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Functional magnetic resonance imaging (fMRI) has provided a window into the brain with wide adoption in research and even clinical settings. Data-driven methods such as those based on latent variable models and matrix/tensor factorizations are being increasingly used for fMRI data analysis. There is increasing availability of large-scale multi-subject repositories involving 1,000+ individuals. Studies...
Accurate imputation and prediction of load data are important prerequisites for many tasks of power systems, especially as renewables and plug-in electric vehicles penetrate the grid. A low-rank and sparse matrix factorization model is considered for load inference tasks to capture spatial as well as temporal structures in multi-site load data. The low-rank structure captures periodic patterns, and...
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