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Traffic flow forecasting is the cornerstone of many applications in transportation which has a great importance in smart city system.The difficulty of this task is how to effectively model the temporal and spatial dependence.Existing methods usually use GNN to model temporal correlation and CNN or RNN to model temporal correlation.When modeling the spatial correlation,only the adjacency matrix is applied to model local relationships while ignoring global spatial information.However,there are some roads in the entire road network whose surrounding structures are similar,and these roads carry similar functions in the road network.Therefore,the characteristics of these similar roads can also be used as the basis for traffic prediction.This paper proposes a traffic flow forecasting model based on multi-adjacent matrix and multi-head attention mechanism.It includes:1)the node2vec algorithm is applied to calculate the vector representation of the road in road network,and the similarity matrix is calculated through the threshold for graph convolution operation to extract global spatial information;2)the multi-channel self-attention mechanism is used to mine the spatial and temporal features of the model.Experiments on public datasets PEMS04 and PEMS08 demonstrate the proposed model’s effectiveness.Its accuracy is improved compared with the mainstream models.

Edge bundling is an effective method to reduce the visual clutter caused by the visualization of the node-link diagram with a large number of complex connections.Generally,the edge bundling based on spatial proximity will lead to independent edge ambiguity and give users a wrong perception.However,focusing only on topological structure of graphs cannot reduce visual clutter caused by dense connections to a large extent.The method based on edge path can control and bundle the edges by using the original nodes in the graph,avoid independent edge ambiguity,and show the advanced mode of data.Therefore,an edge bundling method based on homologous control points is proposed to improve the edge path method.Based on the topology structure information of the graph,the method can calculate homologous control points and select edge control points by using the shortest path algorithm.Then the degree of edge aggregation is optimized with the thinking of gradation.Finally,the edges are smoothed through Bezier curves and colored according to the direction of the edges.The edge bundling method based on homologous control points is used in the US migration dataset and the Chinese railway line dataset.Experimental results show that this method has a good effect on improving the problem of over-bundling.Compared with the original method,this method retains more local data details,balances the bundling degree between the whole and local edges,and can be effectively used for the visualization of complex connected graphs.

Question answering over knowledge graph(KG-QA) systems map the natural language question to the 〈subject,predicate,object〉 triple in the knowledge graph(KG) by semantic analysis of the given question,and infer the triple to get the answer of the question.Due to the diversity of natural languages,a question may be expressed in multiple forms but the triples in KGs are structured data in a standard form.It is challenging to map questions to triples in KGs.This paper proposes a novel Key-Value relational memory network,starting from the perspective of KGs,and focusing on the relationship between the candidate answer knowledge and the relationship between the knowledge in KGs and the question representations.In addition,the attention mechanism is applied in the proposed model,so that it has better interpretability than other baseline models.We evaluate the method on WebQuestions benchmark.Experiment results show that,compared with the best methods based on information extraction,the F1 value of the proposed method increases by 5.9% and is slightly higher than that of the optimal methods based on semantic analysis,which verifies the effectiveness of the proposed method.

The fundamental understanding of the subtle interactions between molecules and plasmons is of great significance for the development of plasmon-enhanced spectroscopy (PES) techniques with ultrahigh sensitivity. However, this information has been elusive due to the complex mechanisms and difficulty in reliably constructing and precisely controlling interactions in well-defined plasmonic systems. Herein, the interactions in plasmonic nanocavities of film-coupled metallic nanocubes (NCs) are investigated. Through engineering the spacer layer, molecule-plasmon interactions were precisely controlled and resolved within 2 nm. Efficient energy exchange interactions between the NCs and the surface within the 1-2 nm range are demonstrated. Additionally, optical dressed molecular excited states with a huge Lamb shift of ≈7 meV at the single-molecule (SM) level were observed. This work provides a basis for understanding the underlying molecule-plasmon interaction, paving the way for fully manipulating light-matter interactions at the nanoscale., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)