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Traffic flow forecasting is the foundation of intelligent transportation systems. Accurate traffic forecasting is crucial for intelligent traffic management and urban development. However, achieving highly accurate traffic flow prediction is challenging due to road networks' complex dynamic spatial and temporal dependencies. Previous work using predefined static adjacency matrices in graph convolutional networks needs to be revised to reflect the dynamic spatial dependencies in the traffic system. In addition, most current methods ignore the hidden dynamic spatial–temporal correlations between road network nodes as they evolve. We propose a spatial–temporal dynamic graph differential equation network (ST-DGDE) for traffic prediction to address the above problems. First, the model captures the dynamic changes between spatial nodes over time through a dynamic graph learning network. Then, dynamic graph differential equations (DGDE) are used to learn the spatial–temporal dynamic relationships in the global space that change continuously over time. Finally, static adjacency matrices are constructed by static node embedding. The generated dynamic and predefined static graphs are fused and input into a gated temporal causal convolutional network to jointly capture the fixed long-term spatial association patterns and achieve a global receiver domain that facilitates long-term prediction. Experiments of our model on two natural traffic flow datasets show that ST-DGDE outperforms other baselines. [ABSTRACT FROM AUTHOR]

The significance of vehicle-to everything (V2X) communication in ensuring road safety is undeniable. In addition, real-time vehicle communication requires an ample amount of spectrum resources. However, the existing spectrum resources are seriously scarce, and the utilization rate is not high, leading to high delays in V2X communication and other unfavorable factors in the case of fast-moving vehicles, bringing great safety risks to driving. Load balancing is one of the most effective methods to improve spectrum utilization. However, the existing load balancing schemes merely focus on static conditions, with a lack of joint scheduling schemes, which cannot support the communication framework of dynamic V2X. To address both of these issues, in this paper, a new communication method is proposed. In addition, this paper studies a joint load balancing scheme of mobility vehicle-to-vehicle (V2V) and user association under incomplete channel state information (CSI) and realizes the load balancing management of a cross-cell V2X network. An algorithm combining power control and resource allocation mode selection is proposed. In particular, according to different coverage areas, different allocation algorithms are adopted to maximize the overall system efficiency. The simulation results show that this strategy can maintain low latency and effectively improve the system energy efficiency of vehicle users. [ABSTRACT FROM AUTHOR]

Few-shot relation extraction aims to identify and extract semantic relations between entity pairs using only a small number of annotated instances. Many recently proposed prototype-based methods have shown excellent performance. However, existing prototype-based methods ignore the hidden inter-instance interaction information between the support and query sets, leading to unreliable prototypes. In addition, the current optimization of the prototypical network only relies on cross-entropy loss. It is only concerned with the accuracy of the predicted probability for the correct label, ignoring the differences of other non-correct labels, which cannot account for relation discretization in semantic space. This paper proposes an attentional network of interaction information to obtain a more reliable relation prototype. Firstly, an inter-instance interaction information attention module is designed to mitigate prototype unreliability through interaction information between the support set and query set instances, utilizing category information hidden in the query set. Secondly, the similarity scalar, which is defined by the mixed features of the prototype and the relation and is added to the focal loss to improve the attention of hard examples. We conducted extensive experiments on two standard datasets and demonstrated the validity of our proposed model. [ABSTRACT FROM AUTHOR]