Program
Date: February 11–13, 2019
Venue: Room 603, 6F High-Rise Wing, Nakano Campus, Meiji University
Monday, February 11
10:00-10:10

Opening: Kokichi Sugihara (Director of MIMS)

Invited Session 1: Particle Filter and Data Assimilation
Chair: Kazuyuki Nakamura (Meiji University, Japan)
10:10-10:40

Arnaud Doucet (University of Oxford, UK)"Iterative Particle Sampling for Bayesian Computation"

10:40-11:10

Norikazu Ikoma (Nippon Institute of Technology, Japan)"On Some Realistic Instances of Particle Filter in Engineering Field"

11:10-11:40

11:40-13:10

Kazuyuki Nakamura (Meiji University, Japan)"Applications of Data Assimilation and Error Analysis to Biological andGeophysical Systems"
Lunch

Invited Session 2: Earth Science
Chair: Hiromichi Nagao (The University of Tokyo, Japan)
13:10-13:40

13:40-14:10

14:10-14:40

Peng Hui (Central South University, China)"State-Space Modeling for Signal Extraction of Earthquake and Precision
Weighing"
Hiromichi Nagao (The University of Tokyo, Japan)"Seismic Wavefield Imaging of Long-Period Ground Motion in the Tokyo
Metropolitan Area, Japan"
Koji Tamaribuchi (Meteorological Research Institute, Japan)"Real-time Monitoring System of Earthquakes by Bayesian Estimation"

14:40-15:00

Coffee break

Invited Session 3: Time Series Modeling
Chair: Seisho Sato (The University of Tokyo/Meiji University, Japan)
15:00-15:30

Genshiro Kitagawa (The University of Tokyo/Meiji University, Japan)"Gaussian-sum Filter and Smoother for Nonlinear or Non-Gaussian Smoothing"

15:30-16:00

16:00-16:30

16:30-16:45

Koki Kyo (Obihiro University of Agriculture and Veterinary Medicine, Japan)"Analysis of the Structure of Economic Growth and Business Cycles in the
Prefectures of Japan"
Seisho Sato (The University of Tokyo/Meiji University, Japan)"Term Structure Models During the Global Financial Crisis: A Parsimonious Text
Mining Approach"
Coffee breakSpecial Short Session I
Chair: Koki Kyo (Obihiro University of Agriculture and Veterinary Medicine, Japan)
16:45-17:00

Takeru Matsuda (The University of Tokyo, Japan)"Oscillator Decomposition of Time Series Data"

17:00-17:15

17:15-17:30

17:30-17:45

Tomoya Haba (The University of Tokyo, Japan)"Decomposition of Multiple Seasonal Components in A Seasonal Adjustment
Model"
Shin-ichi Ito (The University of Tokyo, Japan)"Data Assimilation for Grain Growth Prediction based on A Second-order Adjoint
Method"Masayuki Yokoyama (National Institute for Fusion Science, Japan)"An Attempt for the Thermal Transport Modelling of Fusion Plasmas based on
the Statistical Approach"

Tuesday, February 12
Invited Session 4: Econometrics and Data Analysis
Chair: Naoto Kunitomo (Meiji University, Japan)
10:00-10:30

Duc Khuong Nguyen (IPAG Business School, France)"On the Network of Global Currencies: Does Lead-lag Connectedness Matter?"

10:30-11:00

Naoto Kunitomo (Meiji University, Japan)"Non-Stationary Errors-in-Variables Models and Their Econometric Applications"

11:00-11:30

11:30-12:00

Tatsuyoshi Okimoto (Australian National University, Australia)"How Does Unconventional Monetary Policy Affect the Global Financial Markets?:
Evaluating Policy Effects by Global VAR Models"Group photo

12:00-13:30

Lunch

Invited Session 5: Data Science I
Chair: Genshiro Kitagawa (The University of Tokyo/Meiji University, Japan)
13:30-14:00

Ryo Yoshida (The Institute of Statistical Mathematics, Japan)"Machine Learning for Accelerated Materials Discovery"

14:00-14:30

Ippei Obayashi (RIKEN/Tohoku University, Japan)"Topological Data Analysis for Materials Science"

14:30-15:00

Tomoyuki Miyaji (Meiji University, Japan)"Application of A Topological Computation Method to Biomedical Signals"

15:00-15:20

Coffee breakSpecial Short Session II
Chair: Tomoyuki Miyaji (Meiji University, Japan)
15:20-15:35

Ayuki Sekisaka (Meiji University, Japan)"Motion of Spots on the Curved Surface"

15:35-15:50

Nina Sviridova (The University of Tokyo, Japan)"Filtration Effect on the Dynamical Characteristics of the Photoplethysmogram at
Green Light"

15:50-16:05

Takahiro Tanabe (Meiji University, Japan)"Interaction between Two Intruders in Granular Flow"

16:05-16:20

16:20-16:35

Lorenzo Contento (Meiji University, Japan)"Competitor-mediated Coexistence and Complex Patterns in a Three-species
Competition-diffusion System"
Coffee breakSpecial Short Session III
Chair: Yoshiro Yamamura (Meiji University, Japan)
16:35-16:50

16:50-17:05

17:05-17:20

Kengo Nakai (The University of Tokyo, Japan)"Machine-learning Inference of Variables of a Chaotic Fluid Flow from Data using
Reservoir Computing"Tomoro Kimura (Meiji University, Japan）"Application of Hamiltonian Monte Carlo Method to Bayesian Estimation of Sika
Deer Population Dynamics for Effective Wildlife Management"
Daisuke Kurisu (Tokyo Institute of Technology, Japan）"Nonparametric Inference for Lévy Models"

17:20-17:35

Hiroumi Misaki (University of Tsukuba, Japan）"On the Error of Realized Measures of Volatility in Finance"

18:00-20:00

BanquetSpecial Presentation“Mathematical Study of Impossible Objects in MIMS”
Kokichi Sugihara (Meiji University, Japan)

Wednesday, February 13
Invited Session 6: Data Science II
Chair: Jiancang Zhuang (The Institute of Statistical Mathematics, Japan)
10:00-10:30

10:30-11:00

11:00-11:30

11:30-13:00

Aapo Hyvärinen (University College London, UK)"Nonlinear Independent Component Analysis: A Principled Framework for
Unsupervised Deep Learning"
Shunsuke Ono (Tokyo Institute of Technology, Japan)"Nonsmooth Convex Optimization and Sparse Regularization in Signal andInformation Processing"
Jiancang Zhuang (The Institute of Statistical Mathematics, Japan)"A Semi-parametric Spatiotemporal Hawkes-type Point Process Model with
Periodic Background for Crime Data"
Lunch

Invited Session 7: Financial Time Series Modeling
Chair: Yoko Tanokura (Meiji University, Japan)
13:00-13:30

Yoshiro Yamamura (Meiji University, Japan)"Dynamics of Commercial Real Estate Market in Tokyo"

13:30-14:00

Yoko Tanokura (Meiji University, Japan)"On Trend Change Factors of Financial Markets"

14:00-14:10

Closing: Genshiro Kitagawa (Chair of Organizing Committee)

Invited Session 1: Particle Filter and Data Assimilation
10:10-10:40 on February 11

Iterative Particle Sampling for Bayesian Computation
Arnaud Doucet - Oxford University
October 29, 2018
Abstract
Particle methods, also known as Sequential Monte Carlo methods, are numerical schemes used tosample approximately from a sequence of probability distributions. In statistics and related fields, thesemethods are often used to approximate the posterior distributions of state variables in non-linear nonGaussian state-space models or to approximate a complex posterior distribution by using a sequenceof targets bridging smoothly the prior to the posterior. They also provide unbiased estimators of theirnormalizing constants which have been exploited in many applications to perform inference using thepseudo-marginal method. However, contrary to Markov chain Monte Carlo schemes, standard particle
algorithms require specifying before execution a number of Monte Carlo samples, named particles in thiscontext, and do not allow us to iteratively improve the corresponding Monte Carlo estimators. To address
this limitation, various schemes where particles are added incrementally have been previously proposedso as to make best use of the computational budget available. Unfortunately, these algorithms provide
either biased estimators of the normalizing constants or high variance unbiased estimators of them. We
propose here a novel iterative particle sampling scheme which returns unbiased estimators of normalizing
constants and estimators of expectations with respect to the target distributions whose variances can becontrolled under standard regularity assumptions. This scheme can be additionally parallelized. In thespirit of particle Markov chain Monte Carlo methods, we also propose novel Markov chain Monte Carlo
schemes based upon the iterative particle sampler. We demonstrate the methodology on a variety ofBayesian inference tasks.
Keywords: Conditional sequential Monte Carlo; Normalizing constants; Particle methods; Sequential Monte

Carlo methods; State-space models.

