25 citations · 78 across the 14 of their papers we have counts for
14 papers
Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimation
Hayato Okubo, Yoshifumi Amamoto, Toshimitsu Aritake +5
Spectral deconvolution is essential for extracting peak structures that encode material properties and chemical structures, but conventional automated methods often fail when spect…
Unsupervised feature selection using Bayesian Tucker decomposition
Y-h. Taguchi, Yoh-ichi Mototake
In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regression. Although we have propose…
Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI
Yoh-ichi Mototake, Makoto Sasaki
Physics-informed machine learning (PIML) integrates partial differential equations (PDEs) into machine learning models to solve inverse problems, such as estimating coefficient fun…
Algebraic Geometrical Analysis of Metropolis Algorithm When Parameters Are Non-identifiable
Kenji Nagata, Yoh-ichi Mototake
The Metropolis algorithm is one of the Markov chain Monte Carlo (MCMC) methods that realize sampling from the target probability distribution. In this paper, we are concerned with…
Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra
Hiroyuki Kumazoe, Kazunori Iwamitsu, Masaki Imamura +4
We analyzed the X-ray photoemission spectra (XPS) of carbon 1s states in graphene and oxygen-intercalated graphene grown on SiC(0001) using Bayesian spectroscopy. To realize highly…
Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series
Akifumi Okuno, Yuya Morishita, Yoh-ichi Mototake
This study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function. Traditional approaches in this…
Dynamics-Based Intrinsic Signal Model for High-Dimensional, Small-Sample Data
Yoh-ichi Mototake, Y-h. Taguchi
Signal extraction is difficult when the number of variables is much larger than the number of observations . We address this problem under the working hypothesis that an emp…
Quantitative Prediction of Fracture Toughness of Polymer by Fractography Using Deep Neural Networks
Yoh-ichi Mototake, Kaita Ito, Masahiko Demura
Fracture surfaces provide various types of information about fracture. The fracture toughness , which represents the resistance to fracture, can be estimated using th…
Procedure to Reveal the Mechanism of Pattern Formation Process by Topological Data Analysis
Yoh-ichi Mototake, Masaichiro Mizumaki, Kazue Kudo +1
Topological data analysis (TDA) is a versatile tool that can be used to extract scientific knowledge from complex pattern formation processes. However, the physics correspondence b…
Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks
Yoh-ichi Mototake
Understanding complex systems with their reduced model is one of the central roles in scientific activities. Although physics has greatly been developed with the physical insights…
Semi-flat minima and saddle points by embedding neural networks to overparameterization
Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake +1
We theoretically study the landscape of the training error for neural networks in overparameterized cases. We consider three basic methods for embedding a network into a wider one…
Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)
Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka +2
In this paper, we propose a new method of Bayesian measurement for spectral deconvolution, which regresses spectral data into the sum of unimodal basis function such as Gaussian or…
Bayesian Hamiltonian Selection in X-ray Photoelectron Spectroscopy
Yoh-ichi Mototake, Masaichiro Mizumaki, Ichiro Akai +1
Core-level X-ray photoelectron spectroscopy (XPS) is a useful measurement technique for investigating the electronic states of a strongly correlated electron system. Usually, to ex…
Finding Continuity and Discontinuity in Fish Schools via Integrated Information Theory
Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake +3
Collective behaviour is known to be the result of diverse dynamics and is sometimes likened to a living system. Although many studies have revealed the dynamics of various collecti…