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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Chengwei Zhang, Xueyi Yue, Xianghu Lao, Mingrui Jiang, Tao Wang, Jiawei Zhang, Fubo Chen, Longyong National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China Department of Electrical and Computer and Engineering National University of Singapore Singapore Singapore National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity. © 2024 ACM.

关键词： edge-aware supervision

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Oesophageal cancer-specific mortality risk and public health insurance: Prospective cohort study from China

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World Journal of Gastrointestinal Oncology 2025年第4期17卷 274-285页

作者： Xiang-Lin Wu Xiao-Sheng Li Jing-Han Cheng Lin-Xin Deng Zu-Hai Hu Jun Qi Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Research Center for Medicine and Social Development Chongqing Medical UniversityChongqing 400016China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Department of Thoracic Surgery The People’s Hospital of Changshou ChongqingChongqing 401220China

BACKGROUND Oesophageal cancer is a significant health concern worldwide,with high inci-dence and mortality *** China,the disease burden is particularly high,accounting for a substantial proportion of oesophageal cancer cases and related deaths *** To explore the relationship between the mortality rate of oesophageal cancer patients and insurance type,out-of-pocket ratio,and the joint effects of insurance type and out-of-pocket *** The χ^(2) test was used to analyze patients’demographic and clinical *** logistic regression,the Cox proportional hazard model,and the competitive risk model were used to calculate the cumulative hazard ratios(HRs)of all-cause death and oesophageal cancer-specific death among patients with different types of insurance and out-of-pocket *** Compared with patients covered by basic medical insurance for urban and rural residents,patients covered by urban employee basic medical insurance for urban workers(UEBMI)had a 23.30%increased risk of oesophageal cancer-specific death[HR=1.233,95%confidence interval(CI):1.093-1.391,P<0.005].Compared with patients in the low out-of-pocket ratio group,patients in the high out-of-pocket ratio group had a 25.80%reduction in the risk of oesophageal cancer-specific death(HR=0.742,95%CI:0.6555-0.84,P<0.005).With each 10%increase in the out-of-pocket ratio,the risk of oesophageal cancer-specific death decreased by 10.10%in patients covered by ***,the risk of oesophageal cancer-specific death increased by 26.90%in patients in the high out-of-pocket ratio *** This study reveals the relationships of the specific mortality rate of patients with oesophageal cancer with the out-of-pocket ratio and medical insurance types as well as their combined *** study provides practical suggestions and guidance for the formulation of relevant policies in this area.

关键词： Oesophageal cancer Public health Insurance Prognosis Mortality risk

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Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

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International Journal of Agricultural and Biological Engineering 2024年第4期17卷 265-275页

作者： Weixin Zhai Zhi Xu Jinming Liu Xiya Xiong Jiawen Pan Sun-Ok Chung Dionysis Bochtis Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Department of Smart Agricultural Systems Graduate SchoolChungnam National UniversityDaejeon 34134Republic of Korea Institute for Bio-Economy and Agri-Technology(iBO) Centre for Research and Technology-Hellas(CERTH)Thessaloniki 57001Greece

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory *** the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was ***,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory ***,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature ***,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory *** validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural *** the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.

关键词： road-field trajectory classification efficientNet feature deformation network multi-range feature enhancement agricultural machinery operation mode recognition

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DT-ASGNRU: A Comprehensive Framework for Traffic Flow Forecasting

DT-ASGNRU: A Comprehensive Framework for Traffic Flow Foreca...