Invited Session 1: Particle Filter and Data Assimilation
10:40-11:10 on February 11

On Some Realistic Instances of Particle Filter in Engineering Field
Norikazu IKOMA

Nippon Institute of Technology, Saitama, Japan.

Abstract
Mathematical optimality in sequential state estimation based on state space modeling andsequential Bayes inference is nice property. Many of theoretical papers based on theproperty contain numerical examples in order to show efficiency of each proposed content,
and some of them may contain real data analyses. Despite of good performance in theseexamples / analyses, there still exist significant gap between mathematical theory andpractice in real world where theoretical assumptions are not sufficiently satisfied due tocomplexity of real world. Furthermore, real-time requirement of state estimation isnecessary in many of realistic applications. This invited talk presents a survey on somerealistic instances of particle filter in engineering field, such as human motion estimation[1][2], mobile robot localization[3] with simultaneous mapping[4], reliability estimation
simultaneously with original state variable[5] and its combination with CNN(Convolutional Neural Network)[6], in order to figure out such the gaps and problematic
issues in realistic applications.

References[1] Norikazu Ikoma, "GPGPU Implementation for Steering Hands Tracking of a Car Driverby Particle Filter with Depth Image Sensor", Second International Conference on Robot,
Vision and Signal Processing (RVSP-2013), pp.164-167, 2013.
[2] Sangeun Lee and Keiichi Horio, "Human Tracking with Particle Filter Based on Locally
Adaptive Appearance Model" Journal of Signal Processing (RISP in Japan), Vol.18, No.4,
pp.229-232, 2014.
[3] Jian Mi and Yasutake Takahashi, "Design of an HF-Band RFID System with Multiple
Readers and Passive Tags for Indoor Mobile Robot Self-Localization", Sensors, Vol.16, No.8,#1200, 20 pages, 2016.
[4] Jun Wang and Yasutake Takahashi, "SLAM Method Based on Independent Particle
Filters for Landmark Mapping and Localization for Mobile Robot Based on HF-band RFID
System", Journal of Intelligent & Robotic Systems, Volume 92, Issue 3-4, pp.413-433, 2018.
[5] Naoki Akai, Luis Yoichi Morales and Hiroshi Murase, "Reliability estimation of vehicle
localization result", Proc. of the IEEE Intelligent Vehicles Symposium (IV), pp. 740-747,
2018.
[6] Naoki Akai, Luis Yoichi Morales and Hiroshi Murase, "Simultaneous pose and reliability
estimation using convolutional neural network and Rao-Blackwellized particle filter",
Advanced Robotics, Vol.32, No.17, pp. 930-944, 2018.

Invited Session 1: Particle Filter and Data Assimilation
11:10-11:40 on February 11

Applications of Data Assimilation and Error Analysis to
Biological and Geophysical Systems
Kazuyuki Nakamura1,2
1

School of Interdisciplinary Mathematical Sciences / MIMS, Meiji University, Tokyo 164-8525,
Japan
2 JST, PRESTO, Saitama 332-0012, Japan

Abstract
Data assimilation (DA) is the technique which aims at combining computer simulations withobservation data. DA is originally developed in the meteorology and the oceanography, but
nowadays application fields of DA are expanding, such as life sciences, geotechnics and materialsciences.
In biology, especially in molecular biology, interactions among biomolecules such as proteins are
written as network. In systems biology, these relationships are often modelled as ordinary differential
equations. The parameters included in these models have uncertainties and quantification of these
uncertainties can give useful information on the biological systems. Data assimilation is useful for
quantification of these uncertainties. In this presentation, we will show several assimilation results ofa gene regulatory network and a metabolic pathway system.
To manage and estimate these uncertainties, some tools imported from dynamical system might beuseful. We will also discuss applicability of system error analysis to data assimilation method. Localtranslation error analysis is used for Lorenz 96 model that is a reduced atmospheric flow model.

Invited Session 2: Earth Science
13:10-13:40 on February 11

State-Space Modeling for Signal Extraction of Earthquake andPrecision Weighing
Hui Penga, Genshiro Kitagawab, and Satoshi MiuracaSchool of Information Science and Engineering, Central South University, Changsha,
Hunan 410083, Chinab

Mathematics and Informatics Center, the University of Tokyo, Tokyo 113-8656,
Japanc

Graduate School of Science, Tohoku University, Sendai 980-8578, Japan

Abstract: In this talk, we introduce a time series modeling method for geodetic signal
and small-weight weighing signal, which are contaminated by some environment
variables. Geodetic signals such as strain, gravity and groundwater level data usuallyare greatly affected by atmospheric pressure, earth tide, precipitation, and missing
observations due to instrument maintenance or breakdown. In on-line weighing
process of small-weight drug bottle, the weighing signal is very sensitive to theambient wind pressure and equipment vibration. To detect meaningful geodetic signal
or accurate weighing result from heavily noise-affected data, we build a time seriesmodel for decomposition of the data taking into account the characteristics of effects
from these covariates. This talk presents the time series modeling method for
detecting geodetic signal from observed time series and weighing result fromcontinuous weighing data of small-weight drug bottle. Using the modeling approach,a geodetic time series can be decomposed into several components including geodetic
trend signal, barometric pressure response, earth tidal response, precipitation responseand data level shift due to mechanical maintenance or breakdown., While, a weighing
sample data can be decomposed into weighing result, ambient wind pressure response
and equipment vibration response. In the model parameter estimation process, we
introduce the model stability constraint for guaranteeing model stability. On the other
hand, for the traditional modeling method that regards the process noise as Gauss
random signal with constant variance, by introducing GARCH (Generalized
Autoregressive Conditional Heteroskedasticity) structure into the signal extraction
model, we may improve the modeling performance. Finally, case studies of real
geodetic time series and weighing data demonstrate the effectiveness of the presented
modeling method that leads to some important findings in seismology or obtains
accurate weighing result.

Invited Session 2: Earth Science
13:40-14:10 on February 11

Seismic Wavefield Imaging of Long-Period Ground Motionin the Tokyo Metropolitan Area, Japan
Hiromichi Nagao1,2
Masayuki Kano3, Kenji Nagata4,5, Shin-ichi Ito1,2, Shin’ichi Sakai1,
Shigeki Nakagawa1, Muneo Hori1, and Naoshi Hirata1
1: Earthquake Research Institute, The University of Tokyo, Japan
2: Graduate School of Information Science and Technology, The University of Tokyo, Japan
3: Graduate School of Science, Tohoku University, Japan
4: Artificial Intelligence Research Center, National Institute of Advanced Industrial Science andTechnology, Japan
5: Presto, Japan Science and Technology Agency, Japan
Long-period ground motions due to large earthquakes can cause devastating disasters,
especially in urbanized areas located on sedimentary basins. To assess and mitigate such damage,it is essential to rapidly evaluate seismic hazards for infrastructures, which can be simulated byseismic response analyses that use waveforms at the base of each infrastructure as an input groundmotion. The present study reconstructs the seismic wavefield in the Tokyo metropolitan area
located on the Kanto sedimentary basin, Japan, from seismograms of the Metropolitan Seismic
Observation network (MeSO-net). The obtained wavefield fully explains the observed waveforms
in the frequency band of 0.10-0.20 Hz. This is attributed to the seismic wavefield imaging
technique proposed by Kano et al. (Geophys. J. Int., 2017), which implements the replica
exchange Monte Carlo method to simultaneously estimate model parameters related to thesubsurface structure and source information. Further investigation shows that the reconstructed
seismic wavefield lower than 0.30 Hz is of high quality in terms of variance reduction (VR),which quantifies a misfit in waveforms but that the VR rapidly worsens in higher frequencies.
Meanwhile, the velocity response spectra show good agreement with observations up to 0.90 Hz
in terms of the combined goodness of fit (CGOF), which is a measure of misfit in the velocity
response spectra. Inputting the reconstructed wavefield into seismic response analyses, we canrapidly assess the overall damage to infrastructures immediately after a large earthquake.
References
Kano, M., H. Nagao, K. Nagata, S. Ito, S. Sakai, S. Nakagawa, M. Hori, and N. Hirata, Seismic
wavefield imaging of long-period ground motion in the Tokyo Metropolitan area, Japan, J.
Geophys. Res. Solid Earth, Vol. 122, doi:10.1002/2017JB014276, 2017.
Kano, M., H. Nagao, D. Ishikawa, S. Ito, S. Sakai, S. Nakagawa, M. Hori, and N. Hirata, Seismic
wavefield imaging based on the replica exchange Monte Carlo method, Geophys. J. Int., Vol.
208, pp. 529-545, doi:10.1093/gji/ggw410, 2017.

