Structurally Enhanced Prototype Alignment for Unsupervised Cross-Domain Node Classification Introduction With the advancement of modern information technology, Graph Neural Networks (GNNs) have demonstrated significant success in handling complex network node classification tasks. However, one key challenge is the need for a large amount of high-qu...

Semantic segmentation aims to predict category labels for each pixel in an image (Liu et al., 2021; Wang et al., 2021) and is widely used in scene understanding, medical image analysis, autonomous driving, geographic information systems, and augmented reality (Strudel et al., 2021; Sun et al., 2023). Although the development of deep neural networks...

Application of a Prototype-Based Sample Weighted Distillation Unified Framework in Missing Modality Sentiment Analysis Research Background Sentiment analysis is a significant field in Natural Language Processing (NLP). With the development of social media platforms, people increasingly tend to express their emotions through short video clips. This ...

Background Introduction Network Pruning is a crucial technique for designing efficient Convolutional Neural Network (CNN) models. By reducing memory footprint and computational demands, while maintaining or improving overall performance, it makes deploying CNNs on resource-constrained devices (such as mobile phones or embedded systems) feasible. Th...

With the rapid development of deep learning technology, the importance of data mining and artificial intelligence training techniques in practical applications has become increasingly prominent. Especially in the field of multivariate time series anomaly detection, existing methods, though excellent, still face significant issues when dealing with ...

Background Introduction Multi-source Unsupervised Domain Adaptation (MUDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. However, existing methods often merely seek to blend distributions between different domains or combine multiple single-source models in the decision process through weighted fusio...

Application of Sliding Mode Control in Uncertain Fractional-Order Reaction-Diffusion Memristor Neural Networks In recent years, as neural networks have been widely applied in various fields, the research on their control and stability has gained increasing attention. Fractional-order (FO) memristor neural networks (MNNs), due to their ability to si...

Background and Motivation Understanding fluid motion is crucial for comprehension of our environment and our interactions with it in the field of physics. However, traditional fluid simulation methods face limitations in practical applications due to high computational demands. In recent years, physics-driven neural networks have emerged as a promi...

Distillation of Multi-Class Cervical Lesion Cell Detection via Synthesis-Aided Pre-Training and Patch-Level Feature Alignment Background and Research Significance Cervical cancer is a disease that seriously threatens the life and health of women. According to data from the International Agency for Research on Cancer (IARC), there were approximately...

With the continuous advancement of data acquisition technology, obtaining large amounts of high-dimensional data containing multiple features has become very easy, such as images and vision data. However, traditional machine learning methods, especially those based on vectors and matrices, face challenges such as the curse of dimensionality, increa...