HD^3-2015 Poster Session

RegistraNametion No.

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December 16th, 2015

Affiliation

Title of presentation

Abstract

Tokyo Institute ofTechnology, Japan

Online compressed sensing

An online scheme to carry out compressed sensing is developed on the basis of the Bayesian inference framework and mean field theory.
Co-authors: Paulo Rossi (San Paulo University) and Jun-ichi Inoue (Hokkaido University).

Y-h. Taguchi

Chuo University

Principal component analysis based
unsupervised feature extraction
applied to budding yeast temporally
periodic gene expression

Principal component analysis (PCA) based unsupervised feature extraction (FE) was successfully applied to a wide ranged problems ranging from circulating microRNAbiomarker identification for diseases, proteome analysis, genes associated with aberrant promoter methylation over three autoimmune diseases, identification of disease causing
genes of PTSD mediated heart failure, integrated analysis of genotype/DNA methylation of cancer, identification of ALS causing genes, identification of genes associated withdifferential gene expression as well as aberrant promoter methylation during transgenerational epigentetics and comparison between control/treated cancer cell lines. PCA
based unsupervised FE was also suggested to be equivalent to FE based upon time consuming variational Bayes frameworks and its FE is extremely stable (i.e., relatively
independent of sample selections), while no other methods that were compared with PCA based unsupervised FE could be competitive with it. In this poster, we summarize
usefulness, superiorities towards conventional supervised methodologies, and its application to translational bioinformatics (e.g., findings of desase biomarker and in silico drug
discoveries).

Shaogao Lyu

Partially varying coefficient models (PVCM) provide a useful class of tools for modelling complex data by incorporating a combination of constant and time varying covariate
effects. One natural question is that how to decide which covariates correspond to constant coefficients and which correspond to time-dependent coefficient functions. The
Prediction performance and structure
structure selection problem is fundamentally important, since tackling this problem enhances model interpretation and avoids overfitting, as well as keeps the model flexibility. To
Southwesternselection consistency for highaddress this issue, this paper proposes a new approach to estimation and structure selection for PVCM. Within a highdimensional framework, we derive convergence rates for
University of Finance dimensional partially varying
the prediction risk of the proposed method when each unknown time-dependent coefficient lies in a reproducing kernel Hilbert space. Our upper bounds in ∥ · ∥2 and ∥ · ∥n
and Economics, China coefficient models in reproducing
norms are established under two different kinds of settings, and are shown to be the optimality of our method under their individual settings. Under certain regularity conditions,kernel Hilbert space
we also show that the proposed estimator is able to identify the underlying structure
correctly with high probability.

Yoshiyuki Kabashima

Kengo Nakamura

Ryoko Nakata

Takamitsu Araki

Tohoku University

Extraction of heavy metals
characteristics of the 2011 Tohoku
tsunami deposits using multiple
classification analysis

Tsunami deposits were accumulated due to the impact of the Tohoku-oki earthquake at Tohoku coast areas in Japan. This paper reports an application of principal component
analysis (PCA) and Cluster analysis (CA) to heavy metals content amount and dissolution concentration from tsunami deposits. Results of the heavy metals content anddissolution concentrations from tsunami deposits suggested low environmental risk, geometric means could be calculated to be 16mg/kg and 0.003ml/L for Pb, 1.8mg/kg and0.004ml/L for As, and 0.17mg/kg and 0.0001ml/L for Cd. The CA using excluding outliers from PCA can extract characteristic of heavy metals relationships at content amountand dissolution concentration. The first clusters of content amount are Ni, Fe, Cd, Cu, Al, Cr, Zn and Mn; the second cluster is Pb, Sb, As and Mo. First cluster of dissolution
concentration is Ni, Fe, Al and Cr; the second cluster is Mo, Sb, As, Cu, Zn Pb and Mn. Therefore, heavy metals with higher relationship can be simulated by elution trends, andheavy metals with lower relationship do not show same elution trends. Obviously, Ni and Fe content amounts are assumed for controlling Cd content amount. Pb and As ofcontent amount are considered to reveal a kind of dissolution mechanism using 1N HCl in tsunami deposits.

JAMSTEC, Japan

Geodetic data inversion for spatial
distribution of slip undersmoothness, discontinuity, andsparsity constraints

In geodetic data inversion, insufficient observational data and smoothness constraints for model parameters make it difficult to clearly resolve small-scale heterogeneous
structures with discontinuous boundaries. We therefore developed a novel regularization scheme for the inversion problem that uses discontinuity, sparsity, and smoothness
constraints. The proposed method was applied to synthetic displacements calculated by a ring-shaped afterslip. The afterslip was obtained from reasonable numerical simulationof earthquake generation cycle with a rate- and state- dependent friction law and 3-D plate interface geometry. Obtained afterslip distribution that minimizes the evaluation
function showed a ring-shaped distribution with sharply varying boundary. The same inversion test was conducted with smoothly varying circular slip distribution. Our method
well reproduced both discontinuous and continuous slip distributions.

National Institute ofAdvanced Industrial
Science andTechnology, Japan

Slow slip event (SSE), that is, aseismic transient slip of faults occurs over days, is an important crustal event in subduction zone, and Seismologists have needed to estimate
accurately duration and spatio-temporal distribution of the SSE using only crustal measurement data.
Estimation methods of the spatio-temporal distribution and the duration have been developed separately, and those methods use inappropriate models that increase theSimultaneous estimation of spatio- estimation errors.
temporal distribution and duration of We propose the estimation method that estimates the spatio-temporal distribution and the duration of the SSE simultaneously by using the switching model.
fault slip by switching model
For the observation data measuring one SSE, the switching model represents three forms in three periods. In the first and third periods, the fault is fixed, and in the second
period, the fault is slipping slowly. The time points at which the model changes as well as the parameters of the switching model are estimated by the maximum likelihood
method using the Expectation-Maximization algorithm.
Our method is compared to the conventional methods through their application to synthetic data.

HD^3-2015 Poster Session

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Hiromichi Suetani

Koh Takeuchi,
Yoshinobu Kawahara,
and Tomoharu Iwata

Chongke Bi

Tomoki Tokuda

Thong Pham

Kourosh Meshgi

December 16th, 2015

Manifold learning approach forcollective chaos

It has been known that a certain class of high-dimensional chaotic systems exhibits lower-dimensional motion at a macroscopic level whereas it also keeps high-dimensional
chaos at a microscopic level, called "collective chaos". In this study, we propose an approach based on manifold learning for modeling collective chaos.

