[Poster Titles](Additional information on authors, affiliations and abstracts follows)1.

Predicting Drosophila behavior from a small number of neuron types

2.

Topological data analysis of across-sniff habituation in olfactory cortex

3.

Towards accurate spatial modeling of hydrothermal-originated metal grade by a
combination of geostatistics and physical law

4.

Sparse estimation of neural circuits for internal rewards

5.

Analysis of x-ray fluorescence holography data using sparse modeling on Mn-doped
Bi2Te3 topological insulator

6.

Analysis of x-ray fluorescence holography data using sparse modeling on the
traditional Fe65Ni35 Invar alloy

7.

Statistical properties of interaction parameter estimates in direct coupling analysis

8.

Statistical Mechanics Analysis of Compressed Sensing for Hamiltonian Estimationof the Ising Spin Glass

9.

Time-series reconstruction using compressed sensing with ADMM in 2D magnetic
resonance spectroscopic imaging

10. Solid-state NMR signal assignment by covariance NMR combined with Monte Carlointegration
11. Statistical mechanical analysis of sparse linear regression as a variable selection
problem
12. Geodetic data inversion for spatial distribution of slow slip events using sparsemodelling
13. Simultaneous Screening for Positive-Unlabeled Sparse Support Vector Machines
14. Geochemical-petrological evolution under the paleo subduction zone identified bymachine-learning techniques
15. Bayesian inference of an effective interaction between atomic defects on the surfaceof SrVO3
16. Principal component analysis with electronic wavefunctions for exploration oforganic polymer device materials
17. (Cancelled)
18. Many-variable variational Monte Carlo method with sparse modeling
19. Direct imaging of supermassive black hole by sparse modeling
20. Variational Bayes method for matrix factorization to two sparse factorized matrices
21. Approximate analysis of matrix factorization/completion problem by variational
Bayes method with sparse prior22. Comparison between asteroids and meteorites through multivariate analysis ofvisible to near-infrared reflectance spectra
23. Low-rank matrix completion by simulated annealing
24. Analytical continuation of quantum Monte Carlo data using sparse modeling
25. Regression model for stabilization energy of various anion ordering in perovskite
oxynitrides
26. TimeTubes: Visual Fusion for Detailed and Precise Analysis of Time-Varying MultiDimensional Datasets
27. Model selection on chemical kinetics in water-rock interaction using exchange
Monte Carlo method
28. Bayesian spectroscopy of x-ray photoelectron spectra in graphenes having atomic
thicknesses on SiC substrates
29. Bayesian spectroscopy for spectral and normal mode analyses in solid-statematerials
30. Seismic wavefield imaging of long-period ground motion in the Tokyo metropolitanarea, Japan
31. Bayesian common parameter model in systems biology
32. Bayesian inference for chemical kinetics of catalytic reaction using temporal
analysis of products
33. Bayesian spectroscopy of excitonic photoluminescence spectra in Cu_2O thincrystals
34. Break the Limit of Live Imaging Technology using sparse modeling.
35. Deta-driven analysis of first-principles simulation of chemical reaction
36. Extracting Neuronal Nonlinear Dynamics Based on Sparse Modeling
37. Bayesian inversion analysis of nonlinear spatiotemporal dynamics of heterogeneous
reactions in rock-water interactions
38. Application of compressed sensing for efficient measurement of scanning tunnelingmicroscopy and spectroscopy
39. Bandit Algorithms for Fast Diagnosis by Interactive Measurement

[Posters]
1.

Predicting Drosophila behavior from a small number of neuron types
Takuya HiroshimaA, Laurent BadelB, Hokto KazamaB, and Keiji MiuraA
A

Kwansei Gakuin University

B

RIKEN Brain Science Institute
Although various hypotheses on population coding such as a grandmother cellhypothesis have been proposed, it remains unclear how many neurons are essential
to trigger fly behaviors. Here we tried to predict fly aversive responses to abundantodors from the recorded neural activity of olfactory glomeruli by using algebra-based
sparse estimation of input-output relationships. We found that at least 6 cells arenecessary to explain the behaviors, suggesting that a grandmother cell hypothesis
is insufficient under broad olfactory contexts.

2.

Topological data analysis of across-sniff habituation in olfactory cortex
Yuki Usui, Ayaka Onishi, and Keiji Miura
Kwansei Gakuin University
Although it is well known that animals habituate to odors across sniffs, its
neural basis is still unclear. While many neurons we recorded from the olfactorycortex (anterior piriform cortex) decreased firing rates for the continued odor
stimulation, some increased firing rates. Therefore, it is necessary to characterize
the responses dynamically as a whole to understand the habituation in the olfactorycortex. The principal component analysis revealed that the neural responses at thefirst and second sniffs after the odor delivery looked different. In fact, the topological
data analysis revealed that the orbit of the neural responses for the two sniffs did
not necessarily repeat the same loop twice. These findings suggest that the olfactorycortex could underlie the odor habituation across sniffs.

3.

Towards accurate spatial modeling of hydrothermal-originated metal grade by a
combination of geostatistics and physical law
Lei LuA, Katsuaki KoikeA, Koki KashiwayaA, Mohamad N. HeriawanB, andRyoichi YamadaCA

Kyoto University

B

Bandung Institute of Technology

C

Tohoku University
This study is aimed to develop a method that combines geostatistics and aphysical law for highly precise spatial modeling of metal contents in hydrothermaloriginated ore deposits . Semivariogram clarified spatial correlation structure of themetal data and then kriging estimations and conditional simulation were used togenerate three-dimensional distribution of ore grade. Transports of ore fluid anddepositional process of metals were assumed to be approximated by a physicalprocess with partial differential equation. Numerical solution of advection-diffusion
equation was applied to metal grade from borehole sampling data. Suitable valuesof the advective velocity and diffusion coefficient were determined at each locationaccording to the geostatistical estimation and simulation. The Spatial modeling bya joint use of Physical law & Geostatistics (SPG) method was verified by two typesof ore deposits, kuroko (volcanogenic massive sulfide) type in Japan and porphyry
copper type in Sulawesi Islands, Indonesia. Matsumine and Fukazawa mines,
typical kuroko deposits in the Hokuroku district, Akita Pref., northern Japan, wereselected to verify SPG As the result. High metal content zones are well clarified andcharacterized, and a fluid flow pattern that formed the zones is expressed as acolloidal texture which could suggest temperature and pressure changes in theshallow subvolcanic activities.