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Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Jiaju He Jianhua Xiao Hongbo Xiao Yujun Yang Liming Ding Dunhong Yao Pan Yang School of Computer and Artificial Intelligence (School of Software) Huaihua University Huaihua China key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

Traffic prediction plays a crucial role in alleviating congestion, enhancing road utilization, and reducing traffic accidents. In order to tackle the challenge of dynamic spatiotemporal correlations in traffic data, this paper introduces a new traffic flow prediction model, DT-ASGNRU. This model combines Gated Recurrent Units (GRU), a multi-attention mechanism, and a Graph Convolutional Network (GCN), while introducing an expansion convolution layer to enhance the multi-attention mechanism. This modification expands the acceptance domain, which enhances the model’s capacity to grasp local temporal dependencies. The expanded convolution based on multi-head attention mechanism enhances the sensitivity of the model to time series data and captures the temporal correlation in traffic flow effectively. Additionally, the integration of attention mechanisms with graph convolutions allows for dynamic modeling of spatial relationships within traffic networks. We conducted assessment trials utilizing an extensive collection of genuine traffic information, and the outcomes indicate that DT-ASGNRU surpasses all comparative methodologies in terms of performance.

关键词： Adaptation models Technological innovation Attention mechanisms Correlation Convolution Graph convolutional networks Time series analysis Predictive models data models Forecasting

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An improved graphical convolutional network model based on interactive aspect-level sentiment analysis

An improved graphical convolutional network model based on i...

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2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Shi, Yuanhang Chen, Junjie College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Huhhot01 0018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Hohhot010018 China

ISBN: (纸本)9781510667662

Aspect-level text sentiment analysis is a more complete and specific task of text sentiment analysis, which determines the sentiment information expressed by a text with respect to each aspect. In recent years, the use of syntactic dependency trees as well as sentence structure information for aspect-level sentiment analysis has become more common, but they often ignore the role of sentence semantics involved in sentiment analysis and fail to combine them. In this paper, we improve and propose an interactive graph convolutional network (SInterGCN) model incorporating syntactic semantic structure for text-level sentiment analysis tasks. Specifically, a dependency graph is first constructed for each sentence in the syntactic relational dependency tree. This is followed by combining the graph of structural relations with the associated semantic graph, which is used to capture key aspect-oriented and contextual aspects. Furthermore, in order to interactively extract dependencies between aspect words and other aspects, an aspect-oriented GCN is used to model the representation learned by the aspect-oriented GCN based on the inter-aspect graph. Thus, the model can be aware of important contexts and aspect words when interactively learning aspect-specific sentiment features. Experimental results on four relevant benchmark datasets demonstrate that our proposed model outperforms the original interactive approach, achieving superior performance and validating the effectiveness of our model. © 2023 SPIE.

关键词： Sentiment analysis

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Time-varying neural networks based on local attention mechanism for time series forecasting

Time-varying neural networks based on local attention mechan...

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Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Yuxuan Wang Hongbo Xiao Jianhua Xiao Chunqiao Mi Yujun Yang Liming Ding Lebing Zhang School of Computer and Artificial Intelligence (School of Software) Huaihua University Huaihua China key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

To address the challenges of long-range dependency capture and local dynamics sensitivity in time series prediction, this study proposes a novel time-varying neural network (LATVN) based on the local attention mechanism. The design of the LATVN focuses on mitigating the gradient vanishing problem and enhancing the model’s sensitivity to the local dynamics in the time series. The model efficiently captures key temporal dependencies by focusing on specific parts of the sequence through local attention blocks (LA blocks). Meanwhile, the time-varying structure allows the model parameters to be dynamically adjusted over time steps, thus better adapting to the non-stationary and cyclical changes in the time series. Extensive experimental evaluations on several standard datasets show that LATVN outperforms existing baseline methods in several evaluation metrics, proving its effectiveness and superiority in time series forecasting tasks. The proposal of LATVN provides new perspectives and solutions in the field of time series forecasting, and it is expected to demonstrate its forecasting potentials in a wider range of application scenarios.

