Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iecon49645.2022.9968594

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：情報処理学会  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：一般社団法人日本統計学会  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/itsc45102.2020.9294592

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：統計数理研究所  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Public Library of Science (PLoS)  

BACKGROUND: Japan experienced a nationwide rubella epidemic from 2012 to 2013, mostly in urban prefectures with large population sizes. The present study aimed to capture the spatiotemporal patterns of rubella using a parsimonious metapopulation epidemic model and examine the potential usefulness of spatial vaccination. METHODOLOGY/PRINCIPAL FINDINGS: A metapopulation epidemic model in discrete time and space was devised and applied to rubella notification data from 2012 to 2013. Employing a piecewise constant model for the linear growth rate in six different time periods, and using the particle Markov chain Monte Carlo method, the effective reproduction numbers were estimated at 1.37 (95% CrI: 1.12, 1.77) and 1.37 (95% CrI: 1.24, 1.48) in Tokyo and Osaka groups, respectively, during the growing phase of the epidemic in 2013. The rubella epidemic in 2012 involved substantial uncertainties in its parameter estimates and forecasts. We examined multiple scenarios of spatial vaccination with coverages of 1%, 3% and 5% for all of Japan to be distributed in different combinations of prefectures. Scenarios indicated that vaccinating the top six populous urban prefectures (i.e., Tokyo, Kanagawa, Osaka, Aichi, Saitama and Chiba) could potentially be more effective than random allocation. However, greater uncertainty was introduced by stochasticity and initial conditions such as the number of infectious individuals and the fraction of susceptibles. CONCLUSIONS: While the forecast in 2012 was accompanied by broad uncertainties, a narrower uncertainty bound of parameters and reliable forecast were achieved during the greater rubella epidemic in 2013. By better capturing the underlying epidemic dynamics, spatial vaccination could substantially outperform the random vaccination.

DOI： 10.1371/journal.pone.0205889

PubMed

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Language：Japanese  

DOI： 10.14954/jste.3.2_A_76

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PUBLIC LIBRARY SCIENCE  

Cellular structures are hydrodynamically interconnected, such that force generation in one location can move distal structures. One example of this phenomenon is cytoplasmic streaming, whereby active forces at the cell cortex induce streaming of the entire cytoplasm. However, it is not known how the spatial distribution and magnitude of these forces move distant objects within the cell. To address this issue, we developed a computational method that used cytoplasm hydrodynamics to infer the spatial distribution of shear stress at the cell cortex induced by active force generators from experimentally obtained flow field of cytoplasmic streaming. By applying this method, we determined the shear-stress distribution that quantitatively reproduces in vivo flow fields in Caenorhabditis elegans embryos and mouse oocytes during meiosis II. Shear stress in mouse oocytes were predicted to localize to a narrower cortical region than that with a high cortical flow velocity and corresponded with the localization of the cortical actin cap. The predicted patterns of pressure gradient in both species were consistent with species-specific cytoplasmic streaming functions. The shear-stress distribution inferred by our method can contribute to the characterization of active force generation driving biological streaming.

DOI： 10.1371/journal.pone.0159917

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：COPERNICUS GESELLSCHAFT MBH  

A technique for estimating the age-depth relationship in an ice core and evaluating its uncertainty is presented. The age-depth relationship is determined by the accumulation of snow at the site of the ice core and the thinning process as a result of the deformation of ice layers. However, since neither the accumulation rate nor the thinning process is fully known, it is essential to incorporate observational information into a model that describes the accumulation and thinning processes. In the proposed technique, the age as a function of depth is estimated by making use of age markers and delta O-18 data. The age markers provide reliable age information at several depths. The data of delta O-18 are used as a proxy of the temperature for estimating the accumulation rate. The estimation is achieved using the particle Markov chain Monte Carlo (PMCMC) method, which is a combination of the sequential Monte Carlo (SMC) method and the Markov chain Monte Carlo method. In this hybrid method, the posterior distributions for the parameters in the models for the accumulation and thinning process are computed using the Metropolis method, in which the likelihood is obtained with the SMC method, and the posterior distribution for the age as a function of depth is obtained by collecting the samples generated by the SMC method with Metropolis iterations. The use of this PMCMC method enables us to estimate the age-depth relationship without assuming either linearity or Gaussianity. The performance of the proposed technique is demonstrated by applying it to ice core data from Dome Fuji in Antarctica.

DOI： 10.5194/npg-23-31-2016

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ASSOC COMPUTING MACHINERY  

Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.

DOI： 10.1145/2996913.2997000

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Language：English  

There is a high death rate of lung cancer patients. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are effective in some lung adenocarcinoma patients with EGFR mutations. However, a significant number of patients show primary and acquire resistance to EGFR-TKIs. Although the Akt kinase is commonly activated due to various resistance mechanisms, the key targets of Akt remain unclear. Here, we show that the Akt-β-catenin pathway may be a common resistance mechanism. We analyzed gene expression profiles of gefitinib-resistant PC9M2 cells that were derived from gefitinib-sensitive lung cancer PC9 cells and do not have known resistance mechanisms including EGFR mutation T790M. We found increased expression of Axin, a β-catenin target gene, increased phosphorylation of Akt and GSK3, accumulation of β-catenin in the cytoplasm/nucleus in PC9M2 cells. Both knockdown of β-catenin and treatment with a β-catenin inhibitor at least partially restored gefitinib sensitivity to PC9M2 cells. Lung adenocarcinoma tissues derived from gefitinib-resistant patients displayed a tendency to accumulate β-catenin in the cytoplasm. We provide a rationale for combination therapy that includes targeting of the Akt-β-catenin pathway to improve the efficacy of EGFR-TKIs.

DOI： 10.1038/srep13076

PubMed

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japan Industrial Management Association  

Japan needs to develop data analytics talent quickly in order to catch up with the trend of using data for making business decisions. The Ministry of Education, Culture, Sports, Science and Technology started a three-year project to develop so-called data scientists. This paper reviews the findings of the first year of the project, and discusses future challenges.

DOI： 10.11221/jima.65.334

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

The development of the so-called intelligent tire has changed the role of the tire. Here we discuss a real-time road condition classification system that employs monitoring tire acceleration. Because the tire acceleration is non-stationary and is warped non-linearly in the time domain, we applied the time alignment algorithm to it similarly to speech recognition. High accuracy classification systems robust against tire-size range variations have been achieved in previous studies, in which the same pattern tires with prescribed pressure and several vehicles were used. However, there is no discussion on the execution speed of such a system. Therefore, we validated the algorithm and improved it for use in real-time applications. Specially, the calculation time was reduced by limiting the path of the global alignment kernel, which handles a sequence with time warping. Furthermore, we used SVM with L1 norm regularization instead of L2 norm regularization. We succeeded in achieving a real-time road condition classification system, which was verified in a vehicle test.

DOI： 10.1109/MLSP.2015.7324381

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER CHEMICAL SOC  

The measurement of molecular similarity is an essential part of various machine learning tasks in chemical informatics. Graph kernels provide good similarity measures between molecules. Conventional graph kernels are based on counting common subgraphs of specific types in the molecular graphs. This approach has two primary limitations: (i) only exact subgraph matching is considered in the counting operation, and (ii) most of the subgraphs will be less relevant to a given task. In order to address the above-mentioned limitations, we propose a new graph kernel as an extension of the subtree kernel initially proposed by Ramon and Gartner (2003). The proposed kernel tolerates an inexact match between subgraphs by allowing matching between atoms with similar local environments. In addition, the proposed kernel provides a method to assign an importance weight to each subgraph according to the relevance to the task, which is predetermined by a statistical test. These extensions are evaluated for classification and regression tasks of predicting a wide range of pharmaceutical properties from molecular structures, with promising results.

DOI： 10.1021/ci400403w

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

We have developed a technique for estimating the temporal evolution of the plasmaspheric helium ion density based on a sequence of extreme ultraviolet (EUV) data obtained from the IMAGE satellite. In the proposed technique, the estimation is obtained by incorporating EUV images from IMAGE into a two-dimensional fluid model of the plasmasphere using a data assimilation approach based on the ensemble transform Kalman filter. Since the motion and the spatial structure of the helium plasmasphere is strongly controlled by the electric field in the inner magnetosphere, the electric field around the plasmapause can also be estimated using the ensemble transform Kalman filter. We performed an experiment using synthetic images that were generated from the same numerical model under a certain condition. It was confirmed that the condition that generated the synthetic images was successfully reproduced. We also present some results obtained using real EUV imaging data. Finally, we discuss the possibility of estimating the density profile along a magnetic field line. Since each EUV image was taken from a different direction due to the motion of the IMAGE satellite, we could obtain the information on the density profile along a field line by combining multiple images.

DOI： 10.1002/2013JA019734

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

We have developed a technique by which to estimate the spatial distribution of plasmaspheric helium ions based on extreme ultraviolet (EUV) data obtained from the IMAGE satellite. The estimation is performed using a linear inversion method based on the Bayesian approach. The global imaging data from the IMAGE satellite enable us to estimate a global two-dimensional distribution of the helium ions in the plasmasphere. We applied this technique to a synthetic EUV image generated from a numerical model. This technique was confirmed to successfully reproduce the helium ion density that generated the synthetic EUV data. We also demonstrate how the proposed technique works for real data using two real EUV images.

DOI： 10.1002/2013JA019733

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

The solar minimum between solar cycles 23 and 24 was unusually long and deep. The upward region-1 (R1) field-aligned current (FAC) response to this extreme solar minimum was investigated using Defense Meteorological Satellite Program observations. The solar cycle responses on the dayside are different than those on the nightside. The field-aligned current density (J(//)) on the dayside, at 12-17 magnetic local time (MLT), peaks in the declining phase of the solar cycle, in 2003, when the solar wind speed also peaks, whereas J(//) on the nightside, at 18-23 MLT, appears insensitive to the solar cycle. In 1995-2010, J(//) at 15-17 MLT reaches the lowest value during the extreme solar minimum in 2009, when the solar wind speed also reaches the lowest value. At 12-17 MLT, R1 is located mostly on open field lines or at the boundary layer, where the current is driven mostly by the velocity shear at the magnetopause boundary. However, on the nightside, R1 is located mostly on the closed field lines where J(//) is not driven directly and immediately by the solar wind. The nightside current width () exhibits a solar cycle effect such that is smaller at the solar minimum and smallest in 2009. However, the dayside exhibits little solar cycle effect. As a result, the FAC intensity (latitudinally integrated J(//)) exhibits a solar cycle variation at all local times and the FAC intensity is lower during the extreme solar minimum than that of the previous solar minimum.

DOI： 10.1002/2013JA019452

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

In the present study we observationally address the role of ionospheric conductivity in the solar wind-magnetosphere coupling in terms of global field-aligned currents (FACs). Solar EUV irradiance changes during a solar cycle and so does its contribution to the ionospheric conductivity. We statistically examine how, under fixed external conditions, the intensities of the R1 and R2 currents and their demarcation latitude depend on solar activity (F-10.7). An emphasis is placed on nightside FACs in the dark hemisphere. The result shows that for fixed ranges of interplanetary electric field, the nightside FACs are more intense for higher solar activity irrespective of their polarities or local times. It is also found that the R1-R2 pair, therefore the auroral oval, moves equatorward as the solar activity increases. For both current intensity and latitude, the dependence on F-10.7 is more sensitive at smaller F-10.7 and it levels off with increasing F-10.7. The intensities of dayside FACs reveal similar F-10.7 dependence as expected from the enhancement of the local ionospheric conductance. Interestingly, they also move equatorward with increasing F-10.7. It is expected from force balance that as the dayside R1 current becomes more intense with increasing solar activity, the magnetosphere shrinks on the day side and expands on the night side. This configurational change of the magnetosphere presumably affects the energy transport from the solar wind to the magnetosphere, although its details still remain to be understood. We conclude that the ionospheric conductivity plays an active role in the solar wind-magnetosphere-ionosphere coupling.

DOI： 10.1002/2013JA019410

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Sentinel observations of influenza epidemic in each prefecture indicates basically unimodal trend but with irregular shocks, which are synchronous among prefectures. In this study, we have confirmed that the the strength of inter-regional connection and local probabilistic fluctuation are separately identifiable in a multi-compartment SIR model.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

We have developed a technique for estimating the temporal evolution of the plasmaspheric helium ion density based on a sequence of extreme ultraviolet (EUV) data obtained from the IMAGE satellite. In the proposed technique, the estimation is obtained by incorporating EUV images from IMAGE into a two-dimensional fluid model of the plasmasphere using a data assimilation approach based on the ensemble transform Kalman filter. Since the motion and the spatial structure of the helium plasmasphere is strongly controlled by the electric field in the inner magnetosphere, the electric field around the plasmapause can also be estimated using the ensemble transform Kalman filter. We performed an experiment using synthetic images that were generated from the same numerical model under a certain condition. It was confirmed that the condition that generated the synthetic images was successfully reproduced. We also present some results obtained using real EUV imaging data. Finally, we discuss the possibility of estimating the density profile along a magnetic field line. Since each EUV image was taken from a different direction due to the motion of the IMAGE satellite, we could obtain the information on the density profile along a field line by combining multiple images. Key Points A data assimilation approach is applied for modeling the plasmasphere The proposed technique allows us to estimate the evolution of He+ distribution The parameter for the profile along a field line could also be estimated ©2014. American Geophysical Union. All Rights Reserved.

DOI： 10.1002/2013JA019734

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

We have developed a technique by which to estimate the spatial distribution of plasmaspheric helium ions based on extreme ultraviolet (EUV) data obtained from the IMAGE satellite. The estimation is performed using a linear inversion method based on the Bayesian approach. The global imaging data from the IMAGE satellite enable us to estimate a global two-dimensional distribution of the helium ions in the plasmasphere. We applied this technique to a synthetic EUV image generated from a numerical model. This technique was confirmed to successfully reproduce the helium ion density that generated the synthetic EUV data. We also demonstrate how the proposed technique works for real data using two real EUV images. Key Points A technique to estimate the plasmaspheric He+ density distribution is proposed The estimation is based on EUV data from the IMAGE satellite The uncertainty and sensitivity of the estimate are also evaluated ©2014. American Geophysical Union. All Rights Reserved.

DOI： 10.1002/2013JA019733

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

The solar minimum between solar cycles 23 and 24 was unusually long and deep. The upward region-1 (R1) field-aligned current (FAC) response to this extreme solar minimum was investigated using Defense Meteorological Satellite Program observations. The solar cycle responses on the dayside are different than those on the nightside. The field-aligned current density (J//) on the dayside, at 12-17 magnetic local time (MLT), peaks in the declining phase of the solar cycle, in 2003, when the solar wind speed also peaks, whereas J// on the nightside, at 18-23 MLT, appears insensitive to the solar cycle. In 1995-2010, J// at 15-17 MLT reaches the lowest value during the extreme solar minimum in 2009, when the solar wind speed also reaches the lowest value. At 12-17 MLT, R1 is located mostly on open field lines or at the boundary layer, where the current is driven mostly by the velocity shear at the magnetopause boundary. However, on the nightside, R1 is located mostly on the closed field lines where J// is not driven directly and immediately by the solar wind. The nightside current width (Λ) exhibits a solar cycle effect such that Λ is smaller at the solar minimum and smallest in 2009. However, the dayside Λ exhibits little solar cycle effect. As a result, the FAC intensity (latitudinally integrated J//) exhibits a solar cycle variation at all local times and the FAC intensity is lower during the extreme solar minimum than that of the previous solar minimum. Key Points Upward R1 responds to solar cycle differently in the closed than open field line Dayside R1 J// has a strong solar cycle variation which can be attributed to Vsw R1 FAC intensity exhibits a solar cycle variation at 1200-2300 MLT ©2014. American Geophysical Union. All Rights Reserved.