4.

Sparse estimation of neural circuits for internal rewards
Shintaro NakamuraA, Ju TianB, Naoshige UchidaB, Mitsuko Watabe-UchidaB, andKeiji MiuraA
A

Kwansei Gakuin University

B

Harvard University
Dopamine neurons, representing an internal joy, are known to promote thelearning in which animals select future actions based on past reward experience.
Although the activities of dopamine neurons themselves should be determined by
the projections from the other types of neurons, the functional connections within
the reward system are still unclear. In this study, we construct a sparse regression
model that can reproduce electric activities of dopamine neurons from the others.

5.

Analysis of x-ray fluorescence holography data using sparse modeling on Mn-doped
Bi2Te3 topological insulator
Shinya HosokawaA, Jens R. StellhornA, Tomohiro MatsushitaB, Naohisa HappoC,
Koji KimuraD, Koichi HayashiD, Mamoru KitauraE, and Minoru SasakiEA

Kumamoto University

B

JASRI

C

Hiroshima City University

D

Nagoya Institute of Technology

E

Yamagata University
Mn K-alpha x-ray fluorescence holography (XFH) measurements were

performed on a Bi2Te3Mn0.1 topological insulator single crystal at 300 and 100 K
to search for the Mn impurity sites in this functional crystal. Possible impurity sites
are twofold, i.e., an interlayer site with an octahedral symmetry and a substitutionalsite of Bi and/or Te in the layer. Local structural information was obtained bydetailed analyses using a L1-regularized linear regression for the experimental data,such as the ratio of the Mn impurity sites, the Mn-Te interatomic distances, thelattice distortions, and the positional fluctuations around the impurity Mn atoms. A
distinct temperature dependence is seen in the positional fluctuations of the
impurity Mn atoms. These findings for the impurity sites cannot be obtained bydiffraction or XAFS experiment in the usual way, but only using a combination of
the XFH and XAFS measurements

6.

Analysis of x-ray fluorescence holography data using sparse modeling on the traditional
Fe65Ni35 Invar alloy
Yuki IdeguchiA, Jens R. StellhornA, Shinya HosokawaA, Tomohiro MatsushitaB,
Naohisa HappoC, Kunio YubutaD, Koji KimuraE, and Koichi HayashiEA

Kumamoto University

B

JASRI

C

Hiroshima City University

D

Tohoku University

E

Nagoya Institute of Technology
Fe and Ni Kα x-ray fluorescence holography (XFH) measurements were carriedout on a single crystal Fe<sub>65</sub>Ni<sub>35</sub> Invar alloy at 100 and
300 K to investigate the temperature dependence of the three-dimensional local
structures around the Fe and Ni atoms, respectively. Local structural information
was obtained by detailed analyses using a L1-regularized linear regression for theexperimental data.

At 100 K, the local atomic arrangements around both theelements show fcc structures. At 300 K, however, only the image around Fe shows a
bcc-like neighboring arrangement. In addition, the atomic image around Fe exhibitsa fcc structure by choosing the incident x-ray energy at the high-spin (HS) shoulder
in the XANES spectrum, i.e., the atomic arrangements around the HS atoms are fcclike. From these XFH data, we propose a model that by increasing temperature thatan Fe atom enters around an Fe atom with the low-spin (LS) state at the central
position of the fcc lattice and is stabilized there.

7.

Statistical properties of interaction parameter estimates in direct coupling analysis
Yingying XuA, Erik AurellB, Jukka CoranderC, and Yoshiyuki KabashimaDA

Aalto University

B

KTH-Royal Institute of Technology and Aalto University

C

University of Helsinki and University of Oslo

D

Tokyo Institute of Technology
We consider the statistical properties of interaction parameter estimatesobtained by the direct coupling analysis (DCA) approach to learning interactions
from large data sets, which help to filter out the insignificant elements from learning
results. Two inference methods are considered: the L2 regularized naive mean-eld
inference procedure (regularized least squares, RLS), and the pseudo-likelihood
maximization (plmDCA). Numerical simulations indicate that background
distribution can be characterized by a few system parameters with finite scaling
properties. This property holds for both RLS and plmDCA and may be exploitablefor inferring the distribution of extremely large interactions from simulations forsmaller system sizes.

8.

Statistical Mechanics Analysis of Compressed Sensing for Hamiltonian Estimation of theIsing Spin Glass
Chako TakahashiA, Masayuki OhzekiA, Shuntaro OkadaB, Masayoshi TerabeB,
Shinichiro TaguchiB, and Kazuyuki TanakaA
A

Graduate School of Information Sciences, Tohoku University

B

DENSO CORPORATION
Recently, several powerful machines dedicated to solver of the optimization

problems such as D-wave machine with quantum mechanics have appeared. To
input problems on the machines, a method to estimate coupling constants fromseveral pairs of energy and spin configurations is needed. In this presentation, we
propose such a method by use of compressed sensing. Compressed sensing is atechnique for reconstructing an unknown sparse vector from low dimensional data.
This technique has spread to many fields because of its simple principle,
straightforward implementation, and extensive availability for various applications.
We consider compressed sensing for the above case in fully-connected Ising model,
and analytically evaluate the estimation performance of the coupling constants.
Then we employ the replica method, which was used in the performance analysis of
the compressed sensing in the ordinary problem setting as in [Kabashima et al.,2009]. We show that our result coincides with the results in [Donoho & Tanner, 2005]
and [Kabashima et al., 2009]. Finally, we compare our analytical result withnumerical result using the alternating direction method of multipliers.

9.

