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Full-Text Articles in Physical Sciences and Mathematics
Filtered Subspace Iteration For Selfadjoint Operators, Jay Gopalakrishnan, Luka Grubišić, Jeffrey S. Ovall
Filtered Subspace Iteration For Selfadjoint Operators, Jay Gopalakrishnan, Luka Grubišić, Jeffrey S. Ovall
Portland Institute for Computational Science Publications
We consider the problem of computing a cluster of eigenvalues (and its associated eigenspace) of a (possibly unbounded) selfadjoint operator in a Hilbert space. A rational function of the operator is constructed such that the eigenspace of interest is its dominant eigenspace, and a subspace iteration procedure is used to approximate this eigenspace. The computed space is then used to obtain approximations of the eigenvalues of interest. An eigenvalue and eigenspace convergence analysis that considers both iteration error and dis- cretization error is provided. A realization of the proposed approach for a model second-order elliptic operator is based on a …
Identifying Clouds With Convolutional Neural Networks, Jeff Mullins, Sean Richardson, Peter Drake
Identifying Clouds With Convolutional Neural Networks, Jeff Mullins, Sean Richardson, Peter Drake
Portland Institute for Computational Science Publications
The greatest source of uncertainty in model estimates of projected climate change involve clouds and aerosols. Photographic images of clouds in the sky are simple to acquire and archive, but climate scientists need an automated process for identifying clouds in these images. We bring machine learning to bear on this problem. Specifically, we use convolutional neural networks, which to our knowledge have not previously been applied to this task. We trained a network to identify clear sky, thin cloud, thick cloud, and non-sky pixels in photos taken by the Total Sky Imager. The trained network is capable of classifying 91.9% …