DOI： 10.1002/2013JA019452

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

In the present study we observationally address the role of ionospheric conductivity in the solar wind-magnetosphere coupling in terms of global field-aligned currents (FACs). Solar EUV irradiance changes during a solar cycle and so does its contribution to the ionospheric conductivity. We statistically examine how, under fixed external conditions, the intensities of the R1 and R2 currents and their demarcation latitude depend on solar activity (F 10.7). An emphasis is placed on nightside FACs in the dark hemisphere. The result shows that for fixed ranges of interplanetary electric field, the nightside FACs are more intense for higher solar activity irrespective of their polarities or local times. It is also found that the R1-R2 pair, therefore the auroral oval, moves equatorward as the solar activity increases. For both current intensity and latitude, the dependence on F 10.7 is more sensitive at smaller F10.7 and it levels off with increasing F10.7. The intensities of dayside FACs reveal similar F10.7 dependence as expected from the enhancement of the local ionospheric conductance. Interestingly, they also move equatorward with increasing F10.7. It is expected from force balance that as the dayside R1 current becomes more intense with increasing solar activity, the magnetosphere shrinks on the day side and expands on the night side. This configurational change of the magnetosphere presumably affects the energy transport from the solar wind to the magnetosphere, although its details still remain to be understood. We conclude that the ionospheric conductivity plays an active role in the solar wind-magnetosphere-ionosphere coupling. Key Points Nightside R1 and R2 currents become more intense with increasing solar activity The auroral oval moves equatorward as solar activity increases The dayside conductivity affects the SW-M-I coupling through the R1 system. ©2013. American Geophysical Union. All Rights Reserved.

DOI： 10.1002/2013JA019410

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE INC  

In order to understand the evolution of the 2009 influenza A (H1N1) pandemic within local regions of Japan, we studied the significance of regional migration between these regions. For this purpose, we have employed an extended SEIR model to describe the immigration of infected people and the stochastic variation of the infectious efficiency. We then applied a data assimilation technique in order to study how the agreement of the simulation results with the observed data depends on the presence/absence of immigration and the degree of variation of the infectious efficiency. Reproducibility is evaluated by log-likelihood values. The log-likelihood does not indicate the significance of immigration. Although there are multiple waves in the time course of the number of reported infected individuals, these waves could be explained by the stochastic nature of infectious events. (C) 2013 Elsevier Inc. All rights reserved.

DOI： 10.1016/j.mbs.2013.08.009

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PUBLIC LIBRARY SCIENCE  

Vaccination is a preventive measure against influenza that does not require placing restrictions on social activities. However, since the stockpile of vaccine that can be prepared before the arrival of an emerging pandemic strain is generally quite limited, one has to select priority target groups to which the first stockpile is distributed. In this paper, we study a simulation-based priority target selection method with the goal of enhancing the collective immunity of the whole population. To model the region in which the disease spreads, we consider an urban area composed of suburbs and central areas connected by a single commuter train line. Human activity is modelled following an agent-based approach. The degree to which collective immunity is enhanced is judged by the attack rate in unvaccinated people. The simulation results show that if students and office workers are given exclusive priority in the first three months, the attack rate can be reduced from 30% in the baseline case down to 1-2%. In contrast, random vaccination only slightly reduces the attack rate. It should be noted that giving preference to active social groups does not mean sacrificing those at high risk, which corresponds to the elderly in our simulation model. Compared with the random administration of vaccine to all social groups, this design successfully reduces the attack rate across all age groups.

DOI： 10.1371/journal.pone.0072866

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Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.11295/marketingscience.210103

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Language：Japanese   Publisher：Japan Science and Technology Agency  

To develop an efficient way of generating innovation driven by big data is a central issue in academia as well as industry. Although such a demand grows rapidly, a human resource development that is a key factor for realizing it remains the rarely-discussed problem. A data scientist with sufficient knowledge in statistics and machine learning is exactly a right person who plays a crucial role in making effective use of big data. In fact, the business community, in particular, social network game company has made significant efforts to attract them. In this paper, we address a problem in developing data scientists in Japan, and give a brief introduction of interesting internship program for PhD candidate or post-doctoral researcher which the Silicon Valley technology companies initiated in summer, 2012. Its program is designed to bridge a gap between academia and a career data science. Finally, we consider how to increase the data scientist in Japan effectively, and propose to realize a Japanese version of that program as soon as possible with a financial support of Japanese government.

DOI： 10.1241/johokanri.56.2

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Other Link： https://jlc.jst.go.jp/DN/JALC/10016665773?from=CiNii

Language：English   Publishing type：Research paper (scientific journal)  

Tide gauge observations along the coastline of Japan have recorded the land sinking due to the continuous subduction of the oceanic plates, indicating that stress and strain energies have been accumulating at the plate boundary, which would eventually cause large oceanic trench earthquakes like the 2011 Great East Japan Earthquake. The proposed method extracts such long-term activities of the Earth's crust together with rapid displacements related to earthquakes, even before the establishment of the global positioning system, from monthly mean data of the sea levels. A state space model decomposes the tide gauge time series into trend, seasonal, autoregressive and observation noise components, each of which are estimated using the particle filter algorithm. The spatial and temporal distributions of the extracted trend component clearly indicate high-risk regions, near which giant earthquakes have occurred or are predicted to occur. A multivariate analysis of the observatories located at the northeast coast of Japan successfully determines the past crustal displacement in the case of the 1978 Off-Miyagi Earthquake. The proposed method has the potential application for monitoring crustal activities related to the accumulation of earthquake energy. © 2012 The Author 2012.

DOI： 10.1093/comjnl/bxs139

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Association for Petroleum Technology  

A tracer test is a powerful experiment to identify fractures in underground reservoirs, which significantly affect fluid flow in a porous medium. The complex variable boundary element method (CVBEM) is capable of simulating time-series of the tracer concentration at a producer when the physical properties of the medium and the tracer injection plan are given. Several studies have proposed inversion procedures to identify physical parameters of a fracture in a homogeneous porous medium, using such as the genetic algorithm (GA) or the ensemble Kalman filter (EnKF) combined with iterative computations of CVBEM. The present paper proposes, for this inversion problem, a Markov chain Monte Carlo (MCMC) algorithm, which enables us to obtain a posterior probability density function (PDF) as well as an optimum value for each model parameter that characterizes the fracture. The proposed algorithm is performed to twin experiments that confirm whether properties of a synthetically-assumed fracture are reproduced from time-series of tracer concentration at a producer, which CVBEM has computed in advance. A clustering result of the K-means, which is applied to the samples obtained by MCMC, provides candidate solutions corresponding to local maximums of the posterior PDF. The candidate solutions are ranked by the posterior PDF at the centroid of each cluster. Especially in the case of the two-directional tracer test, in which two pairs of injector and producer are located, the optimum parameter set that maximizes the posterior PDF successfully reproduces both the fracture properties and the concentration curves.

DOI： 10.3720/japt.78.197

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Other Link： https://jlc.jst.go.jp/DN/JALC/10019782927?from=CiNii

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Sentinel observations of influenza epidemic in each prefecture indicates basically unimodal trend but with irregular shocks, which are synchronous among prefectures. In this study, we have examined the possibility that the observed treads can be modeled by SIR with stochastic outer force added. The result shows that this is formally possible but large outer forces are needed in some prefecture.

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Language：Japanese  

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Language：Japanese   Publishing type：Research paper (other academic)   Publisher：公益社団法人日本オペレーションズ・リサーチ学会  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PUBLIC LIBRARY SCIENCE  

Purpose: To identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy.
Patients and Methods: Whole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). "Gefitinib-sensitive" genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer.
Results: The risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively.
Conclusion: The set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis.

DOI： 10.1371/journal.pone.0043923

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Language：Japanese   Publishing type：Research paper (scientific journal)  

The 140 tide gauge observatories located along the coastline of Japan have measured the sea levels for more than 100 years. A number of tide gauge records provide the information of the Earth's crustal activities associated with great earthquakes, e.g., the 2011 Great East Japan Earthquake, that occur periodically at the oceanic plate boundaries. The present paper develops an analysis procedure using the particle filter algorithm, which enables us to extract the crustal displacements from monthly means of the tide gauge records. The proposed method results that the plate boundary called "Nankai Trough" offshore west Japan has been rapidly accumulating the stress and strain energies, which will cause a great earthquake eventually. This result supports the hypothesis that the Tokai-Tonankai-Nankai Earthquake may occur in near future.

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Other Link： http://dl.ndl.go.jp/info:ndljp/pid/10823273

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PUBLIC LIBRARY SCIENCE  

Different models for animal cell cytokinesis posit that the stiffness of the equatorial cortex is either increased or decreased relative to the stiffness of the polar cortex. A recent work has suggested that the critical cytokinesis signaling complex centralspindlin may reduce the stiffness of the equatorial cortex by inactivating the small GTPase Rac. To determine if such a reduction occurs and if it depends on centralspindlin, we devised a method to estimate cortical bending stiffness with high spatio-temporal resolution from in vivo cell shapes. Using the early Caenorhabditis elegans embryo as a model, we show that the stiffness of the equatorial cell surface is reduced during cytokinesis, whereas the stiffness of the polar cell surface remains stiff. The equatorial reduction of stiffness was compromised in cells with a mutation in the gene encoding the ZEN-4/kinesin-6 subunit of centralspindlin. Theoretical modeling showed that the absence of the equatorial reduction of stiffness could explain the arrest of furrow ingression in the mutant. By contrast, the equatorial reduction of stiffness was sufficient to generate a cleavage furrow even without the constriction force of the contractile ring. In this regime, the contractile ring had a supportive contribution to furrow ingression. We conclude that stiffness is reduced around the equator in a centralspindlin-dependent manner. In addition, computational modeling suggests that proper regulation of stiffness could be sufficient for cleavage furrow ingression.

DOI： 10.1371/journal.pone.0031607

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：TAYLOR & FRANCIS LTD  

We consider the problem of generating 3D facial animation of characters. An efficient procedure is realized by using the motion capture data (MoCap data), which is obtained by tracking the facial markers from an actor/actress. In some cases of artistic animation, the MoCap actor/actress and the 3D character facial animation show different expressions. For example, from the original facial MoCap data of speaking, a user would like to create the character facial animation of speaking with a smirk. In this paper, we propose a new easy-to-use system for making character facial animation via MoCap data. Our system is based on the interpolation: once the character facial expressions of the starting and the ending frames are given, the intermediate frames are automatically generated by information from the MoCap data. The interpolation procedure consists of three stages. First, the time axis of animation is divided into several intervals by the fused lasso signal approximator. In the second stage, we use the kernel k-means clustering to obtain control points. Finally, the interpolation is realized by using the control points. The user can easily create a wide variety of 3D character facial expressions by changing the control points.

DOI： 10.1080/02664763.2012.724391

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：COPERNICUS GESELLSCHAFT MBH  

The D-st index has a long-term variation that is not associated with magnetic storms. We estimated the long-term non-storm component of the D-st variation by removing the short-term variation related to magnetic storms. The results indicate that the variation of the non-storm component includes not only a seasonal variation but also an irregular variation. The irregular long-term variation is likely to be due to an anti-correlation with the long-term variation of solar-wind activity. In particular, a clear anti-correlation is observed between the non-storm component of D-st and the long-term variation of the solar-wind dynamic pressure. This means that in the long term, the D-st index tends to increase when the solar-wind dynamic pressure decreases. We interpret this anti-correlation as an indication that the long-term non-storm variation of D-st is influenced by the tail current variation. The long-term variation of the solar-wind dynamic pressure controls the plasma sheet thermal pressure, and the change of the plasma sheet thermal pressure would cause the non-storm tail current variation, resulting in the non-storm variation of D-st.

DOI： 10.5194/angeo-30-153-2012

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

In recent years. the importance of environment is widely recognized and various kinds of environmental policies have been implemented. However. when one establishes a policy, there are few cases that the contents and the period of the policy are evaluated by using prediction method for its effect. In this study, we aim to predict the effects of a policy in micro-level by using agent simulations and estimate the optimal period of the policy implementation by totally evaluating its effect. For sugarcane farmer of an area in Japan. we performed 200 thousand patterns of simulations in order to optimize the subsidy of green manure and its implementation period. In particular, when the values of the parameters in the simulation model are not clearly defined, we are able to show various futures caused by the policy implementation of interest based on large-scale simulations with a huge variety of the parameter values.

DOI： 10.1007/978-3-642-27216-5_25

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

We have developed a parallel agent-based influenza pandemic simulator, in order to study the influenza spread in a city. In the simulator, the city consists of several towns connected tightly by trains. Residents of the towns walk around places such as corporations and schools using trains by need, following to own schedulers. The influenza spread in these congested places is simulated as stochastic processes. We have demonstrated simulations with a realistic scale of population (an order of million) and showed that one simulation run is completed around one hour.

DOI： 10.1007/978-3-642-27216-5_27

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)  

To predict an occurrence of extraordinary phenomena, such as earthquakes, failures of engineering systems and financial market crushes, it is important to identify precursory events in time series. However, existing methods are limited in their applicability for real world precursor detections. Recently, Ide and Inoue [1] have developed an SSA-based change-point detection method, called singular spectrum transformation (SST). SST is suitable for detecting various types of change-points, but real world precursor detections can be far more difficult than expected. In general, precursory events are observed as minute and less-visible fluctuations preceding an onset of massive fluctuations of extraordinary phenomena and therefore they are easily over-looked. To overcome this point, we extend the conventional SST to the multivariable SST. The originality of our strategy is in focusing on synchronism detections of precursory events in multiple sequences of univariate time series. We performed some experiments by using artificial data and showed the superiority of multivariable SST in detecting onset of precursory events. Furthermore, the superiority is also shown statistically in determi

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

Variations in the dayside field-aligned current (FAC) density (J(//)), field-aligned parallel potential drop (Delta phi(//)), peak precipitating electron energy (peak E(e)), and precipitating electron energy flux (epsilon) as functions of solar wind (SW) and interplanetary magnetic field (IMF) are investigated with Defense Meteorological Satellite Program observations and a quasi-stationary low-latitude boundary layer (LLBL)-FAC coupling model. Region 1 (R1) J(//) responses to variations in SW velocity (V(sw)) and density (n(sw)) at 8-16 magnetic local time (MLT) suggest that R1 at these local times is frequently open while R1 at 6-08 and 17-18 MLT is frequently closed. R2 is located mostly on closed field lines. In the afternoon open R1 at 12-16 MLT, an increase in n(sw) increases J(//), decreases maximum peak Ee (proxy for Delta phi(//)), but has little effect on maximum epsilon. In the same R1 region, an increase in V(sw) increases J(//), maximum peak E(e), and maximum epsilon. The dependencies of J(//), maximum peak E(e), and maximum epsilon are consistent with the Knight relation and the voltage generator at the magnetopause boundary in the afternoon open R1. In the midmorning and midafternoon, the response of J(//) to V(sw) is higher for southward than for northward IMF. This can be attributed to the higher-velocity shear at the magnetopause boundary due to higher sunward convection in the LLBL inside the magnetopause. R1 in the closed-field lines near dawn and dusk appears to be more sensitive to merging rate (d Phi/dt = V(4/3)(sw) B(T)(2/3) sin(8/3)(theta(c)/2)) than to SW dynamic pressure.