关键词： Adaptation models Attention mechanisms Sensitivity Time series analysis Neural networks Focusing Proposals Forecasting Information technology Standards

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MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS 11

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTUR...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Zaixi Min, Yaosen Zheng, Shuxin Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Institute of Interdisciplinary Information Sciences Tsinghua University China Microsoft Research United States

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragment-based LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures. Our code is publicly available at https://***/zaixizhang/FLAG. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Molecules

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Habitat Suitability analysis of rice sheath blight based on multi-source data 5

Habitat Suitability analysis of rice sheath blight based on ...

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5th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2024

作者： Huang, Linsheng Shang, Juncheng Huang, Wenjiang Li, Jinsong National Engineering Research Center for Agro-Ecological Big Data Analysis& Application Anhui University Hefei China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Institute of Space Integrated Ground Network Anhui Co. Ltd. Hefei China

ISBN: (纸本)9781510681712

Rice sheath blight is one of the main diseases of rice, seriously affecting the safety of rice production. This study integrates multisource data such as meteorological, spectral, and microwave data, and combines various species distribution models (SDMs) to establish a habitat suitability analysis model for rice sheath blight, thus evaluating the suitability of habitats for sheath blight. The results indicate that integrating multiple Species Distribution Models (SDMs) into an aggregative model yielded AUC values of 0.959 and 0.963 for the years 2019 and 2020 respectively. The True Skill Statistics (TSS) were 0.817 and 0.831 respectively. The overall habitat suitability for rice sheath blight in 2019 was slightly higher than that in 2020. This research provides a method for evaluating the habitat suitability of rice sheath blight, thereby offering support for the scientific prevention and control of crop diseases. © 2024 SPIE.

关键词： Ecosystems

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Crop classification method using multi-feature optimal selection combined with an ensemble learning model: A case study of the Sanjiang Plain 5

Crop classification method using multi-feature optimal selec...

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5th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2024

作者： Huang, Linsheng Lu, Qibao Huang, Wenjiang Li, Jinsong National Engineering Research Center for Agro-Ecological Big Data Analysis& Application Anhui University Hefei China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Science Beijing China Institute of Space Integrated Ground Network Anhui Co. Ltd. Hefei China

ISBN: (纸本)9781510681712

Crop classification is a crucial step in crop monitoring and yield estimation. Integrating multi-source and multi-temporal remote sensing images can effectively enhance the accuracy of crop classification, but it leads to an increase in the dimensionality of features. It is necessary to employ appropriate feature selection methods and classification algorithms to improve the precision and efficiency of the classification. Therefore, this study takes the Sanjiang Plain as an example, combines multi-temporal features of Sentinel-1/2 images, and conducts research on crop classification methods using the XGBoost ensemble learning algorithm through feature selection methods. The research results show that the feature selection combined with the XGBoost algorithm yields the optimal classification results, reducing the feature dimensions from 195 to 33, with spectral features being the most important. The Kappa coefficient reached 0.941, which is 0.024 and 0.059 higher than RF and SVM, respectively. The error in crop area extraction results compared to the official statistical data is less than 14%. This method can accurately and efficiently perform crop classification, providing a reference for crop classification in the Sanjiang Plain and similar regions. © 2024 SPIE.

关键词： Learning systems

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Impact of urban sprawl on land surface temperature in the Mashhad City,Iran:A deep learning and cloudbased remote sensing analysis

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Journal of Arid Land 2025年第3期17卷 285-303页

作者： Komeh ZINAT Hamzeh SAEID Memarian HADI Attarchi SARA LU Linlin Naboureh AMIN Alavipanah KAZEM SEYED Department of Remote Sensing and GIS(Geographic Information System) University of TehranTehran 14178-53933Iran Department of Watershed Management University of BirjandBirjand 97174-34765Iran Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China University of Chinese Academy of Sciences Beijing 100049China

The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development *** study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern *** the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to *** convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land *** obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear *** results revealed a 1.00°C–2.00°C increase in the LST across various land use *** variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use *** LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°*** developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level ***,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,*** conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.

关键词： convolutional neural network machine learning Google Earth Engine land use change random forest

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