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学者姓名：张勇
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Abstract ：
This research aims to investigate the intricate dynamics between energy futures prices and both tropical and traditional commodities futures prices, with a particular focus on the critical role of oil futures prices in predicting the trajectory of agricultural futures prices. The study employs the Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC-MGARCH) model to analyse the interplay among oil and agricultural futures price series over 2008:01-2021:06. Additionally, it incorporates the innovative Spatio-temporal Information Recombination Hypergraph Neural Network (STIR-HGNN) model to highlight the differences in how energy futures prices predict tropical and traditional agricultural futures prices. The findings reveal numerous connections between oil prices and both tropical and traditional agricultural futures prices, underscoring the significant role of oil prices in forecasting agricultural futures price movements. The empirical insights from this study provide valuable guidance for futures market participants, encouraging them to use these findings to refine and optimize their market strategies, thus enhancing their ability to navigate and capitalize on the complexities of these interconnected markets.
Keyword ：
Oil futures markets Oil futures markets STIR-HGNN model STIR-HGNN model DCC-MGARCH model DCC-MGARCH model agricultural futures prices forecast agricultural futures prices forecast
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| GB/T 7714 | Zhang, Wei , Wu, Jiayi , Wang, Shun et al. Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets [J]. | APPLIED ECONOMICS LETTERS , 2024 . |
| MLA | Zhang, Wei et al. "Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets" . | APPLIED ECONOMICS LETTERS (2024) . |
| APA | Zhang, Wei , Wu, Jiayi , Wang, Shun , Zhang, Yong . Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets . | APPLIED ECONOMICS LETTERS , 2024 . |
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Abstract ：
Accurate prediction of origin-destination (OD) demand is critical for service providers to efficiently allocate limited resources in regions with high travel demands. However, OD distributions pose significant challenges, characterized by high sparsity, complex spatial correlations within regions or chains, and potential repetition due to the recurrence of similar semantic contexts. These challenges impede traditional graph-based approaches, which connect two vertices through an edge, from performing effectively in OD prediction. Thus, we present a novel multichannel hypergraph convolutional neural network (MC-HGCN) to overcome the above challenges. The model innovatively extracts distinctive features from the channels of inflows, outflows, and OD flows, to conquer the high sparsity in OD matrices. High-order spatial proximity within regions and OD chains are then modeled by the three adjacency hypergraphs constructed for the above three channels. In each adjacency hypergraph, multiple neighboring stations are treated as vertices, while multiple OD pairs constitute hyperedges. These structures are learned by hypergraph convolutional networks for latent spatial correlations. On this basis, a semantic hypergraph is created for the OD channel to model OD distributions lacking spatial proximity but sharing semantic correlations. It utilizes hyperedges to represent semantic correlations among OD pairs whose origins and destinations both possess similar point-of-interest (POI) functions, before learned by a hypergraph convolutional network (HGCN). Both spatial and semantic correlations intrinsic to OD flows are accordingly captured and embedded into a gated recurrent unit (GRU) to unveil hidden spatiotemporal dependencies among OD distributions. These embedded correlations are ultimately integrated through a multichannel fusion module to enhance the prediction of OD flows, even for minor ones. Our model is validated through experiments on three public datasets, demonstrating its robust performances across long and short time steps. Findings may contribute theoretical insights for practical applications, such as coordinating traffic scheduling or route planning.
Keyword ：
Convolutional neural networks Convolutional neural networks traffic prediction traffic prediction Predictive models Predictive models Deep learning Deep learning Semantics Semantics Logic gates Logic gates Correlation Correlation Feature extraction Feature extraction intelligent transportation system intelligent transportation system origindestination (OD) demand prediction origindestination (OD) demand prediction hypergraph convolutional network (HGCN) hypergraph convolutional network (HGCN)
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| GB/T 7714 | Wang, Ming , Zhang, Yong , Zhao, Xia et al. Traffic Origin-Destination Demand Prediction via Multichannel Hypergraph Convolutional Networks [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 , 11 (4) : 5496-5509 . |
| MLA | Wang, Ming et al. "Traffic Origin-Destination Demand Prediction via Multichannel Hypergraph Convolutional Networks" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 11 . 4 (2024) : 5496-5509 . |
| APA | Wang, Ming , Zhang, Yong , Zhao, Xia , Hu, Yongli , Yin, Baocai . Traffic Origin-Destination Demand Prediction via Multichannel Hypergraph Convolutional Networks . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 , 11 (4) , 5496-5509 . |
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Abstract ：
Recommender systems have become an indispensable tool in today's digital age, significantly enhancing user engagement on various online platforms by curating personalized item recommendations tailored to individual preferences. While the field has long been dominated by the collaborative filtering technique, which primarily leverages user-item interaction data, it often falls short in encapsulating the rich contextual intricacies and evolving dynamics inherent to these interactions. Recognizing this limitation, our research introduces the contextual semantic interaction graph embedding (CSI-GE) method. This advanced model incorporates a dynamic hop window within a multilayer graph convolutional network, ensuring a comprehensive extraction of both immediate and evolving contextual features. By amalgamating self-supervised contrastive learning, we achieve a refinement of user and item embeddings. Furthermore, our innovative variance-invariance-covariance (VIC) regularization-based loss function fortifies the robustness of these embeddings. Through rigorous testing, CSI-GE consistently outperformed contemporary methods, underscoring its superior accuracy and stability.
