arXiv subjects

Kenji Tanaka

Publications and source records attributed to Kenji Tanaka.

10 recordsLinked to original sources

arXiv2025

Enhancement of Mid-/High-Z Impurity Transport by Continuous Li-granule Dropping in a Stellarator Plasma

An enhancement of core impurity transport is observed in high-density plasmas of the stellarator LHD heated by neutral beam injection when continuous lithium (Li) granule dropping is performed. In the experiments reported here, in which the TESPEL is employed to inject trace amounts of titanium (Ti) and molybdenum (Mo) into the plasma core, confinement times for these impurities are seen to reduce significantly when Li dropping is applied, this reduction being more notable for Mo. In order to gain some initial insight into these observations, simulations are performed using the drift-kinetic transport code SFINCS for the Mo case. These simulations indicate that, while neoclassical transport prevails for the main plasma components (electrons, majority ions and low Z impurities), the classical contribution seems to be dominant for transporting Mo impurities. In summary, this work reports the first experimental observation of the degradation of mid-Z and high-Z impurity confinement induced by the continuous dropping of Li granules into a high-density stellarator plasma. In the case of the Mo impurity, simulations suggest that classical transport is the key mechanism underlying the enhanced impurity transport.

physics.plasm-ph↗

arXiv2024

Data assimilation approach for addressing imperfections in people flow measurement techniques using particle filter

Understanding and predicting people flow in urban areas is useful for decision-making in urban planning and marketing strategies. Traditional methods for understanding people flow can be divided into measurement-based approaches and simulation-based approaches. Measurement-based approaches have the advantage of directly capturing actual people flow, but they face the challenge of data imperfection. On the other hand, simulations can obtain complete data on a computer, but they only consider some of the factors determining human behavior, leading to a divergence from actual people flow. Both measurement and simulation methods have unresolved issues, and combining the two can complementarily overcome them. This paper proposes a method that applies data assimilation, a fusion technique of measurement and simulation, to agent-based simulation. Data assimilation combines the advantages of both measurement and simulation, contributing to the creation of an environment that can reflect real people flow while acquiring richer data. The paper verifies the effectiveness of the proposed method in a virtual environment and demonstrates the potential of data assimilation to compensate for the three types of imperfection in people flow measurement techniques. These findings can serve as guidelines for supplementing sparse measurement data in physical environments.

cs.HC↗

arXiv2022

3D scene reconstruction from monocular spherical video with motion parallax

In this paper, we describe a method to capture nearly entirely spherical (360 degree) depth information using two adjacent frames from a single spherical video with motion parallax. After illustrating a spherical depth information retrieval using two spherical cameras, we demonstrate monocular spherical stereo by using stabilized first-person video footage. Experiments demonstrated that the depth information was retrieved on up to 97% of the entire sphere in solid angle. At a speed of 30 km/h, we were able to estimate the depth of an object located over 30 m from the camera. We also reconstructed the 3D structures (point cloud) using the obtained depth data and confirmed the structures can be clearly observed. We can apply this method to 3D structure retrieval of surrounding environments such as 1) previsualization, location hunting/planning of a film, 2) real scene/computer graphics synthesis and 3) motion capture. Thanks to its simplicity, this method can be applied to various videos. As there is no pre-condition other than to be a 360 video with motion parallax, we can use any 360 videos including those on the Internet to reconstruct the surrounding environments. The cameras can be lightweight enough to be mounted on a drone. We also demonstrated such applications.

cs.CV↗

arXiv2022

Physical Deep Learning with Biologically Plausible Training Method

The ever-growing demand for further advances in artificial intelligence motivated research on unconventional computation based on analog physical devices. While such computation devices mimic brain-inspired analog information processing, learning procedures still relies on methods optimized for digital processing such as backpropagation. Here, we present physical deep learning by extending a biologically plausible training algorithm called direct feedback alignment. As the proposed method is based on random projection with arbitrary nonlinear activation, we can train a physical neural network without knowledge about the physical system. In addition, we can emulate and accelerate the computation for this training on a simple and scalable physical system. We demonstrate the proof-of-concept using a hierarchically connected optoelectronic recurrent neural network called deep reservoir computer. By constructing an FPGA-assisted optoelectronic benchtop, we confirmed the potential for accelerated computation with competitive performance on benchmarks. Our results provide practical solutions for the training and acceleration of neuromorphic computation.

cs.NE↗

arXiv2022

"This is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information

False information spreads on social media, and fact-checking is a potential countermeasure. However, there is a severe shortage of fact-checkers; an efficient way to scale fact-checking is desperately needed, especially in pandemics like COVID-19. In this study, we focus on spontaneous debunking by social media users, which has been missed in existing research despite its indicated usefulness for fact-checking and countering false information. Specifically, we characterize the tweets with false information, or fake tweets, that tend to be debunked and Twitter users who often debunk fake tweets. For this analysis, we create a comprehensive dataset of responses to fake tweets, annotate a subset of them, and build a classification model for detecting debunking behaviors. We find that most fake tweets are left undebunked, spontaneous debunking is slower than other forms of responses, and spontaneous debunking exhibits partisanship in political topics. These results provide actionable insights into utilizing spontaneous debunking to scale conventional fact-checking, thereby supplementing existing research from a new perspective.

