7 citations · 8 across the 13 of their papers we have counts for
14 papers
A Message-Passing Perspective on Ptychographic Phase Retrieval
Hajime Ueda, Shun Katakami, Masato Okada
We introduce a probabilistic approach to ptychographic reconstruction in computational imaging. Ptychography is an imaging method where the complex amplitude of an object is estima…
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,…
Stochastic Vector Approximate Message Passing with applications to phase retrieval
Hajime Ueda, Shun Katakami, Masato Okada
Phase retrieval refers to the problem of recovering a high-dimensional vector from the magnitude of its linear transform $\boldsymbol{z} = A \bold…
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…
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…
Basis Function Dependence of Estimation Precision for Synchrotron-Radiation-Based Mössbauer Spectroscopy
Binsheu Shieh, Ryo Masuda, Satoshi Tsutsui +4
Mössbauer spectroscopy is a technique employed to investigate the microscopic properties of materials using transitions between energy levels in the nuclei. Conventionally, in sync…
Quantitative Selection of Sample Structures in Small-Angle Scattering Using Bayesian Methods
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto +3
Small-angle scattering (SAS) is a key experimental technique for analyzing nano-scale structures in various materials.In SAS data analysis, selecting an appropriate mathematical mo…
Teaching Specific Scientific Knowledge into Large Language Models through Additional Training
Kan Hatakeyama-Sato, Yasuhiko Igarashi, Shun Katakami +2
Through additional training, we explore embedding specialized scientific knowledge into the Llama 2 Large Language Model (LLM). Key findings reveal that effective knowledge integra…
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model
Tomohiro Nabika, Kenji Nagata, Shun Katakami +2
In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is n…
Efficient NMR measurement and data analysis supported by the Bayesian inference : The case of the heavy fermion compound YbCo2Zn20
H. Ueda, S. Katakami, S. Yoshida +4
We propose a data-driven technique to infer microscopic physical quantities from nuclear magnetic resonance(NMR) spectra, in which the data size and quality required for the Bayesi…
Bayesian Inference for Small-Angle Scattering Data
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto +3
In this paper, we propose a method for estimating model parameters using Small-Angle Scattering (SAS) data based on the Bayesian inference. Conventional SAS data analyses involve p…
Bayesian Inference of Absorption Spectra Based on Binomial Distribution
Tomohiro Nabika, Kenji Nagata, Shun Katakami +2
In this paper, we propose a Bayesian spectral deconvolution method for absorption spectra. In conventional analysis, the noise mechanism of absorption spectral data is never consid…
Bayesian Inference on Hamiltonian Selections for Mössbauer Spectroscopy
Ryota Moriguchi, Satoshi Tsutsui, Shun Katakami +3
Mössbauer spectroscopy, which provides knowledge related to electronic states in materials, has been applied to various fields such as condensed matter physics and material science…
Bayesian Spectral Deconvolution of X-Ray Absorption Near Edge Structure Discriminating High- and Low-Energy Domains
Shuhei Kashiwamura, Shun Katakami, Ryo Yamagami +6
In this paper, we propose a Bayesian spectral deconvolution considering the properties of peaks in different energy domains. Bayesian spectral deconvolution regresses spectral data…