Explainable recommendation aims to offer high-quality explanations for recommendation results to help users in understanding and making decisions. Benefiting from the intuitive and easy-to-understand nature of natural language, existing work primarily concentrates on text-based explainable recommendation methods with the objective of generating explanatory text. However, most of these methods only focus on textual information while neglecting the multimodal information that can provide more comprehensive preferences and characteristics. To address this issue, we propose a novel Multimodal Enhanced Explainable Recommendation (MEER) model. Specifically, we integrate collaborative features and multimodal features into a multitask learning framework, jointly optimizing the tasks of rating prediction and explanation generation. Firstly, the pre-trained model CLIP is utilized to extract multimodal features. Subsequently, we adopt an attention mechanism to fuse the collaborative and multimodal features. To fully model the cross-modal feature correlations, we introduce a contrastive learning paradigm to constrain the fused features. Furthermore, we developed a personalized multimodal Transformer decoder, which is capable of comprehensively modeling both collaborative information and multimodal information. Experimental results on three real-world datasets demonstrate that our proposed method has achieved competitive performance in both recommendation accuracy and explanation quality.

Soft artificial muscle actuators have gained attention in robotics for their remote control, fast response, and high compliance. However, replicating the intricate and efficient motions of natural muscles remains a challenge. Existing designs often lack the hierarchical and anisotropic properties of muscle sarcomeres, limiting their ability to achieve biomimetic movements. We developed a novel Biomimetic Magnetic Artificial Actuator (BMAA) inspired by muscle sarcomeres. Using a soft magnetic composite material arranged in a hierarchical structure, the actuator mimics the arrangement of actin and myosin filaments. External magnetic fields enable precise control of contraction and relaxation, emulating natural muscle motion. The driver can achieve the motion performance of muscle like motion characteristics, and its driving ability is verified by reptile experiment and elbow experiment. The actuator demonstrates significant deformation, fast response, and excellent controllability, enabling complex and precise movements. This research advances the development of biomimetic soft actuators, offering potential applications in soft robotics, biomedical devices, and artificial muscles, and paving the way for more versatile and intelligent machines.

Zero-shot node classification is a vital task in the field of graph data processing, aiming to identify nodes of classes unseen during the training process. Prediction bias is one of the primary challenges in zero-shot node classification, referring to the model's propensity to misclassify nodes of unseen classes as seen classes. However, most methods introduce external knowledge to mitigate the bias, inadequately leveraging the inherent cluster information within the unlabeled nodes. To address this issue, we employ spectral analysis coupled with learnable class prototypes to discover the implicit cluster structures within the graph, providing a more comprehensive understanding of classes. In this paper, we propose a spectral approach for zero-shot node classification (SpeAr). Specifically, we establish an approximate relationship between minimizing the spectral contrastive loss and performing spectral decomposition on the graph, thereby enabling effective node characterization through loss minimization. Subsequently, the class prototypes are iteratively refined based on the learned node representations, initialized with the semantic vectors. Finally, extensive experiments verify the effectiveness of the SpeAr, which can further alleviate the bias problem.