Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction

In recent years, deep learning has increasingly gained attention in the field of traffic prediction. Existing traffic prediction models often rely on GCNs or attention mechanisms with O(N^2) complexity to dynamically extract traffic node features, which lack efficiency and are not lightweight. Additionally, these models typically only utilize historical data for prediction, without considering the impact of the target information on the prediction. To address these issues, we propose a Pattern-Matching Dynamic Memory Network (PM-DMNet). PM-DMNet employs a novel dynamic memory network to capture traffic pattern features with only O(N) complexity, significantly reducing computational overhead while achieving excellent performance. The PM-DMNet also introduces two prediction methods: Recursive Multi-step Prediction (RMP) and Parallel Multi-step Prediction (PMP), which leverage the time features of the prediction targets to assist in the forecasting process. Furthermore, a transfer attention mechanism is integrated into PMP, transforming historical data features to better align with the predicted target states, thereby capturing trend changes more accurately and reducing errors. Extensive experiments demonstrate the superiority of the proposed model over existing benchmarks. The source codes are available at: https://github.com/wengwenchao123/PM-DMNet.

TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method

Recently, diffusion models have gained significant attention as a novel set of deep learning-based generative methods. These models attempt to sample data from a Gaussian distribution that adheres to a target distribution, and have been successfully adapted to the reconstruction of MRI data. However, as an unconditional generative model, the diffusion model typically disrupts image coordination because of the consistent projection of data introduced by conditional bootstrap. This often results in image fragmentation and incoherence. Furthermore, the inherent limitations of the diffusion model often lead to excessive smoothing of the generated images. In the same vein, some deep learning-based models often suffer from poor generalization performance, meaning their effectiveness is greatly affected by different acceleration factors. To address these challenges, we propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training. We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model while mitigating the over-smoothing issue to a certain extent. During the image generation sampling process, we employ a novel TCKG module and a Coarse-to-Fine sampling scheme. These additions aim to harmonize image texture, expedite the sampling process, while achieving data consistency. Our source code is available at https://github.com/JustlfC03/TC-DiffRecon.