DOI： 10.1029/2011JA016579

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Publishing type：Research paper (scientific journal)  

CiNii Research

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Language：Japanese   Publishing type：Research paper (scientific journal)  

CiNii Books

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Publishing type：Research paper (international conference proceedings)  

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Publishing type：Research paper (international conference proceedings)  

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Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

We reproduce a magnetospheric reconfiguration under southward interplanetary magnetic field (IMF) conditions using numerical magnetohydrodynamic simulations. To investigate the relative displacements of the geomagnetic conjugate points, we trace both footprints of the geomagnetic field lines during the magnetospheric reconfiguration under positive and negative IMF B-y conditions. Several substormlike features, namely, the formation of a near-Earth neutral line, a fast earthward flow, and tailward releases of the plasmoid, occur about 1 h after a southward turning of the IMF. The surveyed field line traced from the near-Earth magnetotail was strongly distorted duskward in the north and south after the substorm onset for positive and negative IMF B-y, respectively. The maximum of the relative longitudinal displacement of both footprints is 4.5 and 5.5 h in magnetic local time for positive and negative IMF B-y, respectively. While observational studies have indicated that the IMF orientation is the main factor controlling the relative displacement of the conjugate points, the present simulation-based study with a constant IMF orientation shows for the first time that the combined effects of plasma pressure, magnetic field intensity, and the field-aligned current density distribution along the field line are likely to be major factors controlling the relative displacement of conjugate points.

DOI： 10.1029/2010JA016155

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SIAM International Workshop on Data Mining for Marketing  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

Variations in the dayside field-aligned current (FAC) density (J //), field-aligned parallel potential drop (δφ //), peak precipitating electron energy (peak Ee), and precipitating electron energy flux (ε) as functions of solar wind (SW) and interplanetary magnetic field (IMF) are investigated with Defense Meteorological Satellite Program observations and a quasi-stationary low-latitude boundary layer (LLBL)-FAC coupling model. Region 1 (R1) J// responses to variations in SW velocity (Vsw) and density (nsw) at 8-16 magnetic local time (MLT) suggest that R1 at these local times is frequently open while R1 at 6-08 and 17-18 MLT is frequently closed. R2 is located mostly on closed field lines. In the afternoon open R1 at 12-16 MLT, an increase in nsw increases J//, decreases maximum peak Ee (δφ//), but has little effect on maximum ε. In the same R1 region, an increase in Vsw increases J//, maximum peak Ee, and maximum ε. The dependencies of J//, maximum peak Ee, and maximum ε are consistent with the Knight relation and the voltage generator at the magnetopause boundary in the afternoon open R1. In the midmorning and midafternoon, the response of J// to Vsw is higher for southward than for northward IMF. This can be attributed to the higher-velocity shear at the magnetopause boundary due to higher sunward convection in the LLBL inside the magnetopause. R1 in the closed-field lines near dawn and dusk appears to be more sensitive to merging rate (dΦ/dt = Vsw4/3 BT2/3 sin8/3(θc/2)) than to SW dynamic pressure. Copyright 2011 by the American Geophysical Union.

DOI： 10.1029/2011JA016579

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

We reproduce a magnetospheric reconfiguration under southward interplanetary magnetic field (IMF) conditions using numerical magnetohydrodynamic simulations. To investigate the relative displacements of the geomagnetic conjugate points, we trace both footprints of the geomagnetic field lines during the magnetospheric reconfiguration under positive and negative IMF By conditions. Several substormlike features, namely, the formation of a near-Earth neutral line, a fast earthward flow, and tailward releases of the plasmoid, occur about 1 h after a southward turning of the IMF. The surveyed field line traced from the near-Earth magnetotail was strongly distorted duskward in the north and south after the substorm onset for positive and negative IMF By, respectively. The maximum of the relative longitudinal displacement of both footprints is 4.5 and 5.5 h in magnetic local time for positive and negative IMF By, respectively. While observational studies have indicated that the IMF orientation is the main factor controlling the relative displacement of the conjugate points, the present simulation-based study with a constant IMF orientation shows for the first time that the combined effects of plasma pressure, magnetic field intensity, and the field-aligned current density distribution along the field line are likely to be major factors controlling the relative displacement of conjugate points. Copyright 2011 by the American Geophysical Union.

DOI： 10.1029/2010JA016155

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

The source regions of region-0 (R0), region-1 (R1), and region-2 (R2) field-aligned currents (FACs) were statistically determined using DMSP particle precipitation and magnetometer data. Each FAC sheet originates from more than one region in the magnetosphere, depending on the latitude and the magnetic local time (MLT). R2 originates mostly from the central plasma sheet (CPS) and boundary plasma sheet (BPS) in the morning and from the CPS, BPS, and inner magnetosphere in the afternoon, all of which are on closed field lines. Near noon, some R2 may originate from the low-latitude boundary layer (LLBL), which is located near the magnetopause and can be open or closed. R1 mostly maps to the BPS, hence on closed field lines, in morning and afternoon, but near noon, it maps mostly to the LLBL. The LLBL source region can be found more frequently in the dawn-noon sector than in the noon-dusk sector. On the other hand, R0 is located mostly on open field lines and is associated mostly with mantle precipitation. However, the mantle precipitation has a dependency on the polarity of R0. Within up-flowing R0, sometimes an upward field-aligned electric field, which accelerates electron downward and retards ion precipitation, modifies mantle distribution to look more like those of polar rain or BPS. This electric field has the opposite polarity to the background electric field that maintains charge-quasi-neutrality and that limits some solar wind electrons from entering the magnetosphere in the mantle and polar rain regions. Implications to current generation mechanisms are discussed.

DOI： 10.1029/2010JA015837

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

The present study statistically examines the location of various precipitation boundaries introduced by Newell et al. (1996) relative to R2 and R1 currents on the night side. Results are summarized as follows: (1) The electron and ion zero-energy boundaries, b1e and b1i, are located mostly inside the R2 current at dusk-to-midnight and near the equatorward boundary of the R2 current or farther equatorward at midnight-to-dawn. (2) The maximum energy flux of ion precipitation (b2i) occurs inside the R2 current irrespective of magnetic local time. (3) The occurrence distributions of the most equatorward (b3a) and poleward (b3b) electron acceleration events indicate that mono-energetic electron precipitation is mostly confined in the upward R1 current at dusk-to-midnight, whereas at midnight-to-dawn, it is more widely distributed including the downward R1 current. (4) The transition between structured and unstructured electron precipitation (b4s) tends to occur around the R2/R1 demarcation, but its occurrence distribution has extending tails. (5) The distributions of the poleward boundaries of the electron and ion auroral ovals, b5e and b5i, are centered around the poleward boundary of the R1 current but have extending tails especially at midnight-to-dawn. Result 1 suggests that the overlap between the ring current and the plasmasphere is more significant at dusk than at dawn. Result 2 indicates that the b2i boundary can be used as an identifier of the R2 current. Results 3-5 suggest that the R1 current is more structured than the R2 current and that the field-aligned current structure is more complex at midnight-to-dawn than at dusk-to-midnight.

DOI： 10.1029/2010JA015444

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：INST ENGINEERING TECHNOLOGY-IET  

The present study describes a frequency spectrum classification method for fault detection of the LP gas pressure regulator using support vector machines (SVMs). Conventional diagnosis methods are inefficient because of problems such as significant noise and non-linearity of the detection mechanism. In order to address these problems, a machine learning method with a divergence-based kernel is introduced into spectrum classification. The authors use the normalised frequency spectrum directly as input with the divergence-based kernel. The proposed method is applied to the vibration spectrum classification of the rubber diaphragm in a pressure regulator. As a result, the classification performance using the divergence-based kernel is shown to be better than when using common kernels such as the Gaussian kernel or the polynomial kernel. The high classification performance is achieved by using an inexpensive sensor system and the machine learning method. The proposed method is widely applicable to other spectrum classification applications without limitation on the generality if the spectra are normalised.

DOI： 10.1049/iet-spr.2008.0195

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Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：OXFORD UNIV PRESS  

Motivation Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation ( DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics.
Results: We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles.

DOI： 10.1093/bioinformatics/btq389

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：OXFORD UNIV PRESS  

Motivation Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation ( DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics.
Results: We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles.

DOI： 10.1093/bioinformatics/btq389

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PubMed

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICIF.2010.5712015

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：WILEY-BLACKWELL  

We propose a method for estimating optimal error covariances in the context of sequential assimilation, including the case where both the system equation and the observation equation are nonlinear. When the system equation is nonlinear, ensemble-based filtering methods such as the ensemble Kalman filter (EnKF) are widely used to deal directly with the nonlinearity. The present approach for covariance optimization is a maximum likelihood estimation carried out by approximating the likelihood with the ensemble mean. Specifically, the likelihood is approximated as the sample mean of the likelihood of each member of the ensemble. To evaluate the sampling error of the proposed ensemble-approximated likelihood, we construct a method for examining the statistical significance using the bootstrap method without extra ensemble computation. We apply the proposed methods to an EnKF experiment where TOPEX/POSEIDON altimetry observations are assimilated into an intermediate coupled model, which is nonlinear, and estimate the optimal parameters that specify the covariances of the system noise and observation noise. Using these optimal covariance parameters, we examine the estimates by the EnKF and the ensemble Kalman smoother (EnKS). The effect of smoothing decreases by 1/e approximately one year after the filtering step. One of the properties of the smoothed estimate is that westerly wind anomalies over the western Pacific are not reproduced around the period of an El Nino event, while those over the central Pacific are strengthened. From additional experiments, we find that (1) the westerly winds in the western Pacific are phenomena outside of the coupled model and are not necessary to model El Nino, (2) the model El Nino is maintained by the westerlies over the central Pacific, and (3) the modelled evolution process of the sea-surface temperature (SST) requires improvement to reproduce the westerly winds over the western Pacific. Copyright (C) 2010 Royal Meteorological Society

DOI： 10.1002/qj.654

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

Any sensor for detecting a certain physical variable is more or less influenced by other physical variables, which are designated as "noise." The objective in conventional sensor design has been to minimize the noise. In this paper, however, we make use of sensing devices that are easily influenced by multiple physical variables andmake full use of their multisensing characteristic. We consider such devices as multiple-input-single-output sensors. First, the output signal derived from multiple input signals must be dissociated. The input signals resulting from physical phenomena have inherent characteristics and can mathematically be modeled. Application of a Kalman filter realized by such models can provide estimates of the state variables of all input models, and thus, the input signals are dissociated. As an example, a novel sensor based on a microphone is presented. This sensor can detect various variables such as pressure and acceleration in the frequency range of 0.1 Hz to 10 kHz, temperature, and even light emission. We apply the sensor to monitor the symptoms of fire, earthquake, and break-in by an intruder from within a house.

DOI： 10.1109/TIM.2009.2030716

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Language：English   Publishing type：Research paper (scientific journal)  

We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.

PubMed

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

The source regions of region-0 (R0), region-1 (R1), and region-2 (R2) field-aligned currents (FACs) were statistically determined using DMSP particle precipitation and magnetometer data. Each FAC sheet originates from more than one region in the magnetosphere, depending on the latitude and the magnetic local time (MLT). R2 originates mostly from the central plasma sheet (CPS) and boundary plasma sheet (BPS) in the morning and from the CPS, BPS, and inner magnetosphere in the afternoon, all of which are on closed field lines. Near noon, some R2 may originate from the low-latitude boundary layer (LLBL), which is located near the magnetopause and can be open or closed. R1 mostly maps to the BPS, hence on closed field lines, in morning and afternoon, but near noon, it maps mostly to the LLBL. The LLBL source region can be found more frequently in the dawn-noon sector than in the noon-dusk sector. On the other hand, R0 is located mostly on open field lines and is associated mostly with mantle precipitation. However, the mantle precipitation has a dependency on the polarity of R0. Within up-flowing R0, sometimes an upward field-aligned electric field, which accelerates electron downward and retards ion precipitation, modifies mantle distribution to look more like those of polar rain or BPS. This electric field has the opposite polarity to the background electric field that maintains charge-quasi-neutrality and that limits some solar wind electrons from entering the magnetosphere in the mantle and polar rain regions. Implications to current generation mechanisms are discussed. Copyright 2010 by the American Geophysical Union.

DOI： 10.1029/2010JA015837

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

The present study statistically examines the location of various precipitation boundaries introduced by Newell et al. (1996) relative to R2 and R1 currents on the night side. Results are summarized as follows: (1) The electron and ion zero-energy boundaries, b1e and b1i, are located mostly inside the R2 current at dusk-to-midnight and near the equatorward boundary of the R2 current or farther equatorward at midnight-to-dawn. (2) The maximum energy flux of ion precipitation (b2i) occurs inside the R2 current irrespective of magnetic local time. (3) The occurrence distributions of the most equatorward (b3a) and poleward (b3b) electron acceleration events indicate that mono-energetic electron precipitation is mostly confined in the upward R1 current at dusk-to-midnight, whereas at midnight-to-dawn, it is more widely distributed including the downward R1 current. (4) The transition between structured and unstructured electron precipitation (b4s) tends to occur around the R2/R1 demarcation, but its occurrence distribution has extending tails. (5) The distributions of the poleward boundaries of the electron and ion auroral ovals, b5e and b5i, are centered around the poleward boundary of the R1 current but have extending tails especially at midnight-to-dawn. Result 1 suggests that the overlap between the ring current and the plasmasphere is more significant at dusk than at dawn. Result 2 indicates that the b2i boundary can be used as an identifier of the R2 current. Results 3-5 suggest that the R1 current is more structured than the R2 current and that the field-aligned current structure is more complex at midnight-to-dawn than at dusk-to-midnight. Copyright 2010 by the American Geophysical Union.

DOI： 10.1029/2010JA015444

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICIF.2010.5712049

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Publishing type：Research paper (scientific journal)  

DOI： 10.1504/IJKESDP.2010.037492

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：WORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC  

To predict an occurrence of extraordinary phenomena, such as earthquakes, failures of engineering system and financial market crushes, it is important to identify precursory events in time series. However, existing methods are limited in their applicability for real world precursor detections. Recently, Ide and Inoue [1] have developed an SSA-based change-point detection method, called singular spectrum transformation (SST). In this paper, we extend the SST so that it is applicable for real world precursor detections, focusing on the wide applicability of the conventional SST. Although the SST is suitable for detecting various types of change-points, detecting precursors can be far more difficult than expected because, in general, real world time series contains measurement noise and non-stationary trends. Furthermore, precursory events are usually observed as minute and less-visible fluctuations preceding an onset of massive fluctuations of extraordinary phenomena and therefore they are easily over-looked. To overcome this, we extend the conventional SST to the multivariable SST, focusing on the synchronism detection of precursory events in multiple sequences of univariate time series. First, we would like to define the problem setting of real world precursory detections and consider its difficulties. Second, the multivariable SST is introduced. Third, we apply SST to geomagnetic time series data and show the multivariable SST is more suitable than the conventional SST for real world precursor detections. Finally, we show further experimental results using artificial data to evaluate the reliability of SST-based precursor detections.

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Publishing type：Research paper (scientific journal)  

DOI： 10.11345/nctam.58.101

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

The estimation of many unknown parameters is carried out using a simplified Sequential Importance Sampling (SIS) algorithm which is implemented in a graphic processing unit (GPU). The aim of the present work is to show technical points to bring out the performance of GPU. Using the implemented code, two numerical experiments are demonstrated. In the first demonstration, it is shown that a parameter estimation involving 109 Monte Carlo samples is completed within eight hours. In the second demonstration, accuracy-guaranteed evaluation of the likelihood is carried out.

