arXiv subjects

Kohei Yoshikawa

Publications and source records attributed to Kohei Yoshikawa.

7 recordsLinked to original sources

arXiv2026

Machine learning evaluation of structural descriptors for supercooled water

The anomalous behavior of liquid water is widely associated with a liquid-liquid phase transition between high- and low-density states in the supercooled regime. At the microscopic level, tetrahedral hydrogen-bond networks govern these properties, motivating structural descriptors that characterize local molecular environments. These structural descriptors quantify features such as tetrahedral order, local density, and the separation between the first and second coordination shells; however, they have largely been proposed independently, with limited systematic comparison. Here we evaluate 16 previously proposed descriptors using a neural-network-based temperature classification framework, enabling an objective assessment of their ability to distinguish temperature-dependent structural changes in supercooled water. We further apply an explainable artificial intelligence method that identifies the structural features responsible for the model predictions. This approach reveals how different descriptors encode local structural information and establishes a data-driven framework for benchmarking structural descriptors in liquid water.

cond-mat.soft↗

arXiv2026

Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach

The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational studies, multiple versions may exist in the treatment. It has been pointed out that ignoring such multiple versions of treatment can lead to biased estimates of causal effects, but a causal inference framework that explicitly deals with the unbiased identification and estimation of version-specific causal effects has not been fully developed yet. Thus, obtaining a deeper understanding for mechanisms of the complex treatments is difficult. In this paper, we introduce the Mixture-of-Experts framework into causal inference and develop a methodology for estimating the causal effects of latent versions. This approach enables explicit estimation of version-specific causal effects even if the versions are not observed. Numerical experiments demonstrate the effectiveness of the proposed method.

stat.ME↗

arXiv2025

Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism

Glass-forming liquids exhibit slow dynamics below their melting temperatures, maintaining an amorphous structure reminiscent of normal liquids. Distinguishing microscopic structures in the supercooled and high-temperature regimes remains a debated topic. Building on recent advances in machine learning, particularly Graph Neural Networks (GNNs), our study automatically extracts features, unveiling fundamental mechanisms driving structural changes at varying temperatures. We employ the self-attention mechanism to generate attention coefficients that quantify the importance of connections between graph nodes, providing insights into the rationale behind GNN predictions. Exploring structural changes with decreasing temperature through the GNN+self-attention using physically-defined structural descriptors, including the bond-orientational order parameter, Voronoi cell volume, and coordination number, we identify strong correlations between high attention coefficients and more disordered structures as a key indicator of variations in glass-forming liquids.

cond-mat.soft↗

arXiv2020

Variable fusion for Bayesian linear regression via spike-and-slab priors

In linear regression models, fusion of coefficients is used to identify predictors having similar relationships with a response. This is called variable fusion. This paper presents a novel variable fusion method in terms of Bayesian linear regression models. We focus on hierarchical Bayesian models based on a spike-and-slab prior approach. A spike-and-slab prior is tailored to perform variable fusion. To obtain estimates of the parameters, we develop a Gibbs sampler for the parameters. Simulation studies and a real data analysis show that our proposed method achieves better performance than previous methods.

stat.ME↗

arXiv2020

Multilinear Common Component Analysis via Kronecker Product Representation

We consider the problem of extracting a common structure from multiple tensor datasets. For this purpose, we propose multilinear common component analysis (MCCA) based on Kronecker products of mode-wise covariance matrices. MCCA constructs a common basis represented by linear combinations of the original variables which loses as little information of the multiple tensor datasets. We also develop an estimation algorithm for MCCA that guarantees mode-wise global convergence. Numerical studies are conducted to show the effectiveness of MCCA.

stat.ML↗

arXiv2019

Sparse Reduced-Rank Regression for Simultaneous Rank and Variable Selection via Manifold Optimization

We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as a singular value decomposition with sparse singular vectors. The traditional estimation procedure for the coefficient parameter often fails when the true rank of the parameter is high. To overcome this issue, we develop an estimation algorithm with rank and variable selection via sparse regularization and manifold optimization, which enables us to obtain an accurate estimation of the coefficient parameter even if the true rank of the coefficient parameter is high. Using sparse regularization, we can also select an optimal value of the rank. We conduct Monte Carlo experiments and real data analysis to illustrate the effectiveness of our proposed method.

stat.ML↗

arXiv2017

Electric-field-induced changes of magnetic moments and magnetocrystalline anisotropy in ultrathin cobalt films

In this study, the microscopic origins of the voltage-controlled magnetic anisotropy (VCMA) in 3d-ferromagnetic metals are revealed. Using in-situ X-ray fluorescence spectroscopy that provides a high quantum efficiency, electric-field-induced changes in orbital magnetic moment and magnetic dipole Tz terms in ultrathin Co films are demonstrated. An orbital magnetic moment difference of 0.013μB. was generated in the presence of electric fields of +(-)0.2 V/nm. The VCMA of Co was properly estimated by the induced change in orbital magnetic moment, according to the perturbation theory model. The induced change in magnetic dipole Tz term only slightly contributed to the VCMA in 3d-ferromagnetic metals.

cond-mat.mtrl-sci↗