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To assure that descriptors were all inclusive, we examined Medical Subject Headings for "military personnel" and "DSs" in PubMed.
The evaluation on the UCF-101 action recognition dataset demonstrates that the descriptor is applicable in challenging unconstrained environments.
However, the limitation of HKS is that the descriptor is not scale invariant.
This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin.
The value κ = 0 indicates that there is no more correlation between descriptors than expected by chance alone, and κ = 1 indicates that the descriptors are fully dependent.
This means that the descriptors are very important in constructing questionnaires and comparing the findings of different surveys.
It is also noted that the descriptors are adult-oriented and unsuitable for young learners.
The experimental results show that our LAI descriptor is highly descriptive and robust with respect to varying resolution and noise, and the LAI descriptor based calibration is effective and repeatable.
It turned out that when a descriptor is normalized to eliminate the influence of protein size, it completely looses its ability to predict folding rates.
The model parameters could be adjusted, so that the individual descriptor is less likely to get the maximal score.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com