Keyword ：
recommender systems recommender systems hop window hop window Data models Data models Vectors Vectors Semantics Semantics Robustness Robustness graph neural networks (GNNs) graph neural networks (GNNs) Collaboration Collaboration Collaborative filtering Collaborative filtering Recommender systems Recommender systems Nonhomogeneous media Nonhomogeneous media self-supervised contrastive learning self-supervised contrastive learning
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| GB/T 7714 | Zhao, Shiyu , Zhang, Yong , Li, Mengran et al. Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 . |
| MLA | Zhao, Shiyu et al. "Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2024) . |
| APA | Zhao, Shiyu , Zhang, Yong , Li, Mengran , Piao, Xinglin , Yin, Baocai . Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 . |
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Abstract ：
Traffic prediction plays a significant role in Intelligent Transportation Systems (ITS). Although many datasets have been introduced to support the study of traffic prediction, most of them only provide time-series traffic data. However, urban transportation systems are always susceptible to various factors, including unusual weather and traffic accidents. Therefore, relying solely on historical data for traffic prediction greatly limits the accuracy of the prediction. In this paper, we introduce Beijing Text-Traffic (BjTT), a large-scale multimodal dataset for traffic prediction. BjTT comprises over 32,000 time-series traffic records, capturing velocity and congestion levels on more than 1,200 roads within the 5th ring area of Beijing. Meanwhile, each piece of traffic data is coupled with a text describing the traffic system (including time, location, and events). We detail the data collection and processing procedures and present a statistical analysis of the BjTT dataset. Furthermore, we conduct comprehensive experiments on the dataset with state-of-the-art traffic prediction methods and text-guided generative models, which reveal the unique characteristics of the BjTT. The dataset is available at https://github.com/ChyaZhang/BjTT.
Keyword ：
Task analysis Task analysis Data collection Data collection Transportation Transportation Blogs Blogs Traffic prediction Traffic prediction Roads Roads Social networking (online) Social networking (online) large-scale large-scale new dataset new dataset Meteorology Meteorology
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| GB/T 7714 | Zhang, Chengyang , Zhang, Yong , Shao, Qitan et al. BjTT: A Large-Scale Multimodal Dataset for Traffic Prediction [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
| MLA | Zhang, Chengyang et al. "BjTT: A Large-Scale Multimodal Dataset for Traffic Prediction" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024) . |
| APA | Zhang, Chengyang , Zhang, Yong , Shao, Qitan , Feng, Jiangtao , Li, Bo , Lv, Yisheng et al. BjTT: A Large-Scale Multimodal Dataset for Traffic Prediction . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
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Abstract ：
Graph convolutional networks (GCNs) are widely used in social computation such as urban traffic prediction. However, when faced with city-level forecasting challenges, the graph-based deep learning methods struggle to process large-scale multivariate data effectively. To address the challenges of limited scalability, a traffic prediction framework based on a hypergraph message passing network (HMSG) is proposed in this article. The model represents the urban transportation network with hypergraph, where nodes denote transportation hubs and hyperedges represent their relationship at geographical and feature level. Compared with pairwise edges, hyperedges are more scalable and flexible, providing a more descriptive representation of traffic information. The HMSG algorithm updates node and hyperedge features in two steps, facilitating effective and efficient integration of hidden spatial features across layers. The proposed framework is evaluated on large-scale historical datasets and demonstrates its completion of city-scale traffic prediction tasks. The results also show that it matches the accuracy of existing traffic prediction methods on small-scale datasets. This validates the potential of the traffic prediction model based on the HMSG algorithm for intelligent transportation applications.