Invited Session 2: Earth Science
14:10-14:40 on February 11

Real-time Monitoring System of Earthquakes
by Bayesian estimation
Koji Tamaribuchi1
1

Meteorological Research Institute, Japan Meteorological Agency, Tsukuba 305-0052, Japan
Abstract

Earthquake Early Warning (EEW) system is a real-time monitoring alert system of earthquake that
detects signals of seismic waves, estimates hypocenters, and predicts future ground shakes. After the2011 Mw 9.0 Tohoku earthquake, the extremely active seismic activity caused serious issues for theearthquake real-time monitoring system in Japan. Since the hypocenter was estimated based on
arrival times of the seismic waves, the EEW system issued many false alarms due to the failure ofcorrectly determining the source location and magnitude in the case of the multiple concurrent
earthquakes. It can be regarded as a classification problem which earthquake triggers observation
stations. Conventionally, it was difficult to properly classify simultaneous occurrence of earthquakes
automatically because it was based only on the arrival times of earthquakes.
We then proposed the IPF method (Integrated Particle Filter method) to distinguish multiple
concurrent events in real time by using a particle filter. We used amplitudes as well as P-wave arrival
times of triggered stations. The particle filter can also consider the uncertainty of the hypocenter. The
IPF method have started its operation for the JMA EEW in December 2016.
The production system of earthquake catalog in JMA also faced severe problems. Since the all
events were required the visual inspection, the large number of aftershocks and induced earthquakes
caused delay of the catalog production. We also applied Bayesian estimation to identify multiple
concurrent earthquakes of the JMA unified earthquake catalog which includes micro-earthquake that
nobody felt shaking. This method (Phase combination Forward search method; PF method) makes
integrated use of P- and S-wave arrival times and maximum amplitude. The PF method have started
its operation in April 2016. In the 2016 Kumamoto earthquake, which began on April 14, the abilityof the method was fully demonstrated. The real-time monitoring technology is an indispensable andextremely important task. We have to develop the monitoring system that process their big data inreal time.

Invited Session 3: Time Series Modeling
15:00-15:30 on February 11

ICMMA 2018

Gaussian-sum Filter and Smootherfor Nonlinear or Non-Gaussian Smoothing
Genshiro Kitagawa
Mathematics and Informatics Center, the University of Tokyo, Tokyo 113-8656, Japan
Abstract:
Gaussian-sum filter can produce very accurate prediction and filtering
distributions for linear state-space model with the noise inputs whose distributions are
given as a mixture of several Gaussian distributions. On the other hand, a criticalproblem with this method is that the number of Gaussian components increased
exponentially with time step. Therefore, in order to apply this filter for actual timeseries modeling, we need to develop an efficient method of reducing the number ofGaussian components in terms of both accuracy and computational cost.
In this talk, mixture reduction methods such as the one proposed by Runnalls(2007) and a resampling method are considered to improve the previously developed
filtering and smoothing methods. The developed methods are tested for jumpdetection problem and seasonal adjustment problems.
The developed method will be also applied to nonlinear smoothing problem.
Based on the Taylor expansion of the nonlinear functions in the state and observation
models, the extended Kalman filter can be applied to nonlinear state-space model.
However, the main problem with this extended Kalman filter appears when thepredictive distribution becomes bimodal or multi-model due to the nonlinear
transition function in the system model. Namely, if the state distribution becomes
bimodal, it may yield disastrous results. We develop a Gaussian-sum extended
Kalman filter that can properly handle multi-modal state distributions. By awell-known nonlinear test example, it will be shown that a Gaussian-sum extended
Kalman smoother can recover unknown signal of nonlinear system based on astate-dependent predictive density.
References
Kitagawa, G. (1994). The two-filter formula for smoothing and an implementation of
the Gaussian-sum smoother. Annals of the Institute of Statistical Mathematics,
46(4), 605-623.
Kitagawa, G. (2014). Computational aspects of sequential Monte Carlo filter andsmoother. Annals of the Institute of Statistical Mathematics, 66(3), 443-471.
Runnalls, A. R. (2007). Kullback-Leibler approach to Gaussian mixture reduction.
IEEE Transactions on Aerospace and Electronic Systems, 43(3), 1-10.

Invited Session 3: Time Series Modeling
15:30-16:00 on February 11

Analysis of the Structure of Economic Growth andBusiness Cycles in the Prefectures of Japan
Koki Kyo
Obihiro University of Agriculture and Veterinary Medicine, Japan
In this study, we analyze the structure of economic growth and business cycles
in the prefectures of Japan. We take a quarterly time series of gross domestic
product (GDP) in whole Japan as a reference. So, for a quarterly time series yn (inlogarithmic scale) of GDP in Japan, we use the soft of Decomp to decompose the timeseries yn into a trend component tn , a seasonal component, a cyclical component rn ,and a irregular component.
Based on the above results, we construct a model for analyzing the structure of
the prefectural GDP in Japan as follows:(1)(2)
xmi = ai + bi fm+ ci fm(L) + dmi + ϵmi ,ϵmi ∼ N(0, σi2 ) (i = 1, 2, . . . , 47).

In this model, xmi is an annual time series of GDP (in logarithmic scale) for
(1)

(1)

the i-th prefecture, fm the ﬁrst common factor which is determined by fm =
4

(2)
(2)

j=1 exp(t4∗(m−1)+j ), fm the second common factor which is given by fm (L) =log 4j=1 exp(r4∗(m−1)+j−L ), where t4∗(m−1)+j and r4∗(m−1)+j denote the estimates

log

for the trend component and the cyclical component in the GDP of whole Japan.
dmi the individual trend for the i-th prefecture, and ϵmi the error term, ai , bi andci are constants, especially, bi and ci are the factor loadings of the common factors, Also, L is a lag parameter expressing the lead-lag relationship between cyclicalmovements in GDP for each prefecture and the whole Japan in the number of quarter. We introduce a second order smoothness prior to dmi . So, a set of state spacemodels for dmi is constructed, the estimate of dmi is obtained based on Kalman ﬁlter
algorithm and so on.
Thus, following knowledge about the prefectural economies can be obtained. (a)From the value of L, we see the lead-lag relationship between the movements of
GDP in the whole Japan and each prefecture. (b) From the estimates of bi and ci
we can analyze the economic dependence of each prefecture on whole Japan. (c)From the estimates of dmi , the feature of the individual trend for each prefecture
can be investigated. Furthermore, the similarities between the individual trends for
all prefectures can analyzed by employing methods of cluster analysis.

Invited Session 3: Time Series Modeling
16:00-16:30 on February 11

Term Structure Models During the Global Financial
Crisis: A Parsimonious Text Mining Approach
Kiyohiko G. Nishimura
National Graduate Institute for Policy Studies (GRIPS) and CARF, University of Tokyo

Seisho Sato
Graduate School of Economics and CARF, University of Tokyo

Akihiko Takahashi
Graduate School of Economics and CARF, University of Tokyo

Abstract
This work develops and estimates a three-factor term structure model with explicit sentiment factors in a period including the global ﬁnancial crisis, where market
conﬁdence was said to erode considerably. It utilizes a large text data of real time,
relatively high-frequency market news and takes account of the diﬃculties in incorporating market sentiment into the models. To the best of our knowledge, this is
the ﬁrst attempt to use this category of data in term-structure models.
Although market sentiment or market conﬁdence is often regarded as an important driver of asset markets, it is not explicitly incorporated in traditional empiricalfactor models for daily yield curve data because they are unobservable. To overcome
this problem, we use a text mining approach to generate observable variables which
are driven by otherwise unobservable sentiment factors. Then, applying the Monte
Carlo ﬁlter as a ﬁltering method in a state space Bayesian ﬁltering approach, weestimate the dynamic stochastic structure of these latent factors from observablevariables driven by these latent variables.

