Hierarchical Temporal Sequence Segmentation for Weakly Supervised Video Anomaly Detection

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Expert Systems with Applications 2025年

作者： Nuku Atta Kordzo Abiew Lijian Gao Qirong Mao School of Computer Science and Engineering Jiangsu University Zhenjiang China Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang Jiangsu China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang Jiangsu China

Temporal detection and localization of anomalies with weak labels is challenging due to the limited availability of labeled data and the dynamic nature of anomalies. To address these challenges, we introduce a novel Hierarchical Temporal Sequence Segmentation (HiTESS) approach that enhances anomaly detection and localization by leveraging multi-level temporal segmentation. Our method begins by segmenting the video into progressively smaller temporal units, enabling the model to capture anomalies at varying scales. The core contribution of our approach is the hierarchical selection of temporal segments, where multi-level scoring aggregates predictions across different time scales. While Bidirectional LSTMs and Multi-Head Attention refine temporal features, the hierarchical aggregation of predictions significantly enhances anomaly detection accuracy. This hierarchical analysis not only improves feature representation but also enhances the detection accuracy of subtle anomalies that might be overlooked in traditional methods. Experimental results demonstrate that the HiTESS method outperforms existing approaches by 1.32% AUC and 4.8% AP on the UCF-Crime and XD-Violence datasets, respectively, highlighting its effectiveness in utilizing aggregated features at varied temporal levels for capturing and localizing anomalies in video sequences.

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Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model

arXiv

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arXiv 2024年

作者： Hou, Jinbo Qiu, Kehai Zhang, Zitian Yu, Yong Wang, Kezhi Capolongo, Stefano Zhang, Jiliang Li, Zeyang Zhang, Jie The Department of Electronic and Electrical Engineering the University of Sheffield Sheffield United Kingdom The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom Brunel University London United Kingdom The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China The Design & Health Lab Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Italy The Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom The State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang China The Centre for Wireless Innovation Queen’s University of Belfast BelfastBT3 9DT United Kingdom

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building’s wireless performance can be significantly enhanced while ensuring indoor daylight performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Daylighting

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Real-time human cross-race aging-related face appearance detection with deep convolution architecture

Real-time human cross-race aging-related face appearance det...

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作者： Tian, Qing Zhang, Wenqiang Mao, Junxiang Yin, Hujun School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

Human age estimation (AE) is an emerging research topic in computer vision and machine learning and has attracted increasing amount of research due its wide potential applications. In the process of human aging, facial appearances change from glabrous to crinkly similarly across all races, from European, Hispanic and African to Asian. To specially explore the relationships between aging and facial appearances across races, this paper is devoted to determining the correspondence between facial aging and facial appearances. Specifically, we first extract appearance vector features from facial images with their spatial structure preserved. Then, we propose to select the aging-related features shared by different races to explore their aging-related common facial regions, while removing redundant features. Thirdly, we improve the proposed model by incorporating potential cross-race relationships in an automated learning manner. Additionally, we extend our model with deep convolution architecture. Finally, we evaluate the proposed methodologies on a large face aging database with real-time efficiency. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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