Higher Order Fused Regularizationfor Supervised Learning withGrouped Parameters

We often encounter situations in supervised learning where there exist possibly overlapping groups that consist of more than two parameters. For example, we might work on
parameters that correspond to words expressing the same meaning, music pieces in the same genre, and books released in the same year. Based on such auxiliary information,
we could suppose that parameters in a group have similar roles in a problem and similar values. In this paper, we propose the Higher Order Fused (HOF) regularization that can
incorporate smoothness among parameters with group structures as prior knowledge in supervised learning. We define the HOF penalty as the Lov\'{a}sz extension of asubmodular higher-order potential function, which encourages parameters in a group to take similar estimated values when used as a regularizer. Moreover, we develop an
efficient network flow algorithm for calculating the proximity operator for the regularized problem. We investigate the empirical performance of the proposed algorithm by usingsynthetic and real-world data.

RIKEN

Interactive In-situ Visualization forLarge-Scale Simulations on the K
ComputerA large-scale simulation usually needs several days, or even several months to be executed on the supercomputer. It is desired by researchers to observe the visualization
results of such kind of simulations in real time for analysis and adjustment of initial simulation parameters. However, currently most of such kinds of simulation datasets areanalyzed by post-visualization. It is usually necessary to execute these large-scale simulations several times for selecting a set of good initial simulation parameters andvisualization parameters. This leads to a time consuming process for researchers. Interactive in-situ visualization can help us to minimize this issue by enabling the visualizationof the simulation results immediately after several time steps are finished. By analyzing these real time visualization results, researchers can adjust the simulation parameters
and restart the simulation at any time. As a result, it becomes possible to avoid the repetitive execution of the entire simulation just for the parameter selection process. In thispaper, a compression based interactive in-situ visualization method is proposed. This is achieved by using proper orthogonal decomposition (POD), which can extract data
features for getting a good compression ratio for in-situ visualization. We also proposed a data sharing approach between the simulation code and the compression code toobtain the best performance. Furthermore, the visualization framework, called Heterogeneously Integrated Visualization Environment (HIVE) is employed in our system for therendering and the interactive visualization.

OIST

We propose a novel method for multiple clustering that assumes a co-clustering structure (partitions both in rows and columns of data matrix) in each view, which is applicable tohigh dimensional data. This method can be considered as a sparse model for clustering by means of partitioning features that contribute to several sample clustering solutions.
Multiple co-clustering based on
The method is based on a nonparametric Bayesian approach in which the number of views and the number of feature-/subject-clusters are inferred in a data-driven manner. We
nonparametric mixture models with simultaneously model different types of distribution family such as Gaussian, Poisson and multinomial in each cluster block. This leads our method to be applicable to datasets
heterogeneous marginal distributions consisting of both numerical and categorical variables which is common in biomedical data analysis. The clustering solutions are obtained based on variational inference withmean field approximation. We apply our method to a real dataset with no true cluster structure available, from which useful implication is drawn about possible clustering
structures of the data.

Osaka University

The famous "rich-gets-richer" and "fit-gets-richers" phenomena have long been conjectured to be the main driving forces behind the evolution of various kinds of systems
founded in diverging fields such as biology, ecology, sociology, economics, etc. The field of complex network analysis is of course no exception. "Rich-gets-richer" and "fit-getsrichers" phenomena, now under the new names of "preferential attachment" and "fitness", have been hypothesized to be the underlying mechanisms of the evolution of some
Efficient joint estimation of rich-getsimportant types of complex networks. As in any scientific conjectures, the hypothesized presences of preferential attachment and fitness mechanisms call for the real need ofricher and fit-gets-richer effects invalidating such hypotheses in real-world networks. In our case, this amounts to actually estimate the preferential attachment (PA) function and node fitnesses from observed
complex networks
data. In the first part of our research, we provide a statistical method to jointly estimate PA function and fitnesses from a time-series of network snapshots. In the second part, weconsider the case when we could only observe a single snapshot. Even in this case, which is frequently encountered in real-world situations, we show that not all hope is lost. By
introducing the key concept of timeline, one can actually recover the PA function and node fitnesses to some extent.

Kyoto University

Every year many trackers are introduced to address many visual tracking challenges such as illumination variances and partial occlusion of the objects. The biggest obstacle toconstruct the holy-grail of tracking from current trackers is the complexity of integrating mechanisms and contradictory objectives pursued by each of them.
In this study, we propose a framework, MIMIC, to imitate the behaviors of an arbitrary black-box tracker, with a relatively simple non-linear tracker benefiting from a pool ofvarious features. This study is based on a premise that a linear combination of sufficiently expressive features along with a flexible but still simple non-linear observation model
can roughly approximate the behaviors of many popular trackers.
To suppress over-fitting in this high-dimensional learning task, we employed the "dropout” technique. By emphasizing on optimization over model ensemble, this algorithm
investigates different combinations of the features and prevents over-fitting by approximating the training dataset by means of a virtual ensemble of the models employing varietyof feature subsets. The proper combination of the L1 regularization and the dropout algorithm is expected to work well in the simultaneous task of feature selection and feature
weighting, by avoiding over-fitting even when the number of available videos is limited.

Oita University, JapanNTT and Osaka
University

Imitation Game: How a Imitate any
Visual Tracker using a few VideosHD^3-2015 Poster Session

December 16th, 2015

Conventional methods for the reconstruction of multidimensional NMR spectra from undersampled time-domain data were not effective for the accurate determination of thesignal intensity ratios of the crowded two-dimensional spectra of proteins. Here, we developed an NMR spectra reconstruction method, “Conservation of experimental data in
ANAlysis of FOuRier” (Co-ANAFOR), to reconstruct the crowded spectra from the undersampled time-domain data. The number of sampling points required for the transferredcross-saturation (TCS) experiments between membrane proteins, photosystem I and cytochrome b6f, and their ligand, plastocyanin, with Co-ANAFOR was half of that needed
for linear prediction, and the peak height reduction ratios of the spectra reconstructed from truncated time-domain data by Co-ANAFOR were more accurate than thosereconstructed from non-uniformly sampled data by compressed sensing (Ueda et al., Journal of Biomolecular NMR, 2015). Application of Co-ANAFOR to the TCS experimentsof chemokine receptors, CCR1 and CCR5, and their ligand MIP-1a revealed that the residues on the N-loop and b-sheets of MIP-1a are close to both CCR1 and CCR5, andthose in the C-terminal helix region are close to CCR5, although the NMR measurement time for CCR1 and CCR5 samples was limited to 24 hours, due to their low stability(Yoshiura et al., Journal of Biomolecular NMR, 2015).