DOI： 10.1109/icif.2010.5712017

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

The present study statistically addresses the dependence of large-scale field-aligned currents (R1/R2) and particle precipitation in the premidnight sector on solar illumination at the ionosphere. The energy flux and average energy of precipitating electrons and ions are examined for downward R2 and upward R1 currents. Results are summarized as follows: (1) The R1 current density is larger in the dark hemisphere, whereas the dependence of R2 density on solar illumination is unclear. (2) For R1 currents the electron energy flux is larger and the average electron energy is higher in the dark hemisphere. (3) For R2 currents, a similar preference for the dark hemisphere is found for the electron energy flux, which, however, is significantly lower than for R1 currents. The average electron energy is similar between the two hemispheres. (4) For both R1 and R2 currents, ion precipitation is more intense and energetic in the dark hemisphere. (5) For a given field-aligned current density, whether R1 or R2 currents, both electron and ion precipitation is more intense and energetic in the dark hemisphere. The Pedersen conductivity is estimated from electron precipitation and solar illumination. The result suggests that in the dark hemisphere the absence of solar illumination is often overcompensated by more intense and energetic electron precipitation. The interhemispheric difference in electron acceleration may be interpreted in terms of plasma density in the acceleration region, which is known to be significantly lower in the dark hemisphere, and therefore electrons need to be more accelerated along the field line to carry imposed currents.

DOI： 10.1029/2009JA014115

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

The D(st) index is the most popular measure of a scale of magnetic storms, and it is widely used as a monitor of the conditions of the Earth's magnetosphere. Since the D(st) index contains contributions from multiple magnetospheric phenomena, it is important to distinguish each of the contributions in order to obtain meaningful information about the conditions of the magnetosphere. There have been several efforts which modeled temporal evolution of the D(st) index empirically. and these empirical models considers some contributions separately. However, they take only short-term varations into accout, and contributions from phenomena which show long-term variations are neglected. In the present study, we have developed a technique for estimating the component of long-term variations of the D(st) index using solar wind data and a nonlinear empirical model. The newly-developed technique adopts an algorithm which is similar to the particle filter. This algorithm allows an on-line processing of a long sequence of D(st) data, which would enable a real-time estimation of system variables in a nonlinear system model. The estimates of the long-term variations can be used for accurate estimation of other contributions to the D(st) index, which would provide credible information about the conditions of the magnetosphere. The framework proposed in the present study could be applied for the purpose of continuous real-time monitoring of the environment of the magnetosphere.

DOI： 10.1587/transinf.E92.D.1382

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

An accurate prediction of ocean tides in southeast Alaska is developed using a regional, barotropic ocean model with a finite difference scheme. The model skill is verified by the observational tidal harmonics in southeast Alaska including Glacier Bay. The result is particularly improved in Glacier Bay compared to the previous model described by Foreman et al. (2000). The model bathymetry dominates the model skill. We re-estimate tidal energy dissipation in the Alaska Panhandle and suggest a value for tidal energy dissipation of 3.4 GW associated with the M(2) constituent which is 1.5 times the estimation of Foreman et al. (2000). A large portion of the M(2) energy budget entering through Chatham Strait is dissipated in the vicinity of Glacier Bay. Moreover, it is shown that the developed model has the potential to correct the ocean tide loading effect in geodetic data more efficiently than the model of Foreman et al. (2000), especially around Glacier Bay.

DOI： 10.1007/s10872-009-0031-y

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Publishing type：Research paper (other academic)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

The relationship between the Region 2 field-aligned currents and the solar wind dynamic pressure is investigated using magnetic field data from Defense Meteorological Satellite Program-F13 (DMSP-F13). The generation of the Region 2 currents is associated with the direction of the magnetic pressure gradient in the magnetosphere, in relation to that of the thermal pressure gradient. Past spacecraft observations have suggested that the spatial distribution of the magnetic pressure in the magnetosphere varies with the solar wind dynamic pressure. Therefore, we can expect that the Region 2 currents would depend on the solar wind dynamic pressure. We compared the Region 2 field-aligned current intensity at the altitude of the ionosphere, as derived using magnetic field data from DMSP-F13, with the solar wind dynamic pressure derived from 0MNI2 hourly data. It was confirmed that the Region 2 current intensity depends on the solar wind dynamic pressure during magnetic storms. During nonstorm times, however, the correlation between the Region 2 currents and the solar wind dynamic pressure is weak. The weak correlation during nonstorm times suggests that the plasma pressure in the ring current region is also essential for the effect of the solar wind dynamic pressure on the Region 2 currents. © 2009.

DOI： 10.1029/2008JA013674

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

The relationship between the Region 2 field-aligned currents and the solar wind dynamic pressure is investigated using magnetic field data from Defense Meteorological Satellite Program-F13 (DMSP-F13). The generation of the Region 2 currents is associated with the direction of the magnetic pressure gradient in the magnetosphere, in relation to that of the thermal pressure gradient. Past spacecraft observations have suggested that the spatial distribution of the magnetic pressure in the magnetosphere varies with the solar wind dynamic pressure. Therefore, we can expect that the Region 2 currents would depend on the solar wind dynamic pressure. We compared the Region 2 field-aligned current intensity at the altitude of the ionosphere, as derived using magnetic field data from DMSP-F13, with the solar wind dynamic pressure derived from OMNI2 hourly data. It was confirmed that the Region 2 current intensity depends on the solar wind dynamic pressure during magnetic storms. During nonstorm times, however, the correlation between the Region 2 currents and the solar wind dynamic pressure is weak. The weak correlation during nonstorm times suggests that the plasma pressure in the ring current region is also essential for the effect of the solar wind dynamic pressure on the Region 2 currents.

DOI： 10.1029/2008JA013674

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

The present study statistically addresses the dependence of large-scale field-aligned currents (R1/R2) and particle precipitation in the premidnight sector on solar illumination at the ionosphere. The energy flux and average energy of precipitating electrons and ions are examined for downward R2 and upward Rl currents. Results are summarized as follows: (1) The Rl current density is larger in the dark hemisphere, whereas the dependence of R2 density on solar illumination is unclear. (2) For Rl currents the electron energy flux is larger and the average electron energy is higher in the dark hemisphere. (3) For R2 currents, a similar preference for the dark hemisphere is found for the electron energy flux, which, however, is significantly lower than for Rl currents. The average electron energy is similar between the two hemispheres. (4) For both Rl and R2 currents, ion precipitation is more intense and energetic in the dark hemisphere. (5) For a given field-aligned current density, whether Rl or R2 currents, both electron and ion precipitation is more intense and energetic in the dark hemisphere. The Pedersen conductivity is estimated from electron precipitation and solar illumination. The result suggests that in the dark hemisphere the absence of solar illumination is often overcompensated by more intense and energetic electron precipitation. The interhemispheric difference in electron acceleration may be interpreted in terms of plasma density in the acceleration region, which is known to be significantly lower in the dark hemisphere, and therefore electrons need to be more accelerated along the field line to carry imposed currents. Copyright 2009 by the American Geophysical Union.

DOI： 10.1029/2009JA014115

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DOI： 10.1109/ICASSP.2008.4517999

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：WORLD SCIENTIFIC PUBL CO PTE LTD  

The aim of this paper is to demonstrate the potential power of large-scale particle filtering for the parameter estimations of in silico biological pathways where time course measurements of biochemical reactions are observable. The method of particle filtering has been a popular technique in the field of statistical science, which approximates posterior distributions of model parameters of dynamic system by using sequentially-generated Monte Carlo samples. In order to apply the particle filtering to system identifications of biological pathways, it is often needed to explore the posterior distributions which are defined over an exceedingly high-dimensional parameter space. It is then essential to use a fairly large amount of Monte Carlo samples to obtain an approximation with a high-degree of accuracy. In this paper, we address some implementation issues on large-scale particle filtering, and then, indicate the importance of large-scale computing for parameter learning of in silico biological pathways. We have tested the ability of the particle filtering with 10(8) Monte Carlo samples on the transcription circuit of circadian clock that contains 45 unknown kinetic parameters. The proposed approach could reveal clearly the shape of the posterior distributions over the 45 dimensional parameter space.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：WORLD SCIENTIFIC PUBL CO PTE LTD  

The aim of this paper is to demonstrate the potential power of large-scale particle filtering for the parameter estimations of in silico biological pathways where time course measurements of biochemical reactions are observable. The method of particle filtering has been a popular technique in the field of statistical science, which approximates posterior distributions of model parameters of dynamic system by using sequentially-generated Monte Carlo samples. In order to apply the particle filtering to system identifications of biological pathways, it is often needed to explore the posterior distributions which are defined over an exceedingly high-dimensional parameter space. It is then essential to use a fairly large amount of Monte Carlo samples to obtain an approximation with a high-degree of accuracy. In this paper, we address some implementation issues on large-scale particle filtering, and then, indicate the importance of large-scale computing for parameter learning of in silico biological pathways. We have tested the ability of the particle filtering with 10(8) Monte Carlo samples on the transcription circuit of circadian clock that contains 45 unknown kinetic parameters. The proposed approach could reveal clearly the shape of the posterior distributions over the 45 dimensional parameter space.

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：OXFORD UNIV PRESS  

Motivation: Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential. first step to. find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision.
Results: The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e. g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways.

DOI： 10.1093/bioinformatics/btn483

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

In the marketing field, activities such as One-to-One Marketing and Customer Relationship Management (CRM) have gotten a lot of attention for its effectiveness. The concept of those activities is based on the notion of consumer heterogeneity in the marketing. It is necessary to incorporate consumer heterogeneity and dynamic trait into a statistical model for evaluating the activities adequately. In this paper, we propose dynamic individual models for analyzing the shopping trip behavior individually. We formulate the model with framework of a general state space model. We realize an estimation of the model by using particle filter/fixed lag smoother. We show that the proposed model allow us to discriminate an individual's shopping trip behavior correctly using frequent shoppers data.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

We present a technique for estimating ring current ion distributions and electric potential in the inner magnetosphere by incorporating data from the High-Energy Neutral Atom (HENA) imager on the IMAGE satellite into a kinetic ring current model in the context of data assimilation. Data assimilation is an approach which adjusts a physicsbased model according to differences with observations. We perform the data assimilation using the particle filter (PF) which is applicable to high-dimensional systems and observations with relatively low computational cost. In the present technique, the magnetospheric electric potential distribution is represented by the sum of a Volland-Stern field and a deviation, and the deviation is improved in the assimilation process. The ring current ion distribution is then determined from the electric potential distribution. The method is tested by assimilating artificial data generated by another simulation. The results demonstrate that the ring current ion distribution is successfully reconstructed by the proposed algorithm and that the distorted structures of the electric potential distribution are also well reproduced. An example to illustrate how the present technique could be applied for assimilating a real IMAGE/HENA data set is also demonstrated. This technique provides a useful tool for investigating the global dynamic structure of ring current and electric potential. Copyright 2008 by the American Geophysical Union.

DOI： 10.1029/2006JA011853

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Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/ve.sicetr1965.44.444

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

We present a technique for estimating ring current ion distributions and electric potential in the inner magnetosphere by incorporating data from the High-Energy Neutral Atom (HENA) imager on the IMAGE satellite into a kinetic ring current model in the context of data assimilation. Data assimilation is an approach which adjusts a physics-based model according to differences with observations. We perform the data assimilation using the particle filter (PF) which is applicable to high-dimensional systems and observations with relatively low computational cost. In the present technique, the magnetospheric electric potential distribution is represented by the sum of a Volland-Stern field and a deviation, and the deviation is improved in the assimilation process. The ring current ion distribution is then determined from the electric potential distribution. The method is tested by assimilating artificial data generated by another simulation. The results demonstrate that the ring current ion distribution is successfully reconstructed by the proposed algorithm and that the distorted structures of the electric potential distribution are also well reproduced. An example to illustrate how the present technique could be applied for assimilating a real IMAGE/HENA data set is also demonstrated. This technique provides a useful tool for investigating the global dynamic structure of ring current and electric potential.

DOI： 10.1029/2006JA011853

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：OXFORD UNIV PRESS  

We have developed a new geodetic inversion method for space-time distribution of fault slip velocity with time-varying smoothing regularization in order to reconstruct accurate time histories of aseismic fault slip transients. We introduce a temporal smoothing regularization on slip and slip velocity through a Bayesian state space approach in which the strength of regularization (temporal smoothness of slip velocity) is controlled by a hyperparameter. The time-varying smoothing regularization is realized by treating the hyperparameter as a time-dependent stochastic variable and adopting a hierarchical Bayesian state space model, in which a prior distribution on the hyperparameter is introduced in addition to a conventional Bayesian state space model. We have tested this inversion method on two synthetic data sets generated by simulated aseismic slip transients. Results show that our method reproduces well both rapid changes of slip velocity and steady-state velocity without significant oversmoothing and undersmoothing, which has been hard to overcome by the conventional Bayesian approach with time-independent smoothing regularization. Application of this method to transient deformation in 2002 caused by a silent earthquake off the Boso peninsula, Japan, also shows similar advantages of this method over the conventional approach.

DOI： 10.1111/j.1365-246X.2007.03722.x

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：OXFORD UNIV PRESS  

Motivation: Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway.
Results: In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis.

DOI： 10.1093/bioinformatics/btm639

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Language：English   Publishing type：Research paper (international conference proceedings)  

Fusion of a simulation model and observation data has been investigated extensively for the purpose of data assimilation in geophysics. The inaccuracy of the parameters, initial conditions, or boundary conditions causes a discrepancy in the simulation results and the actual phenomenon. The present paper describes the parameter identification of a pressure regulator with a nonlinear structure by sequential Bayes estimation in the framework of data assimilation. A damping coefficient of feedback system in the pressure regulator that cannot be observed directly is estimated using a particle filter and a nonlinear state space model. The data assimilation concept is demonstrated using a pressure regulator as an engineering application.

DOI： 10.1109/ICIF.2008.4632304

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In the kernel method, the appropriate selection or design of the kernel function is important for the construction of a high-performance classifier. The present paper describes a dynamic spectrum classification method using kernel classifiers with the divergence-based kernel and its application to the detection of worn-out banknotes. We introduce the divergence-based kernel that was proposed as a measure between two probability distributions into the dynamic spectrum classification. The present method is applied to the detection of worn-out banknotes by using acoustic signals for the facilitation of identifying counterfeit banknotes. As a result, the classification performance using the divergence-based kernel is shown to have better performance than those using common kernels such as the Gaussian kernel or the polynomial kernel.

DOI： 10.1109/ICASSP.2008.4517999

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IMPERIAL COLL PRESS  

We propose a statistical strategy to predict differentially regulated genes of case and control samples from time-course gene expression data by leveraging unpredictability of the expression patterns from the underlying regulatory system inferred by a state space model. The proposed method can screen out genes that show different patterns but generated by the same regulations in both samples, since these patterns can be predicted by the same model. Our strategy consists of three steps. Firstly, a gene regulatory system is inferred from the control data by a state space model. Then the obtained model for the underlying regulatory system of the control sample is used to predict the case data. Finally, by assessing the significance of the difference between case and predicted-case time-course data of each gene, we are able to detect the unpredictable genes that are the candidate as the key differences between the regulatory systems of case and control cells. We illustrate the whole process of the strategy by an actual example, where human small airway epithelial cell gene regulatory systems were generated from novel time courses of gene expressions following treatment with (case)/without(control) the drug gefitinib, an inhibitor for the epidermal growth factor receptor tyrosine kinase. Finally, in gefitinib response data we succeeded in finding unpredictable genes that are candidates of the specific targets of gefitinib. We also discussed differences in regulatory systems for the unpredictable genes. The proposed method would be a promising tool for identifying biomarkers and drug target genes.

