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pro vyhledávání: '"Kou, S"'
Autor:
Yu, Kun-Hsing, Lee, Tsung-Lu Michael, Yen, Ming-Hsuan, Kou, S C, Rosen, Bruce, Chiang, Jung-Hsien, Kohane, Isaac S
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 8, p e16709 (2020)
BackgroundChest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rarely
Externí odkaz:
https://doaj.org/article/625b85cde8474d2cbfd9705b3c9fa8a4
Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-cour
Externí odkaz:
http://arxiv.org/abs/2212.10653
Catalytic prior distributions provide general, easy-to-use, and interpretable specifications of prior distributions for Bayesian analysis. They are particularly beneficial when the observed data are inadequate to stably estimate a complex target mode
Externí odkaz:
http://arxiv.org/abs/2208.14123
This article presents the MAGI software package for the inference of dynamic systems. The focus of MAGI is on dynamics modeled by nonlinear ordinary differential equations with unknown parameters. While such models are widely used in science and engi
Externí odkaz:
http://arxiv.org/abs/2203.06066
Exceptional points are interesting physical phenomena in non-Hermitian physics at which the eigenvalues are degenerate and the eigenvectors coalesce. In this paper, we find that the universal feature of arbitrary non-Hermitian two level systems with
Externí odkaz:
http://arxiv.org/abs/2109.05980
Big data generated from the Internet offer great potential for predictive analysis. Here we focus on using online users' Internet search data to forecast unemployment initial claims weeks into the future, which provides timely insights into the direc
Externí odkaz:
http://arxiv.org/abs/2010.09958
Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data is a vital task in many fields. We propose a fast and accurate method, MAGI (MAnifold-constrained Gaussian pr
Externí odkaz:
http://arxiv.org/abs/2009.07444
For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the
Externí odkaz:
http://arxiv.org/abs/2006.02927
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Publikováno v:
In Progress in Materials Science September 2023 138