cs.SI↗

arXiv2021

Attempt to Predict Failure Case Classification in a Failure Database by using Neural Network Models

With the recent progress of information technology, the use of networked information systems has rapidly expanded. Electronic commerce and electronic payments between banks and companies, and online shopping and social networking services used by the general public are examples of such systems. Therefore, in order to maintain and improve the dependability of these systems, we are constructing a failure database from past failure cases. When importing new failure cases to the database, it is necessary to classify these cases according to failure type. The problems are the accuracy and efficiency of the classification. Especially when working with multiple individuals, unification of classification is required. Therefore, we are attempting to automate classification using machine learning. As evaluation models, we selected the multilayer perceptron (MLP), the convolutional neural network (CNN), and the recurrent neural network (RNN), which are models that use neural networks. As a result, the optimal model in terms of accuracy is first the MLP followed by the CNN, and the processing time of the classification is practical.

cs.DC↗

arXiv2021

Retrospective Analysis of Controversial Subtopics on COVID-19 in Japan

For efficient political decision-making in an emergency situation, a thorough recognition and understanding of the polarized topics is crucial. The cost of unmitigated polarization would be extremely high for the society; therefore, it is desirable to identify the polarizing issues before they become serious. With this in mind, we conducted a retrospective analysis of the polarized subtopics of COVID-19 to obtain insights for future policymaking. To this end, we first propose a framework to comprehensively search for controversial subtopics. We then retrospectively analyze subtopics on COVID-19 using the proposed framework, with data obtained via Twitter in Japan. The results show that the proposed framework can effectively detect controversial subtopics that reflect current reality. Controversial subtopics tend to be about the government, medical matters, economy, and education; moreover, the controversy score had a low correlation with the traditional indicators--scale and sentiment of the subtopics--which suggests that the controversy score is a potentially important indicator to be obtained. We also discussed the difference between subtopics that became highly controversial and ones that did not despite their large scale.

cs.SI↗

arXiv2021

Characterizing the Anti-Vaxxers' Reply Behavior on Social Media

Although the online campaigns of anti-vaccine advocates, or anti-vaxxers, severely threaten efforts for herd immunity, their reply behavior--the form of directed messaging that can be sent beyond follow-follower relationships--remains poorly understood. Here, we examined the characteristics of anti-vaxxers' reply behavior on Twitter to attempt to comprehend their characteristics of spreading their beliefs in terms of interaction frequency, content, and targets. Among the results, anti-vaxxers more frequently conducted reply behavior with other clusters, especially neutral accounts. Anti-vaxxers' replies were significantly more toxic than those from neutral accounts and pro-vaxxers, and their toxicity, in particular, was higher with regard to the rollout of vaccines. Anti-vaxxers' replies were more persuasive than the others in terms of the emotional aspect, rather than linguistical styles. The targets of anti-vaxxers' replies tend to be accounts with larger numbers of followers and posts, including accounts that relate to health care or represent scientists, policy-makers, or media figures or outlets. We discussed how their reply behaviors are effective in spreading their beliefs, as well as possible countermeasures to restrain them. These findings should prove useful for pro-vaxxers and platformers to promote trusted information while reducing the effect of vaccine disinformation.

cs.SI↗

arXiv2020

Neural Schr\"{o}dinger Equation:Physical Law as Neural Network

We show a new family of neural networks based on the Schr\"{o}dinger equation (SE-NET). In this analogy, the trainable weights of the neural networks correspond to the physical quantities of the Schr\"{o}dinger equation. These physical quantities can be trained using the complex-valued adjoint method. Since the propagation of the SE-NET can be described by the evolution of physical systems, its outputs can be computed by using a physical solver. As a demonstration, we implemented the SE-NET using the finite difference method. The trained network is transferable to actual optical systems. Based on this concept, we show a numerical demonstration of end-to-end machine learning with an optical frontend. Our results extend the application field of machine learning to hybrid physical-digital optimizations.

physics.comp-ph↗

arXiv2004

Maximum Mutual Information of Space-Time Block Codes with Symbolwise Decodability

In this paper, we analyze the performance of space-time block codes which enable symbolwise maximum likelihood decoding. We derive an upper bound of maximum mutual information (MMI) on space-time block codes that enable symbolwise maximum likelihood decoding for a frequency non-selective quasi-static fading channel. MMI is an upper bound on how much one can send information with vanishing error probability by using the target code.

cs.IT↗