Explores protein structure determination using X-ray crystallography and NMR spectroscopy, covering historical significance, crystal formation, diffraction patterns, and challenges in crystallization.
Discusses challenges in comparing non-Euclidean data, proposing a Laplacian-based solution for graph alignment and exploring optimal transport for graph distance computation.
Explores protein structure determination using NMR and Cryo-EM techniques, covering chemical shifts, isotope labeling, NOE, and high-resolution imaging methods.
Explores the integration of machine learning into discrete choice models, emphasizing the importance of theory constraints and hybrid modeling approaches.