Special Short Session I
16:45-17:00 on February 11

Oscillator decomposition of time series data
Takeru Matsuda
The University of Tokyo
Many time series are considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing timeseries into such oscillation components like the Bayesian seasonal adjustment method.
The proposed method is based on the following Gaussian linear state-space model,(1)(K)
which describes several oscillators xt , · · · , xt underlying the given time series yt .
((k)

xt+1,1(k)

xt+1,2)yt,1 =) ( (k) ) ( (k) )
vt,1
xt,1
cos(2πfk ∆t) − sin(2πfk ∆t)(k = 1, · · · , K),
= ak
(k) +
(k)
sin(2πfk ∆t) cos(2πfk ∆t)
xt,2
vt,2
)
(
(( ) ( 2
))
(k)
vt,1
0
σk 0
∼ N2
,
(k = 1, · · · , K),
(k)
0
0 σk2
vt,2

K
∑

(

(k)
xt,1 + wt,1 ,

k=1

yt,j =

K
∑

(k)

(k)

(cjk,1 xt,1 + cjk,2 xt,2 ) + wt,j

k=1

(j = 2, · · · , J),(wt,1 , · · · , wt,J )⊤ ∼ NJ (0, τ 2 I).

The model parameters and the number of oscillators are determined from data byusing the empirical Bayes method and Akaike Information Criterion (AIC), respectively. Thus, the proposed method extracts underlying oscillators in a data-driven
manner and enables investigation of the phase dynamics of a given time series. We
apply the proposed method to real data from various ﬁelds such as astronomy, ecology, chronobiology, and neuroscience. For example, from the mouse actogram data,
an oscillator corresponding to the circadian rhythm is extracted and its phase reveals
the progression of jet lag.

References[1] T. Matsuda and F. Komaki. Time series decomposition into oscillation components and phaseestimation. Neural Computation, 29, 332–367, 2017.
[2] T. Matsuda and F. Komaki. Multivariate time series decomposition into oscillation components.
Neural Computation, 29, 2055–2075, 2017.

Special Short Session I
17:00-17:15 on February 11

Decomposition of multiple seasonal components
in a seasonal adjustment model
Tomoya Haba, Hiromichi Nagao and Shin-ichi Ito
The University of Tokyo, Tokyo, 113-0033, Japan
A nonparametric seasonal adjustment model [1] is possible to decompose a given
time series yt into multiple components such as trend component ut , seasonal component st and observation noise wt , i.e., yt = ut + st + wt . The Kalman ﬁlter conducts
such a decomposition based on appropriate system model deﬁned for each of the components. A unique decomposition is possible when the system model of the seasonal
component st is given as(1 + B + · · · + B p−1 )st = vs,t ,(1)where B is a backwards shift operator, e.g., Bst = st−1 , p is the period of st , and vs,t isa system noise that follows a normal distribution. However, when an observation model
contains multiple seasonal components with diﬀerent periods, e.g., yt = ut +s1t +s2t +wt ,the conventional decomposition method using the system model equation (1) for eachseasonal component is often impossible to uniquely decompose the trend and seasonal
components.
In this study, we propose a method to extract multiple seasonal components in theframework of the seasonal adjustment. In the case that an observation model contains
two seasonal components, our method considers three cases according to the relation
between the periods of seasonal components, and gives system model that achieves
the unique decomposition for each case. We also propose a selection method using the
Akaike Information Criterion (AIC) [2], which enables us to select dominant seasonal
components in a given time series. Numerical experiments and applications to real
data show the validity of our method.

References[1] G. Kitagawa and W. Gersch: A smoothness priors-state space modeling of timeseries with trend and seasonality. Journal of the American Statistical Association．
Vol. 79, No. 386, pp. 378–389, 1984.
[2] H. Akaike: Information theory and an extension of maximum likelihood principle.
In Proceedings of 2nd International Symposium on Information Theory． pp. 267–
281, Akademiai Kiàdo, Budapest, 1973.

Special Short Session I
17:15-17:30 on February 11

Data assimilation for grain growth prediction
based on a second-order adjoint method
Shin-ichi Ito1,2, Hiromichi Nagao1,2, Takashi Kurokawa2,
Tadashi Kasuya3, and Junya Inoue4,3

1 Earthquake Research Institute, The University of Tokyo, Tokyo 113-0032, Japan

2 Graduate School of Information Science and Technology, The University of Tokyo, Tokyo

113-8656, Japan

3 Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan

4 Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo

153-8904, Japan
Abstract

Data assimilation (DA) is a fundamental computational technique that integrates numericalsimulation models and observation data on the basis of Bayesian statistics. Originally developed for
meteorology, especially weather forecasting, DA is now an accepted technique in various scientificfields such as materials science to evaluate unknown material parameters and/or unmeasurable inner
structures in objective materials. One key issue that remains controversial is the implementation of
DA in massive simulation models used in materials science under the constraints of limited
computation time and resources. For the purpose of obtaining accurate prediction of the grain growth
in metals and steel alloys, we propose an adjoint-based DA method that produces optimum estimates
and their uncertainties within reasonable computation time and resource constraints. The
uncertainties are given as several diagonal elements of an inverse Hessian matrix, which is thecovariance matrix of a multivariate normal distribution that approximates the target posterior
probability density function in the neighborhood of the optimum. Although conventional algorithms
for deriving the inverse Hessian matrix require a huge computational cost since a full description of
the Hessian matrix is needed, our methodology implementing a second-order adjoint method enables
us to extract the diagonal elements without computing the full description of the matrix. The
proposed method is validated through numerical tests using some massive phase-field models, which
describe growing dynamics of grain structure. We confirm that the proposed method correctly
reproduces the parameter and initial grain structure assumed in advance, and successfully evaluates
the uncertainty of the parameter. Furthermore, we confirm that the influence of the obtained
uncertainties to the grain growth prediction is correctly estimated. Such information regarding
uncertainty is valuable, as it can be used to optimize the design of experiments.
References
1. Ito, S., H. Nagao, A. Yamanaka, Y. Tsukada, T. Koyama, M. Kano, and J. Inoue, Data assimilation
for massive autonomous systems based on second-order adjoint method, Physical Review E 94,
043307 (2016).
2. Ito, S., H. Nagao, T. Kasuya, and J. Inoue, Grain growth prediction based on data assimilation byimplementing 4DVar on multi-phase-field model, Science and Technology of Advanced Materials
18:1, 857-869 (2017).
3. Ito, S., H. Nagao, T. Kurokawa, T. Kasuya, and J. Inoue, Bayesian inference of grain growth
prediction via multi-phase-field models, Physical Review Materials, submitted.

Special Short Session I
17:30-17:45 on February 11

An attempt for the thermal transport modelling of fusion plasmas
based on the statistical approach
Masayuki Yokoyama1,2

1 National Institute for Fusion Science (NIFS), National Institutes of Natural Sciences (NINS),
2 SOKENDAI (The Graduate University for Advanced Studies), Toki 509-5292, Japan

yokoyama@nifs.ac.jp
Abstract
A statistical approach has been attempted for the thermal transport modelling for plasmas in fusion
experiment [1]. It can provide regression expressions for the heat diffusivities, to be valuable forpredicting the temperature profile evolution. This approach should outstrip the conventional scalinglaws for the global confinement time since it also deals with profiles (temperature, density, heating
depositions etc.). This approach has become possible with the analysis database accumulated by theextensive application of the integrated transport analysis suite, TASK3D-a [2], to the LHD (Large
Helical Device) experiment at NIFS [3]. After the first attempt that was published in Ref. [1], thevalidity check of the obtained regression expression for the ion heat diffusivity profiles has been
made through its implementation into the predictive TASK3D calculations to be compared withseveral LHD experimental results. Furthermore, the improvement for statistical treatment has beenpromoted by establishing collaborations with experts in statistics. The first attempt [1] and thesuccessive progress will be reported in the workshop, as one of possible and innovative “data-driven”
modelling approaches in fusion research.
Acknowledgements: The author appreciates valuable advices on statistics from Prof. K. Shimizu
and Prof. Y. Iba (The Institute of Statistical Mathematics, ISM). Dr. H. Yamaguchi (National Institutefor Fusion Science) is also acknowledged his contributions for validity check of this approach. This
work has been supported by the NIFS Collaborative Research Programs, NIFS14KNTT025,
NIFS14UNTT006 and NIFS18KNTT046, and by the ISM Collaborative Research Program,
H30-1002. The participation to this workshop is supported by the NINS Joint Research Program.
References:[1]

M. Yokoyama, Plasma and Fusion Research 9 (2014) 1302137.

[2]

M. Yokoyama et al., Nuclear Fusion 57 (2017) 126016.

[3]

Y. Takeiri, IEEE Trans. Plasma Sciences 46 (2018) 1141.

Invited Session 4: Econometrics and Data Analysis
10:00-10:30 on February 12

On the network of global currencies: Does lead - lagconnectedness matter?
Tan T.M. Lea,b , Franck Martina , Duc K. NguyencaFaculty of Economics, University of Rennes 1, Rennes, FRANCEb
Hue College of Economics - Hue University, Hue, VIETNAM
c
Ipag Business School, Paris, FRANCE

Abstract
In this paper, we augment Billio et al. (2012) by using conditional Granger causality to measure lead-lag network connectedness among the most globally tradedcurrencies. From this, we then investigate the informational content of systemwide dynamic connectedness and examine the relationship between node connectedness and exchange rate variations. Our panel regression results reveal that
node connectedness has significant effects on mean return, standard deviation
and Value at Risk after controlling for certain fundamental and market-behavior
variables. More importantly, increase in a currency’s lead-lag connectedness predicts greater variations in its values vis-à-vis the USD in the following year. We
also find evidence that node with higher centrality before the global financial
crisis faced more extreme depreciation in the crisis period. At the broader level,
the dynamic system-wide lead-lag connectedness is seen to spike during high-risk
episodes, become more stable in lower-risk environment and co-integrate with
VIX, Vstoxx and rolling TED spread. It could also capture major systemic events
like Lehman Brothers’ collapse, the get-through of European Stability Mechanism
in September 2012 as well as Brexit.
Keywords: exchange rates, conditional granger causality, network,
connectedness, systemic risk, risk and return.

