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...

Low-Field MRI Image Reconstruction Using Physics-Informed Deep Learning Background: The application of magnetic resonance imaging (MRI) technology in low-field magnetic resonance imaging has gained increasing attention in recent years. Low-field MRI, due to its low cost and simplified maintenance, is considered to have a broad application prospect ...

DVMark: A Multi-Scale Deep Learning Framework for Video Watermarking Video watermarking technology achieves data hiding by embedding information into the cover video. The DVMark model proposed in this paper is a multi-scale video watermarking solution based on deep learning that boasts high robustness and practicality, capable of resisting various ...

Transparent Deep Image Aesthetic Assessment Based on Tag Content Descriptions Academic Background With the proliferation of social media platforms such as Instagram and Flickr, there is an increasing demand for Image Aesthetics Assessment (IAA) models. These models can help social network service providers optimize image ranking or recommendation r...

Bilingual Speech Neuroprosthesis Driven by Cortical Speech Representations Background In the development of neuroprostheses, research on decoding language from brain activity has primarily focused on decoding a single language. Thus, the extent to which bilingual speech production relies on unique or shared cortical activity between different langu...