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Takumi Ueda

Graduate School of
Pharmaceutical
Sciences, theUniversity of Tokyo

Development of an NMR spectra
reconstruction method to elucidate
the CCR1- and CCR5- binding
modes of MIP-1a

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Tomoya Sakai

Nagasaki University

Sparse modeling of lung sounds and We present and demonstrate a convex optimization technique for separating lung sounds acquired through an electronic stethoscope.
their separation by convex
Our technique exploits sparse and low-rank properties of the lung sounds in the Fourier, wavelet, and time-frequency domains. Co-authors: Senya Kiyasu, Sueharu Miyahara,
optimization
and Yasushi Obase (Nagasaki University)

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Masaaki Nagahara,
Katsuyuki Kunida^A

Kyoto University, The
University of Tokyo^A

Dynamical Sparse Modeling

The core idea of sparse modeling is to elicit a few essential parameters from a very large data set, often referred to as Big Data, with a fast and efficient algorithm. In this study,
we consider data from dynamical systems, and propose a sparse modeling method based on dynamical models. This can be described as sparse optimization subject todifferential equations, which can be solved via sparse optimal control theory that the presenter has recently proposed. We call this new sparse modeling "dynamical sparsemodeling." The optimization can be efficiently solved by recent optimization algorithms such as alternating direction method of multipliers (ADMM), after discretization. The poster
presentation shows the mathematical formulation of dynamical sparse modeling, efficient algorithms, and numerical simulation results.

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Motoki Shiga

Gifu University

Advances in scanning transmission electron microscopy (STEM) techniques have enabled us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX)
spectral datasets from a specified region of interest (ROI) at an arbitrary step width, called spectral imaging (SI). Instead of manually identifying the potential constituent chemicalcomponents from the ROI and determining the chemical state of each spectral component from the SI data stored in a huge three-dimensional matrix, it is more effective andefficient to use a statistical approach for the automatic resolution and extraction of the underlying chemical components. Among many different statistical approaches, we adopt a
Nonnegative Matrix Factorization for
non-negative matrix factorization (NMF) technique, mainly because of the natural assumption of non-negative values in the spectra and cardinalities of chemical components,
Spectral Imaging Data Analysis
which are always positive in actual data. We propose a new NMF model with two penalty terms: i) an automatic relevance determination (ARD) prior, which optimizes thenumber of components, and ii) a soft orthogonal constraint, which clearly resolves each spectrum component. Numerical experiments using real STEM-EDX/EELS SI datasets
demonstrate that the ARD prior successfully identifies the correct number of physically meaningful components. The soft orthogonal constraint is also shown to be effective,
particularly for STEM-EELS SI data, where neither the spatial nor spectral entries in the matrices are sparse.

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Jun-ya Gotoh, Akiko
Takeda, and Katsuya
Tono

Chuo University,
Japan;The Universityof Tokyo, Japan;The
University of Tokyo,
Japan

Proximal DC algorithm for Sparse
Optimization

We propose a non-convex sparsity-inducing regularizer and an iterative algorithm we call proximal DC (Difference of Convex functions) algorithm, which repeatedly linearizes the
non-convex terms at a subgradient. Our algorithm can be regarded as a generalized iterative shrinkage thresholding algorithm (ISTA) and converges linearly to a critical pointfrom any initial point. Through experiments, we demonstrate that the proposed algorithm finds a better solution with desired sparsity in almost the same order of computationaltime compared to ISTA.

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Takuma Kasai and
Takanori Kigawa

RIKEN Quantitative
Biology Center

NMR spectrum analysis with error
detection mechanism

Nuclear magnetic resonance (NMR) is a useful method for protein analysis since properties of NMR signals; intensities, chemical shifts, and line widths, reflect their structuraland dynamic properties. However, low signal-to-noise ratio often leads to wrong interpretation of the NMR spectra. Here we report that methods from information science, suchas error detection, can be incorporated for reliable analyses of the NMR spectra. We previously developed a distinctive stable-isotope labeling strategy, SiCode (Stable isotope
encoding), to determine amino-acid type of amide signals with a small number of labeled protein samples. In the SiCode strategy, information of amino-acid type is encoded tostable-isotope labeling ratios of the samples and decoded from signal intensities of the observed NMR spectra. We introduced an additional labeled sample to the SiCodecorresponding to a check digit to certainly improve the noise tolerance through error detection in amino-acid typing especially under the condition of low signal-to-noise ratio.

HD^3-2015 Poster Session

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December 16th, 2015

^A^Kumamoto
Kazunori Iwamitsu^A^,
University, Japan,
Shingo Aihara^A^,^B^The University of
Masato Okada^B,C^
Tokyo, Japan,
and Ichiro Akai^A^^C^RIKEN, Japan.

Bayesian estimation of excitonic
absorption spectra with theMetropolis algorithm

Katsuaki Koike

Demand for metal resources has largely increased because of constructing sustainable society and innovative technologies. Based on that background, this study is aimed to (i)develop a method for highly precise spatial modeling of metal contents in a metal deposit and (ii) clarify a physical law that formed a deposit. Matsumine mine, the largest kuroko
Spatial modeling of metal contents in deposit in northern Japan, is selected as a case study site. The metal content data of Cu, Zn, and Pb at 1457 points were used for the spatial analyses over a region of 500 m×a kuroko-type deposit with
1000 m×300 m. High metal-content zones by geostatistics were revealed to be overlapped with the patchy kuroko zones and extend horizontally as connecting the zones.
application to estimating
Assuming that the transport of ore solutions and the deposition of metals are approximated by an advective-diffusion spread phenomenon, the advective velocities and the
diffusion coefficients were calculated from the metal content model. The result suggested the main paths of ore solutions in the distributions of silicious ore and rhyolite. Our next
step is to improve the metal content estimation by combining a physical law of the deposit formation and kriging.

Kyoto University

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Hiromichi Nagao

Seismic wave field imaging in the
The University of Tokyo Tokyo metropolitan area based on
lasso

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Wataru Kurebayashi

Aomori University

In our study, we analyzed the excitonic absorption spectra of Cu2O thin films sandwiched by MgO plates with the Metropolis algorithm of a Bayesian estimation. Consequently,
we clarify probability distributions of all the spectral parameters only using one absorption spectrum.