This research is supported by Chaire Fondation Rennes 1 ”Nouveaux défis de la banque”.
Email address: to-minht-tan.le@univ-rennes1.fr (Tan T.M. Le)

Preprint submitted to Elsevier

October 5, 2018

Invited Session 4: Econometrics and Data Analysis
10:30-11:00 on February 12

Non-Stationary Errors-in-Variables Models and their
Econometric Applications
Naoto Kunitomo

Abstract:
Kunitomo, Sato and Kurisu (2018, Springer) have developed a new way to handle nonstationary (high-frequency) financial data with micro-market noise.

We apply their

method (the separating information maximum likelihood method) to analyze many
economic data including macroeconomic time series.
characteristics of macro-economic time series.

There are at least three important(i) the time series observations are

mixtures of non-stationary components, seasonal components and stationary components,(ii) measurement errors usually exist because macro-economic (official) data are usuallyconstructed from various sources including sampling surveys, and (iii) the sample size ofmacro-economic data is usually small. The main interest of statistical analysis of macroeconomic variables has been to find statistical relationships among multivariate timeseries. Since the sample size is small and there are several components, it is important touse an appropriate statistical procedure in both time domain and frequency domain to
extract information on trend components, seasonal components and noise (ormeasurement error) components in a systematic way from non-stationary and noisy timeseries data.

We propose a simple filtering method to detect trend components andseasonal components from noise components.
Invited Session 4: Econometrics and Data Analysis
11:00-11:30 on February 12

How Does Unconventional Monetary Policy Aﬀect the
Global Financial Markets?: Evaluating Policy Eﬀects by
Global VAR Models
Tomoo Inoue∗

Tatsuyoshi Okimoto†

Abstract
Since the beginning of the new century, we have observed a number of so-called “unconventional monetary policies,” conducted by central banks in the major economies.
In this paper, we re-investigate the policy eﬀects of the Bank of Japan and the Federal Reserve (Fed), in regards to ﬁnancial markets, by employing the Global Vector
Autoregression (GVAR) approach.
Using generalized impulse response function analysis, we compare the monetary
policy eﬀects of the BOJ and those of the Fed on four ﬁnancial indices, i.e., sovereign
bond price, corporate bond price, equity price, and exchange rate, both in the domestic
markets and the global markets. Possible structural changes during the sample period
are also modeled.
Some of the main results are: 1) the BOJ’s expansionary monetary policy rises theJapanese corporate bond price only temporarily, but sovereign bond price and equity
price of the domestic market persistently. However, in terms of its global inﬂuence,the eﬀects are rather limited; 2) the Fed’s expansionary monetary policy increases US
sovereign bond price temporarily, but corporate bond and equity price for a long-run;and 3) the diﬀerent magnitude of policy eﬀects in the diﬀerent time periods by twocentral banks are illustrated by the use of ST-GVAR.
Keywords: Monetary policy, Bank of Japan, Federal Reserve, GVAR, Financial linkage
JEL Codes: C32, E44, F41∗

Professor, Faculty of Economics, Seikei University. E-mail: inoue@econ.seikei.ac.jp
Associate Professor, Crawford School of Public Policy, Australian National University and Visiting Fellow,
Research Institute of Economy, Trade, and Industry. E-mail: tatsuyoshi.okimoto@anu.edu.au†

Invited Session 5: Data Science I
13:30-14:00 on February 12

Machine Learning for Accelerated Materials Discovery
Ryo Yoshida
The Institute of Statistical Mathematics, Research Organization of Information and Systems
10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Phone: +81-50-5533-8534
E-mail: yoshidar@ism.ac.jp
The ability of machine intelligence trained on massive amounts of data to reach oreven outperform humans has been demonstrated in intellectually demanding tasks acrossvarious fields. Under such circumstance, there has been a growing interest in using machinelearning (ML) to accelerate the discovery and development of new materials. In this talk, I will
describe some key drivers of ML technologies to achieve this goal.
The first topic is the ML-assisted materials design. In general, the material spaces are
considerably high-dimensional. For instance, the chemical space of small organic molecules isknown to contain as many as 1060 candidates. The problem entails a considerably complicatedcombinatorial optimization where it is impractical to fully explore the vast landscape ofstructure-property relationships. We developed an inverse material design algorithm by the
integration of ML and quantum chemistry calculation. The design objective is to generatepromising hypothetical materials that exhibit desired properties of various kinds. The
emergence of such ML algorithms to exhaustively search in such a huge space is expected toaccelerate the pace of expanding the frontier in the vast universe of materials.
The second topic is a subject of data scarcity. In recent years, various kinds of databases
have begun to emerge with the aim to transform materials science into being fully data driven.
However, the volume and diversity of data being accumulated remain far from enabling us to
fully enjoy remarkable advances recently made in ML. A ML framework called transfer
learning has the great potential to break this barrier. For a target property with a limited supplyof training data, models on physically related proxy properties are pre-trained on large amountsof data, which capture features of materials generally applicable to the target task. Re-purposing
such ML-acquired knowledge on a new task provides an outstanding prediction ability as highly
experienced experts can make rational inferences even on considerably less experienced tasks.
We have developed a pre-trained model library, XenonPy.MDL, which can be used to predictvarious properties of small molecules, polymers and inorganic solid-state materials. Along withthis library, I will demonstrate some outstanding successful applications of transfer learning.
References
1) Ikebata, H. et al. (2017) J Comput Aided Mol Des, 31(4):379-391.
2) XenonPy: http://xenonpy.readthedocs.io/en/late3) iQSPR: https://github.com/yoshida-lab/iqspr

