Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education.

Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domainspecific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks.

CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation.

Accurate retinal vessel segmentation is the prerequisite for early recognition and treatment of retina-related diseases. However, segmenting retinal vessels is still challenging due to the intricate vessel tree in fundus images, which has a significant number of tiny vessels, low contrast, and lesion interference. For this task, the u-shaped architecture (U-Net) has become the de-facto standard and has achieved considerable success.