Mobile Healthcare System for Preventive of Metabolic Syndrome

Recently, metabolic syndrome affects a great number of people in Japan. Glycemic control can delay the onset and slow the progression of vascular complications. Lifestyle modification including weight reduction can contribute significantly to glycemic control. This paper describes the mobile application of the healthcare support system for metabolic patients

Two-Step Contribution to Intermediate Energy (p,p') and (p,n) Reactions

We calculate the two-step contribution to (p,p') and (p,n) reactions at intermediate energy. We describe the motion of the incident nucleon with plane wave and compare the contribution from the two-step processes with that from the one-step processes. To describe the two-step processes, we extende the response functions into the nondiagonal ones with respect to the momentum transfer q. We performed a numerical calculation for the cross sections of the $^{12}$C, $^{40}$Ca(p,p') scatterings and the spin longitudinal and the spin transverse cross sections of the $^{12}$C,$^{40}$Ca(p,n) reactions at 346 MeV and 494 MeV. We found that the two-step contribution is appreciable in comparison with the one-step processes in higher energy transfer region for the spin longitudinal and the spin transverse (p,n) reactions. We also found that the two-step processes give larger contribution to the spin transverse (p,n) reaction than to the spin longitudinal reaction. This finding is very encouraging to interpret the discrepancy between the DWIA calculation and the experimental results of the spin longitudinal and the spin transverse cross sections.Comment: LaTeX, 18 pages, 11 Postscript file

Simultaneous Selection of Optimal Bandwidths for the Sharp Regression Discontinuity Estimator

A new bandwidth selection rule that uses different bandwidths for the local linear regression estimators on the left and the right of the cut-off point is proposed for the sharp regression discontinuity estimator of the mean program impact at the cut-off point. The asymptotic mean squared error of the estimator using the proposed bandwidth selection rule is shown to be smaller than other bandwidth selection rules proposed in the literature. An extensive simulation study shows that the proposed method's performances for the sample sizes 500, 2000, and 5000 closely match the theoretical predictions

Characterization of the asymptotic distribution of semiparametric M-estimators

This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification. Our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature. The first-stage nonparametric estimation may depend on finite dimensional parameters. We characterize: (1) conditions under which the first-stage estimation of nonparametric components do not affect the asymptotic distribution, (2) conditions under which the asymptotic distribution is affected by the derivatives of the first-stage nonparametric estimator with respect to the finite-dimensional parameters, and (3) conditions under which one can allow non-smooth objective functions. Our framework is illustrated by applying it to three examples: (1) profiled estimation of a single index quantile regression model, (2) semiparametric least squares estimation under model misspecification, and (3) a smoothed matching estimator. © 2010 Elsevier B.V. All rights reserved

Data Mining by Soft Computing Methods for The Coronary Heart Disease Database

For improvement of data mining technology, the advantages and disadvantages on respective data mining methods should be discussed by comparison under the same condition. For this purpose, the Coronary Heart Disease database (CHD DB) was developed in 2004, and the data mining competition was held in the International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES). In the competition, two methods based on soft computing were presented. In this paper, we report the overview of the CHD DB and the soft computing methods, and discuss the features of respective methods by comparison of the experimental results

Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data

Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns. Such architecture is well known to represent higher learning capability compared with some conventional models if the best set of parameters in the optimal network structure is found. We have been developing the adaptive learning method that can discover the optimal network structure in Deep Belief Network (DBN). The learning method can construct the network structure with the optimal number of hidden neurons in each Restricted Boltzmann Machine and with the optimal number of layers in the DBN during learning phase. The network structure of the learning method can be self-organized according to given input patterns of big data set. In this paper, we embed the adaptive learning method into the recurrent temporal RBM and the self-generated layer into DBN. In order to verify the effectiveness of our proposed method, the experimental results are higher classification capability than the conventional methods in this paper.Comment: 8 pages, 9 figures. arXiv admin note: text overlap with arXiv:1807.03487, arXiv:1807.0348