DOI： 10.11234/gi1990.21.101

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IMPERIAL COLL PRESS  

We propose a statistical strategy to predict differentially regulated genes of case and control samples from time-course gene expression data by leveraging unpredictability of the expression patterns from the underlying regulatory system inferred by a state space model. The proposed method can screen out genes that show different patterns but generated by the same regulations in both samples, since these patterns can be predicted by the same model. Our strategy consists of three steps. Firstly, a gene regulatory system is inferred from the control data by a state space model. Then the obtained model for the underlying regulatory system of the control sample is used to predict the case data. Finally, by assessing the significance of the difference between case and predicted-case time-course data of each gene, we are able to detect the unpredictable genes that are the candidate as the key differences between the regulatory systems of case and control cells. We illustrate the whole process of the strategy by an actual example, where human small airway epithelial cell gene regulatory systems were generated from novel time courses of gene expressions following treatment with (case)/without(control) the drug gefitinib, an inhibitor for the epidermal growth factor receptor tyrosine kinase. Finally, in gefitinib response data we succeeded in finding unpredictable genes that are candidates of the specific targets of gefitinib. We also discussed differences in regulatory systems for the unpredictable genes. The proposed method would be a promising tool for identifying biomarkers and drug target genes.

DOI： 10.11234/gi1990.21.101

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In order to estimate accurate gene networks from time course gene expression data, replicated time course data are useful. However, existing methods do not clearly distinguish between biological and technical replicates, while these two kinds of replicates have different features. In this paper, we propose a statistical model based on state space models to use biologically and technically replicated time course data and show an algorithm to estimate a gene network that is a graphical representation of gene-gene regulation. To our knowledge, for estimating gene networks, the proposed model is the first model that can simultaneously use two types of replicated time course data. We show the effectiveness of the proposed method through the analysis of the microarray human T-cell data. © 2008 IEEE.

DOI： 10.1109/AMS.2008.150

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

We study the statistical characteristics of four-sheet structures of large scale field-aligned currents (FACs) with an emphasis on particle precipitation collocated with each FAC sheet observed in dayside using magnetic field and particle precipitation data from the Defense Meteorological Satellite Program (DMSP). A total of 3247 four-sheet events were identified by applying an automatic procedure to the magnetic field data. The occurrence frequency of the events was less than 10% in each magnetic local time (MLT) sector for both hemispheres. The selected events were classified into two groups (type W and type M) with respect to the polarity of FACs. The polarity of the most equatorward type W (type M) FAC sheet is upward (downward). Type W (type M) events are observed predominantly in the morning (evening) sector and display a clear interplanetary magnetic field (IMF) BY dependence. Type W (type M) events appear for negative (positive) IMF BY in the Northern Hemisphere with the opposite dependence in the Southern Hemisphere. For each FAC sheet for type W events, we examined the occurrence of different precipitation regions identified with another automatic procedure. The most equatorward FAC sheet is collocated predominantly with central plasma sheet precipitation except for the prenoon sector, where boundary plasma sheet (BPS) precipitation has the highest occurrence. For the second and the third equatorward sheets, boundary layer-like precipitation, BPS near dawn and low-latitude boundary layer near noon, is dominant. In the most poleward sheet, mantle precipitation was observed frequently near noon, while BPS precipitation was dominant at earlier MLTs. These results, especially precipitation characteristics near noon, are consistent with our interpretation of the four-sheet structures, which is based on IMF BY dependence of convection patterns consisting of lobe, merging, and viscous cells. Copyright 2007 by the American Geophysical Union.

DOI： 10.1029/2006JA012036

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

We study the statistical characteristics of four-sheet structures of large scale field-aligned currents (FACs) with an emphasis on particle precipitation collocated with each FAC sheet observed in dayside using magnetic field and particle precipitation data from the Defense Meteorological Satellite Program (DMSP). A total of 3247 four-sheet events were identified by applying an automatic procedure to the magnetic field data. The occurrence frequency of the events was less than 10% in each magnetic local time (MLT) sector for both hemispheres. The selected events were classified into two groups (type Wand type M) with respect to the polarity of FACs. The polarity of the most equatorward type W (type M) FAC sheet is upward (downward). Type W (type M) events are observed predominantly in the morning (evening) sector and display a clear interplanetary magnetic field (IMF) B-Y dependence. Type W (type M) events appear for negative (positive) IMF B-Y in the Northern Hemisphere with the opposite dependence in the Southern Hemisphere. For each FAC sheet for type W events, we examined the occurrence of different precipitation regions identified with another automatic procedure. The most equatorward FAC sheet is collocated predominantly with central plasma sheet precipitation except for the prenoon sector, where boundary plasma sheet (BPS) precipitation has the highest occurrence. For the second and the third equatorward sheets, boundary layer-like precipitation, BPS near dawn and low-latitude boundary layer near noon, is dominant. In the most poleward sheet, mantle precipitation was observed frequently near noon, while BPS precipitation was dominant at earlier MLTs. These results, especially precipitation characteristics near noon, are consistent with our interpretation of the four-sheet structures, which is based on IMF B-Y dependence of convection patterns consisting of lobe, merging, and viscous cells.

DOI： 10.1029/2006JA012036

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

In the framework of sensor fusion, multiple sensors corresponding to the number of physical variables that must be measured are used. In this paper, we propose a novel sensing approach that simultaneously deals with heterogeneous physical variables with a sensor. It is fundamentally different from sensor fusion. The proposed approach takes into consideration the fact that any sensor that detects a certain physical variable is influenced to a degree by other physical variables, which are designated as noise. The objective in conventional sensor design has been the minimization of noise. In contrast, the proposed approach takes advantage of sensors that are easily influenced by many physical variables and makes full use of the multisensing characteristics of these sensors. The system designed using this concept has advantages in terms of cost performance and system simplification compared to existing approaches. This concept can be realized by developing a novel multiple-input/single-output sensor that can detect various variables, including pressure, acceleration, temperature and incandescent light emission, by a single device. We apply the sensor to monitor the symptoms of fire, earthquakes, and break-ins for the purpose of home security. The proposed security system is realized through statistical signal processing and machine learning techniques.

DOI： 10.1109/JSEN.2007.894887

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：COPERNICUS PUBLICATIONS  

A new filtering technique for sequential data assimilation, the merging particle filter (MPF), is proposed. The MPF is devised to avoid the degeneration problem, which is inevitable in the particle filter (PF), without prohibitive computational cost. In addition, it is applicable to cases in which a nonlinear relationship exists between a state and observed data where the application of the ensemble Kalman filter (EnKF) is not effectual. In the MPF, the filtering procedure is performed based on sampling of a forecast ensemble as in the PF. However, unlike the PF, each member of a filtered ensemble is generated by merging multiple samples from the forecast ensemble such that the mean and covariance of the filtered distribution are approximately preserved. This merging of multiple samples allows the degeneration problem to be avoided. In the present study, the newly proposed MPF technique is introduced, and its performance is demonstrated experimentally.

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Artificial Intelligence  

The number of competing-brands changes by new product's entry. The new product introduction is endemic among consumer packaged goods firm and is an integral component of their marketing strategy. As a new product's entry affects markets, there is a pressing need to develop market response model that can adapt to such changes. In this paper, we develop a dynamic model that capture the underlying evolution of the buying behavior associated with the new product. This extends an application of a dynamic linear model, which is used by a number of time series analyses, by allowing the observed dimension to change at some point in time. Our model copes with a problem that dynamic environments entail: changes in parameter over time and changes in the observed dimension. We formulate the model with framework of a state space model. We realize an estimation of the model using modified Kalman filter/fixed interval smoother. We find that new product's entry (1) decreases brand differentiation for existing brands, as indicated by decreasing difference between cross-price elasticities
(2) decreases commodity power for existing brands, as indicated by decreasing trend
and (3) decreases the effect of discount for existing brands, as indicated by a decrease in the magnitude of own-brand price elasticities. The proposed framework is directly applicable to other fields in which the observed dimension might be change, such as economic, bioinformatics, and so forth.

DOI： 10.1527/tjsai.22.200

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The present paper describes a frequency spectrum classification method for fault diagnosis of the LP gas pressure regulator using Support Vector Machines. Conventional diagnosis methods are not efficient because of problems such as significant noise and nonlinearity of the detection mechanism. In order to solve these problems, a machine learning method with the Kullback-Leibler (KL) kernel based on the KL divergence is introduced into spectrum classification. We use the normalized frequency spectrum directly as input with the KL kernel. The proposed method demonstrates a higher accuracy than popular kernels, such as polynomial or Gaussian kernels, or the conventional fault diagnosis method and Gaussian Mixture Model with the KL kernel for the examined problem. The high classification performance is achieved by using an inexpensive sensor system and the machine learning method. This method is widely applicable to other spectrum classification applications without limitation on the generality if the spectrums are normalized.

DOI： 10.1109/MLSP.2006.275593

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：METEOROLOGICAL SOC JAPAN  

We report an application of the ensemble Kalman filter (EnKF) and smoother (EnKS) to an intermediate coupled atmosphere-ocean model of Zebiak and Cane, into which the sea surface height (SSH) anomaly observations by TOPEX/POSEIDON (T/P) altimetry are assimilated. Smoothed estimates of the 54,403 dimensional state are obtained from 1,981 observational points with 2,048 ensemble members. While assimilated data are SSH anomalies alone, the estimated sea surface temperature (SST) anomalies reproduce primary temporal characteristics of the actual SST. The smoothed estimate of the zonal wind anomalies is also consistent with the observation except for the westerly anomalies in the western Pacific.

DOI： 10.2151/sola.2007-002

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IMPERIAL COLL PRESS  

We propose a novel method to classify samples where each sample is characterized by a time course gene expression profile. By exploiting the mixture of state space model, the proposed method addresses the following tasks: (1) clustering samples according to temporal patterns of gene expressions, (2) automatic detection of genes that discriminate identified clusters, (3) estimation of a restricted autoregressive coefficient for each cluster. We demonstrate the proposed method along with the cluster analysis of 53 multiple sclerosis patients under recombinant interferon beta therapy with the longitudinal time course expression profiles.

DOI： 10.11234/gi1990.18.258

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Publishing type：Research paper (scientific journal)   Publisher：社団法人 人工知能学会  

The number of competing-brands changes by new products entry. The new product introduction is endemic among consumer packaged goods firm and is an integral component of their marketing strategy. As a new products entry affects markets, there is a pressing need to develop market response model that can adapt to such changes. In this paper, we develop a dynamic model that capture the underlying evolution of the buying behavior associated with the new product. This extends an application of a dynamic linear model, which is used by a number of time series analyses, by allowing the observed dime...

DOI： 10.1527/tjsai.22.200

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：OXFORD UNIV PRESS  

One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail owing to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.

DOI： 10.1093/bioinformatics/btl129

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A track inspection car, which measures the shape of railway tracks (hereafter, rail geometry) while it is running on rails, discretizes the measurement results at nearly fixed spatial intervals. However, the distance between the discretized locations (spatial sampling intervals) may shorten or lengthen locally due to slipping or sliding of the car wheel, and this prevents the sampling locations from aligning with those of a dataset obtained with another measuring run. The authors developed an algorithm for approximately aligning the sampling locations of the measurement datasets obtained with different runs. First, they considered this problem as the selection of the series of data corresponding to each supervised data from a training dataset, which was constructed by interpolation in order to minimize the evaluation function of a number sequence representing data points. Next, they used the maximum likelihood method to identify the unknown parameters contained in the evaluation function. This problem uses two features of the evaluation function. The first is that the evaluation function is minimized by dynamic programming, and the obtained optimum sequence is equivalent to a maximum a posteriori (MAP) estimate in the Bayesian framework. The second is that by converting the evaluation function to a general state space representation, the log likelihood of the model that includes the parameters is obtained by a nonlinear filtering method. Also, to simplify the search for the identification, they devised a parameter search procedure for the parameters in the autoregressive (AR) model. © 2005 Wiley Periodicals, Inc.

DOI： 10.1002/scj.20313

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We propose a novel statistical method for estimating gene networks based on microarray gene expression data together with information from biological knowledge databases. Although a large amount of gene regulation information has already been stored in some biological databases, there are still errors and missing facts due to experimental problems and human errors. Therefore, we cannot blindly use them for understanding gene regulation and a robust procedure with a statistical model for using such database information is required. By using gene expression data, we provide a probabilistic framework of a joint learning model for repairing database information and for estimating a gene network based on dynamic Bayesian networks, simultaneously. To show the effectiveness of the proposed method, we analyze Saccharomyces cerevisiae cell-cycle gene expression data together with KEGG information. © 2005 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.stamet.2005.09.013

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

We report the first application of the ensemble Kalman filter (EnKF) to an intermediate coupled atmosphere-ocean model by [1], into which the sea surface height (SSH) anomaly observations by TOPEX/POSEIDON (T/P) altimetry are assimilated. Smoothed estimates of the 54,403 dimensional state are obtained from 1981 observational points with 2048 ensemble members. While data assimilated are SSH anomalies alone, an ensemble experiment of 2002/03 El Ni (n) over tildeo event based on the EnKF can predict consistent Ni (n) over tildeo 3 sea surface temperature (SST) anomalies about 5 months in advance.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：INDERSCIENCE ENTERPRISES LTD  

We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from hidden state variables in a system. To identify the system, we estimate parameters of the model by EM algorithm and determine the dimension of the state variable by BIC.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

Data assimilation is a method of combining an imperfect simulation model and a number of incomplete observation data. Sequential data assimilation is a data assimilation in which simulation variables are corrected at every time step of observation. The ensemble Kalman filter is developed for a sequential data assimilation and frequently used in geophysics. On the other hand, the particle filter developed and used in statistics is similar in view of ensemble-based method, but it has different properties. In this paper, these two ensemble based filters are compared and characterized through matrix representation. An application of sequential data assimilation to tsunami simulation model with a numerical experiment is also shown. The particle filter is employed for this application. An erroneous bottom topography is corrected in the numerical experiment, which demonstrates that the particle filter is useful tool as the sequential data assimilation method.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Any sensor to detect a certain physical variable is influenced to some degree by other physical variables designated as "noise". The objective in conventional sensor design is to minimize such noise. In this study, conversely we welcome sensing devices that are easily influenced by many physical variables and make full use of their multi-sensing characteristic. We consider such devices as multiple-inputs and single-output sensors. The output signal derived from multiple input signals must be dissociated. The input signals resulting from physical realities have inherent characteristics and can be mathematically modeled. Application of a Kalman filter realized by such models can provide estimates of state variables of all input models, and thus the input signals can be dissociated. As an example, a novel sensor based on a microphone is presented. It can detect various variables such as pressure and acceleration in the frequency range of 0.1 Hz to 10 kHz, temperature, and even light emission. We use the sensor to monitor the symptoms of fire, earthquake and break-in by intruders from within a house.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Conventional sensors have been designed to minimize noise effects. Any sensor that is designed to detect a certain physical variable is influenced to a certain degree by other physical variables. This suggests that any sensor is potentially capable of detecting multiple physical variables. In the present study, we consider sensing devices that are easily influenced by several physical variables and make full use of their multi-sensing characteristics through statistical signal processing and machine learning techniques with a wide variety of prior information. The proposed sensor design approach is completely different from the conventional approach with respect to system design and has advantages in terms of cost and system simplification compared to existing approaches. This new idea can be realized by developing a novel multiple-input/single-output sensor that can detect various variables such as pressure, acceleration, temperature and light emission by a single device. The sensor is applied to monitor the symptoms of fire, earthquake and break-in for the purpose of home security. The proposed security system consists of the following three steps: (1) Detection of disaster by a probabilistic outlier detection procedure using an auto-regressive model, (2) Disaster feature extraction by Kaiman filter on a state space model, and (3) Disaster classification by multiclass support vector machine.

