arXiv subjects

Takao Namiki

Publications and source records attributed to Takao Namiki.

7 recordsLinked to original sources

arXiv2026

One-shot prediction of noise-induced bifurcations with reservoir computing

Dynamical systems can exhibit complex responses when noise is injected. In particular, dynamics can be qualitatively altered by dynamic noise, a phenomenon known as noise-induced bifurcation. Predicting noise-induced bifurcations is a critical challenge in nonlinear physics. Recently, it has been reported that reservoir computing, a machine learning framework, can reconstruct the unseen global structure of a dynamical system, including bifurcations, from limited time series data. However, learning global structures in random dynamical systems has not yet been systematically addressed. In this study, we report that a simple reservoir computing framework can predict the noise-induced bifurcation structure from the time series at a single noise condition. We demonstrate dynamic noise cancellation and the reconstruction of entire noise-induced bifurcation structures, including noise-induced chaos and noise-induced order, in representative dynamical systems. Additionally, we provide a theoretical explanation for noise cancellation and demonstrate noise cancellation of a neuromorphic spintronics device. Our results provide significant insights into understanding and harnessing real-world noisy complex dynamics.

nlin.CD↗

arXiv2025

Cluster Analysis for Globally Coupled Map using Optimal Transport Distance and Complexity of Attractor-ruin

In this paper, we show the results of the strength of attractorruins for a globally coupled map. The globally coupled map (GCM) is a discrete dynamical system, and here we consider a model in which the logistic map is globally coupled. An attractor-ruin is a set in which the attractor is destabilized by a change in parameters, which is characterized by a Milnor attractor. Intermittent phenomena called chaotic itinerancy, in which orbits transition between attractor-ruin, have been observed in various complex systems, and their onset mechanisms and statistical properties have attracted attention. In this study, the instability of orbits of GCM is analyzed from the perspective of clustering using the optimal transport distance, and the strength of attractor-ruins is numerically evaluated by applying this method. As a result, it was found that the strength of various attractor-ruins is high in the parameter region called the partially ordered phase, where chaotic itinerancy occurs.

math.DS↗

arXiv2024

Trans-Bifurcation Prediction of Dynamics in terms of Extreme Learning Machines with Control Inputs

By extending the extreme learning machine by additional control inputs, we achieved almost complete reproduction of bifurcation structures of dynamical systems. The learning ability of the proposed neural network system is striking in that the entire structure of the bifurcations of a target one-parameter family of dynamical systems can be nearly reproduced by training on transient dynamics using only a few parameter values. Moreover, we propose a mechanism to explain this remarkable learning ability and discuss the relationship between the present results and similar results obtained by Kim et al.

nlin.CD↗

arXiv2018

Fully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images

Sublingual vein is commonly used to diagnose the health status. The width of main sublingual veins gives information of the blood circulation. Therefore, it is necessary to segment the main sublingual veins from the tongue automatically. In general, the dataset in the medical field is small, which is a challenge for training the deep learning model. In order to train the model with a small data set, the proposed method for automatically segmenting the sublingual veins is to re-train U-net model with different sets of the limited number of labels for the same training images. With pre-knowledge of the segmentation, the loss of the trained model will be convergence easier. To improve the performance of the segmentation further, a novel strategy of data augmentation was utilized. The operation for masking output of the model with the input was randomly switched on or switched off in each training step. This approach will force the model to learn the contrast invariance and avoid overfitting. Images of dataset were taken with the developed device using eight near infrared LEDs. The final segmentation results were evaluated on the validation dataset by the IoU metric.

cs.CV↗

arXiv2009

Experimental DML over digital repositories in Japan

In this paper the authors show an overview of Virtual Digital Mathematics Library in Japan (DML-JP), contents of which consist of metadata harvested from institutional repositories in Japan and digital repositories in the world. DML-JP is, in a sense, a subject specific repository which collaborate with various digital repositories. Beyond portal website, DML-JP provides subject-specific metadata through OAI-ORE. By the schema it is enabled that digital repositories can load the rich metadata which were added by mathematicians.

cs.DL↗

arXiv2004

Symmetry of distribution for the one-dimensional Hadamard walk

In this paper we study a one-dimensional quantum random walk with the Hadamard transformation which is often called the Hadamard walk. We construct the Hadamard walk using a transition matrix on probability amplitude and give some results on symmetry of probability distributions for the Hadamard walk.

quant-ph↗

arXiv2003

Absorption problems for quantum walks in one dimension

This paper treats absorption problems for the one-dimensional quantum walk determined by a 2 times 2 unitary matrix U on a state space {0,1,...,N} where N is finite or infinite by using a new path integral approach based on an orthonormal basis P, Q, R and S of the vector space of complex 2 times 2 matrices. Our method studied here is a natural extension of the approach in the classical random walk.

quant-ph↗