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We prove a recursive lower bound on the coarse Ricci curvature of multiple steps in the Markov chain, making our method broadly applicable.
More importantly, expensive field surveys can be avoided for sample labeling, making our method suitable for practical large-scale SAR image understanding.
Furthermore, Minkowski metric is utilized to take into account the influence of the L-p distance (p ≥ 0), making our method adaptive to different application tasks.
In the latter case, manual intervention could be needed in order to exclude background tissues, thus making our method more observer dependent.
We next examined whether these same genes could be identified using more standard analytic approaches (making our method redundant) or whether our approach provides truly independent information.
Our method has several limitations: Some instances of the problem still require exponential running time, making our method impractical for finding a minimal perturbation for large models.
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(For example, compressed sensing is often applied to image reconstruction from scattered light intensities. These intensities are continuously valued and not discrete. It seems possible that the scattering medium might introduce nonlinearities, thereby making our methods of interest).
Sixth, except for motor vehicle accidents [ 16], we used the Global Burden of Disease ICD-10 cause of death categories, making our methods similar to national and international studies [ 15, 21].
Importantly, our approach does not require any priori knowledge, which makes our method more practical in medical decision making.
This makes our method more efficient and faster.
This would make our method likely overestimate the actual number of GSR events.
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