Time-series reconstruction using compressed sensing with ADMM in 2D magnetic
resonance spectroscopic imaging
Utako YamamotoA, Hirohiko ImaiA, Kei SanoA, Masayuki OhzekiB, Tetsuya MatsudaA,
and Toshiyuki TanakaAA

Kyoto University

B

Tohoku University
In order to reduce the imaging time, we employ the compressed sensing, which

can make the image of dynamics of the substances in vivo even from the undersampling observation data of 2D 1H-13C heteronuclear multiple quantum coherence(HMQC) magnetic resonance spectroscopic imaging (MRSI).
For MR measurement, an animal 7T MR device (Bruker BioSpin) and a 1H-13C
nuclear transmission / reception volume coil were used. Using a sequence
incorporating 2D 1H-13C HMQC, signals were collected with a sampling patternbased on the Sobol sequence which is a set of quasi-random numbers. Metabolic
processes of about 7 hours after 13C -labeled glucose was intraperitoneallyadministered to tumor-bearing mouse were measured. For reconstruction bycompression sensing, alternating direction method of multipliers (ADMM) was used.
Time series changes of the spatial distribution of each substance were reconstructedusing the base spectrum of three substances (glucose, lactic acid, and fat).
Under the current imaging condition, in the case of the conventional full sampling,
the imaging time is about 4.5 hours. On the other hand, our approach allowed us toreconstruct 2D spatial distribution of metabolites with a time resolution of 8.5
minutes from the data collected in about 7 hours.

10. Solid-state NMR signal assignment by covariance NMR combined with Monte Carlointegration
Hajime TamakiA, Yuta SaitoB, Toshimichi FujiwaraA, and Makoto DemuraC
A

Institute for Protein Research, Osaka University

B

Graduate School of Life Science, Hokkaido University

C

Faculty of Advanced Life Science, Hokkaido University
Solid-state NMR can exhibit tremendous power for structural and dynamical

analyses of large and/or insoluble proteins. However, the analyses are often
prevented due to difficulty of peak picking in chemical shift assignment. Covariance
NMR approach that can integrate several spectra into the high dimensional space
using covariance calculation between spectra, can reduce the difficulty of peak
picking because signals are dispersed in a high dimensional space. The mostconcerned problem was the covariance map calculation in a high dimensional spacerequires high computational cost. To tackle this problem, we applied Monte Carlointegration to obtaining the covariance map. The approach was verified using 3DCANCO, NCACO and NCOCA solid-state NMR spectra of GB1 microcrystal. We
could construct the 6D covariance map within a few minutes using a laptop computer.
Thereby, we successfully assigned the backbone chemical shifts of GB1 derived fromthe approximated 6D covariance map without human intervention. Thus, our Monte
Carlo integration based covariance NMR approach is helpful for backbone chemical
shift assignments.

11. Statistical mechanical analysis of sparse linear regression as a variable selection
problem
T. ObuchiA, Y. Nakanishi-OhnoB, M. OkadaC, and Y. KabashimaAA

Dept. of Math. and Comp. Sci. Tokyo Tech.

B

Grad. Sch. Arts. Sci. Univ. TokyoC

Grad. Sch. Front. Sci. Univ. Tokyo
The information theoretic performance of compressed sensing or related

variable-selection problems is analytically evaluated when a design matrix is given
by an overcomplete random matrix. The replica method is employed to derive theresult. The analysis is conducted through evaluation of the entropy, an exponential
rate of the number of combinations of variables giving a specific value of error to
given data which is assumed to be generated from a linear process using the design
matrix. This reveals the achievable limit of the fit error to the data when solving a
representative L0 problem, as well as the presence of unfavorable phase transitions
preventing local search algorithms from reaching the minimum-error configuration.
The associated phase diagrams are shown. A noteworthy outcome of the phase
diagrams is, however, that there exists a wide parameter region where any phasetransition is absent from the high temperature to the lowest temperature at which
the minimum-error configuration or the ground state is reached. This implies thatcertain local search algorithms possibly find the ground state with moderatecomputational costs in that region. A numerical simulation based on simulated
annealing is conducted, which well supports this theoretical prediction.

12. Geodetic data inversion for spatial distribution of slow slip events using sparse modelling
Ryoko NakataA, Hideitsu HinoB, Tatsu KuwataniC,D, Shoichi YoshiokaE, Masato OkadaF,
and Takane HoriA
A

Research and Development Center for Earthquake and Tsunami, Japan Agency for

Marine-Earth Science and Technology, JapanB

Department of Computer Science, University of Tsukuba, JapanC

Department of Solid Earth Geochemistry, Japan Agency for Marine-Earth Science andTechnology, JapanD

PRESTO, Japan Science and Technology Agency, Japan

EResearch Center for Urban Safety and Security, Kobe University, Japan
FGraduate School of Frontier Sciences, The University of Tokyo, JapanWe estimated spatial distribution of the long-term slow slip events (L-SSEs)
beneath the Bungo channel in southwest Japan. The total slip distributions of these
L-SSEs estimated by geodetic data have been smoothed to some extent due to priorconstraints on inversion analyses. In this study, by using fused regularization, a typeof sparse modelling suitable for detecting discontinuous changes in the model
parameters, we found that the largest slip abruptly becomes zero at the down-dip
limit of the seismogenic zone, and is immediately reduced to half at the up-dip limitof the deep low-frequency tremors, and becomes zero near its down-dip limit. Suchcorrespondences imply that some thresholds exist in the generation processes forboth tremors and SSEs. It suggests that geodetic data inversion with sparsemodelling can detect such abrupt changes in the slip distribution.

13. Simultaneous Screening for Positive-Unlabeled Sparse Support Vector Machines
Taishin SugiyamaA,B and Takanori MaeharaB
A

Tokyo Institute of Technology

B

RIKEN Center for Advanced Intelligence ProjectSparse support vector machine (Sparse SVM) is a standard method forclassification problem. However, when the number of samples and features are verylarge, it requires large computational cost. Therefore, finding efficient algorithms
for sparse SVM is an important problem. In many real real-world applications, given
data are not completely labeled. In particular, we often have a few positive labeleddata and many unlabeled data. Learning from such dataset is referred to as positiveunlabeled learning (PU-learning). In this study, we propose an efficient algorithm
for the PU-learning version sparse SVM problem (PU pparse SVM). We introduce a
new convex loss function for PU sparse SVM problem. Then we adopt thesimultaneous screening method for sparse SVM to our loss function. We evaluate
the proposed method in real-world dataset.