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Publishing type：Research paper (scientific journal)   Publisher：1  

DOI： 10.11234/gi1990.17.46

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Publishing type：Research paper (scientific journal)   Publisher：2  

Cell Illustrator is a model building tool based on the Hybrid Functional Petri net with extension (HFPNe). By using Cell Illustrator, we have succeeded in modeling biological pathways, e.g., metabolic pathways, gene regulatory networks, microRNA regulatory networks, cell signaling networks, and cell-cell interactions. The recent development of tandem mass spectrometry coupled with liquid chromatography (LC/MS/MS) technology has enabled researchers to quantify the dynamic profile of a wide range of proteins within the cell. The proteomic data obtained by using LC/MS/MS has been considerably useful for introducing dynamics to the HFPNe model. Here, we report the first introduction of the time-series proteomic data to our HFPNe model. We constructed an epidermal growth factor receptor signal transduction pathway model (EFGR model) by using the biological data available in the literature. Then, the kinetic parameters were determined in the data assimilation (DA) framework with some manual tuning so as to fit the proteomic data published by Blagoev et al. (Nat. Biotechnol., 22:1139-1145, 2004). This in silico model was further refined by adding or removing some regulation loops using biological background knowledge. The DA framework was used to select the most plausible model from among the refined models. By using the proteomic data, we semi-automatically constructed a well-tuned EGFR HFPNe model by using the Cell Illustrator coupled with the DA framework.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

Conventional sensors have been designed to minimize noise effects. Any sensor that is designed to detect a certain physical variable is influenced to a certain degree by other physical variables. This suggests that any sensor is potentially capable of detecting multiple physical variables. In the present study, we consider sensing devices that are easily influenced by several physical variables and make full use of their multi-sensing characteristics through statistical signal processing and machine learning techniques with a wide variety of prior Information. The proposed sensor design approach is completely different from the conventional approach with respect to system design and has advantages in terms of cost and system simplification compared to existing approaches. This new idea can be realized by developing a novel multiple-input/single-output sensor that can detect various variables such as pressure, acceleration, temperature and light emission by a single device. The sensor is applied to monitor the symptoms of fire, earthquake and break-in for the purpose of home security. The proposed security system consists of the following three steps: (1) Detection of disaster by a probabilistic outlier detection procedure using an auto-regressive model, (2) Disaster feature extraction by Kaiman filter on a state space model, and (3) Disaster classification by multiclass support vector machine. © 2006 IEEE.

DOI： 10.1109/ijcnn.2006.247296

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japan Society for Industrial and Applied Mathematics  

It is known that an execution of price and/or a non-price promotion has a strong influence on the sales of a brand sold in a supermarket. Usually, we can easily obtain information on a price promotion from a POS data. On the other hand, unless investigator collects information on an execution of non-price promotion in every retail store, we can not obtain such information. In this paper, we consider a problem to identify whether or not non-price promotion is conducted. We treat a non-price promotion execution/non-execution as a state variable. An unknown stationary probability matrix is assumed to describe the probability of a transition between states. Each state is characterized by a different stationary time series model with unknown parameters. An objective of the analysis is to identify the model and to assign a probability model for each state at each time instant. Finally, we give a high precision estimator of a past non-price promotion based on the proposal model.

DOI： 10.11540/jsiamt.15.4_537

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Applied Statistics  

A dataset that contains missing regions is assumed to arise from two or more populations. In order to decompose the data into meaningful component distributions, a normal mixture model can be applied. A problem with this approach is that the estimated parameters are biased by fitting the standard normal mixture model. To correct the bias, a log-likelihood function for missing region probabilities is constructed, and the maximum likelihood estimators of the parameters - i.e. mix-ing proportions, mean vectors, and variance-covariance matrixes - are derived in the context of the EM algorithm. The performance of the model is verified by numerical experiments, and the model is applied to plasma velocity data.

DOI： 10.5023/jappstat.34.57

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

[1] The present study statistically compares large-scale field-aligned currents (FACs) under sunlit and dark ionospheric conditions. A total of similar to 74,000 auroral oval crossings are selected from magnetic field measurements from the DMSP F7 and F12 to F15 satellites. For the dayside FAC it is reconfirmed that both current intensity and density are statistically larger in the illuminated events than in the unilluminated events. As for the nightside FAC, in contrast, a few important features become clear for the first time, which can be summarized as follows: ( 1) At 20 <= MLT < 02 both R1 and R2 intensities tend to be larger when the ionosphere is dark than when it is sunlit. ( 2) Although dependence on the ionospheric condition is less clear for the FAC density, a systematic preference of the occurrence of strong FACs for the dark ionosphere can be found for both R1 and R2 currents in the dusk-to-premidnight ( 16 <= MLT < 22) sector and for the R1 current in the postmidnight sector ( 00 <= MLT < 02). ( 3) For both FAC intensity and density the difference between the illuminated and unilluminated events tends to increase with increasing geomagnetic activity as measured by the IMF B-Z component. Result 1 can be partially explained in terms of the interhemispheric asymmetry of the magnetospheric configuration, which, however, should not affect the FAC density. Therefore result 2 strongly suggests that the solar-induced conductivity controls the M-I coupling in a different way in certain nightside MLT sectors than in dayside sectors.

DOI： 10.1029/2005JA011057

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

[ 1] The present study examines seasonal variations of large- scale field- aligned current ( FAC) systems in terms of the dipole tilt and clock angles. Magnetic field measurements from the DMSP F7 and F12- F15 satellites are used. This data set consists of a total of similar to 185,000 auroral oval crossings, out of which similar to 121,000 crossings were selected for the present analysis. Focus is placed on the latitude at the demarcation between the region 2 ( R2) and region 1 ( R1) currents and the intensities of these currents. It is found that the dayside FAC moves poleward and equatorward in the summer and winter hemispheres, respectively, and the nightside FAC has the opposite seasonal dependence. In the midday sector the peak- to- peak variation of the FAC latitude over the entire range of the dipole tilt is similar to5degrees, whereas it is similar to4degrees around midnight. In the flank sectors the average FAC latitude is higher around the solstices than around the equinoxes irrespective of hemisphere. The corresponding dependence on the dipole clock angle can actually be found for almost all local time sectors, although the peak- to- peak variation of the expected semiannual variation, 2 degrees around noon and < 1 ° in other local time sectors, is smaller than that of the annual variation except for the flank sectors. A comparison with a model magnetic field strongly suggests that the dipole tilt effect on the magnetospheric configuration is the primary cause of the annual variation, whereas the semiannual variation is inferred to reflect the fact that geomagnetic activity tends to be higher around the equinoxes. The average dayside FAC intensity is larger in the summer hemisphere than in the winter hemisphere, which can be explained in terms of the seasonal variation of the ionospheric conductivity. The dayside R1 current intensity depends more strongly on the dipole tilt than the dayside R2 current intensity, and it changes by a factor of 2 - 3 over the entire range of the dipole tilt angle. In contrast, the annual variation of the nightside FAC intensity is more complicated, and the nightside R2 current seems to be more intense in the winter hemisphere than in the summer hemisphere. The dependence of the FAC intensity on the dipole clock angle is less significant especially for the R1 system. Nevertheless, the result suggests that the FAC tends to be more intense around the equinoxes, which is consistent with the semiannual variation of geomagnetic activity.

DOI： 10.1029/2004JA010634

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Language：Japanese   Publisher：統計数理研究所  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

The present study statistically compares large-scale field-aligned currents (FACs) under sunlit and dark ionospheric conditions. A total of ∼74,000 auroral oval crossings are selected from magnetic field measurements from the DMSP F7 and F12 to F15 satellites. For the dayside FAC it is reconfirmed that both current intensity and density are statistically larger in the illuminated events than in the unilluminated events. As for the nightside FAC, in contrast, a few important features become clear for the first time, which can be summarized as follows: (1) At 20 ≤ MLT &lt
02 both R1 and R2 intensities tend to be larger when the ionosphere is dark than when it is sunlit. (2) Although dependence on the ionospheric condition is less clear for the FAC density, a systematic preference of the occurrence of strong FACs for the dark ionosphere can be found for both R1 and R2 currents in the dusk-to-premidnight (16 ≤ MLT &lt
22) sector and for the R1 current in the postmidnight sector (00 ≤ MLT &lt
02). (3) For both FAC intensity and density the difference between the illuminated and unilluminated events tends to increase with increasing geomagnetic activity as measured by the IMF BZ component. Result I can be partially explained in terms of the interhemispheric asymmetry of the magnetospheric configuration, which, however, should not affect the FAC density. Therefore result 2 strongly suggests that the solar-induced conductivity controls the M-I coupling in a different way in certain nightside MLT sectors than in dayside sectors. Copyright 2005 by the American Geophysical Union.

DOI： 10.1029/2005JA011057

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

In gene network, estimation from time series microarray data, dynamic models such as differential equations and dynamic Bayesian networks assume that the network structure is stable through all time points, while the real network might changes its structure depending on time, affection of some shocks and so on. If the true network structure underlying the data changes at certain points, the fitting of the usual dynamic linear models fails to estimate the structure of gene network and we cannot obtain efficient information from data. To solve this problem, we propose a dynamic linear model with Markov switching for estimating time-dependent network-struture from time series gene expression data. Using our proposed method, the network structure between genes and its change points are autoniatically estimated We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle time series data.

DOI： 10.1109/CSB.2005.32

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

In gene network, estimation from time series microarray data, dynamic models such as differential equations and dynamic Bayesian networks assume that the network structure is stable through all time points, while the real network might changes its structure depending on time, affection of some shocks and so on. If the true network structure underlying the data changes at certain points, the fitting of the usual dynamic linear models fails to estimate the structure of gene network and we cannot obtain efficient information from data. To solve this problem, we propose a dynamic linear model with Markov switching for estimating time-dependent network-struture from time series gene expression data. Using our proposed method, the network structure between genes and its change points are autoniatically estimated We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle time series data.

DOI： 10.1109/CSB.2005.32

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Blackwell Publishing Ltd  

The present study examines seasonal variations of large-scale field-aligned current(FAC) Systems in terms of the dipole tilt and clock angles. Magnetic field measurements from the DMSP F7 and F12-F15 satellites are used. This data set consists of a total of ∼185,000 auroral oval crossings, out of which ∼121,000 crossings were selected for the present analysis. Focus is placed on the latitude at the demarcation between the region 2 (R2) and region 1 (R1) currents and the intensities of these currents. It is found that the dayside FAC moves poleward and equatorward in the summer and winter hemispheres, respectively, and the nightside FAC has the opposite seasonal dependence. In the midday sector the peak-to-peak variation of the FAC latitude over the entire range of the dipole tilt is ∼5°, whereas it is ∼4° around midnight. In the flank sectors the average FAC latitude is higher around the solstices than around the equinoxes irrespective of hemisphere. The corresponding dependence on the dipole clock angle can actually be found for almost all local time sectors, although the peak-to-peak variation of the expected semiannual variation, 2° around noon and &lt
1° in other local time sectors, is smaller than that of the annual variation except for the flank sectors. A comparison with a model magnetic field strongly suggests that the dipole tilt effect on the magnetospheric configuration is the primary cause of the annual variation, whereas the semiannual variation is inferred to reflect the fact that geomagnetic activity tends to be higher around the equinoxes. The average dayside FAC intensity is larger in the summer hemisphere than in the winter hemisphere, which can be explained in terms of the seasonal variation of the ionospheric conductivity. The dayside R1 current intensity depends more strongly on the dipole tilt than the dayside R2 current intensity, and it changes by a factor of 2-3 over the entire range of the dipole tilt angle. In contrast, the annual variation of the nightside FAC intensity is more complicated, and the nightside R2 current seems to be more intense in the winter hemisphere than in the summer hemisphere. The dependence of the FAC intensity on the dipole clock angle is less significant especially for the R1 system. Nevertheless, the result suggests that the FAC tends to be more intense around the equinoxes, which is consistent with the semiannual variation of geomagnetic activity. Copyright 2005 by the American Geophysical Union.

DOI： 10.1029/2004JA010634

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

In this paper, we propose a new clustering procedure for high dimensional microarray data. Major difficulty in cluster analysis of microarray data is that the number of samples to be clustered is much smaller than the dimension of data which is equal to the number of genes used in an analysis. In such a case, the applicability of conventional model-based clustering is limited by the occurence of overlearning. A key idea of the proposed method is to seek a linear mapping of data onto the low-dimensional subspace before proceeding to cluster analysis. The linear mapping is constructed such that the transformed data successfully reveal clusters existed in the original data space. A clustering rule is applied to the transformed data rather than the original data. We also establish a link between this method and a probabilistic framework, that is, a penalized likelihood estimation of the mixed factors model. The effectiveness of the proposed method is demonstrated through the real application.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

We propose a robust estimation method of gene networks based on microarray gene expression data. It is well-known that microarray data contain a large amount of noise and some outliers that interrupt the estimation of accurate gene networks. In addition, some relationships between genes are nonlinear, and linear models thus are not enough for capturing such a complex structure. In this paper, we utilize the moving boxcel median filter and the residual bootstrap for constructing a Bayesian network in order to attain robust estimation of gene networks. We conduct Monte Carlo simulations to examine the properties of the proposed method. We also analyze Saccharomyces cerevisiae cell cycle data as a real data example.

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：BLACKWELL PUBLISHING LTD  

In the last decade, continuous Global Positioning System (GPS) networks have observed transient crustal deformation associated with various types of aseismic fault-slip event in many subduction zones. It is important to precisely clarify the entire time history of these events to understand the physical process of earthquake generation. For this purpose, we have developed a new time-dependent inversion method for imaging transient fault slips from geodetic data. Segall & Matthews (1997) presented a time-dependent inversion method to infer the spatiotemporal distribution of fault slip from geodetic data. They modelled a transient crustal deformation associated with fault-slip events using a linear Gaussian state space model and employed a Kalman filter. They introduced a scaling parameter that represents the temporal smoothness of the fault slip, and assumed that the scaling parameter is constant over the observation period. Under this assumption, abrupt changes of slip have been overly smoothed, whereas estimated slips in a 'quiet' steady-state period have been oscillatory. To improve the method, we developed a new filtering technique, a Monte Carlo mixture Kalman filter (MCMKF), and apply it to time-dependent inversion. The MCMKF allows variations of the temporal smoothness of slips by regarding it as a stochastic variable. The MCMKF is based on a Monte Carlo method in which conditional probability density functions of the stochastic variable are estimated recursively. We examine the validity of the introduced MCMKF-based inversion scheme through numerical experiments using simulated displacement time-series. Then, the results are compared with those obtained by a conventional Kalman filter-based scheme. In all cases, MCMKF gives a significantly smaller Akaike information criterion (AIC) values than the Kalman filter. This indicates that MCMKF yields better state estimates than the Kalman filter. We also find that MCMKF is capable of imaging the initiation process of transient slip events in cases with a high signal-to-noise ratio, while the Kalman filter is not. Furthermore, MCMKF is superior to the Kalman filter in detecting small signals from noisy data sets. From all of the results above, we conclude that the new filtering approach introduced here may provide a powerful tool for imaging the time history of fault slips.

