In frequency division duplex model, the massive multiple-input multiple-output (MIMO) systems rely on feedback of channel state information (CSI) to perform precoding operations. This can increase the potential transmission gain of the system. The use of large scale antennas increases the feedback overhead of CSI exponentially. Therefore, CSI must be compressed for feedback. However, convolution based feedback methods lack long-range dependency modeling of the CSI, resulting in limited effects at high compression ratios. In this letter, we propose a novel neural network based on the convolutional transformer architecture to improve the performance of CSI compression and reconstruction at high compression rates. The experimental results show that the average accuracy of ours is improved by 5.39% over the state-of-the-art method at a high compression rate of 1/64. At the same time, the overall performance of the system has been improved.

Single-image dehazing is a challenging problem due to its ill-posed nature. Existing methods rely on a suboptimal two-step approach, where an intermediate product like a depth map is estimated, based on which the haze-free image is subsequently generated using an artificial prior formula. In this paper, we propose a light dual-task Neural Network called LDTNet that restores the haze-free image in one shot. We use transmission map estimation as an auxiliary task to assist the main task, haze removal, in feature extraction and to enhance the generalization of the network. In LDTNet, the haze-free image and the transmission map are produced simultaneously. As a result, the artificial prior is reduced to the smallest extent. Extensive experiments demonstrate that our algorithm achieves superior performance against the state-of-the-art methods on both synthetic and real-world images.

Left ventricular ejection fraction (LVEF) is a critical indicator for assessing cardiac function and diagnosing heart disease. LVEF can be derived by estimating the left ventricular volume from end-systolic and end-diastolic frames through echocardiography segmentation. However, current algorithms either focus primarily on single-frame segmentation, neglecting the temporal and spatial correlations between consecutive frames, or often fail to effectively address the inherent challenges posed by the low-contrast and fuzzy edges characteristic of echocardiography, thereby resulting in suboptimal segmentation outcomes. In this study, we propose a novel self-attention-based dual-task learning approach for automatic left ventricle segmentation. First, we introduce a multi-scale edge-attention U-Net to achieve supervised semantic segmentation of echocardiography. Second, an optical flow network is developed to capture the changes in the optical flow fields between frames in an unsupervised manner. These two tasks are then jointly trained using a temporal consistency mechanism to extract spatio-temporal features across frames. Experimental results demonstrate that our model outperforms existing segmentation methods. Our proposed method not only enhances the performance of semantic segmentation but also improves the consistency of segmentation between consecutive frames.

Chinese idiom cloze-style reading comprehension task is of great significance for improving the machine’s ability to understand Chinese idioms, which is one of the essential application requirements in advanced artificial intelligence. Existing methods suffer from an insufficient deep semantic understanding of the text. To solve this problem, this paper proposes a novelRetrospective Multi-granularity Fusion Network (RMFNet)for Chinese idiom cloze-style reading comprehension. Our RMFNet is equipped with two novel modules to model deeper contextual information of passage and Chinese idioms, respectively. First, we propose a novelMulti-granularity Passage Fusion (MgPF)module, which enhances the passage representation by integrating different semantic perspectives. Second, we propose aRetrospective Reading (Re\(^2\))module that implements a back-and-forth reading mechanism to concentrate on critical Chinese idioms, thereby generating an ultimate memory for the whole text. Notably, the intuition of the MgPF module and the Re \(^2\) module is based on human reading strategies in the real world. The strategies in these modules are similar to how humans perceive the text. Extensive experiments are conducted on Chinese benchmark datasets to evaluate the effectiveness and superiority of the proposed method. Our RMFNet achieves state-of-the-art performance and in-depth analysis verifies its capability for understanding the deep semantics of the text.