Accelerating the MRI acquisition process is always a key issue in modern medical practice, and great efforts have been devoted to fast MR imaging. Among them, multi-contrast MR imaging is a promising and effective solution that utilizes and combines information from different contrasts. However, existing methods may ignore the importance of the high-frequency priors among different contrasts. Moreover, they may lack an efficient method to fully utilize the information from the reference contrast. In this paper, we propose a lightweight and accurate High-frequency Modulated Transformer (HFMT) for multi-contrast MRI super-resolution. The key ideas of HFMT are high-frequency prior enhancement and its fusion with global features. Specifically, we employ an enhancement module to enhance and amplify the high-frequency priors in the reference and target modalities. In addition, we utilize the Rectangle Window Transformer Block (RWTB) to capture global information in the target contrast. Meanwhile, we propose a novel cross-attention mechanism to fuse the high-frequency enhanced features with the global features sequentially, which assists the network in recovering clear texture details from the low-resolution inputs. Extensive experiments show that our proposed method can reconstruct high-quality images with fewer parameters and faster inference time.

Predicting block popularity is of crucial importance for data placement in multi-tiered multimedia storage systems. Traditional methods, such as least recently used and exponential smoothing, are commonly employed to predict future block access frequencies and fail to achieve good performance for complex and changing access patterns. Recently, deep neural networks have brought great success to pattern recognition and prediction, which motivates us to introduce deep learning to solve the problem of block popularity prediction. In this paper, we first analyze and verify the temporal and spatial correlations among the multimedia I/O traces. Then, we design a multi-dimension feature to capture such correlations, which serves as the input of the designed deep neural network. A spatial-temporal-sequential neural network (STSNN) and its variants that capture the locality information, time dependency information, and block sequential information are proposed to predict the block popularity. We systematically evaluate our STSNN models against six baseline models from three different categories, i.e., heuristic methods, regression methods and neural network-based methods. Experiment results show that our proposed STSNN models are very promising for predicting block access frequencies under some of Huawei and Microsoft datasets and particularly achieve 2-6 times better performance compared with the baselines in terms of the I/O hit ratio, I/O recall rate and I/O prediction ratio under the Microsoft 64 MB-block dataset.