After a morphological analysis of the given real sample, we proposed a germ-grain model with germs scattered around some nucleation planes, and spherical random grains, depending on a set of 11 unknown parameters.
To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based.
To maximize the chances of detecting the drug in serum samples, we propose the use of this specific ELISA test as a high-throughput screening method, combined with a classic isoelectric focusing test as a confirmatory assay.
In cluster randomized designs structured with balanced ranked set sampling, we propose a pooled pivotal test to improve the power of detecting any treatment effect by stabilizing the estimation of variance components when groups are unevenly sized.
To compromise the two regimes (RW's high-degree dominance vs. MHRW's stratified sampling), we propose an extension to the MHRW algorithm.
According to multiple previous reports, a large amount of different lipid molecules, including cardiolipin, phosphotidylcholine, and phosphotidylethanolamine, should be present in the isolated samples, we propose these lipid molecules may occupy these gaps to further stabilize the respirasome (Gu et al., 2016).
In this respect, to characterize the analyze samples we propose a chemometric representation base on Principal Component Analysis which is compared to the ones based only on the relative amount of the two types of carbonate or on carbonate and collagen.
Furthermore, instead of using the random patches as the negative samples, we propose a reasonable selection criterion, in which both the saliency confidence and similarity are considered with the benefits that confusors in the surrounding background are incorporated into the classifiers update process before the drift occurs.
While it is possible that these species were simply missed, after four years of extensive sampling we propose that this is unlikely.
Especially in the case of sparse sampling we propose to use weighting functions for the uncertainties.
