arXiv subjects

Tetsuya Yamamoto

Publications and source records attributed to Tetsuya Yamamoto.

4 recordsLinked to original sources

arXiv2026

Detecting outliers of pursuit eye movements: a preliminary analysis of autism spectrum disorder

Background: Autism spectrum disorder (ASD) is characterized by significant clinical and biological heterogeneity. Conventional group-mean analyses of eye movements often mask individual atypicalities, potentially overlooking critical pathological signatures. This study aimed to identify idiosyncratic oculomotor patterns in ASD using an "outlier analysis" of smooth pursuit eye movement (SPEM). Methods: We recorded SPEM during a slow Lissajous pursuit task in 18 adults with ASD and 39 typically developed (TD) individuals. To quantify individual deviations, we derived an "outlier score" based on the Mahalanobis distance. This score was calculated from a feature vector, optimized via Principal Component Analysis (PCA), comprising the temporal lag ($\Delta$t) and the spatial deviation ($\Delta$s). An outlier was statistically defined as a score exceeding $\sqrt{10}$ (approximately 3.16$\sigma$) relative to the TD normative distribution. Results: While the TD group exhibited a low outlier rate of 5.1%, the ASD group demonstrated a significantly higher prevalence of 38.9% (7/18) (binomial P = 0.0034). Furthermore, the mean outlier score was significantly elevated in the ASD group (3.00 $\pm$ 2.62) compared to the TD group (1.52 $\pm$ 0.80; P = 0.002). Notably, these extreme deviations were captured even when conventional mean-based comparisons showed limited sensitivity. Conclusions: Our outlier analysis successfully visualized the high degree of idiosyncratic atypicality in ASD oculomotor control. By shifting the focus from group averages to individual deviations, this approach provides a sensitive metric for capturing the inherent heterogeneity of ASD, offering a potential baseline for identifying clinical subtypes.

q-bio.NC↗

arXiv2023

Energy Spectrum Analysis on a Red Blood Cell Model

It is important to understand the dynamics of red blood cells (RBCs) in blood flow. This requires the formulation of coarse-grained RBC models that reproduce the hydrodynamic properties of blood accurately. One of the models that successfully reproduce the rheology and morphology of blood has been proposed by Fedosov et al. [D. A. Fedosov, B. Caswell, and G. E. Karniadakis, Comput. Methods Appl. Mech. Eng., Vol. 199, 1937-1948 (2010)]. The proposed RBC model contains several parameters whose values are determined either by various experiments or physical requirements. In this study, we developed a new method of determining the parameter values precisely from the fluctuations of the RBC membrane. Specifically, we studied the relationship between the spectra of the fluctuations and model parameters. Characteristic peaks were observed in the spectra, whose peak frequencies were dependent on the parameter values. In addition, we investigated the spectra of the radius of gyration. We identified the peaks originating from the spring potential and the volume-conserving potential appearing in the spectra. These results lead to the precise experimental determination of the parameters used in the RBC model.

cond-mat.soft↗

arXiv2015

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

We propose a novel method for multiple clustering that assumes a co-clustering structure (partitions in both rows and columns of the data matrix) in each view. The new method is applicable to high-dimensional data. It is based on a nonparametric Bayesian approach in which the number of views and the number of feature-/subject clusters are inferred in a data-driven manner. We simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block. This makes our method applicable to datasets consisting of both numerical and categorical variables, which biomedical data typically do. Clustering solutions are based on variational inference with mean field approximation. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

stat.ML↗

arXiv2015

Contact angle saturation in electrowetting: Injection of ions into the surrounding media

We use the Poisson-Boltzmann theory to predict contact angle saturation of aqueous droplets in electrowetting. Our theory predicts that injection of ions from the droplet into its surrounding medium is responsible for the deviation of the apparent contact angle from the Young-Lippmann equation for large applied voltages. The ion injection substantially decreases the Maxwell stress and increases the osmotic pressure at the interface between the two media, leading to saturation of the apparent contact angle. Moreover, we find that the contact angle does not saturate, but only has a broad minimum that increases again upon further increase of the applied voltage, in agreement with experiments.

cond-mat.soft↗