Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI ...

1 Department of Geological Science, University of Science and Technology, Daejeon, Republic of Korea 2 Petroleum and Future Energy Research Center, Resource Exploration and Development Research ...

Abstract: Revealing the latent low-dimensional geometric structure of high-dimensional data is a crucial task in unsupervised representation learning. Traditional manifold learning, as a typical ...

The SECI1013 Discrete Structure course has enhanced my understanding of the foundational concepts in computer science, especially through its focus on proof techniques and graph theory. By connecting ...

Understanding the preferences of potential users of digital health products is beneficial for digital health policy and planning. Stated preference methods could help elicit individuals’ preferences ...

Matroid theory is a way mathematicians describe the property of independence in a space, with uses across mathematics, physics, computer science and more. Carnegie Mellon University Mathematician ...

Proteins, vital macromolecules, are characterized by their amino acid sequences, which dictate their three-dimensional structures and functions in living organisms. Effective generative protein ...

1 College of Science, North China University of Science and Technology, Tangshan, China. 2 Hebei Provincial Key Laboratory of Data Science and Applications, North China University of Science and ...

A biological model based on slime mold has provided astronomers with new insights into the structure and evolution of the universe. (Artist’s concept.) Credit: SciTechDaily.com Utilizing a slime mold ...

Abstract: In recent years, the fine-grained classification issue in plants is a hot topical in computer vision. However, most existing fine-grained classification methods focus on finding ...