Rapid prediction of damage due to large earthquakes on constructions through a numerical simulation would provide important information for making a decision relating to
rescue and rehabilitation activities. Such a simulation requires ground motion input to each construction, which usually distributes much denser than seismometers.
We propose a new method based on lasso for the purpose of data-driven imaging of seismic wave field in an urban area from seismograms obtained by a dense array. The
coefficient matrix in a Taylor’s expansion model is estimated by group lasso rather than the ordinary lasso in order to avoid the dependency of subjective coordinate settings intime and space.
We have applied the proposed method to synthetic seismograms obtained from an analytic solution of seismic wave field assuming that an earthquake of M7 class occurs in ahorizontally-layered underground. The resulting image reproduced better than those obtained by the ordinary least square, ridge regression or lasso. We have also applied to theactual MeSO-net data when the 2011 Earthquake off the Pacific coast of Tohoku occurred, and confirmed that group lasso reproduced the actual seismic wave field moreaccurately than the other methods.

Efficient estimation of Koopman
operators from time series data

The mode decomposition of time series data based on the Koopman operator has been a hot topic in applied mathematics and fluid mechanics. We propose an efficient kernelbased method for estimating Koopman operators from time series data.

The period analysis is a fundamental tool to study variable stars in which we estimate one or a few periodic signals in the time-series data of stellar brightness (so-called the"light curve"). In the case of ground-based astronomical observations, the sampling pattern of the light curve is so irregular that aliases occasionally disturb the detection of real
signals. Here, we report the capability and application of our period analysis methods using the LASSO and group-LASSO for variable stars. They are definitely useful for thereconstruction of power spectra because we can assume them sparse when we know the objects are periodic variables. We demonstrate that, for periods longer than 1 day, theastronomical sampling pattern achieves a reconstruction quality similar to that by random sampling. For periods shorter than 1 day, however, the reconstruction quality issignificantly degraded. Multi-longitude observations can improve it even if the data size is small for each observatory. We also introduce applications to real data, in which sharp
signals in the LASSO periodogram emphasize a small, but significant period change in periodic humps in dwarf novae.

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Makoto Uemura

Hiroshima University,
Japan

Period analysis of variable stars
using the group LASSO

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Motonobu Kanagawa

SOKENDAI (ISM),Japan

Convergence Rates of Quasi Monte Quasi Monte Carlo (QMC) methods deterministically generate samples, thereby achieving numerical integration with convergence rates faster than Monte Carlo integration.
Carlo Integration under Misspecified These samples are generated based on smoothness assumptions on integrands. We discuss situations where such assumptions are violated. We show that QMC can be
Assumptions
consistent even for such situations, and can achieve certain convergence rates.

HD^3-2015 Poster Session

December 16th, 2015

Large amounts of visible and near-infrared reflectance spectra data of solar system small bodies have been obtained by ground- and space-based telescopic observations andclose-up observations by spacecraft in the past few decades. Small bodies, including asteroids, comets, dwarf planets and trans-Neptunian objects have been classified into
several types, based on the shape of reflectance spectra. The physical meaning of reflectance spectra, however, is still poorly constrained because of the lack of forward
models. In addition, different types of meteorite samples can show similar shape of reflectance spectra, suggesting the application limit of classification scheme based solely onreflectance spectra. In this study, we show the relationship between spectral type and geometric albedo mainly obtained by ground- and space-based observations, in order toseparate similar reflectance spectra inherent in small bodies. Furthermore, we compare the observational results obtained by close-up observations with spectra-albedo
relationship to extract physical meaning of reflectance spectra, such as the highest temperature that a small body has experienced.

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Peng Hong

The University ofTokyo, Japan

Physical understanding ofreflectance spectra of solar systemsmall bodies based on data-driven
approach

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Koujin Takeda,
Toshiyuki Tanaka

Ibaraki University,
Kyoto University

Application of partial parallelinterference cancellation to sparse
signal recovery

We propose a scheme for how to construct sparse signal recovery algorithms in compressed sensing under general measurement matrix, by applying the idea of partial parallelinterference cancellation in CDMA demodulation to iterative soft thresholding. As a result, we arrive at a novel algorithm, which is equivalent to approximate message passing(AMP) under an appropriate assumption on the measurement matrix. This means that our novel algorithm is one of generalizations of AMP. In addition, via numerical experiment
we verify that the proposed algorithm shows better convergence than the original AMP when the measurement matrix does not satisfy the assumption of AMP.

Shinsuke Uda

Kyushu University,
Japan

Estimation method of sparse partial
correlation matrix from omics data
set with missing values

Omics data analysis is important to understand whole picture of living system. However, analytical methods of omics data do not fully developed owing to high-dimension-lowsample-size setting and missing values. In this study, we developed an estimation method of sparse partial correlation matrix from omics data set with missing values, taken
together with data base of biological knowledge. The partial correlations between molecular species is estimated by sparse regression. In addition, prior knowledge of network
structure stored in database such as the KEGG is used by tuning the hyper parameter of L1 norm regularization for each pathway. The missing values in omics data set are
estimated simultaneously with the partial correlations by low rank approximation of data matrix.

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Hajime Tamaki

In nuclear magnetic resonance (NMR) spectroscopy, data sampling reduction by combination of compressed sensing (CS) and non-uniform sampling (NUS) is an attractive
approach to obtain high-resolution spectra and gain sensitivity. This approach successfully introduced to solution NMR measurement of many types of multi-dimensional spectra.
Graduate School ofApplication of compressed sensing
However, the effectivity of the approach is still unclear in solid-state NMR due to its broadened line width and low sensitivity, especially in the case of large size protein
Life Science, Hokkaido to solid-state NMR measurements ofmeasurement. We applied NUS to a 13C-13C chemical shift correlation spectrum of a heptahelical membrane protein recorded by magic-angle sample spinning solid-state NMR
University, Japan
heptahelical membrane protein
and reconstructed by CS using L1-norm as a regularization term. Our results show that the spectral features include linearity of the signal intensity were reproduced by 30 % of afully linearly sampled data set. Combination of NUS and CS is useful even the case of the heptahelical membrane protein measurements in solid-state.