14. Geochemical-petrological evolution under the paleo subduction zone identified bymachine-learning techniques
Kenta YoshidaA, Tatsu KuwataniA,B, Takao HirajimaC, Hikaru IwamoriA, andShotaro AkahoD
A

Japan Agency for Marine-earth Science and Technology

B

PRESTO, Japan Science and Technology Agency

CKyoto University

D

National Institute of Advanced Industrial Science and Technology
Geochemical-petrological evolutions of the subducting rocks are identified bymeans of multivariate statistical analyses applied for the metamorphic rocks formed
under ca. 15-60 km of the paleo subduction zone. Chemical composition ofmetamorphic rocks is commonly represented by more than 10 components such as
SiO2, TiO2, and others. To detect the characteristics of a series of rock compositions,this contribution performed k-means cluster analysis (KCA) combined with thewhitening of the dataset (Iwamori et al., 2017) and non-negative matrix
factorization (NMF) to the dataset of metapelites of the Sanbagawa metamorphic
belt in central Shikoku, collected by Goto et al. (1996) and Kiminami & Ishihama(2003) which comprise 235 samples with 14 elements. Both multivariate analyses
indicate an evolutional change of chemical composition during the progressive
metamorphism. In the higher metamorphic grade part, a decrease in SiO2 and Na2O
and compensating increase in other components are recognized. To be noted, theendmember decomposition using NMF revealed that the change of rock composition
is approximated to a stoichiometric increase of garnet-like component. This
metamorphic evolution can be best explained by the precipitation of garnet andleaching of other components during progressive dehydration.

15. Bayesian inference of an effective interaction between atomic defects on the surface of
SrVO3
Masamichi J. MiyamaA and Koji HukushimaA,B
A

University of Tokyo

B

NIMS
We invented the new methodology to extract atomic positions from STM

topography data [arXiv:1703.08643]. We thus applied our method to realexperimental data of an oxide metal, SrVO_3. On the surface of the material, theoxygen atoms take a cubic lattice arrangement, and there are atomic defects that
correspond to lower electron density spots in a topography image. According to ourfurther analysis, it is found that these atomic defects are randomly distributed inspace beyond four lattice constants, meanwhile it exhibits an exclusive distribution
within the distance. In order to understand the spatial distribution, we infer an
effective interaction between the atomic defects by using a lattice gas model throughthe Bayesian statistics. In our poster, we show our inference framework by using
MCMC and our recent results.

16. Principal component analysis with electronic wavefunctions for exploration of organic
polymer device materials
Takeo HoshiA, Hiroto ImachiA,* Kentaro OohiraA, Yukiya AbeA, and Koji HukushimaB
A

Tottori University

B

The University of Tokyo(*) The present affiliation: Preferred Networks, inc.
Principal component analysis (PCA) was carried out so as to analyze large-scale
electronic state calculation data for exploration of organic polymer device materials[1]. The method is given by the dimensional reduction of electronic wavefunctions,since the original data size is huge. The reduction is realized, when the participation
ratio of wavefunctions, a measure of quantum localization, is chosen as thedescriptor. The proposed method was used as an extension of our previous researchin organic polymer devices materials [2]. The computation was carried out forelectronic states for 40,000 samples of disordered organic polymers with 1,200 atoms
by our large-scale electronic state calculation code ELSES on the K computer. As
results, the polymer samples are classified into four groups correctly and thephysical meaning of the principal components is clarified. The present method is
general and forms a rigorous foundation of the data-driven material science.
References: [1] H. Imachi et al., in ASIAN19, National Chiao Tung Univ., Taiwan,
31. Oct.- 2. Nov. (2016); H. Imachi, D. Thesis, Tottori U., Mar. 2017; Manuscript in
preparation. [2] T. Hoshi, et al., Proc. ScalA16 in SC16, pp.33-40, (2016).

17.

(Cancelled)18. Many-variable variational Monte Carlo method with sparse modeling
Yuichi Motoyama, Takahiro Misawa, and Kazuyoshi Yoshimi
The University of Tokyo
The variational Monte Carlo (VMC) method is one of the most powerful solver
for strongly correlated quantum system. In VMC, we search for the ground state of
the Hamiltonian by minimizing the energy of a parametrized wave function. In orderto obtain the real ground state, the Hilbert space spanned by the parametrized wave
functions should include it. It is non-trivial unfortunately how we can construct a
good parameter space. Many-variable VMC (mVMC) method overcomes this obstacle
simply by increasing the number of variables. This causes another problem, that is,
an increase of numerical cost, but this has been resolved by advance in hardwareand optimization method referred as stochastic reconfiguration. Now mVMC cantreat many of condensed matter physics problems, for example a spin liquid phases
in quantum spin model. However, there remain some models that mVMC method
has not yet been able to deal with because of a slow optimization. In this study, we
combine the sparse modeling framework with the mVMC method to prepare a better
initial parameter than random one and to speed up optimization steps.

19. Direct imaging of supermassive black hole by sparse modeling
Mahito SasadaA, Fumie TazakiA, Kazuki KuramochiA,B, Kazuhiro HadaA, Mareki HonmaA,
Kazunori AkiyamaA,C, Shiro IkedaD, Mariko KimuraE, Taichi KatoE, Daisaku NogamiE,
Makoto UemuraF, Taise AbeF, and Yurika YamadaF
A

The National Astronomical Observatory of JapanB

The University of TokyoC

Massachusetts Institute of Technology

D

The Institute of Statistical Mathematics

E

Kyoto University

FHiroshima University

Black hole is one of the most interesting topics in the universe. Direct imagingof the black hole is the primary purpose of the project of Event Horizon Telescope(EHT), which is an international collaboration on a very long baseline
interferometer (VLBI) observation. A current specification of the EHT observation
will achieve a spatial resolution of 25 microarcsec. The angular size of black hole is,however, considered from 35 to 50 microarcsec in the case of Sgr A*. We develop a
new imaging tool to obtain a higher-resolution image of black hole with a sparsemodeling (SpM) algorithm. An image with the SpM imaging technique has achieved
over three times higher resolution than a conventional method of a VLBI analysis
from the same data set. A polarimetric image is also generated with over three times
higher resolution. We apply the SpM to not only the simulation but also an actual
data. We try to apply the SpM to other astronomical topics. In this presentation, we
will report results of application of SpM to the VLBI imaging and other astronomical
studies.