Keyword ：
Transportation Transportation Message passing Message passing traffic prediction traffic prediction Convolution Convolution Vectors Vectors Urban areas Urban areas message passing message passing Feature extraction Feature extraction hypergraph learning hypergraph learning Predictive models Predictive models Graph neural network (GNN) Graph neural network (GNN)
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| GB/T 7714 | Wang, Jingcheng , Zhang, Yong , Hu, Yongli et al. Large-Scale Traffic Prediction With Hierarchical Hypergraph Message Passing Networks [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 . |
| MLA | Wang, Jingcheng et al. "Large-Scale Traffic Prediction With Hierarchical Hypergraph Message Passing Networks" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2024) . |
| APA | Wang, Jingcheng , Zhang, Yong , Hu, Yongli , Yin, Baocai . Large-Scale Traffic Prediction With Hierarchical Hypergraph Message Passing Networks . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 . |
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Abstract ：
Trajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeling capability for trajectory data. However, deep learning-based models face challenges in effectively utilizing scene information and accurately modeling agent interactions, largely due to the complexity and uncertainty of real-world scenarios. To mitigate these challenges, this study presents a novel multiagent trajectory prediction model, termed the global-local scene-enhanced social interaction graph network (GLSESIGN), which incorporates two pivotal strategies: global-local scene information utilization and a social adaptive attention graph network. The model hierarchically learns scene information relevant to multiple intelligent agents, thereby enhancing the understanding of complex scenes. Additionally, it adaptively captures social interactions, improving adaptability to diverse interaction patterns through sparse graph structures. This model not only improves the understanding of complex scenes but also accurately predicts future trajectories of multiple intelligent agents by flexibly modeling intricate interactions. Experimental validation on public datasets substantiates the efficacy of the proposed model. This research offers a novel model to address the complexity and uncertainty in multiagent trajectory prediction, providing more accurate predictive support in practical application scenarios. Comparison of scene information learning approaches. image
Keyword ：
trajectory prediction trajectory prediction scene-aware information integration scene-aware information integration multiagent systems multiagent systems
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| GB/T 7714 | Lin, Xuanqi , Zhang, Yong , Wang, Shun et al. Multiagent trajectory prediction with global-local scene-enhanced social interaction graph network [J]. | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2024 , 35 (3) . |
| MLA | Lin, Xuanqi et al. "Multiagent trajectory prediction with global-local scene-enhanced social interaction graph network" . | COMPUTER ANIMATION AND VIRTUAL WORLDS 35 . 3 (2024) . |
| APA | Lin, Xuanqi , Zhang, Yong , Wang, Shun , Piao, Xinglin , Yin, Baocai . Multiagent trajectory prediction with global-local scene-enhanced social interaction graph network . | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2024 , 35 (3) . |
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Abstract ：
Precipitation nowcasting within 2 h is an important and hard issue in the weather research area. Benefiting from the outstanding nonlinear relationship modeling capability, methods based on deep learning (DL) have achieved significant success in the task of precipitation nowcasting compared to the others. However, existing DL-based methods always disregard the intricate high-order correlations and lack substantial connections with the evolution of the precipitation system, which would lead to blurred forecasts and implausible predictions. To address these issues, we proposed a new Precipitation Nowcasting Network within a 2-h model based on the Hypergraph Neural Network (PN-HGNN). In this work, a Hypergraph Neural Network is first adopted for extracting spatiotemporal dynamic echo features. Second, regulation evolution is in charge of capturing the memory features to guide the extrapolation. Finally, we design a dual branch module to extrapolate the radar echoes. The proposed model has been assessed on the dataset HKO-7. The experimental results demonstrate that PN-HGNN achieved better prediction performance than the six representative echo extrapolation models.
