Major improvements to BiCal using new SVD-Parallel Jacobi:
a)  Was able to process much higher degrees of the polynomial - 25 from previous max 15.  New
image looks great and better.  Not able to do it in previous versions due to numerical instability of the
20000x20000 matrix solution using old SVD-NumericalRecipes (QR-decomposition).  Jacobi algorithm is simpler but more stable.
b)  At Degree=10 SVD takes 1% of calculation time, at 25 degree it takes 99% of calculation  time.
But now SVD is 100% parallel and scalable with 10x faster than  previous version.
c) On BPD datasets,  results for      WM/GM/CSM improved from  0.14/0.14/0.064  down to 0.10/0.11/0.49 for degree 25.
d) But calculation times become prohibitive at the higher degress ~40 min.