20. Variational Bayes method for matrix factorization to two sparse factorized matrices
Tomoki Tamai and Koujin Takeda
Ibaraki University
Matrix factorization is the problem of finding two low-rank matrices from theirproduct. This idea is used in many areas such as collaborative filtering, sentiment
analysis and drug interaction analysis. For this problem we use variational Bayes
method. In the past study of Bayesian analysis, multivariate Gaussian prior is used
for both factorized matrices for convenience of analysis. In our study, we assume thatthe observed matrix is the product of two sparse matrices, and the prior of eachmatrix element is exponential distribution. This describes the case of nonnegative
matrix factorization, which is significant in the application to many practicalproblems. Under these assumptions, we will report the result of our analysis andmatrix factorization experiment.

21. Approximate analysis of matrix factorization/completion problem by variational Bayes
method with sparse prior
Ryota Kawasumi and Koujin Takeda
Ibaraki University
We study matrix factorization, which is the problem of finding low-rankfactorized matrices from their product. This can be applied to various problems likecollaborative filtering, which is used for quick and precise analysis of customer
preference. We also study matrix completion, where some elements in matrix
product are unobserved and we need to fill such elements by the idea of matrix
factorization. Here we mainly deal with the matrix factorization with noise, where
the observed matrix is expressed by the product of dense and sparse matrices withadditional noise. For the analysis we use variational Bayes method, where Gaussianprior for dense matrix and Laplace prior for sparse matrix are assumed. With thismethod and under several approximations, we obtain an analytical expression of
matrix factorization solution for a given observed matrix. Using our analytical result,
we conduct numerical experiment of matrix factorization for the product of dense
and sparse matrices with additional noise, and compared the result with anotheranalytical work, where Gaussian priors are assumed for both factorized matrices.
We also report Bayesian formulation of matrix completion, and the result of
numerical experiment.

22. Comparison between asteroids and meteorites through multivariate analysis of visible
to near-infrared reflectance spectra
Peng K Hong, Takafumi Niihara, and Hideaki Miyamoto
The University of Tokyo
Asteroids and meteorites have been considered as remnants of the early
evolution of the solar system. Although meteorites are considered to originate fromasteroids, their spectral relationship, remains poorly constrained except for a few
cases. The major obstacle to compare asteroids with meteorites is that theclassification schemes for the two are fundamentally different, because the two
databases have significantly different data coverage, precision and resolution. In
order to connect asteroids with meteorites, we develop large databases for theapplication of multivariate analysis. Our reflectance spectra database ranges from
0.4 to 4 μm, including 3 μm region, where one can observe absorptions due tohydrated silicates. We performed principal component analyses and found thatusing spectra from 0.4 to 2.5 μm, accuracy of separation among ordinary chondrites,
carbonaceous chondrites, HED meteorites is significantly improved compared withthe case using spectra from 0.4 to 1.0 μm. We also found that the accuracy ofseparation is not significantly improved when using meteorite spectra from 0.4 to 4
μm. These results suggest that unlike previous expectation, 3 μm band may not be
useful for classification of some meteorites, because 3 μm band is widely observed
among chondrites and achondrites.

23. Low-rank matrix completion by simulated annealing
Joji Mikami, Yuya Seki, Masayuki Ohzeki, and Kazuyuki Tanaka
GSIS
The method to solve L0-norm sparse approximation by using simulated
annealing has been proposed. We apply a similar method to find the low-rank matrix
by selecting the relevant components. In our method, we randomly select a singularvalue and decide whether or not to delete it by using an energy function following
the Monte-Carlo manner. This is a kind of the simulated annealing to find the
optimal selection of the singular values. When the temperature is high, singularvalues are randomly deleted. As temperature decreases, it is often to decide to
decrease the value of the energy function. When the temperature becomes near 0,
the planted solution can be found. We investigated its performance in the noiselessand noisy cases by numerical experiments. Also, we compared its performance byusing quantum annealing.

24. Analytical continuation of quantum Monte Carlo data using sparse modeling
K. YoshimiA, J. OtsukiB，M. OhzekiC, and H. ShinaokaD
A

ISSP, The University of Tokyo

B

Dep. of. Phys., Tohoku Univ.

C

Faculty of Info. Sci., Tohoku Univ.

D

Dept. of Phys., Saitama Univ.
In quantum Monte Carlo simulation, dynamic physical quantities such as single

particle and magnetic excitation spectrum can be obtained by analyticalcontinuation of imaginary-time data. However, analytical continuation is the illconditioned inverse problem and thus sensitive to noise and statistical errors. To
solve this problem, analysis using the maximum entropy method or statisticalmethod, etc., has been proposed, but a definitive method has not yet been established.
In this presentation, we propose a method of analytical continuation using sparsemodeling [1]. In this method, analytical continuation becomes robust against noise
since relevant basis unaffected by the noise is automatically selected. We also showthat these compression basis gives the model-independent compact representationof imaginary-time data [2]. In addition, we will introduce the open source software“SpM” implementing the new method of analytical continuation [3]. [1] J. Otsuki, M.
Ohzeki, H. Shinaoka, and K. Yoshimi, Phys. Rev. E 95 (2017), 061302(R). [2] H.
Shinaoka, J. Otsuki, M. Ohzeki, and K. Yoshimi, Phys. Rev. B 96 (2017), 035147. [3]https://github.com/j-otsuki/SpM"25. Regression model for stabilization energy of various anion ordering in perovskite
oxynitrides
Masanori KanekoA,B, Mikiya FujiiA,B, and Koichi YamashitaA,B
A

