Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and links in a graph. This is more effective at preserving semantic knowledge when representing data interactions in real-world graph structures. Unfortunately, most heterogeneous graph neural networks tend to transform heterogeneous graphs into homogeneous graphs when using meta-paths for representation learning. This paper therefore presents a novel motif-based hierarchical heterogeneous graph attention network algorithm, MBHAN, that addresses this problem by incorporating a hierarchical dual attention mechanism at the node-level and motif-level. Node-level attention aims to learn the importance between a node and its neighboring nodes within its corresponding motif. Motif-level attention is capable of learning the importance of different motifs in the heterogeneous graph. In view of the different vector space features of different types of nodes in heterogeneous graphs, MBHAN also aggregates the features of different types of nodes, so that they can jointly participate in downstream tasks after passing through segregated independent shallow neural networks. MBHAN’s superior network representation learning capability has been validated by extensive experiments on two real-world datasets.

This study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the most excellent image density estimators. However, the local nature of convolutional networks causes PixelCNN to concentrate only on nearby information, neglecting important information at greater distances. Although deepening the network can broaden the receptive field, excessive depth can compromise model stability, leading to issues like gradient degradation. To address these challenges, this study combines causal attention modules with residual connections, proposing a residual causal attention module to enhance the PixelCNN model. This innovation not only resolves convergence problems arising from network deepening but also expands the receptive field. It facilitates the extraction of crucial image details while capturing the global structural information of the image, significantly enhancing the modeling capabilities for pulsar data. In the experiments, the model is trained and validated using the HTRU1 dataset. This study compares the average negative log-likelihood score with baseline models like the GMM, STM, and PixelCNN. The results demonstrate the superior performance of our model over other models. Finally, this study introduces the practical compression encoding process by combining the proposed model with arithmetic coding.

Due to the fact that client data do not need to leave the local area, a distributed machine learning framework can aggregate training from several clients while preserving data privacy. In this essay, the development of CAI both domestically and internationally is reviewed and summarized, and the current state of CAI is examined. Communication analysis has so far been a key academic and theoretical area in federated learning, and some theoretical contributions have become the crucial theoretical foundations for understanding, defending, and guiding various human social behaviors. The major objective of knowledge distillation based on model responses is to provide students the ability to rapidly replicate the teacher’s model’s output. The experimental results demonstrate that the optimized Smith Regan model adopts the “Smith Logan” teaching design model, selects the courseware structure and record preservation as the teaching content in the fundamental CAI courseware design, and optimizes the teaching design from the perspectives of learning environment analysis, learner characteristics analysis, etc. Based on this, the model’s accuracy and robustness are increased by 7.34%.

The study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the most excellent image density estimators. However, the local nature of convolutional networks causes PixelCNN to concentrate only on nearby information, neglecting important information at greater distances. Although deepening the network can broaden the receptive field, excessive depth can compromise model stability, leading to issues like gradient degradation. To address these challenges, the study combines causal attention modules with residual connections, proposing the Causal Residual Attention Module to enhance the PixelCNN model. This innovation not only resolves convergence problems arising from network deepening but also widens the receptive field. It effectively utilizes global features, particularly capturing vertically correlated features prominently present in subgraphs of candidates. This significantly enhances its capability to model pulsar data.In the experiments, the model is trained and validated using the HTRU1 dataset. The study compares the average negative log-likelihood score with baseline models like GMM, STM, and PixelCNN. The results demonstrate the superior performance of the our model over other models. Finally, the study introduces the practical compression encoding process by combining the proposed model with arithmetic coding.

To enhance pulsar candidate recognition performance and improve model generalization, this paper proposes the cross-feature hybrid associative prior network (CFHAPNet). CFHAPNet incorporates a novel architecture and strategies to integrate multi-class heterogeneous feature subimages from each candidate into multi-channel data processing. By implementing cross-attention mechanisms and other enhancements for multi-view feature interactions, the model significantly strengthens its ability to capture fine-grained image texture details and weak prior semantic information. Through comparative analysis of feature weight similarity between subimages and average fusion weights, CFHAPNet efficiently identifies and filters genuine pulsar signals from candidate images collected across astronomical observatories. Additionally, refinements to the original loss function enhance convergence, further improving recognition accuracy and stability. To validate CFHAPNet’s efficacy, we compare its performance against several state-of-the-art methods on diverse datasets. The results demonstrate that under similar data scales, our approach achieves superior recognition performance. Notably, on the FAST dataset, the accuracy, recall, and F1-score reach 97.5%, 98.4%, and 98.0%, respectively. Ablation studies further reveal that the proposed enhancements improve overall recognition performance by approximately 5.6% compared to the original architecture, achieving an optimal balance between recognition precision and computational efficiency. These improvements make CFHAPNet a strong candidate for future large-scale pulsar surveys using new sensor systems.