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Traffic flow prediction is crucial for intelligent transportation system, such as traffic management, congestion alleviation and public risk assessment. Recently, attention mechanism and deep neural networks are utilized to capture traffic dependencies. However, two challenges have yet to be well addressed: (i) previous works overlook the global dependencies across different regions; (ii) how to integrate spatial and temporal information aggregation with latent channel-aware semantics. To tackle these issues, we propose a deep spatial–temporal convolutional graph attention network for citywide traffic flow prediction. We first apply the multi-resolution transformer network to capture traffic dependencies among different regions with the encoding of multi-level periodicity. Spatial dependencies are captured by the attentive graph neural networks followed by convolutional networks from local view to global view. We further propose to inject spatial contextual signals into our framework with the designed channel-aware recalibration residual network, which effectively endows model with the capability of mapping spatial–temporal data patterns into different representation subspaces of latent semantics. The extensive experiments on four real-world datasets demonstrate at least 5% performance gain of our framework by comparing with 19 state-of-the-art traffic prediction methods. [ABSTRACT FROM AUTHOR]

• New CNN design with complex-valued operations. • Practical complex-valued CNN for image denoising. • Proposed CNN is effective for handling unseen noises. • State-of-the-art results are achieved. While complex-valued transforms have been widely used in image processing and have their deep connections to biological vision systems, complex-valued convolutional neural networks (CNNs) have not seen their applications in image recovery. This paper aims at investigating the potentials of complex-valued CNNs for image denoising. A CNN is developed for image denoising with its key mathematical operations defined in the complex number field to exploit the merits of complex-valued operations, including the compactness of convolution given by the tensor product of 1D complex-valued filters, the nonlinear activation on phase, and the noise robustness of residual blocks. The experimental results show that, the proposed complex-valued denoising CNN performs competitively against existing state-of-the-art real-valued denoising CNNs, with better robustness to possible inconsistencies of noise models between training samples and test images. The results also suggest that complex-valued CNNs provide another promising deep-learning-based approach to image denoising and other image recovery tasks. [ABSTRACT FROM AUTHOR]