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In our implementation, in addition to 20 amino acids we use X as a 21st character to represent non-standard amino acids.
In our implementation, in order to avoid any distortion, we set k to the maximum value as determined by the data (and is found and returned by the method).
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We use the projective model in our implementation and in the layout derivation in the Appendix.
In addition, in our implementation of DPD, some new methods are introduced that result in the computation time scaling linearly with the number of DPD particles.
You should see that A* finds the optimal solution slightly faster than uniform cost search (about 549 vs. 620 search nodes expanded in our implementation, but ties in priority may make your numbers differ slightly).
In our implementation, the user performs in front of three video cameras; the resulting silhouettes are used to estimate his orientation and body configuration based on a set of discriminative local features.
In our implementation we rounded in the nearest integer number for more accurate bit allocation.
Considering the image size is larger in [12], the occupied resource in our implementation is much more than in [12].
The feature set to be used in our implementation will be decided in future experiments.
We have evaluated the correlation performance in terms of accuracy in our implementation.
However, in our implementation, we use the second way in this paper.
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