Invited Session 5: Data Science I
14:00-14:30 on February 12

TOPOLOGICAL DATA ANALYSIS FOR MATERIALS SCIENCEIPPEI OBAYASHI∗,∗∗

In this presentation, I will talk about Persistent Homology (PH) and its applications tomaterials science.
PH is one of the most important tool of Topological Data Analysis (TDA) [1, 2]. TDA is adata analysis approach from the viewpoint of topology, and TDA enables us to characterize
the “shape of data”. Homology is a classical mathematical theory in the ﬁeld of topology
and homology has 100 years history. Persistent homology [3, 4, 5] is invented to apply theidea of homology to the data analysis, and is developed from the theory to the applications.
Persistent homology characterizes the shape of data quantitatively and eﬀectively by using
homology.
From the viewpoint of mathematics, PH is the homology theory on an increasing sequenceof topological spaces, called a ﬁltration, and PH relates many mathematical ﬁelds suchas algebraic topology, representation theory, and probability theory. From the viewpointof data analysis, we can encode multiscale information on a ﬁltration to characterize theshape of data. PH has an advantage for the analysis of disordered and heterogeneous
data. PH is applied to various data analysis, such as materials science [6, 7] and biology[8].
Computer science such as algorithms[9], computer geometry, and machine learning[10], arealso important for the practical applications of PH. We need various sciences and technologies
for PH.
In this talk, I introduce the fundamental concepts of PH, and some applications to materials science. HomCloud[11], the software based on PH developed by our research group,
is also introduced.
References[1] Gunnar Carlsson. Topology and data. Bull. Amer. Math. Soc., 46:255–308, January 2009.
[2] Herbert Edelsbrunner and John Harer. Computational topology: an introduction. American Mathematical Soc., 2010.
[3] Herbert Edelsbrunner, David Letscher, and Afra Zomorodian. Topological persistence and simplification. Discrete & Computational Geometry, 28(4):511–533, Nov 2002.
[4] Afra Zomorodian and Gunnar Carlsson. Computing persistent homology. Discrete & Computational
Geometry, 33(2):249–274, Feb 2005.
[5] Nina Otter, Mason A. Porter, Ulrike Tillmann, Peter Grindrod, and Heather A. Harrington. A roadmap
for the computation of persistent homology. EPJ Data Science, 6(1):17, Aug 2017.
[6] Yasuaki Hiraoka, Takenobu Nakamura, Akihiko Hirata, Emerson G. Escolar, Kaname Matsue, andYasumasa Nishiura. Hierarchical structures of amorphous solids characterized by persistent homology.
Proceedings of the National Academy of Sciences, 113(26):7035–7040, 2016.
[7] Takashi Ichinomiya, Ippei Obayashi, and Yasuaki Hiraoka. Persistent homology analysis of craze formation. Phys. Rev. E, 95:012504, Jan 2017.
[8] Joseph Minhow Chan, Gunnar Carlsson, and Raul Rabadan. Topology of viral evolution. Proceedingsof the National Academy of Sciences, 110(46):18566–18571, 2013.
[9] Ulrich Bauer, Michael Kerber, Jan Reininghaus, and Hubert Wagner. Phat persistent homology algorithms toolbox. Journal of Symbolic Computation, 78:76 – 90, 2017. Algorithms and Software for
Computational Topology.
[10] Ippei Obayashi, Yasuaki Hiraoka, and Masao Kimura. Persistence diagrams with linear machine learningmodels. Journal of Applied and Computational Topology, 1(3):421–449, Jun 2018.
[11] Ippei Obayashi and HomCloud developer team. HomCloud. http://www.wpi-aimr.tohoku.ac.jp/
hiraoka_labo/homcloud/index.en.html.
∗ Center for Advanced Intelligence Project (AIP), RIKEN ∗∗ Advanced Institute for Materi-als Research (AIMR), Tohoku University

Invited Session 5: Data Science I
14:30-15:00 on February 12

Application of a topological computation method tobiomedical signals
Tomoyuki Miyaji1, Nina Sviridova2, Kazuyuki Aihara2, Tiejun Zhao3, Akimasa Nakano4
1 MIMS, Meiji University, Tokyo 164-8525, Japan
2 IIS, The University of Tokyo, Tokyo 153-8505, Japan
3 Faculty of Agro-Food Science, Niigata Agro-Food University, Niigata 959-2702, Japan
4

Ministry of Agriculture, Forestry and Fisheries, Tokyo 100-8950, Japan

Abstract

In this study, biomedical photoplethysmogram (PPG) signals are analyzed from aviewpoint of dynamical systems. The PPG is used for estimating the heart rates andblood oxygen saturation via non-invasive stationary and wearable health monitoring
devices. Although dynamics and applications of PPG signals have been extensively
studied for several decades, the understanding of PPG signals is not yet sufficient for
further development of the PPG applications for human health monitoring. Moreover,no mathematical model, which is realistic and capable of simulation of PPG signals
under various conditions, has been established. For creating such a model, it isimportant to understand essential dynamical properties of PPG signals. In this study, we
employ topological computation methods. Originally, these methods were developed forstudying the phase space structure of a given dynamical system. These enable us tocapture not only attractors but also repellers and saddles, in contrast to ordinary
computation methods. Recently, there have been some attempts to apply such a method
to experimental data. This study is aimed at capturing the dynamical structure including
saddle equilibria from PPG signals.

Special Short Session II
15:20-15:35 on February 12

Motion of spots on the curved surface
Ayuki Sekisaka
MIMS, Meiji University, Tokyo 164-8525, Japan

Abstract
Reaction-diffusion systems are expected to describe the several phenomena. In particular,
spot solutions are important because they correspond to spot-like pattern in phenomena,and they may trigger a complicated phenomenon. In order to avoid difficulty of the analysis,reaction-diffusion systems are usually considered in the Euclidian space. However, when we
consider the polarity of chemical reaction on the cell membrane, the space is not flat and thecurvature of space must be considered.
From the above motivation, we consider the spot solution on the curved surface. We assume
that the spot solution of reaction-diffusion systems on the two dimensional Euclidian space
exist. We replace the Laplacian to the Laprace-Bertrami operator to consider the reactiondiffusion system on the curved surface. The Laprace-Bertrami operator depends on the
Riemannian metric tensor, and it is determined by the diffusivity of specific material (forexample, it is given by a chemical substance) on the curved surface. We then introduce thereduction equation in the local coordinate from the spot solution of reaction-diffusion
system on the curved surface. The motion of spot solution on the curved surface is given by
the solution of the reduction equation. In particular, our analysis does not depend on the
Riemannian metric and hence, it is applicable to many phenomena.
In this short talk, we show the developed method, and several phenomena of the motion of
spot solutions on the curved surface.

Special Short Session II
15:35-15:50 on February 12

Filtration effect on the dynamical characteristics of thephotoplethysmogram at green light
Nina Sviridova1, Kazuyuki Aihara1, Tiejun Zhao2, Akimasa Nakano3
1 Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
2 Faculty of Agro-Food Science, Niigata Agro-Food University, Niigata, 959-2702, Japan
3 Ministry of Agriculture, Forestry and Fisheries, 1-2-1 Kasumigaseki, Chiyoda, Tokyo,

100-8950, Japan

Abstract

Human photoplethysmogram (PPG) is a biomedical signal which is widely used for
health monitoring in clinical settings and wearable devices. PPG signal is measured by
an optical and noninvasive technique from the surface of skin tissue. The signal
measurement in realistic settings cannot be entirely controlled, and therefore the raw
signal always contains a certain amount of experimental noise and various artifacts that
may limit the PPG applicability. Various filtration techniques are typically applied to the
PPG signal for denoising prior to the calculation of the physiological parameters thatcan be used for health condition estimations, such as heart rates. While, indeed,
application of filtering may decrease the amount of noise in the data and does notdemonstrate any negative effect on the heart rate estimation, the dynamic properties of
the complex PPG signal might be affected. This study explores the effects produced bycommonly used filtration techniques on the dynamical characteristics of the PPG signal.
Among various types of the PPG, the signal measured with green light is investigated.

Special Short Session II
15:50-16:05 on February 12

Interaction between two intruders in granular flow
Takahiro Tanabe1 , Hisao Hayakawa2 and Hiraku Nishimori3
1

MIMS, Meiji University, Tokyo 164-8525, Japan

2 YITP, Kyoto University, Kyoto 606-8502, Japan
2 Graduate school of science, Hiroshima University, Hiroshima 739-8511, Japan

Abstract
There are various effective interactions in nonequilibrium situations, for examples, depletion force,
which is an attractive force between suspending large colloidal particles in a dilute solution, andCasimir-like force in jammed-granular system, which shows both the attractive and repulsive
interactions due to long-ranged fluctuations [1, 2]. Although there are variety of interactions
depending on the environments, we focus on the interactions in two-dimensional granular media.
Since granular materials are of significant for our daily life and industrial applications, it is one of
the important themes to understand the behavior of the interaction in the granular flow. Recently, thestudies of the drag forces for one-intruder in the granular media have been proposed, nevertheless,there are few studies of the interactions between intruders in granular media [3]. In this presentation,
we consider the interactions between two intruders surrounded by granular disks on a plane under auniform flow and oscillatory flow based on discrete element method (DEM) simulations.
The interaction is repulsive under uniform granular disks flow. By contrast, the interaction shows
both attractive and non-attractive interactions under oscillatory flow depending on their oscillation
conditions. We will discuss the mechanism and origin of the interactions.
[1] S. Asakura and F. Oosawa, J. Chem. Phys., 22, 1255, (1954).
[2] Juan-Jose Lietor-Santos and Justin C. Burton, Soft Matter, 13, 1142-1155, (2017).
[3] Y. Takehara and K. Okumura, Phys. Rev. Lett., 112, 1 (2014).

Special Short Session II
16:05-16:20 on February 12

Competitor-mediated coexistence andcomplex patterns in a three-species
competition-diffusion system
Lorenzo Contento1 and Masayasu Mimura2
1

2

MIMS, Meiji University, Tokyo
Department of Mathematical Engineering, Musashino University,
Tokyo

The competitive exclusion principle states that two biological species competing forthe same limited resources cannot coexist, resulting in the eventual extinction of oneof them. However, a rich biodiversity is observed in natural ecosystems. The search
for the mechanisms behind such biodiversity is an ongoing field of research in theoretical ecology. One of such mechanisms is competitor-mediated coexistence, in which
coexistence of two competing species is made possible by a third species invading theecosystem from outside. The simplest model for this phenomenon is a three-species
competition-diffusion system of Lotka-Volterra type. For some choices of the parameters, such system admits two different stable one-dimensional traveling wave solutions.
Depending on their relative velocity, such waves interact in several different ways andthe study of such interaction offers insight about whether or not coexistence occurs in
two-dimensional domains.