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Yasuko SugaseMiyamoto1, Narihisa
Matsumoto1, Kenji
Kawano2, Masato
Okada330

Dai Yonebayashi, Peter University of Toyama
Davis, Tatsuto
and Telecognix
Murayama
Corporation

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1AIST, Japan; 2Kyoto
University, Japan;
3Univ Tokyo, Japan

Face-responsive neurons in monkey temporal visual cortex differentiate both global category of faces (human vs. monkey faces) and their rotation in the picture-plane (uprightvs. inverted presentations). To examine how neurons contribute to global categorization (GL), upright vs. inverted categorization of human faces (HUI), and upright vs. inverted
Global category of faces and uprightcategorization of monkey faces (MUI), we analyzed activities of 119 face-responsive neurons in area TE of two rhesus monkeys, performing a fixation task. The test stimuli wereversus inverted category of faces
colored pictures of monkey and human faces, and their inverted pictures. The population activity vectors consisting of mean spike counts across trials to each stimulus were
were sparsely represented bycomputed in 115-165 ms time window after stimulus onset. L1-regularized logistic regression revealed that the minimal number of neurons to categorize the GL, HUI, and MUIneurons in monkey temporal visual
was 2, 2, and 10, respectively. The two neurons for the GL were different from the two for the HUI and from the 10 for the MUI. But the two neurons for the HUI were identicalcortex.
with two of the 10 neurons for the MUI. The results suggest that different members of the neuronal population contribute to categorize GL and HUI or MUI, and that HUI and MUIhave contributing members in common.

Noisy Data Aggregation with Polar
Codes

The quality of collective estimation of a source state involves a difficult tradeoff between sensing quality which sometimes requires a sufficient allocation of our total bandwidth foreach of individual sensors, and aggregation quality which requires the use of as many devices as possible under the resource constraint. Therefore it makes sense to consider astrategy for optimal aggregation for an ensemble of independent noisy observation with constrained system capacity. In this poster presentation, we examine such a tradeoff byapplying the ``polar codes,'' widely known as the state-of-the-art in the field of lossy source coding, and show some numerical results.

HD^3-2015 Poster Session

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Chako Takahashi

Mean-field approximation for
Gaussian-Bernoulli restricted
Boltzmann machine

A machine learning technique referred to as deep learning has been widely used in many research fields. Restricted Boltzmann machine (RBM), that is the Boltzmann machine
defined on a bipartite graph, is one of the most fundamental components of it. Gaussian-Bernoulli restricted Boltzmann machine (GBRBM) is the extended model of RBM thatallows us to treat continuous data. As with RBM, GBRBM consists of two different layers: a visible layer and a hidden layer. The visible layer consists of real-valued visible
variables and the hidden layer consists of binary hidden variables.
Many effective learning algorithms, such as the contrastive divergence, have been proposed. However, inference algorithms have not been much sophisticated.
In this study, we consider inference algorithms in GBRBM on the basis of the naïve mean-field approximation. We derive two types of mean-field approximations: the one is themean-field approximation for whole variables in GBRBM and the other is that for the marginal distribution of GBRBM. We compare the two different mean-field approximations
qualitatively and quantitatively, and show that the latter one is better in both points of view.

A theory of phase transitions andcrossovers in statistical estimation:
Toward a data-driven approach forphysical science

Extracting hidden structures behind observed data is what natural sciences is all about, though the data are always limited in accuracy and amount. Any structures are not
identified if an amount of data is not enough large. Some structures are identified if the amount is enough large. The qualitative change in statistical estimation like this cannot be
explained by the asymptotic theory of statistical estimation. In this study, we develop a theory of the qualitative change in statistical estimation based on the fact that statisticalestimation is mathematically equivalent to statistical physics. We introduce a novel function, which is called Bayesian specific heat, and explain the qualitative change as acrossover in statistical physics. We show that Bayesian specific heat asymptotically corresponds to real log canonical threshold, and explains phase transitions, which occur in
infinite systems. We apply a finite size scaling to Bayesian specific heat and show that the scaling function explains crossovers. We take a regression problem, which is called
Bayesian spectral deconvolution, as a case study and show the effectiveness of Bayesian specific heat. We discuss a necessary data amount and noise level from a viewpoint of
our theory.

^A^The University ofAuto-Extraction of Effective Spin
Tokyo, Japan,
Hamiltonians from Electronic^B^Waseda University, Structural Calculations by Bayesian
Japan
Inference

We describe a novel method for extracting effective classical spin Hamiltonians automatically from mean-field type electronic structural calculations by means of Bayesianinference. The method is applied to a NiS_2 triangular lattice in NiGa_2S_4 with a spin disordered ground state. Starting from unrestricted Hartree-Fock calculations for the spin
configurations of 16 Ni sites, we estimated that not only the strongest superexchange interaction between the third nearest neighbor sites but also those between the nearest
and the second nearest neighbor sites should be taken into account to extract effective classical spin Hamiltonians for NiGa_2S_4. We also show results obtained from theabove calculations with the Boltzmann factor. This method may enable mean-field type electronic structural calculations and magnetic experiments to be compared seamlessly
because the Boltzmann factor takes into account the finite temperature effect. As a result, we estimated that the superexchange interaction between the nearest neighbor sites isferromagnetic, which is consistent with magnetic experiment results. This supports the theory that the competition between the antiferromagnetic third neighbor interaction andthe ferromagnetic nearest neighbor interaction may lead to a novel magnetic order, called the quantum spin liquid.

Yamagata University

32

Satoru Tokuda, Kenji
The University of
Nagata, Masato Okada Tokyo, Japan

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Hikaru Takenaka^A^,
Kenji Nagata^A^,
Takashi Mizokawa^B^,
and Masato Okada^A^

RIKEN AICS, Japan

Sequential data assimilation for
single-molecule FRET photoncounting data

34

Yasuhiro Matsunaga

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1. Graduate School ofInformatics andEngineering, The
University of ElectroMasashi Sato(1), Okito
Information spreading ofCommunications,
Yamashita(2,3), Masamagnetoencephalography source
Japan, 2. ATR Neuralaki Sato(2), Yoichi
localization and its effect on neural
Information Analysis
Miyawaki(4)decoding.
Laboratories, Japan, 3.
Brain Functional
Imaging Technologies
Group, CiNet, Japan,

36

December 16th, 2015

Hirotaka Sakamoto^A^, The University of
Yoshinori NakanishiTokyo^A^, ResearchOhno^A,B^, Masato
Fellow of JSPS^B^,
Okada^A,C^
RIKEN BSI^C^, Japan

Bayesian hyper-parameter
estimation on Markov random field
influenced by using image data of
low quality

Data assimilation is a statistical method designed to improve the quality of numerical simulations in combination with real observations. We have developed a sequential data
assimilation method that incorporates one-dimensional time-series data of single-molecule FRET (smFRET) photon-counting into conformational ensembles of biomolecules
derived from "replicated" molecular dynamics (MD) simulations. A particle filter using a large number of replicated MD simulations with a likelihood function for smFRET photoncounting data is employed to screen the conformational ensembles that match the experimental data. In the poster, we show the details of our method as well as preliminary
results obtained by an application to the smFRET data of protein folding dynamics.

