9,398 research outputs found
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
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. Ā© 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
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 C,
Ca(p,p') scatterings and the spin longitudinal and the spin transverse
cross sections of the C,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
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