The internal structure and operation mechanism of large-scale language models are
analyzed theoretically, especially how Transformer and its derivative architectures can …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Image-text matching is a key multimodal task that aims to model the semantic association
between images and text as a matching relationship. With the advent of multimedia …

The diagnosis of brain cancer relies heavily on medical imaging techniques, with MRI being
the most commonly used. It is necessary to perform automatic segmentation of brain tumors …

With the proliferation of various online and mobile payment systems, credit card fraud has
emerged as a significant threat to financial security. This study focuses on innovative …

This study introduces a novel unsupervised medical image feature extraction method that
employs spatial stratification techniques. An objective function based on weight is proposed …

This paper investigates convolutional neural network (CNN)-based approaches for image
classification and semantic segmentation, with a focus on addressing spatial detail loss and …

The rapid decline in groundwater around the world poses a significant challenge to
sustainable agriculture. To address this issue, agricultural managed aquifer recharge (Ag …

The accurate recognition and interpretation of multi-scale visual information is a critical focus
within contemporary computer vision research. To this end, this study explores and …

We consider the problem of federated Q-learning, where $ M $ agents aim to collaboratively
learn the optimal Q-function of an unknown infinite-horizon Markov decision process with …