1

Special Short Session III
16:35-16:50 on February 12

Machine-learning inference of variables of a chaotic fluid flowfrom data using reservoir computing
Kengo Nakai1 and Yoshitaka Saiki,2,3,4
1 Graduate School of Mathematical Sciences, The University of Tokyo,

Tokyo 153-0041, Japan
2 Graduate School of Business Administration, Hitotsubashi University,

Tokyo 186-8601, Japan
3 JST PRESTO, 4-1-8 Honcho, Kawaguchi-shi, Saitama 332-0012, Japan
4 Institute for Physical Science and Technology, University of Maryland,College Park, MD 20742, USA
Abstract
We infer macroscopic behaviors of a three-dimensional fluid flow with chaotic behaviors using
reservoir computing. A reservoir is a recurrent neural network whose internal parameters are notadjusted to fit the data in the training process. What is done is to train the reservoir by feeding it an
input time-series and fitting a linear function of the reservoir state variables to a desired output
time-series. Due to this approach of reservoir computing we can save a great amount ofcomputational costs, which enables us to deal with a complex deterministic behavior.
Recently it is reported that the reservoir computing is effective in the inference of time-series andsome characteristics using the Lorenz system, Rossler system and Kuramoto-Sivashinsky system.
We apply the same method to a chaotic fluid flow.
In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid
flow except that its behavior is complex but deterministic. We present an inference of the complex
behavior, which requires only past time-series data as training data.
We show that the reservoir dynamics constructed from only past data of energy functions caninfer the future behavior of energy functions and reproduce the energy spectrum. It is also shownthat we can infer a time-series data from only one measurement by using the delay coordinates.
These imply that the obtained two reservoir systems constructed without the knowledge of
microscopic data are equivalent to the dynamical systems describing macroscopic behavior ofenergy functions. It should be remarked that such dynamical systems describing macroscopic
behaviors cannot be derived from the Navier-Stokes equation.

Special Short Session III
16:50-17:05 on February 12

Application of Hamiltonian Monte Carlo Method
to Bayesian Estimation of Sika Deer Population Dynamics
for Effective Wildlife Management.
Tomoro Kimura and Kazuyuki Nakamura
1

1

1,2,3

Graduate School of Advanced Mathematical Sciences, Meiji University
2

MIMS, Meiji University, Tokyo 164-8525, Japan
3

JST, PRESTO, Saitama 332-0012, Japan

Abstract

For the past several decades, overabundant deer have caused problems across Japan. In Hokkaido
prefecture, agricultural damage exceeded five billion yen in 1996. For ecosystem management, it isnecessary to estimate population and to lead them to desirable state artificially. To estimate deer
population dynamics temporally and spatially, state-space models integrating multiple observation
data are suitable. These models often include high dimensional parameters and complex
distributions. Markov chain Monte Carlo (MCMC) method is generally adopted for the parameter
estimation. Especially, many previous researches have applied Gibbs sampling which is one of analgorithm of MCMC method. However, this algorithm requires too much time for calculation in thecase of large parameter size.

In this study, we adopted Hamiltonian Monte Carlo (HMC) method instead of Gibbs sampling. HMC
method is also one of a MCMC algorithm. We can obtain samples from target distribution quickly withthis algorithm. To verify its efficiency, we conducted numerical experiment. We estimated deer
population using simulation data with the two algorithms respectively, and compared those results. As aresult, it was confirmed that the number of samples which must be abandoned as burn-in period andthinning interval is very small in HMC method. Deer population model applied in this research includes
more than 200 parameters and complex distributions such as Gaussian CAR model which express spatial
correlations. We conclude that HMC method is efficient algorithm for estimation of deer population
dynamics. This method enables us to expand models with higher dimensional parameters. Throughsuch expansion, HMC method will promote beneficial discovery of new ecological knowledge forsustainable ecosystem management.

Special Short Session III
17:05-17:20 on February 12

Nonparametric inference for Lévy models
Kengo Kato, Cornell University
Daisuke Kurisu, Tokyo Institute of Technology

1 Abstract
This paper develops bootstrap methods to construct uniform conﬁdence bands for nonparametric spectral estimation of Lévy densities under high-frequency observations. We assume that we observe n discreteobservations at frequency 1/∆ > 0, and work with the high-frequency setup where ∆ = ∆n → 0 andn∆ → ∞ as n → ∞. We employ a spectral (or Fourier-based) estimator of the Lévy density, and develop

novel implementations of Gaussian multiplier (or wild) and empirical (or Efron’s) bootstraps to constructconﬁdence bands for the spectral estimator on a compact set that does not intersect the origin. We provide conditions under which the proposed conﬁdence bands are asymptotically valid. Our conﬁdence
bands are shown to be asymptotically valid for a wide class of Lévy processes. We also develop a practical method for bandwidth selection (inspired by the idea in Bissantz et al. (2007)), conduct simulation
studies to investigate the ﬁnite sample performance of the proposed conﬁdence bands, and apply the
proposed method to the Lévy density of the 15 minute bars data of log-GBP/AUD exchange rate.

References
Bissantz, N., Dümbgen, L., Holzmann, H., and Munk, A. (2007). Non-parametric conﬁdence bands indeconvolution density estimation. J. Roy. Stat. Soc. Ser. B Stat. Methodol. 69 483-506.
Kato, K. and Kurisu, D. (2017), Bootstrap conﬁdence bands for spectral estimation of Lévy densities
under high-frequency observations. arXiv1705.00586. Revision resubmitted to Stochastic Processes andtheir Applications.

Department of Industrial Engineering and Economics, School of Engineering, Tokyo Institute of Technology 2-12-1
Ookayama, Meguro-ku, Tokyo 152-8552, Japan. kurisu.d.aa@m.titech.ac.jp.

Special Short Session III
17:20-17:35 on February 12

On the Error of Realized Measures of Volatility in Finance
Hiroumi Misaki1
1Faculty of Engineering, Information and Systems, University of Tsukuba,
Ibaraki 305-8577, Japan
Abstract

For estimating daily volatility with high-frequency data in financial markets, several realizedmeasures of volatility have been introduced such as the two-scale (TS) estimator, the realized kernel(RK) estimator, the pre-averaging (PA) estimator and the separating information maximum
likelihood (SIML) estimator. Even though they are all consistent estimators of true volatility, actual
estimates do not coincide among them in empirical analysis. Therefore, it is worth making acomparison on these estimators.
First, we investigate the finite sample properties of the estimators of the integrated volatility basedon a set of simulations. The SIML estimator seems to have robustness to the forms of noise, eventhough it is not the best in respect to the convergence rate in a standard case. Second, empiricalanalysis is conducted with these estimators. We assess the fitting to some time series models such asheterogeneous autoregressive (HAR) model and the accuracy of prediction of value-at-risk (VaR)
among the competing estimates. Third, our discussion is extended to multivariate case, in which the
major interest is to estimate realized covolatility and correlation for the purpose of hedging andportfolio management.
In addition, we incorporate the realized measurement error, which is defined as the difference
between estimates of realized measures and true (latent) volatility, by state space modeling andfiltering.