The University of Tokyo

B

JST-CREST
Recently, Perovskite ABX3 has been studied for various applications due to its

various interesting properties, e.g., as a water splitting photocatalyst and solar cell.
ABO3-xNx (0<x<1) has better activity than ABO3 and ABO2N in some cases. When
first-principles calculations are performed to study ABO3-xNx (0<x<1), it isnecessary to consider anion-ordering and tilting. There are a huge number of
combinations of the structure configuration. Thus, performing a quantum chemical
calculation for each combination. However, we cannot calculate all-structures
because of the high computational cost of quantum chemical calculations. The mostexpensive calculation in the quantum chemical calculation is structure optimization.
Therefore, we have developed a model that can predict a stabilization energy by
structure optimization from the unoptimized structure with good accuracy using
ridge regression. Using this model, we can omit the structure optimization andpredict the stabilization energy by the structure optimization from only the
unoptimized structure. Finally, using this method, it is possible to predict the moststable anion-ordering without optimizing the structure.

26. TimeTubes: Visual Fusion for Detailed and Precise Analysis of Time-Varying MultiDimensional Datasets
Naoko SawadaA, Masanori NakayamaA, Hsiang-Yun WuA,B, Makoto UemuraC, andIssei FujishiroA
A

Keio University

B

TU WienC

Hiroshima University
TimeTubes is a visualization tool for observed datasets of blazars. It allows

astronomers to geometrically analyze the dynamic behavior of and relationshipamong multiple time-varying variables in the datasets. This poster mainly focuses
on a novel functionality of TimeTubes—visual data fusion, which is intended tovisually fuse multiple datasets from different observatories for the same blazar. To
analyze the behavior of blazars, temporally denser observation is required, because
blazars fluctuate drastically in a wide time range, which can be from a few minutes
to several years. However, there are many intrinsically unobservable periods in adataset, due to daylight, bad weather, and so on, which may cause astronomers tomiss important events of the blazar. Thus, by increasing the amount of observed
data to visualize, visual data fusion can meet astronomers' need of a more detailed
and precise analysis. Additionally, comparison of multiple datasets for the same
blazar, taking into account observation conditions and precision, can contribute to
the reduction of unreliability caused by errors in datasets. Our interface effectively
supports a more precise analysis, which can be achieved by visually fusing multiple
datasets by allowing for their global gaps.

27. Model selection on chemical kinetics in water-rock interaction using exchange Monte
Carlo method
Ryosuke Oyanagi, Atsushi Okamoto, and Noriyoshi Tsuchiya
Graduate School of Environmental Studies, Tohoku University, Japan
Earth is a water planet, and water-rock interaction plays essential roles on
various geological phenomena in surface and crustal environments. Chemical
reactions among rock and water are essentially complex heterogeneous reactions,where reactive surface of mineral is important, and often are associated with masstransport, and the observable data (i.e. solution chemistries and dissolved or
precipitated minerals) are noisy and sparse. Accordingly, estimates of kinetic
parameters for the water-rock interaction is quite difficult inversion problem. In thisstudy, we conducted a long-term (over 6 months) experiments under hydrothermal
conditions for development of reaction zones at the boundaries between mantle(olivine, Mg2SiO4) and crustal rocks (quartz, SiO2). This reaction system produces
talc (Mg3Si4O10(OH)2), serpentine (Mg3Si2O5(OH)4), and brucite (Mg(OH)2) withincreasing the distance from the olivine-quartz boundary, which spatial variationsin the abundance of product and reactant minerals and pores can be modeled by onedimensional equation of coupled seven reactions and diffusion processes. We
estimated eight kinetic parameters (the reaction rate constant and diffusion
coefficient) by using the exchange Monte Carlo method (Hukushima and Nemoto,1996). We also carried out the cross validation for selecting the most appreciatemodels of reactive surface areas of minerals.

28. Bayesian spectroscopy of x-ray photoelectron spectra in graphenes having atomic
thicknesses on SiC substrates
S. ArishimaA, K. IwamitsuB, K. TakahashiC, M. OkadaD, and I. AkaiE
AGSST, Kumamoto Univ.

B

Fac. Sci., Kumamoto Univ.

C

SL Center, Saga Univ., Grad.

D

Sch. Frontier Sci., The Univ. of Tokyo

E

IPPS, Kumamoto Univ.
We have analyzed x-ray photoelectron spectra(XPS) in graphenes having atomic

thicknesses on a SiC substrate by Bayesian spectroscopy. To elucidate binding
energies and their chemical shifts of bonding states in materials, we have todecompose the XPS to spectral components for the respective species in suchmaterials. In graphene samples formed on SiC substrates, it is considered that two
kinds of buffer layers exist between graphenes and the SiC substrate. In this report,
we performed the Bayesian spectroscopy to decompose a C-1s XPS to spectral
components in such layers. We employed four kinds of symmetric Doniach-Ŝunjić
spectral functions for peak structures of graphenes, two buffer layers and the SiC,
where we assume that each spectral function has spectral widths of a common
homogeneous broadening and a common inhomogeneous broadening. In addition, a
Shirley spectral function was also considered to reproduce the backgroundcomponent in the XPS. Through sufficient samplings by a replica exchange Monte
Carlo method, we succeeded for getting posterior probability distributions of all
spectral parameters for the graphene monolayer, two kinds of buffer layers and thesubstrate. In addition, we can reproduce a measured XPS by using the mean valuesof their distributions.