Keyword ：
Extrapolation Extrapolation Predictive models Predictive models precipitation nowcasting precipitation nowcasting Task analysis Task analysis Neural networks Neural networks Meteorology Meteorology Radar Radar Hypergraph neural network (HGNN) Hypergraph neural network (HGNN) Precipitation Precipitation MotionRNN MotionRNN radar echo extrapolation radar echo extrapolation
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| GB/T 7714 | Sun, Xiaoni , Zhang, Yong , Piao, Xinglin et al. PN-HGNN: Precipitation Nowcasting Network Via Hypergraph Neural Networks [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Sun, Xiaoni et al. "PN-HGNN: Precipitation Nowcasting Network Via Hypergraph Neural Networks" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Sun, Xiaoni , Zhang, Yong , Piao, Xinglin , Wu, Jiayi , Jing, Guodong , Yin, Baocai . PN-HGNN: Precipitation Nowcasting Network Via Hypergraph Neural Networks . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Abstract ：
Missing data in time series is a pervasive problem that serves as obstacles for subsequent traffic data analysis. Consequently, extensive research works have been conducted on traffic missing data imputation tasks. The state-of-the-art traffic data imputation models are mostly based on recurrent neural networks. However, these methods belong to autoregressive models which are highly susceptible to error propagation. The attention-based methods are non-autoregressive models that can avoid compounding errors and help achieve better imputation quality. Moreover, the attention-based methods in now widely applied and have achieved remarkable results, whereas their application on traffic data imputation is still limited. Thus, this paper proposes Self-Attention Graph Convolution Imputation Network (SAGCIN) for spatio-temporal traffic data. To ensure the accuracy of data imputation, it is necessary to fully capture the spatio-temporal contextual information of traffic data to impute missing values. To this end, the SAGCIN model incorporates self-attention mechanism with diffusion graph convolution network. The SAGCIN model consists of two spatio-temporal blocks with a spatio-temporal encoder and an imputation decoder. The encoder learns spatio-temporal representations specialized for traffic data imputation tasks. Based on the learned representation, the decoder performs two stages of imputation operator for missing data. A joint-optimization training approach of imputation and reconstruction is introduced for SAGCIN to perform missing value imputation for traffic data. Empirical results demonstrate that SAGCIN model outperforms state-of-the-art methods in imputation tasks on relevant real-world benchmarks.
Keyword ：
diagonally-masked self-attention diagonally-masked self-attention missing values missing values diffusion graph convolution diffusion graph convolution imputation model imputation model Traffic data Traffic data
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| GB/T 7714 | Wei, Xiulan , Zhang, Yong , Wang, Shaofan et al. Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
| MLA | Wei, Xiulan et al. "Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024) . |
| APA | Wei, Xiulan , Zhang, Yong , Wang, Shaofan , Zhao, Xia , Hu, Yongli , Yin, Baocai . Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
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Abstract ：
Frontal person images contain the richest detailed features of humans, which can effectively assist in behavioral recognition, virtual dress fitting and other applications. While many remarkable networks are devoted to the person image generation task, most of them need accurate target poses as the network inputs. However, the target pose annotation is difficult and time-consuming. In this work, we proposed a first frontal person image generation network based on the proposed anchor pose set and the generative adversarial network. Specifically, our method first classify a rough frontal pose to the input human image based on the proposed anchor pose set, and regress all key points of the rough frontal pose to estimate an accurate frontal pose. Then, we consider the estimated frontal pose as the target pose, and construct a two-stream generator based on the generative adversarial network to update the person's shape and appearance feature in a crossing way and generate a realistic frontal person image. Experiments on the challenging CMU Panoptic dataset show that our method can generate realistic frontal images from arbitrary-view human images.
Keyword ：
arbitrary-view images arbitrary-view images frontal person image generation frontal person image generation frontal pose estimation frontal pose estimation deep learning deep learning generative adversarial networks generative adversarial networks
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| GB/T 7714 | Zhang, Yong , Zhang, Yuqing , Chen, Lufei et al. Frontal person image generation based on arbitrary-view human images [J]. | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2024 , 35 (4) . |
| MLA | Zhang, Yong et al. "Frontal person image generation based on arbitrary-view human images" . | COMPUTER ANIMATION AND VIRTUAL WORLDS 35 . 4 (2024) . |
| APA | Zhang, Yong , Zhang, Yuqing , Chen, Lufei , Yin, Baocai , Sun, Yongliang . Frontal person image generation based on arbitrary-view human images . | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2024 , 35 (4) . |
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Abstract ：
Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model's perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.
Keyword ：
histology image histology image attention mechanism attention mechanism gene expression prediction gene expression prediction spatial transcriptomics spatial transcriptomics hypergraph hypergraph gradient enhancement gradient enhancement
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| GB/T 7714 | Li, Bo , Zhang, Yong , Wang, Qing et al. Gene expression prediction from histology images via hypergraph neural networks [J]. | BRIEFINGS IN BIOINFORMATICS , 2024 , 25 (6) . |
| MLA | Li, Bo et al. "Gene expression prediction from histology images via hypergraph neural networks" . | BRIEFINGS IN BIOINFORMATICS 25 . 6 (2024) . |
| APA | Li, Bo , Zhang, Yong , Wang, Qing , Zhang, Chengyang , Li, Mengran , Wang, Guangyu et al. Gene expression prediction from histology images via hypergraph neural networks . | BRIEFINGS IN BIOINFORMATICS , 2024 , 25 (6) . |
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