DOI： 10.1111/j.1365-246X.2004.02383.x

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

CiNii Books

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：社団法人日本オペレーションズ・リサーチ学会  

CiNii Books

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：1  

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application. © Imperial College Press.

DOI： 10.1142/S021972000400048X

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：KLUWER ACADEMIC PUBL  

The execution of price and/or display promotion has a significant effect on the sales of a brand sold in a supermarket. Information on price and/or sales is available from POS data. However, unless an investigator collects information on the execution of display promotions from every retail store, such information is unavailable. This paper presents a method of identifying whether display promotion has been executed without having to visit individual stores. We treat the execution/non-execution of a display promotion as a state variable. An unknown stationary probability matrix is assumed to describe the probability of a transition between states. Each state is characterized by a different stationary time series model with unknown parameters. The objective of the analysis is to identify the model and to assign a probability model for each state at each time instant. Finally, we provide a high precision estimator of a past execution/non-execution of a display promotion based on the proposed model.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

When we cluster tissue samples on the basis of genes, the number of observations to be grouped is much smaller than the dimension of feature vector In such a case, the applicability of conventional model-based clustering is limited since the high dimensionality of feature vector leads to overfitting during the density estimation process. To overcome such difficulty, we attempt a methodological extension of the factor analysis. Our approach enables us not only to prevent from the occurrence of overfitting, but also to handle the issues of clustering, data compression and extracting a set of genes to be relevant to explain the group structure. The potential usefulness are demonstrated with the application to the leukemia dataset.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

When we cluster tissue samples on the basis of genes, the number of observations to be grouped is much smaller than the dimension of feature vector In such a case, the applicability of conventional model-based clustering is limited since the high dimensionality of feature vector leads to overfitting during the density estimation process. To overcome such difficulty, we attempt a methodological extension of the factor analysis. Our approach enables us not only to prevent from the occurrence of overfitting, but also to handle the issues of clustering, data compression and extracting a set of genes to be relevant to explain the group structure. The potential usefulness are demonstrated with the application to the leukemia dataset.

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Language：English  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

[1] The occurrence epochs and duration periods of the 1969, 1978, and 1991 geomagnetic jerks are estimated for 65 geomagnetic observatories by applying a statistical time series model to monthly means of the geomagnetic eastward component. It is found for every jerk that the occurrence epochs around South Africa and South Pacific Ocean are delayed several years from those in other regions. Moreover, it is found for the 1969 and 1978 jerks that the duration times around South Africa, which are defined as the lengths of the duration periods, are longer than those in other regions. The distribution of these anomalous regions of the jerks (i.e., South Africa and South Pacific Ocean) may imply the existence of higher conductivities in the lower mantle beneath these regions, which possibly relates to the fact that the velocity of a seismic S wave in the D" layer beneath these regions is slower than that in other regions. The mantle conductivities beneath South Africa, South Pacific Ocean, and other regions are estimated through a numerical simulation; the magnetic diffusion equation in the mantle is solved for an abrupt magnetic change generated at the core-mantle boundary. Comparing the results obtained by the simulation with those obtained by the data analysis, it is plausible that the mantle conductivities beneath South Africa and South Pacific Ocean are higher than that beneath other regions, although there is an ambiguity in the estimated magnitudes of the mantle conductivity.

DOI： 10.1029/2002JB001786

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

This article is addressed to the problem of modeling and exploring mean value structure of large-scale time series data and time-space data. A smoothness prior modeling approach (Smoothness Prior Analysis of Time Series, Lecture Notes in Statistics, vol. 116, Springer, New York, 1996.) is taken. In this approach, the observed series are decomposed into several components each of which are expressed by smoothness priors models. In the analysis of POS and GPS data, various useful information were extracted by this decomposition, and result in discoveries in these areas. (C) 2002 Elsevier Science B.V. All rights reserved.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework; and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method We analyze Saccharomyces cerevisiae gene expression data as an application.

DOI： 10.1109/CSB.2003.1227309

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：TERRA SCIENTIFIC PUBL CO  

The geomagnetic jerk amplitudes, which are defined as abruptness of changes in the trends of geomagnetic time series, are investigated with geomagnetic monthly means computed from hourly mean values at each local time. A statistical time series model in which the trend component is expressed by a second order spline function with variable knots is constructed for each time series. The optimum parameter values of the model including positions of knots are estimated by the maximum likelihood method, and the optimum number of parameters including the number of knots are determined based on the Akaike Information Criterion (AIC). The jerks are detected objectively and automatically by regarding the optimized positions of knots as the occurrence epochs. This analysis reveals that the spatial distributions of jerk amplitudes essentially do not depend on the local time, which indicates that the jerks cannot be explained by abrupt changes in intensities of latitudinally flowing external currents such as the field-aligned currents. Longitudinally flowing currents, on the other hand, such as the ring current could explain the distributions. The abrupt changes of the ring current intensity are estimated from the distributions of jerk amplitudes in the eastward component in 1969, 1978, and 1991 supposing that an abrupt change in the ring current intensity causes a jerk. However those estimated changes cannot consistently explain the distributions of the jerks in the northward and downward components. Therefore it is plausible that the jerks which occur-red in 1969, 1978, and 1991 are not caused by external sources but internal ones. It is also confirmed that the occurrence epochs of jerks in the southern hemisphere are a few years after those of the 1969 and 1978 jerks in the northern hemisphere. and it is also found that the jerk in the southern hemisphere occurred a few years after the occurrence of the 1991 jerk in Europe. Taking these time tags in occurrence epochs into account, it can be said that the 1969, 1978, and 1991 jerks are global phenomena.

DOI： 10.1186/BF03351712

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：PHYSICA-VERLAG GMBH & CO  

Tracking problem of maneuvering target is treated with assumption that the maneuver is unknown and its acceleration has abrupt changes sometimes. To cope with unknown maneuver, Bayesian switching structure model, which includes a set of possible models and switches among them, is used. It can be formalized into general (nonlinear, non-Gaussian) state space model where system model describes the target dynamics and observation model represents a process to observe the target position. Heavy-tailed uni-modal distribution, e.g. Cauchy distribution, is used for the system noise to accomplish good performance of tracking both for constant period and abrupt changing time point of acceleration. Monte Carlo filter, which is a kind of particle filter that approximates state distribution by many particles in state space, is used for the state estimation of the model. A simulation study shows the efficiency of the proposed model by comparing with Gaussian case of Bayesian switching structure model.

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Language：Japanese   Publisher：The Japan Society for Industrial and Applied Mathematics  

DOI： 10.11540/bjsiam.12.2_216

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：PHYSICA-VERLAG GMBH & CO  

By the progress of fast computing facilities and various computing technologies, it becomes realistic to apply computer intensive methods to statistical analysis. In time series analysis, sequential Monte Carlo methods was developed for general state space models which enables to consider very complex nonlinear non-Gaussian models.
In this paper, we show algorithms, implementations and parameter estimation for Monte Carlo filter and smoother. Various ways of the use of parallel computer. are also discussed. The usefulness of the general state space modeling is illustrated with several examples.

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/3-540-45884-0_27

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/3-540-45884-0_26

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

Recent space plasma observations have provided us with three-dimensional velocity distributions having multiple peaks. We propose a method for analyzing such velocity distributions via a multivariate Maxwellian mixture model where each component of the model represents each of the multiple peaks. The parameters of the model are determined through an iterative nonlinear optimization technique, specifically the expectation-maximization (EM) algorithm. For the automatic judgment of the preferable number of components in the mixture model, we introduce a method of examining the number of extrema of a resulting mixture model. We show applications of our method to ion observations in the plasma sheet boundary layer (PSBL) and in the central plasma sheet (CPS) of the Earth's magnetotail. From an analysis of the PSBL and CPS, low-energy ions that have properties similar to those of lobe ions were detected also in the PSBL and CPS. In the PSBL, middle-energy ion component which is flowing dawnward and directed the neutral sheet was extracted. We suggested that magnetic field fluctuations in the PSBL can be explained only when the two ion components were properly treated.

DOI： 10.1029/2001JA900079

DOI： 10.1029/2001ja900079

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In this study,we consider a time series model which combines the partial nonGaussian state space model and self-organizing state space model (SOSSM), where the SOSSM has been proposed to an extension of the generalized state space model. The competing different system/observation models for the state vector can be simultaneously dealt in this model as introducing a switching structure, and appropriate system/observation models among them is automatically determined as a function of time. As a result, we are free from a procedure of selecting models among competing models. Of course, this model allows us to consider an inclusion of two system/observation models which conflicts each other in some sense. Namely,we need not havewell-organized knowledge about modeling of the time series. We therefore call this model the evolutionary time series model. We regard the approach based on the evolutionary time series model as one of the machine learning approaches. The evolutionary time series model can be formulated within a framework of an extension of SOSSM whichtakes a convenient form from a computational point of view.

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Publishing type：Research paper (international conference proceedings)  

Abstract. Electron velocity distribution obtained by direct spacecraft observation in space is contaminated by photoelectrons. The photoelectrons are generated due to the solar ultraviolet ray, and are regarded as artificial noise from a viewpoint of scientific research. We propose a method for separating photoelectron component from ambient electron component. Our method uses multivariate normal mixture model, whose parameters are determined via the Expectation-Maximization (EM) algorithm. Initial parameters of the EM algorithm are computed through the classification of the velocity space by a spherical surface of some arbitrary radius. 1

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

The study of large-scale field-aligned currents (LSFACs) has depended on visual inspection of data plots, which is often subjective and time-consuming, and therefore limits the efficiency of analyses. The present paper reports a new procedure to automatically identify the spatial structure of LSFACs from satellite magnetic field measurements. The procedure is based on the concept of the first-order B spline fitting with variable node positions, which may be envisioned as fitting line segments to a line plot. The fitting is made for the maximum variance component of magnetic variations in the plane perpendicular to the background magnetic field. If the distribution of LSFACs can be approximated as an infinite sheet, each slope of the plot corresponds to the crossing of a FAC sheet. The number of node points, which determines the number of FAC sheets, is one of the fitting parameters and is optimized for each orbit so that the Akaike information criterion (AIC) is minimized. Whereas other methods, such as a spherical harmonic fitting, seek to obtain two-dimensional distributions of FACs from assembled data, the present method is basically the automation of the way we visually examine a plot of satellite magnetic field data, and it can be applied even to a single satellite pass. Therefore the procedure can be implemented to real-time data processing and now-casting. The procedure should also provide a powerful tool for data mining. Magnetic field data from the Defense Meteorological Satellite Program-F7 (DMSP-F7) were used for demonstration, and the present paper reports the initial results.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER GEOPHYSICAL UNION  

The present study investigates the statistical characteristics of four-sheet structures of large-scale field aligned currents (FACs) observed by the Defense Meteorological Satellite Program-F7 (DMSP-F7) satellite in the dayside high-latitude region. A total of 495 four-FAG-sheet events were selected from the entire set of DMSP-F7 magnetometer data. With respect to the polarity of FACs, the selected events are classified into two groups, which are named type M and type W after the shape of a plot of the azimuthal magnetic component. The polarity of the equatorward most FAC is downward for the type M structure, whereas it is upward for the type W structure. Type W and type M events tend to take place in the prenoon and postnoon sectors, respectively, but the transition takes place slightly before noon. The type W structure tends to be observed for negative and positive interplanetary magnetic field (IMF) B-y,in the Northern and Southern Hemispheres, respectively. For a given hemisphere, the favorable sign of IMF B-Y, for the type M structure is opposite to that for the type W structure. It is also found that both types occur more often when the IMF B-Z, is northward than when it is southward. The four-sheet structure can be interpreted in terms of two zonal ionospheric convection flows separated in latitude. The equatorward one is a part of the viscous cell and, possibly, also the merging cell, whereas the poleward one is a part of the lobe cell. The direction (of the major cell) of the lobe convection is determined by the sign of IMF By and is different in different hemispheres, explaining the discovered IMF B-Y dependence. The preference for northward IMF B-Z, is consistent with the required development of the lobe convection. It is inferred from the expected polar distribution of FACs that the four-sheet structure is a superposition of a pair of midday region 0 and region 1 currents and a pair of conventional region 1 and region 2 currents.

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：AMER GEOPHYSICAL UNION  

The present paper reports a newly developed procedure to automatically identify : a spatial structure of large-scale field-aligned currents (FACs) from satellite magnetic field measurements. The procedure is based on the concept of the first-order B-spline fitting with variable (i.e., movable) node positions, which may be envisioned as fitting line segments to a,plot of a magnetic field component. The number of node points is optimized for each current sheet crossing so that a certain information criterion is minimized. The properties of a FAC system such as its location and intensity can be easily calculated from the information of node points. The procedure is applied to the entire set of nightside magnetic field measurements (1339 days) made by the DMSP-F7 satellite and for demonstration, the intensity of nightside region-1 FACs is examined as a function of magnetic latitude. The results not only confirm the general expectation that intense region 1 currents tend to be observed at lower latitudes but also quantify the relationship between the current intensity and latitude. The capability of such quantification should be useful for future studies including practical applications such as space weather forecast.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：AMER GEOPHYSICAL UNION  

Large-scale field-aligned current (FAC) systems occasionally disappear in the midday sector (9 < MLT < 13), even though this is the local time sector where FACs tend to be most intense. The present study investigates 26 such events observed by the DMSP-F7 satellite. It is found that the events tend to occur in the winter hemisphere in a certain UT range when the dipole axis was inclined most antisunward and therefore the contribution of solar illumination to the ionospheric conductivity is minimum. Before most events, IMF B(Z) was positive, and the solar wind bulk flow momentum was below the average. Although one might expect to see such events when the sign of B(X) was unfavorable for merging at the high-latitude tail magnetopause in the winter hemisphere, no clear preference for IMF B(X) was found. It was also found that the asymmetry of the ionospheric conductivity between the summer and winter hemispheres suppresses the IMF-dependent characteristics of particle precipitation in the polar cap. These results suggest that ionospheric conductivity not only determines the intensity of FACs for the convection electric field imposed by the solar wind-magnetosphere interaction, but also affects how the magnetosphere interacts with the solar wind.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

DOI： 10.1007/3-540-46846-3_51

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

This article is addressed to the problem of modeling and exploring time series with mean value structure of large scale time series data and time-space data. A smoothness priors modeling approach [11] is taken and applied to POS and GPS data. In this approach, the observed series are decomposed into several components each of which are expressed by smoothness priors models. In the analysis of POS and GPS data, various useful information were extracted by this decomposition, and result in some discoveries in these areas.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

The statistical information processing can be characterized by using the Likelihood function defined by giving an explicit form for an approximation to the true distribution from which the data are generated. This mathematical representation as an approximation, which is usually called a model, is built based on not only the current data but also prior knowledge on the object and the objective of the analysis. Akaike ([2] and [3]) showed that the log-likelihood can be considered as an estimate of the Kullback-Leibler information which defines the similarity between the predictive distribution of the model and the true distribution and proposed the Akaike information criterion (AIC). By the use of this AIC, it becomes possible to evaluate and compare the goodness of many models objectively and it enables us to select the best model among many candidates. In consequence, the minimum AIC procedure allows us to develop automatic modeling and signal extraction procedures. In this study, we give a simple explanation of statistical modeling based on the AIC and demonstrate four examples of applying the minimum AIC procedure to an automatic transaction of signals observed in the earth sciences. In each case, the AIC plays an important role in making the procedure automatic and objective, and promises to realize a detail examination of a large amount of data sets, which provides us with an opportunity to discover new information.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：European Geosciences Union  

In order to determine the normal direction of the magnetopause, the minimum variance analysis technique is frequently used: it is applied to the magnetic field data of a magnetopause crossing observed by a satellite, and provides the direction along which the magnetic field variation is minimum. In this study we propose a method to extend naturally the framework of the minimum variance analysis so that it includes all continuity conditions across a planar magnetopause, so that we could use all physical quantities observed by the spacecraft. We first extend it to the electric field: we discuss how to determine the normal direction and the speed of the magnetopause along the normal by using both the magnetic and electric field data. We next discuss the full extension to all continuity conditions across the magnetopause. Finally, we discuss how to extend our method further so that it can deal with the magnetopause which is accelerating. A discussion of application to observations will appear separately.