Magnetoencephalography (MEG) is widely used as a tool to investigate dynamics of human neural information processing. For this purpose, source localization methods areused to estimate spatial patterns of the cortical current corresponding to recorded MEG signals. However, it remains unclear whether spatial patterns of the cortical current are
estimated accurately. Here, we simulated MEG signals for artificial experimental conditions and applied neural decoding analysis to examine how accurately spatial patterns of
the cortical current are estimated by several MEG source localization methods. All methods successfully localized cortical current with large amplitude in target areas where thecurrent sources were assumed. However, the trained neural decoder predicted the experimental conditions from spatial patterns of the estimated cortical current in non-target
areas as well as in the target areas. These results show information represented in the cortical activity patterns in the specific areas spread to other areas through MEG source
localization. To avoid such false positiveness, we propose a method to evaluate the most informative cortical area for neural decoding.
Acknowledgements: This study was partially supported by JSPS KAKENHI Grant Number 26120514.

Recently, a lot of image data are acquired in various areas of natural science. Since these image data are typical of high-dimensional data, it is important to estimate the valuesof latent variables such as diffusion coefficients. We study a Bayesian framework to estimate the values of diffusion coefficients, represented by hyper-parameters in Markov
random field models, from image data. This framework allows us to evaluate the reliability of the estimates as well. Our motivation is to examine how the estimated values andtheir reliability are influenced by methods used in data analysis and acquisition. In this study, we focus on the pre-processing of image averaging in the case of the measurementof low S/N ,and the use of down-sampled data. First, we show that the method of averaging has no effect on image restoration in terms of root mean square error but decreases
the reliability of hyper-parameter estimation. Next, we show that down-sampled data bias the estimates of hyper-parameters. We discuss the relation between this bias and thedimension of image data using the concept of renormalization group developed in statistical mechanics.

HD^3-2015 Poster Session

37

Yuri Kokura, Yukinojo
Kitakami, Takashi
Ohnishi, Hideaki
Haneishi

Chiba University,
Japan

38

Yukinojo Kitakami,
Takashi Ohnishi,
Hideaki Haneishi

4D-MRI Reconstruction Using LowChiba University,Japan Rank and Sparse Matrix
Decomposition

39

40

41

42

Masamichi J. Miyama
and Koji Hukushima

University of Tokyo,Japan

Enhanced Angiography Under
Natural Respiration Using Robust
Principal Component Analysis

December 16th, 2015

In digital subtraction angiography (DSA) targeting thoracoabdominal organs, patients have to hold their breath in order to achieve the subtraction successfully. However, steadybreath hold is sometimes difficult especially for elder patients. In this paper, we propose a blood vessel enhancement method with only consecutive digital angiographic images
under the natural breathing. The method consists of two-steps. In the first step, we use a robust principal component analysis to separate the consecutive images into a low-rank(L) component and a sparse (S) component. The S component mainly corresponds to the pattern of the contrast agent. At this step, deformation between sequential images due
to respiration is corrected using L component. In the second step, we generate a widely-spread blood vessel pattern from the sequence of motion-corrected S-component
images. We decomposed many sets of angiographic images to L + S components and could observe satisfactory enhanced angiography images.

We have previously developed a method for obtaining 4D-MRI to visualize and quantify the three-dimensional motion of the thoracoabdominal organ due to respiration. However,a data collection time of approximately 30 min is needed. Now, we propose to reduce the number of encoded samples in the k-space for time shortening and to apply a sparsemodel-based reconstruction algorithm for preserving image quality. The applied technique is a low-rank plus sparse matrix decomposition (L+S). We performed a simulation
experiment where the encoded data were reduced to one-third of the full sampling. We compared the ideal image reconstructed with the full sampling data, the image
reconstructed by the conventional method with the k-space data filling the missing space with zero, and the image reconstructed by the L+S technique. We confirmed that L+Stechnique can reduce the artifacts and noise and provide image quality similar to that of the ideal image.

We propse the new framework of the real-space data analysis of the STM (Scanning Tunneling Microscope) topography image, which enables us to determine the positions ofatoms to the degree where we can extract the locally strain of the atom's configuration from the noisy datasets. In our analysis, we utilize the relevant vector machine (RVM) as aSparse modeling approach for STM data model, a 2D Gaussian function as a base function that corresponds to a position of a single atom peak, and LASSO as the prior information. In our framework, it isdata analysis by using LARS-LASSO necessary to choose the suitable coefficient of LASSO term for obtaining the desirable solution of the RVM in order to determine the positions of atoms. One solution for thisprocedure is a cross-validation technique, however it takes high cost when a model containes the large number of variables as in our case. In our poster presentation, we
present the application of LARS for our problems and discuss the result of the optimization of our inference tequnique.

Satoshi Takabe

University of Tokyo,
Japan

Statistical-mechanical analysis of
Boolean compressed sensing

Detecting a small number of defects among items is a traditional but essential issue in statistical inference. Given the number of items $N$ and defects $K$, $O(N)$ tests arenaively required for detection. If some of items are tested at one by pooling them, however, the number of tests reduces to $O(K \log N)$. This technique is called the group
testing and is applied to the DNA cloning library screening, for example. Recently, focusing on the sparsity of defects, Boolean compressed sensing (BCS) is proposed to
improve detection performance of the group testing. In this presentation, we report its implementation based on the Max-Sum algorithm and analysis of its typical behavior
averaged over random pools. Our analysis successfully estimates its detection ratio even in the case of a finite number of items. It also shows that our method based on BCS
asymptotically needs less number of tests than other greedy methods; a half of the combinatorial matching pursuit and a quarter of the combinatorial basis pursuit.

Toshiya Takami

Kyushu University,
Japan

Parallelization of Irregular Sparse
Computations with One-sided
Communications

Sparse linear algebra is one of the basic operations for managing large-scale data sets in physics and chemistry. We often encounter non-symmetric sparse matrices in actual
applications. In this poster, we concentrated on parallel computations for those irregular sparse operations by the use of one-sided communications. Over many years,
send/receive-type communications in Message Passing Interface (MPI) has been widely used for parallel implementations in scientific computations. Recently, MPI3 provides aproper implementation for one-sided communications, where we can use put/get-type operations without disturbing corresponding processes. We will analyze how nonsymmetric sparse operations are effectively parallelized by the use of those one-sided operations.

