6 papers
Development of ultra-high efficiency soft X-ray angle-resolved photoemission spectroscopy equipped with deep prior-based denoising method
Kohei Yamagami, Yuichi Yokoyama, Yuta Sumiya +3
Soft X-ray angle resolved photoemission spectroscopy (SX-ARPES) is one of the most powerful spectroscopic techniques to visualize the three-dimensional bulk electronic structure in…
Sequential Exchange Monte Carlo: A Sampling Method for Bayesian Data Analysis without Parameter Tuning
Tomohiro Nabika, Kenji Nagata, Shun Katakami +2
Bayesian data analysis is widely used across many disciplines, and representative examples in materials science include spectral analysis and sparse modeling. In such applications,…
A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy
Naoki Wada, Yuta Kimura, Masaichiro Mizumaki +3
Quantitative understanding of coupled reaction and transport processes in lithium-ion battery (LIB) composite electrodes remains challenging because key internal states cannot be m…
Deep prior-based denoising for state-of-the-art scientific imaging and metrology
Yuichi Yokoyama, Kohei Yamagami, Yuta Sumiya +2
Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientifi…
Bayesian Inference for Small-Angle Scattering Data II: Core-Shell Samples
Keigo Oyama, Yui Hayashi, Shigeo Kuwamoto +4
Small-angle scattering (SAS) techniques, which utilize neutrons and X-rays, are employed in various scientific fields, including materials science, biochemistry, and polymer physic…
Mesoscopic Bayesian Inference by Solvable Models
Shun Katakami, Shuhei Kashiwamura, Kenji Nagata +2
The rapid advancement of data science and artificial intelligence has affected physics in numerous ways, including the application of Bayesian inference, setting the stage for a re…