29. Bayesian spectroscopy for spectral and normal mode analyses in solid-state materials
Kazunori IwamitsuA, Masato OkadaB,C, and Ichiro AkaiD
A

Faculty of Science, Kumamoto University

B

Department of Complexity Science and Engineering, The University of TokyoC

Research and Service Division of Materials Data and Integrated System, National

Institute for Material Science
D

Institute of Pulsed Power Science, Kumamoto University
We analyze excitonic absorption spectra and coherent phonon (CP) signals insolid-state materials by the Bayesian spectroscopy. In absorption spectra due toelectronic elementary excitations, since multiple excitonic absorption peaks appear
being overlapped with other background optical transitions, their spectral
decomposition is difficult. However, with the Bayesian spectroscopy, the band gap
and the excitonic binding energies can be estimated accurately by introducing
physical laws concerning to the excitonic transitions. On the other hand, theBayesian spectroscopy for the CP signals can also estimate the frequency andvibrating initial phase of the normal mode with high accuracies. Although theconventional Fourier transform spectrum has a broad spectral width on accountingof damping behaviors of the CP signals, the Bayesian spectroscopy with a physicalmodel of a damped oscillation improves the estimation accuracy of the normal mode
frequency in two orders of magnitude compared to the Fourier transform method.
We also present the results of virtual measurement analyses to discuss the abilitiesof the Bayesian spectroscopy in the both cases of the absorption spectra and the CP
signals.

30. Seismic wavefield imaging of long-period ground motion in the Tokyo metropolitan area,Japan
Masayuki KanoA, Hiromichi NagaoA,B, Kenji NagataC,D, Shin-ichi ItoA, Shin'ichi SakaiA,
Shigeki NakagawaA, Muneo HoriA, and Naoshi HirataAA

Earthquake Research Institute, The University of Tokyo

B

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

C

Artificial Intelligence Research Center, National Institute of Advanced Industrial Scienceand Technology
D

Presto, Japan Science and Technology Agency
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 forinfrastructures, which can be simulated by seismic response analyses that use
waveforms at the base of each infrastructure as an input ground motion. 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. The obtained wavefield fully explains the observed waveforms
in the frequency band of 0.10–0.20 Hz. This is attributed to the seismic wavefieldimaging technique, which implements the replica exchange Monte Carlo method tosimultaneously estimate model parameters related to the subsurface structure andsource information. Further investigation shows that the reconstructed seismic
wavefield lower than 0.30 Hz is of high quality in terms of variance reduction, andthat the velocity response spectra show good agreement with observations up to 0.90
Hz in terms of the combined goodness of fit. Inputting the reconstructed wavefield
into seismic response analyses, we can rapidly assess the overall damage toinfrastructures immediately after a large earthquake.

31. Bayesian common parameter model in systems biology
Shinsuke Uda
Kyushu University
In systems biology, ordinary differential equation (ODE) models areconventionally employed to describe biological or life phenomena in the level ofmolecular biology. Although the parameters of ODE models are estimated fromexperimental data sets, generally, the parameter estimation is not easy because theparameter estimation problem is equivalent to nonlinear regression and the sample
size of data set is not enough large. To elucidate the mechanism of disease, thecomparison between healthy and disease subject of ODE model is demanded. Given
that, the mechanism of disease is described by the different values of parameters of
between disease and healthy ODE model rather than the model structure, we canconsider that the disease ODE model has common parameters with the healthy ODE
model, because the biochemical pathways necessary for survival need to be kept
normal. We developed the parameter estimation method from Bayesian approach todetect the different values of parameters between two models, which have samemodel structure. The developed methods is based on hierarchical Bayes model andthe posterior probability distribution is computed by replica exchange Monte Carlo
method.

32. Bayesian inference for chemical kinetics of catalytic reaction using temporal analysis ofproducts
Masafumi AbeA, Yuta MizunoA, Yoshinori Nakanishi-OhnoA, Koji HukushimaA,
Kentaro ItakoB, and Takehiko SasakiB
A

Graduate School of Arts and Sciences, The University of Tokyo

B

Graduate School of Frontier Sciences, The University of Tokyo
We develop a Bayesian inference framework to estimate chemical kinetics ofcatalytic reactions from temporal analysis of products (TAP) data. In TAP
experiments, reactant gas is injected from a pulse valve into a reactor with catalyst
sample and exit gas flow is measured with sub millisecond time resolution. By
analyzing TAP data, we study chemical kinetics of the reaction between the catalytic
surface and reactant gas. It is important to estimate effective diffusivity andreaction rate constant of a reaction-diffusion equation system which models the
behavior of reactant gas. The purpose of this study is to solve these problems byapproximate Bayesian computation which is a combination between Bayesianinference and numerical simulations. This time, we took account of the property ofexperimental apparatus in a reaction-diffusion equation model and examined thevalidity of the model by applying it to argon data. In this presentation, we report theresults of estimating parameters such as effective diffusivity with the method ofleast squares.

33. Bayesian spectroscopy of excitonic photoluminescence spectra in Cu_2O thin crystals
A. KiridoshiA, S. ArishimaA, M. MizumakiB, K. IwamitsuC, and I. AkaiD
A

Graduate School of Science and Technology, Kumamoto University

B

Japan Synchrotron Radiation Research InstituteC

Faculty of Science, Kumamoto University

D

Institute of Pulsed Power Science, Kumamoto University
We analyze polarization dependences of photoluminescence (PL) spectra in

Cu_2O thin crystals sandwiched by paired MgO plates with Bayesian inference. In
such thin crystals, the degeneracy of the yellow exciton states in Cu_2O will be liftedby stress effects induced by a small lattice mismatch between the Cu_2O and MgO.
Consequently, the PL spectra exhibit obvious/remarkable As the result, the thincrystal provides complicated PL spectra, which include multiple resonance PL peaksof the split exciton states, their phonon side-band PL and another PL band. To study
the stress effects and energy structure of the split exciton states, we have todecompose such complicated PL spectra into the respective spectral components and
we introduced a physical model with Gaussian and Maxwell-Boltzmann spectral
functions, which explain the PL profiles of the resonance PL and the phonon sideband PL, respectively. In synthesized spectra composed by these spectral
components, we can completely reproduce the complicated spectra and cansatisfactory get the probability distributions of spectral parameters in suchfunctions. In addition, a measured PL spectrum was also successfully decomposedto the resonance PL peaks and other spectral components.