DOI： 10.1007/s00585-996-1019-1

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Language：English   Publishing type：Research paper (scientific journal)  

The minimum variance analysis technique introduced by Sonnerup and Cahill (1967) is a useful tool in space physics. However the statistical errors appearing in this method are difficult to estimate accurately because of the complicated form of the eigenvalue decomposition. To deal with this problem, this paper introduces the bootstrap method (Efron, 1979) which replaces analytical solutions with repeated simple calculations. We apply this method to the estimation of the statistical errors in the minimum variance direction and the average component in the minimum variance direction, and show that this method accurately estimates the errors. Copyright 1995 by the American Geophysical Union.

DOI： 10.1029/94GL02969

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

The statistical information processing can be characterized by using the likelihood function defined by giving an explicit form for an approximation to the true distribution from which the data are generated. This mathematical representation as an approximation, which is usually called a model, is built based on not only the current data but also prior knowledge on the object and the objective of the analysis. Akaike ([2] and [3]) showed that the log-likelihood can be considered as an estimate of the Kullback-Leibler information which defines the similarity between the predictive distribution of the model and the true distribution and proposed the Akaike information criterion (AIC). By the use of this AIC, it becomes possible to evaluate and compare the goodness of many models objectively and it enables us to select the best model among many candidates. In consequence, the minimum AIC procedure allows us to develop automatic modeling and signal extraction procedures. In this study, we give a simple explanation of statistical modeling based on the AIC and demonstrate four examples of applying the minimum AIC procedure to an automatic transaction of signals observed in the earth sciences. In each case, the AIC plays an important role in making the procedure automatic and objective, and promises to realize a detail examination of a large amount of data sets, which provides us with an opportunity to discover new information.

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1.1 Generalized state space model The generalized state space model (GSSM) that we deal with in this study is de ned by a set of two equations system model xt = f(xt;1 � vt) � and (1.1)

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Total pages：209  

ASIN

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Total pages：xii, 204p   Language：Japanese  

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Total pages：xvii, 349p   Language：Japanese   Book type：Scholarly book

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Total pages：xiii, 433p   Language：Japanese   Book type：Scholarly book

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Total pages：x, 323p, 図版 [2] p   Language：Japanese  

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Total pages：vii, 262p   Language：Japanese  

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Responsible for pages：pp429-444   Language：English  

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Total pages：2冊   Language：Japanese  

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)   Publisher：Japan Science and Technology Agency  

DOI： 10.1241/johokanri.59.629

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Other Link： http://search.jamas.or.jp/link/ui/2017054109

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)  

DOI： 10.1241/johokanri.59.331

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Language：Japanese   Publisher：ダイヤモンド社  

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J-GLOBAL

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：Japan Science and Technology Agency  

DOI： 10.1241/johokanri.59.53

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Other Link： https://jlc.jst.go.jp/DN/JLC/20022241236?from=CiNii

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)   Publisher：日本統計協会  

CiNii Books

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

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J-GLOBAL

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Language：Japanese   Publisher：(公社)日本生化学会  

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Language：Japanese   Publisher：日本分子腫瘍マーカー研究会  

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J-GLOBAL

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)  

DOI： 10.7210/jrsj.33.68

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Language：Japanese   Publisher：Japanese Society for Molecular Tumor Marker Research  

DOI： 10.11241/jsmtmr.31.46

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Language：Japanese   Publisher：The Japanese Society of Snow and Ice / Japan Society for Snow Engineering  

DOI： 10.14851/jcsir.2015.0_63

J-GLOBAL

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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J-GLOBAL

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Language：English   Publisher：American Geophysical Union  

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Language：English   Publisher：American Geophysical Union  

CiNii Books

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：Transdisciplinary Federation of Science and Technology  

This document gives a brief explanation for what is "Statistical Mathematics," and describeshow its notion has been expanding to a wide variety of research fields. The recent interests ofstatistical mathematics are focused on generating more flexible mathematical models for describingcomplex phenomena in terms of data generation mechanism and/or its function. Such efforts demandcollaborative works with the machine learning community in a big data era which we are now facedwith.

DOI： 10.11487/trafst.8.1_14

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Other Link： https://jlc.jst.go.jp/DN/JALC/10035139040?from=CiNii

Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

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Language：Japanese  

J-GLOBAL

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Language：Japanese   Publisher：Japan Society for Simmulation Technology  

CiNii Books

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Language：Japanese   Publisher：Japan Society for Simmulation Technology  

CiNii Books

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Language：Japanese   Publisher：小宮山印刷工業  

CiNii Books

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Language：Japanese   Publisher：The Japanese Society of Snow and Ice / Japan Society for Snow Engineering  

DOI： 10.14851/jcsir.2013.0_153

J-GLOBAL

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J-GLOBAL

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Language：Japanese   Publisher：ダイヤモンド社  

CiNii Books

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

CiNii Books

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Language：English  

J-GLOBAL

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Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

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J-GLOBAL

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CiNii Books

J-GLOBAL

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)   Publisher：日本流体力学学会  

CiNii Books

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Other Link： https://projects.repo.nii.ac.jp/?action=repository_uri&item_id=377136

Language：English   Publishing type：Research paper, summary (international conference)   Publisher：AMER ASSOC CANCER RESEARCH  

DOI： 10.1158/1538-7445.AM2012-LB-99

Web of Science

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

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Language：Japanese   Publisher：日本評論社  

CiNii Books

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

CiNii Books

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

We propose a new system which enables us to generate a high quality animation of 3D characters via Motion Capture (MoCap) Data. The MoCap Data allows us to create an animation which resembles the motion in a video recording. In some cases of artistic animation, however, the motion capture actor/actress and the 3D character facial animation shows different expressions. The proposed procedure enables us to edit the facial expressions flexibly to generate an artistic animation, while making good use of the original facial motion capture data.

CiNii Books

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J-GLOBAL

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Language：Japanese   Publisher：情報処理学会  

一般に，前兆現象は突発現象にそのものに比べて非常に目立ちにくく，その開始時刻は曖昧である．従来よく用いられてきた変化点検出法を適用した場合，このような微小で緩慢な変化は見逃されやすい．Tokunaga et al.1) では，Ide and Inoue2) の提案した特異スペクトル分析を応用した変化点検出法 (SST) を，多次元データを用いたアルゴリズム (MSST) へと拡張することで，鋭敏に前兆現象の開始時刻を推定出来ることを示した．MSST は，緩慢な変化も検出できる鋭敏な手法であるが，実データへの適用では誤検出が問題になる．本稿では，突発現象の大まかな開始時刻を予め検出し，さらに検出された時刻の前後で前兆現象の開始時刻と終了時刻を個別に探索することで，誤検出を劇的に減少させることができることを示す．

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Language：Japanese   Publisher：東京 : 岩波書店  

CiNii Books

CiNii Research

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Other Link： http://id.ndl.go.jp/bib/10887823

Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

Data Assimilation (DA) is a technique for a synthesis of information from a dynamic numerical model and observation data. In statistical methodology, DA can be formulated in the generalized state space model. We briefly explain a recent advancement in the ensemble-based sequential DA (EnSDA), and discuss on how EnSDA is employed in combining CFD and EFD.

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J-GLOBAL

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Language：English   Publishing type：Research paper, summary (international conference)   Publisher：AMER ASSOC CANCER RESEARCH  

DOI： 10.1158/1538-7445.AM10-LB-132

Web of Science

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Language：Japanese   Publisher：宇宙航空研究開発機構  

第6回宇宙環境シンポジウム (2009年2月29日-30日. 北九州国際会議場)
6th Spacecraft Enivironment Symposium (February 29-30, 2009. Kitakyushu International Conference Center)
形態: カラー図版あり
Physical characteristics: Original contains color illustrations
資料番号: AA0064542030
レポート番号: JAXA-SP-09-006

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Other Link： http://id.nii.ac.jp/1001/00067005/

Language：Japanese  

States of living cells are controlled by a series of biochemical reactions, e.g. binding and phosphorylation of protein molecules, gene expressions intermediated by transcription factors. In systems biology, mathematical modeling and simulation, based on biochemical rate equations, have proved to be an efficient approach for unraveling complex cellular mechanisms of the biological pathways. The aim of this paper is to show some recent developments of statistical technologies for the data-driven construction of in silico biological pathways, which are referred to as Genomic Data Assimilation (GDA). The proposed method involves the following protocols: (1) Knowledge-based model construction of biological pathway based on rate equations, (2) Estimation of model parameters (e.g. kinetic rate constants), (3) Statistical evaluation and comparison of hypothetical models, (4) Remodeling where needed. After showing a brief review, we point out some issues to be addressed from statistical points of view. We also report our recent developments in this area.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

An evolutionary algorithm (EA) is applied to a regional ocean tide model to optimize a boundary condition and a physical parameter in the model. The bathymetry is one of the boundary conditions in ocean models and is frequently given by global sea depth data based on estimation derived from the satellite gravity observation. Accuracy of the satellite-derived depth data is often insufficient for accurate ocean tide simulations. We apply the EA to modify the mean depth in specific regions in the Alaska Panhandle, U.S. The mean depth in those regions is optimized to be close to the multibeam-derived depth data which is generally accurate further more than the satellite-derived data but its spatial coverage is sparse. Also, a region with large errors of the satellite-derived data is successfully found through the optimization. The EA has a prospect of correction of erroneous boundary conditions for accurate ocean modeling.

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Other Link： http://id.nii.ac.jp/1001/00061902/

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J-GLOBAL

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：English   Publisher：SPRINGER HEIDELBERG  

DOI： 10.1007/s10463-008-0208-y

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Language：Japanese   Publisher：日本行動計量学会  

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

Building in silico simulation models of genetic regulatory networks provides a rigorous tool for unraveling complex machinery of biological pathways. To proceed to in silico simulations, it is an essential first step to find the effective values of kinetic rate constants, which are difficult to measure directly from in vivo and in vitro experiments. The aim of this research is to present a new statistical technology, called Genomic Data Assimilation, for the data-driven construction of in silico simulation models.

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Language：Japanese   Publisher：日本知能情報ファジィ学会  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：Japanese   Publisher：日本評論社  

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Language：Japanese   Publisher：総合研究大学院大学  

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Language：Japanese   Publisher：日本行動計量学会  

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Language：Japanese   Publisher：一般社団法人 人工知能学会  

本研究ではバイオインフォマティクスのデータから飽和頻出木をマイニングすることを試みる。対象データの構造はDAGなので、我々のツリーマイニングアルゴリズムDryadeをＤＡＧに適用可能なように改良した。実験でこの効果を確かめる。

DOI： 10.11517/pjsai.JSAI06.0.7.0

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

In this study, we apply a state space model to time course gene expression data of yeast (Saccharomyces cerevisiae). We estimate parameters characterizing the system by using EM algorithm and determine the optimal dimension of hidden state variable by an information criterion. Then we compare the result with those in other papers using the same model and the same data but with different methods to estimate the parameters and to determine the internal dimension.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Daily sales of a catering establishment is affected by several factors: A day of the week, national holidays, weather, events taken place near the site, etc. Therefore, it is no doubt beneficial to make a model decomposing a time series of the sales into such factors. Then the model allows us to predict the future sales with a good accuracy in order to plan various levels of management strategies such as purchase, staff assignments, new store openings, etc. In this study, we have proposed a prediction method for daily sales of a catering establishment based on a state space model framework in which an optimal model chosen by an information criterion; we have applied it to daily sales data for two years of a restaurant which is adjacent to a large-scale convention center and office buildings.

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Language：English   Publisher：COPERNICUS GESELLSCHAFT MBH  

The present study statistically examines the dependence of the intensities of dayside (MLT = 8-12 h) large-scale field-aligned currents (FACs) on the ionospheric conductance using the summary data of DMSP-F7 constructed by the procedure of Higuchi and Ohtani (2000). We have found that, in the dayside region, R1 and R0 have a higher correlation between ionospheric conductivity and FAC intensity than R2, suggesting that R0 and R1 are driven by a more voltage-like source than R2. This result is consistent with the idea that R1 and R0 are driven by the interaction between the solar wind and the open magnetospheric magnetic field. We have also found that dayside FAC intensities are latitudinally well balanced when they have a three sheet structure (R0, R1 and R2); on the other hand, for a two sheet structure (R1 and R2), the intensity of R1 is larger than that of R2, so that the net current has the polarity of R1.

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Language：Japanese   Publisher：統計数理研究所  

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Language：Japanese   Publisher：Japan Aerospace Exploration Agency  

The purpose of this study is to establish the error evaluation of Atmospheric Motion Vectors (AMVs) which reflects influence of the image spatial resolution and the change of cloud shape that are the essential primary factor of AMVs' error. In past meteorological researches AMVs' error is defined as the standard deviation of neighboring AMVs. In this definition, however, the AMVs' error is larger than the target physical phenomenon. In this study, by statistical presumption technique, which presumes the detail information from the limited information, we evaluate the AMVs' error which reflects influence of the image spatial resolution and the change of cloud shape. In order to verify this method, we make simple and fractal cloud patterns, and calculate the AMVs and the error estimated by statistical presumption technique and the error by past error evaluation. The result is that the error by statistical presumption technique is as small as a quarter of the error by past error evaluation. In this study, by statistical presumption technique the error evaluation of AMVs that reflects influence of the image spatial resolution and the change of cloud shape is established.

CiNii Books

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Other Link： http://repository.tksc.jaxa.jp/pl/dr/IS0200054000

Language：Japanese   Publisher：日本統計学会  

CiNii Books

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Other Link： http://id.nii.ac.jp/1141/00024500/

Language：Japanese   Publisher：CA研究グループ  

CiNii Books

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Language：Japanese   Publisher：日本統計学会  

CiNii Books

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Other Link： http://id.nii.ac.jp/1141/00024580/

Language：Japanese   Publisher：日本統計学会  

CiNii Books

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Other Link： http://id.nii.ac.jp/1141/00024583/

Language：Japanese   Publisher：日本統計学会  

CiNii Books

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Other Link： http://id.nii.ac.jp/1141/00024405/

Language：Japanese   Publisher：日本統計学会  

CiNii Books

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Other Link： http://id.nii.ac.jp/1141/00024411/

Language：Japanese   Publisher：日本統計学会  

CiNii Books

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Other Link： http://id.nii.ac.jp/1141/00024409/