Kazuki Kuramochi

At the heart of almost every major galaxy lurks a supermassive black hole, which is the most extreme object predicted by Einstein's General Theory of Relativity. The supermassive black hole Sgr A* residing the center of our Milkey way galaxy exhibits the largest apparent angular size in the Universe. Sgr A* provides an excellent opportunity toimage the immediate vicinity of the event horizon with an Earth-size radio interferometer Event Horizon Telescope (EHT).
Imaging the silhouette of the Galactic Two important issues have remained for imaging the silhouette of Sgr A*. First, its size is comparable to the diffraction limit of EHT, requiring a super-resolution imaging. This
The University of Tokyo
supermassive black hole with theissue can be resolved with LASSO, a technique of the sparse modeling, providing a good fidelity image even in a super-resolution regime (Honma et al. 2014). Another issue is/NAOJ, Japansparse modelingthe interstellar scattering effects caused by the tenuous interstellar plasma in our line of sight that scatters radio waves from Sgr A*. It blurs an interferometric image and reduces
its effective resolution.
We present realistic simulated observations of Sgr A* with EHT including the interstellar effects. We found that LASSO can overcome also the effect of interstellar scattering,making it promising to image the giant black hole in our galaxy.

HD^3-2015 Poster Session

43

Takeo Hoshi

Tottori University,
Japan

Unsettled data problems appeared in large-scale quantum material simulations mainly of organic device materials. The simulations were carried out on the K supercomputer by
our novel simulator ELSES(http://www.elses.jp/) with original massively parallel numerical algorithms for upto 100-nm-scale systems. The present paper focuses on industrialorganic materials and related ones. Since they are characterized by large-scale disordered pi-electron networks, the data analysis methods like sparse modeling are crucial so
Data problems that appeared inas to obtain material design principle for industrial products. Our preliminary results indicate proto-typical data problems on (i) sp2-sp3 nano-composite carbon solids and (ii)large-scale quantum material
quantum (hole wavepacket) dynamics in organic device materials of polymers (1D-like system) and thin films (2D-like system). The first topic gives a static (time-independent)
simulations on the K supercomputer
data problem for spatial domain analysis. The second one gives dynamical (time-dependent) data problems for electronic device performance (mobility). These topics require thecollaboration between computational science and data science. As a future outlook, the softwares and the data should be integrated on supercomputers as a cloud-type service
for academic and industrial researchers. The author thanks to Koji Hukushima (U. Tokyo) for ongoing discussions on the topics.

FMLO: a New HD^3 Spectroscopic
Method for Radio Astronomy with
Removing Correlated Noises

We develop a new observing method for (sub)millimeter spectroscopy in radio astronomy using high-dimensional time series data. We focus on the behavior of noises that they
are fluctuated slowly in time (< 1 Hz) and correlated in spectrum (correlated noise). FMLO, our new method, obtains highly-sampled (10 Hz) time series spectra whereastronomical signals are frequency-modulated (FM), which is realized by changing the frequency of local oscillator (LO) in heterodyne receiver. Correlated noises are therefore
removed in time series spectra by principal component analysis (PCA). FMLO never requires off-point (referential) observations and thus dramatically improves efficiency of
single-dish spectroscopy, especially faint & broad molecular line observations toward cosmological distant galaxies. In this poster we will present the principle and the data
reduction method of FMLO. For the faster and more objective data reduction, we introduce an iterative algorithm and probabilistic PCA (PPCA), which is used for machinelearning field. PPCA can correctly determine the number of principal component used to reconstruct the true correlated noise and thus model the astronomical signals. We will
also show some observational results of FMLO implemented on the Nobeyama 45m telescope in Nagano, including optimization of FM patterns and FMLO-mapping, which will
be more efficient on-the-fly observations.

44

Akio Taniguchi

45

Masahiro Haze^1^,
ISSP, Univ. of
Yoshinori NakanishiTokyo^1^, Grad. Sch.
Ohno^2^, Yasuo
Front. Sci., Univ.
Yoshida^2^, Koji
Tokyo^2^, Grad. Sch.
Hukushima^3^, Masato
Arts. Sci., Univ.
Okada^2^ and Yukio
Tokyo^3^
Hasegawa^1^

Scanning tunneling microscopy and spectroscopy (STM/S) enables us to observe quasi-particle interference (QPI) patterns, which are formed by scattering and interference ofquasi-particles at surfaces. By taking the Fourier transformation of QPI patterns at various energies, we obtain the energy dispersion of the quasi-particles, which is related to theelectronic band structure. This technique is widely used in a community of condensed matter physics to obtain the electronic structure of materials such as unconventional
Compressed sensing for eﬃcient
superconductors and topological insulators,
measurements of quasiparticle
However, QPI measurements are time-consuming because tunneling spectroscopy has to be performed over wide area in high spatial and energy resolutions to obtain
interference using scanning
information in sufficient momentum space (q-space). In order to improve the time-efficiency, we have used compressed sensing (CS); a signal-processing technique for thetunneling microscopy / spectroscopy
recovery of signals from a small number of data points. Our application of CS is based on the sparseness of Fourier-transformed QPI in q space. We applied a CS techniquecalled least absolute shrinkage and selection operator (LASSO) to analyze an STM image of the Ag(111) surface whose Fourier-transformed QPI shows a circular pattern.
Through numerical simulations, we revealed that LASSO performs well in reconstructing the circular pattern from reduced number of data points.

46

Masaaki Imaizumi

University of Tokyo

Nonparametric multivariate
regression with tensor-product
RKHS

We consider a nonparametric multivariable regression problem, and estimate a function in tensor-product reproducing kernel Hilbert space (RKHS). The multivariate regressionfunction is represented by tensor-product of elements in several RKHSs, and the representer theorem enables us to solve the optimization problem with the regression function.
We introduce elastic-net type regularization for the each element of the RKHSs, and investigate the convergence of the estimator. We show that our method is suitable for thevariable selection problem, and it is also applicable for complex data analysis.

47

Kazuyuki Hara

Nihon University

Dropout acts as an ensemble
learning

Dropout is used in the deep net act as a regularizer. However, the dropout updates the hidden units asynchronous way and this property may produce the diversity of the hidden
units activities. We propose novel function of dropout to act as an ensemble learning.