34. Break the Limit of Live Imaging Technology using sparse modeling.
Maina SogabeA, Masayuki OhzekiB, and Atsuko SeharaAA

Kyoto University

B

Tohoku University
Live imaging is a novel method which is received the most attention in the fieldof life science and medicine. However, detailed observation of biological events in thelive imaging for a long time often suffers from the fluorescence diminishment. Also
as the imaging duration increases, the imaging area decreases. And so theinformation that can be obtained per unit time is reduced. What is needed for live
imaging in the future is how to use photon budget on optimal conditions and increase
the information obtained per unit time.

But these problems are currently

hampered by the performance limit of the microscope. To solve the crucial problem
in the live imaging, we developed a new technique for obtaining the detailed cellmorphological data, by using the compressed sensing, which can elucidate the most
relevant part from a small amount of data, and by the reduction of the laser
irradiation mass for decrease in the damage to fluorescence protein. By using thistechnology, we succeeded in reducing imaging duration by half, and reduced
fluorescent damage becomes possible in live imaging.

35. Deta-driven analysis of first-principles simulation of chemical reaction
Yasunobu Ando
CD-FMat, AISTIn this poster, we present the data-driven analysis of first-principles simulation
for chemical reactions. First principles simulation based on the density functional
theory is common tool for the research on the chemical reactions. By using it, we cancalculate the activation energy of reactions and identify the transition states onreaction coordinates. Once we simulate them, we obtain several data setscorresponding to the reactions. For example, reaction pathway, electronic structure
along reaction pathway, and activation barriers. It is truly difficult to analyze thesewhole data sets because these are high dimensional. To achieve deep understandingof the chemical reactions from the simulation data sets, data-driven analyticalprocedure based on machine learning is indispensable. From this perspective, we
attempt to develop new method to create low-dimensional reaction coordinate formhigh dimensional ones, and to catch up the differences of density of states along
reaction coordinates to find important energy range of electrons and atoms in thesystem. These method is based on the simple machine learning way; principle
component analysis, and k-means clustering. We would like to present that such
simple methods give us simple viewpoints to understand what is happening in thechemical reactions in the simulation.

36. Extracting Neuronal Nonlinear Dynamics Based on Sparse Modeling
Shinya Otsuka and Toshiaki Omori
Kobe University
We propose a sparse modeling approach for extracting nonlinear dynamics insingle neurons from observable data. Using our proposed sparse modeling approach,
we show that latent neuronal dynamics can be extracted from a number of candidate
nonlinear dynamics.

37. Bayesian inversion analysis of nonlinear spatiotemporal dynamics of heterogeneous
reactions in rock-water interactions
Ryota MorimotoA, Tatsu KuwataniB, Atsushi OkamotoC, Koji HukushimaD,E, andToshiaki OmoriAA

Kobe University

B

Japan Agency for Marine-Earth Science and Technology

C

Tohoku University

D

University of Tokyo

E

National Institute for Materials Science
We propose a Bayesian statistical framework for extracting nonlinear

spatiotemporal dynamics of surface heterogeneous reactions from sparse and noisy
observable data. Surface heterogeneous reactions are chemical reactions withconjugation of multiple phases, and they have the intrinsic nonlinearity of theirdynamics caused by the effect of surface-area between different phases. Using our
proposed method, we show that the rate constants of dissolution and precipitation
reactions, which are typical examples of surface heterogeneous reactions, and the
diffusion constants, as well as the spatiotemporal changes of solid reactants andproducts, were successfully estimated only from the observable temporal changes in
the concentration of the dissolved intermediate product.

38. Application of compressed sensing for efficient measurement of scanning tunnelingmicroscopy and spectroscopy
Yasuo YoshidaA, Masahiro HazeB, Yoshinori Nakanishi-OhnoC, Koji HukushimaC,
Masato OkadaD, and Yukio HasegawaA
A

Institute for solid state physics, University of Tokyo

B

Department of Physis, Kyoto University

C

Graduate School of Arts and Sciences, University of Tokyo

D

Graduate School of Frontier Sciences, University of Tokyo
The interference of electrons is one of the manifestations of their particle-wave

duality in quantum mechanics. When electrons are scattered by local disorderedstructures, the reflected electronic wave interferes with the injected one to form aspatial modulation of surface charge density, called a quasi-particle interference(QPI) pattern. QPI patterns is observable with scanning tunneling microscope(STM), and the Fourier transform of the patterns taken at various energies enables
us to obtain the energy dispersion of the quasiparticles closely related to the band
structure of the system. But on the other hand, this experiment requires quite a longmeasurement time, which sometimes even becomes a week, making it difficult towork on. Here, we apply data analysis methods based on statistical theory, called
compressed sensing (CS), to the QPI measurements. These techniques allow
efficient detection of an original signal that is mostly composed of zero-value data
points. We have numerically examined the effectiveness of random down sampling
and the performance of a sparsity-inducing algorithm based on QPI data taken on anoble metal surface, demonstrating the possible significant reduction of themeasurement time.

39. Bandit Algorithms for Fast Diagnosis by Interactive Measurement
Atsuyoshi Nakamura, Koji Tabata, and Tamiki Kamatsuzaki
Hokkaido University
For some disease diagnosis, existence of bad cells can be effectively checked bysome measurement like Raman-spectra measurement. In such measurement,interactive measurement that minimizes the number of points to measure seems
important technology to achieve fast diagnosis. We regard this problem as a kind of
multi-armed bandit problem, in which the player judges the existence of an arm
whose expected reward is more than a given threshold by drawing as small numberof arms as possible. We first consider a simpler problem called an arm discrimination
problem whose solver algorithm must discriminate whether a given slot machine
has an expected reward at least a given threshold or not by drawing as small numberof arms as possible. We give an algorithm for this problem and show an upper boundof the sample complexity (the number of arm draws). We construct a successive
elimination algorithm and a successive checking algorithm that make use of thealgorithm for the arm discrimination problem as a subroutine, and also show thesample complexity upper bounds of those algorithms.