October 2023 Vol. 21 No. 10102023

https://www.ntt-review.jp/archive/2023/202310.html

Front-line Researchers

Takahiro Kawabe, Senior Distinguished Researcher, NTT
Communication Science Laboratories▼Abstract
Head-mounted displays for cross reality (XR) have been released by various
companies and are gradually gaining popularity. Such XR displays present
three-dimensional stereoscopic images that take advantage of the characteristics ofthe human visual system and give users a greater sense of reality. However,illusions are not just created by sight; they are the result of a combination of sightwith other senses, such as hearing and touch. Takahiro Kawabe, a seniordistinguished researcher at NTT Communication Science Laboratories, is engaged
in research on illusions related to the texture of objects created by the combinationof multiple senses. We interviewed him about his research on presenting the textureof objects for touchless user interfaces, the fusion of psychology and engineering,and his attitude as a researcher.

Feature Articles
Designing a Future Where Everyone Can Flourish by Sharing Diverse
Knowledge and Technologies

Takahiro Kawabe, Senior Distinguished Researcher, NTT Communication Science Laboratories▼Abstract

Rising Researchers

Since its establishment, NTT Communication
Science Laboratories (CS Labs) has been
engaged in researching basic theories that
address the essence of human science andinformation science and creating innovativetechnologies that will bring about changes insociety to achieve communication that reaches theheart between humans and between humans andcomputers. This article introduces recent research
activities of CS Labs from the perspective ofdeciphering individuals, society, and the Earth.

Yuka Hashimoto, Distinguished Researcher, NTT Network Service Systems Laboratories

Feature Articles
Designing a Future Where Everyone Can Flourish by Sharing
Diverse Knowledge and Technologies
Design a World Where Everyone Can Flourish by Deciphering the Future of Individuals, Society,and the Earth—Communication Science That Connects the Past, Present, and Future throughDiverse Knowledge and Technologies
Machine Learning That Reproduces Physical Phenomena from Data
Understanding Mindful Awareness in Mindfulness Meditation—Investigation of the Psychological,
Physiological, and Neural Mechanisms of Mindfulness Meditation
Dilemma between Quantum Speedup and Computational Reliability—Overcoming Errors withEfficient Verification Methods for Quantum Computing

Global Standardization Activities
Technical Trends in ISO/IEC Joint Technical Committee 1

Practical Field Information about Telecommunication Technologies
A Case Study of Malfunction of Wireless Communication System Caused by Electromagnetic
Disturbance at a Construction Site

High arousal
moya moya

shonbori

un unniko nikoTime
Choose how
you feel now.
niko niko

0.3

doki doki

1. Buy the movie ticket
EEE: waku waku

0.2

sowa sowa
biku biku

2. Advertisements before
the movie starts
EEE: sowa sowa

0.1

Watching an action
moviesukkiri

3. Soon after the moviestarts (action scenes)
EEE: hara hara
4. During the movie(The hero is in danger)
EEE: gyaa

Enter your current emotional state on
your smartphone.

5. During the movie(The hero escapesdanger)
EEE: huu

yosshayoshi

hara harawaa

iki ikizawa zawa

gyaa

waai
waku waku
uki ukiniko nikoshakiin

kyun

un un

piri piri
0.0 Unpleasant
eegee
oee
ira ira−0.1muka muka haa uun

uttori
Pleasant
hokkorihosukkiri
yare yare heto heto huu
kachin−0.2 moya moya
guttarigaanbohgakkuri
uto utoshonbori−0.3−0.4−0.2

Low arousal

0.00.2

Experiencing amassage chair
1. Recline on a chair
EEE: heto heto
2. Turn on the switch
EEE: un un
3. During massage
EEE: huu
4. Massage withmoderate intensity
EEE: uto uto
5. After the massage isfinished
EEE: sukkiri

0.4

Machine Learning That Reproduces Physical Phenomena
from Data▼Abstract
Machine learning has made remarkable progress andhas been used successfully in various applications. Our
research goal is to use machine learning to simulate
physical phenomena. In this article, I introducemachine-learning models that can accurately reproduce
physical phenomena from observed data by using priorknowledge of physics. I also discuss the prospects ofand value that can be created with this research.

Equation
Conventional approach:
Equations are handcrafted.

∂u
∂u
∂ 3u
−β= −α u
∂t
∂x
∂x 3

High cost: difficult to model complex phenomena
Parameter∂u= ƒ(u ; θ )∂tOur approach:
Machine learning with laws of physics
No equation design required: learning complex
phenomena by using machine learning techniques

Sensing
Physical phenomena

Data

Machinelearning modelLearning

Simulation:
Predict physical phenomena given
any initial condition
Physicalphenomena(Ex. fluids)

Location x

Front-line Researchers

Design a World Where Everyone Can Flourish by Deciphering
the Future of Individuals, Society, and the Earth—Communication Science That Connects the Past, Present,
and Future through Diverse Knowledge and Technologies

Time

Significance:
Simulation of physical phenomenapreviously difficult to model forscientific discovery, engineering
design, etc.