TimeTubes: Preliminary Design ofVisualization Tool for Timedependent, Multivariate Blazar
Datasets

Blazars are active galactic nuclei whose relativistic jets ejected from the central black hole are pointing towards the earth. It is still a challenge to analyze time-dependent,
multivariate datasets for the blazars with conventional visualization methods such as scatter plot matrices. This poster presents TimeTubes, a new visualization scheme thatallows users to analyze dynamic changes and causality among representative time-varying dimensions. In our initial attempt to visualize blazar datasets provided by Hiroshima
Astrophysical Science Center, we selected six dimensions from the originals, including polarization parameters (Q/I and U/I) and their corresponding errors, intensity and color.
Our basic idea is to align ellipse-transformed and colorized snapshots of these dimensions in parallel, to form a tube in 3D space. The 2D coordinates of the center of each
ellipse and its major and minor axes are given by (Q/I, U/I) and the error of Q/I and U/I, respectively. The resulting tube is then colorized by the observed intensities and colors of
the blazar. We designed a designated interface with 10 feature functions to control the view of TimeTubes. In the presentation, the usability of TimeTubes along with feedback
from experts from the center will also be discussed.
Co-authors: Masanori NAKAYAMA: Keio University, Japan, Hsiang-Yun WU: Keio University, Japan, Makoto UEMURA: Hiroshima University, Japan, Issei FUJISHIRO:
Keio University, Japan.

48

Longyin Xu

University of Tokyo

December 16th, 2015

Keio University, JapanHD^3-2015 Poster Session

49

50

Yusuke Niibe

Making many-to-many parallel
Keio University, Japan coordinate plots scalable byasymmetric biclustering

December 16th, 2015

Many-to-many parallel coordinate plots enable users to grasp at a time correlations between arbitrary pairs of dimensions in a given multi-dimensional dataset.However, themore pairs of dimensions we visualize, the smaller area for displaying each of the pairs becomes. This problem makes it difficult for the users to discover specific highlycorrelated pairs in the datasets. This poster presents our attempt to make the many-to-many parallel coordinate plots scalable by biclustering the underlying dataset in terms ofdata samples and dimensions asymmetrically with different methods. The effectiveness of this approach is empirically proven through applications of the current prototype
system to several practical datasets.
Co-authors: Hsiang-Yun Wu, Keio University, Japan, Kazuho Watanabe, Toyohashi University of Technology, Japan, Shigeo Takahashi, The University of Aizu,
Japan, Issei Fujishiro, Keio University, Japan

Visual object representation in higher visual cortex, inferior temporal cortex (IT), has long been studied either with awake or anesthetized monkeys. Neural
responses to objects in awake and anesthetized conditions are generally considered to have identical property. However, there are no studies directly comparing
Comparison of visual response
responses under these two conditions with the same neuronal populations. In addition, time courses of responses were not well studied in anesthetized animals while itproperties of inferior temporal cortex has been reported that object responses in early and late time phases were different in awake animals. Here, we chronically implanted mul(IT) in Awake and Anesthetized
ti-electrode-array to IT and compared object responses in awake and anesthetized conditions. We found that object response tuning curves calculated from mean
Macaquefiring rates were significantly correlated in 82% of the sites (r=0.6±0.13). However, time-to-time analysis revealed that correlation was not consistent across entire
time period. Responses with mean firing rates in anesthetized condition response was correlated particularly in early phase of responses in the awake condition. We will also
discuss time courses of spatial pattern of responses in awake condition in relation to previously reported spatial patterns of responses with anesthetized animals.

Noriko Katsumata

RIKEN BSI, Japan

51

Takenori Oida,
Kazuhiro Tamiwa

Ultra-low field MRI (ULF-MRI) is one of the modalities in bio-medical images, which has an advantage to obtain high contrast T1-wighted images. However, since it is difficult toSampling strategy of compressed
utilize wide bandwidth, long readout time is required to obtain enough frequency resolution. On the other hand, compressed sensing MRI has been proposed by Lustig et. al. forsensing for improving the contrast of rapid scan of high field MRI. In this study, we propose a sampling strategy of compressed sensing for improving the contrast of T1-weighted images in ULF-MRI. To evaluate our
Kyoto University, Japan
T1-weighted images in ultra-low field strategy, the reconstruction simulations with the fast composite splitting algorithms (FCSA) were carried out. The results of reconstruction simulations show that our sampling
MRI
strategy is effective to reduce readout time. In ULF-MRI, since the readout time occupy the large part of the repetition time (TR), TR can be reduced by short readout time.
Therefore, T1 contrast in ULF-MRI is able to be improved by our sampling strategy.

52

Toshiyuki TANAKA,
Kyoto University, Shiv
Shashi PRABH, Yiyan
Nadar University
LIU

Mean-field hard-core models forthroughput equalization in CSMAbased wireless networks

Hard-core models for wireless ad-hoc networks represent interference between users in terms of hard-core interactions. In mean-field hard-core models one ignores geometricaspects of networks such as locations of wireless nodes, which is justifiable in modeling scattering-rich environments. We study the problem of throughput equalization in meanfield hard-core models. A node with a large degree will suffer from more frequent interference from nodes neighboring in the conflict graph, so that it has to try transmission more
frequently in order to equalize throughput with nodes with smaller degrees. We have obtained via a statistical-mechanics approach a simple scheme to determine frequencies oftransmission attempts which achieves throughput equalization in conflict graphs in the large-system limit.

53

Chihiro Nakajima

Tohoku University

Compressed sensing and semisupervised learning in materialscience

Two applications of informatics for material science, an analysis of rock-textures by image segmentation based on semi-supervised learning and 3D-reconstruction of atomic
arrangement of a gold nano-cluster, are introduced.

New York University

Dynamic digital phantom for
optimization of GRASP DCE-MRI
reconstruction

A realistic digital phantom for optimization of Golden-Angle Radial Sparse Parallel (GRASP) Dynamic Contrast Enhanced (DCE) MRI of the upper abdomen was created using a
set of existing dynamic MR images as a template. Anatomical Region of Interest (ROI)s were manually drawn for the aorta, liver, kidney, muscles, and the averaged signal
change over time was measured. By using this phantom and simulated coil sensitivity maps, k-space data simulating golden-angle radialacquisition was generated. Images were reconstructed by enforcing temporal sparsity using different values for the regularization parameter (λ). Signal changes for each ROI
were compared to determine the optimal λ value. Results obtained from the digital phantom were similar to the results using experimental GRASP data.

54

Koji Fujimoto