This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach especially designed for ECG is introduced, which can jointly represent the morphology and rhythm of the heartbeat and alleviate the influence of inter-patient variation through baseline correction. The symbolic representation of the heartbeat is used by a multi-perspective convolutional neural network (MPCNN) to learn features automatically and classify the heartbeat. We evaluate our method for the detection of the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) on MIT-BIH arrhythmia dataset. Compared with the state-of-the-art methods based on manual features or deep learning models, our method shows superior performance: the overall accuracy of 96.4%, F1 scores for SVEB and VEB of 76.6% and 89.7%, respectively. The ablation study on our method validates the effectiveness of the proposed symbolization approach and joint representation architecture, which can help the deep learning model to learn more general features and improve the ability of generalization for unseen patients. Because our method achieves a competitive inter-patient heartbeat classification performance without complex handcrafted features or the intervention of the human expert, it can also be adjusted to handle various other tasks relative to ECG classification.

Accurate prediction of sea surface temperature (SST), a crucial indicator of global climate and ecosystem changes, holds significant economic and social benefits. Deep learning has shown preliminary success in modeling the dynamic spatial-temporal dependencies within SST signals, yet it remains challenging to obtain precise SSTs due to the inherent variabilities across multiple temporal and spatial scales, driven by distinct physical processes. In this paper, we propose a novel multi-scale spatio-temporal attention network, named MUSTAN, tailored for the SST prediction problem. MUSTAN achieves multi-scale fusion through a progressive scale expansion paradigm, where sub-scale representations are iteratively merged with its counterpart scale units, enabling the propagation of fine-scale SST changes across broader scales. For each scale, MUSTAN introduces temporal attention to characterize dynamic SST patterns in different ocean regions, and spatial attention to capture intricate SST evolution interplay among these regions. Extensive experiments conducted on datasets from the Bohai Sea, Yellow Sea, and South China Sea consistently validate the effectiveness and superiority of our design, outperforming the state-of-the-art methods on SST prediction tasks.

Remote sensing text-image retrieval aims to retrieve valuable information from diverse and complex remote sensing data, attracting significant attention. However, the performance is limited due to the complexity of scenes and their substantial content differences from natural domain images. To address these issues, we propose a simple but effective Text-guided Knowledge Transfer (TGKT) method for remote sensing image-text retrieval. TGKT utilizes CLIP to encode remote sensing data and transfer its rich semantic knowledge from natural to remote sensing domain. The textual information without significant domain differences is employed to bridge the semantic gap between these two domains, thereby enhancing image features. The extensive experimental results on both RSICD and RSITMD datasets demonstrate the effectiveness of our method.