Invited Session 6: Data Science II
10:00-10:30 on February 13

Nonlinear independent component analysis:
A principled framework for
unsupervised deep learning
Aapo Hyvärinen
Gatsby Computational Neuroscience Unit,
University College London, UK;
Dept of Computer Science,
University of Helsinki, Finland
Abstract
Unsupervised learning, in particular learning general nonlinear representations, is one of the deepest problems in machine learning. Estimating
latent quantities in a generative model provides a principled framework,and has been successfully used in the linear case, e.g. with independent
component analysis (ICA) and sparse coding. However, extending ICA
to the nonlinear case has proven to be extremely difficult: A straightforward extension is unidentifiable, i.e. it is not possible to recover thoselatent components that actually generated the data. Here, we show that
this problem can be solved by using additional information either in theform of temporal structure or an additional, auxiliary variable. We startby formulating two generative models in which the data is an arbitrary
but invertible nonlinear transformation of time series (components) which
are statistically independent of each other. Drawing from the theory of
linear ICA, we formulate two distinct classes of temporal structure of thecomponents which enable identification, i.e. recovery of the original independent components. We show that in both cases, the actual learning canbe performed by ordinary neural network training where only the input isdefined in an unconventional manner, making software implementations.
We further generalize the framework to the case where instead of temporal structure, an additional auxiliary variable is observed (e.g. audio in
addition to video). Our methods are closely related to “self-supervised”
methods heuristically proposed in computer vision, and further provide atheoretical foundation for such methods.
The talk is based on the following papers:
http://www.cs.helsinki.fi/u/ahyvarin/papers/NIPS16.pdf
http://www.cs.helsinki.fi/u/ahyvarin/papers/AISTATS17.pdf
https://arxiv.org/pdf/1805.08651

Invited Session 6: Data Science II
10:30-11:00 on February 13

Nonsmooth Convex Optimization and Sparse Regularizationin Signal and Information Processing
Shunsuke Ono1,2
1 FIRST, Tokyo Institute of Technology, Kanagawa 226-8503, Japan
2 PRESTO, Japan Science and Technology Agency, Saitama 332-0012, Japan

Abstract
In this talk, we give an introductory overview of nonsmooth convex optimization and sparse
regularization techniques with applications to signal and information processing. Specifically, we
start by introducing L1 regularized sparse estimation as a typical example of nonsmooth convex
optimization. Then, we move on to various application examples, including signal reconstruction
and decomposition, image restoration, remote sensing, and graph signal processing, which illustrate
the power of sparse regularization techniques via convex optimization.

Invited Session 6: Data Science II
11:00-11:30 on February 13

A semi-parametric spatiotemporal Hawkes-type point
process model with periodic background for crime data
Jiancang Zhuang
Jorge Mateu

Institute of Statistical Mathematics, Tokyo, Japan
University Jaume I of Castellón, Castellón, Spain

Past studies have shown that crime events are often clustered1 . Different from past studies,
where the excitation effect has been emphasized, we focus on disentangling the periodic components
from the long-term trend in the background rate. The reason for such a separation is straightforward: crime behavior is influenced by the criminal’s biological clock and the rhythms of our social
life. This study proposes a new spatiotemporal Hawkes-type point process model which has a
conditional intensity in the form of: t−  g(t − s) h(x − u, y − v) N (du × dv × ds), (1)λ(t, x, y) = µ0 µt (t) µd (t) µw (t) µb (x, y) + A−∞

S

where the µt (t), µd (t), and µw (t) represent the trend term, the daily periodicity, and the weekly
periodicity in the temporal components of the background rate, respectively, µb (x, y) represents
the spatial homogeneity of the background rate, and g(t − s, x − u, y − v) represents the subprocess
triggered by an event previously occurring at location (u, v) and time s. average values of the trend
term µt (t), the daily periodicity µd (t), µw (t), and the weekly periodicity µb (x, y) are all normalized
to 1, the temporal response g and spatial response h are p.d.f.s for the clustering component, andtwo relaxation coefficients,A and µ0 , are introduced to stabilize and fasten the estimation process.
Consequently, we generalize the stochastic reconstruction technique,2, 3 which has been used toestimate Hawkes-type models with a simple background rate, to extract and to estimate different
periodic components from the background rate. We introduce an E-M type iterative algorithm
to estimate the two relaxation parameters by using MLE and all the functions in the conditional
intensity simultaneously.
This model is used to analyze the robbery-related violence data in Castellon city, Spain, during
the years of 2012 and 2013. The results show that the robbery crime is highly influenced by
the daily life rhythms, revealed by its daily and weekly periodicity, and that about 3% of suchcrimes can be explained by clustering. Further diagnostic analysis show that the model could be
improved by considering the following ingredients: (1) The daily occurrence patterns are different
between weekends and working days; (2) in the city center, robbery activity shows different temporal
patterns, in both weekly periodicity and long-term trend, from other suburb areas.

References
1

G. O. Mohler, M. B. Short, P. J. Brantingham, F. P. Schoenberg, and G. E. Tita. Self-exciting point
process modeling of crime. Journal of the American Statistical Association, 106(493):100–108, 2011.

2

J. Zhuang. Second-order residual analysis of spatiotemporal point processes and applications in model
evaluation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(4):635–653,
2006.

3

J. Zhuang, Y. Ogata, and D. Vere-Jones. Analyzing earthquake clustering features by using stochastic
reconstruction. Journal of Geophysical Research, 109(3):B05301, 2004.

Invited Session 7: Financial Time Series Modeling
13:00-13:30 on February 13

Dynamics of Commercial Real Estate Market in Tokyo
Yoshiro Yamamura1 and Yoko Tanokura2
1

Graduate School of Global Business, Meiji University, Tokyo 101-8301, Japan

2 Graduate School of Advanced Mathematical Sciences, Meiji University, Tokyo 164-8525, JapanThe subprime problem 10 years ago triggered the global economic crisis, which was the
manifestation of the potentially risks in the securitized financial products in the US real estatemarket (eg. mortgage backed securities). The development of securitization technology has made thecorrelation between the financial markets and real estate markets increasing, so that the risk of theeconomic crisis due to the collapse of the market is increasing. In U.S., the Case-Shiller house priceindex (Case and Shiller (1987)) widely used in practice. Meanwhile, domestically house priceindices based on transaction data has been made by the Ministry of Land, Infrastructure andTransport (Shimizu et al. (2010)). However, the indices that adequately indicate price trends in thecommercial real estate market have not fully developed since the access to the transaction records inreal estate market has been restricted generally. Therefore, in order to accurately express the
dynamics of the commercial real estate market, we have firstly developed not only price indices but
also rent and investment return indices in Tokyo, base on hedonic approach. These indices can bebeneficial to investors in the real estate markets. Secondly, we have extracted trends of the real estate
to adequately understand the market, based on a seasonal adjustment model proposed by Gersch andKitagawa (1983) and Kitagawa and Gersch (1984). The specific characteristics in the commercial
real estate market in Tokyo will be discussed.
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Fig. Market Trend in Commercial Real Estate
References
Case, K.E. and R.J. Shiller (1987), “Prices of single-family homes since 1970,” New England
Economic Review 1987: 45–56.
Gersch, W. and G. Kitagawa (1983), “The prediction of time series with trends and seasonalities,”
Journal of Business and Economic Statistics, 1, 253-264.
Kitagawa, G. and Gersch, W. (1984), “A smoothness priors-state space modeling of time series withtrend and seasonality”, Journal of the American Statistical Association, 79, 378-389.
Shimizu, C., K. G. Nishimura and T. Watanabe (2010), “House Prices in Tokyo - A Comparison of
Repeat-sales and Hedonic Measures-,” Journal of Economics and Statistics, 230 (6), 792-813.

Invited Session 7: Financial Time Series Modeling
13:30-14:00 on February 13

On Trend Change Factors of Financial Markets
Yoko Tanokura, Graduate School of Advanced Mathematical Sciences, Meiji University
Seisho Sato, Graduate School of Economics, The University of Tokyo
Genshiro Kitagawa, Mathematics and Informatics Center, The University of Tokyo
Political events and economic problems in one country have increasingly influenced the economies
and financial markets worldwide. For example, the global economic crisis occurred at the end of 2008
was first triggered by the US subprime mortgage problem in the summer of 2007. In a financial market,
a trend of the asset price can be regarded as the gradually changing long-term fluctuations caused bycharacteristics specific to the asset. On the other hand, the short-term cyclical fluctuations around thetrend can sensitively be influenced by those of any other asset prices, and they may behave as only aprice adjustment or may lead to a future change in the trend direction. Aiming to analyze factors
causing changes of the trend, we extract the trend component from a financial market index, based ona seasonal adjustment model proposed by Gersch and Kitagawa (1983) and Kitagawa and Gersch(1984). Then, we investigate the fluctuation structures of the return series of the TOPIX (Japan), S&P
500 (USA) and the DAX (Germany), focusing on the mutual relationship of their trend component
fluctuations for about 25 years, by conducting generalized power contribution analysis (Tanokura andKitagawa 2015). This talk will report the characteristics of these fluctuations.

References:
Gersch, W. and G. Kitagawa (1983), The prediction of time series with trends and seasonalities, Journal of Businessand Economic Statistics, 1, 253-264.
Kitagawa, G. and Gersch, W. (1984), A smoothness priors-state space modeling of time series with trend and seasonality,
Journal of the American Statistical Association, 79, 378-389.
Tanokura, Y. and G. Kitagawa (2015), Indexation and Causation of Financial Markets -Nonstationary Time Series
Analysis Method-, Springer, Tokyo.