Quantitative selection of sample structures in small-angle scattering using Bayesian methods.

Small-angle scattering (SAS) is a key experimental technique for analyzing nanoscale structures in various materials. In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting. This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models. The performance of the method is assessed through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that this proposed analysis approach yields highly accurate and interpretable results. The ability of the method to analyze a range of mixing ratios and particle size ratios for mixed components is also discussed, along with its precision in model evaluation by the degree of fitting. The proposed method effectively facilitates quantitative analysis of nanoscale sample structures in SAS, which has traditionally been challenging, and is expected to contribute significantly to advancements in a wide range of fields.

New Poisson denoising method for pulse-count STEM imaging.

With the recent progress in the development of detectors in electron microscopy, it has become possible to directly count the number of electrons per pixel, even with a scintillator-type detector, by incorporating a pulse-counting module. To optimize a denoising method for electron counting imaging, in this study, we propose a Poisson denoising method for atomic-resolution scanning transmission electron microscopy images. Our method is based on the Markov random field model and Bayesian inference, and we can reduce the electron dose by a factor of about 15 times or further below. Moreover, we showed that the method of reconstruction from multiple images without integrating them performs better than that from an integrated image.

Bayesian active learning with model selection for spectral experiments.

Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments. Therefore, we proposed an active learning with model selection method using multiple parametric models as learning models. Important points for model selection and its parameter estimation were actively measured using Bayesian posterior distribution. The present study demonstrated the effectiveness of our proposed method for spectral deconvolution and Hamiltonian selection in X-ray photoelectron spectroscopy.

Fast reconstruction of scanning transmission electron microscopy images using Markov random field model.

In this study, we proposed a fast method of reconstruction for scanning transmission electron microscopy images. The proposed method is based on the Markov random field model and Bayesian inference, and we found that the method can reconstruct such images of sizes 512 × 512 and 264 × 240 in less than 200 ms and 100 ms, respectively. Furthermore, we showed that the method of reconstruction from multiple images without averaging them has better reconstruction performance than that from the averaged image.

Bayesian parameter estimation from dispersion relation observation data with Poisson process.

In this study, we estimate the distribution of lattice model parameters based on Bayesian estimation using the dispersion relation spectral data of lattice vibration. The dispersion relation of lattice vibration is observed using inelastic scattering of neutrons or x rays and is used to analyze the speed of sound and interatomic force. However, the current analysis method of dispersion relation observation data in the field of experimental physics requires manually fitting parameters, so the analysis is costly and cannot effectively handle high-dimensional data and large amounts of data. Moreover, it is impossible to discuss the estimation accuracy with the conventional method. Therefore, we solve these problems by estimating the distribution of parameters using Bayesian inference. We propose a lattice model parameter estimation method that uses Bayesian inference with a physical observation stochastic process and determine the method's effectiveness using artificial data.

Recurrent Connections Might Be Important for Hierarchical Categorization.

Visual short-term memory is an important ability of primates and is thought to be stored in area TE. We previously reported that the initial transient responses of neurons in area TE represented information about a global category of faces, e.g., monkey faces vs. human faces vs. simple shapes, and the latter part of the responses represented information about fine categories, e.g., facial expression. The neuronal mechanisms of hierarchical categorization in area TE remain unknown. For this study, we constructed a combined model that consisted of a deep neural network (DNN) and a recurrent neural network and investigated whether this model can replicate the time course of hierarchical categorization. The visual images were stored in the recurrent connections of the model. When the visual images with noise were input to the model, the model outputted the time course of the hierarchical categorization. This result indicates that recurrent connections in the model are important not only for visual short-term memory but for hierarchical categorization, suggesting that recurrent connections in area TE are important for hierarchical categorization.