Underflow problems persist in the scaling computation if the dimension
of the input representation is too high (approx. 380) to the sparse
encoding for the current problem application.

(1) Test different input representations using 
	Non-negative matrix factorisation or Principle Component Analysis
	for input encoding dimension reduction
	--> msa profile (created)
	--> msa + hyydrophobic moment profiles (created)
	--> single seq + msa profile (created)

(2) PSICOV --> contact map estimation
	--> look for significant local correlation as most people only search
		for non-local correlation for beta-strands
	--> data stored on server at /gluster/data/tmb_maps.tgz

(3) Different grammars, mixtures, and window sizes
	--> tied at mixture level not state level
	--> different mixture numbers (1,3)
	--> window sizes (11,19,21,31)
Underflow problems persist if the dimension of the input encoding is too
large as this leads to sparse representation in the scaling computation
for the forward matrix algorithm.

(1) Test different input representations all using Non-negative matrix
	factorisation or Principle Component Analysis for input encoding
	dimension reduction
	--> msa profile (created)
	--> msa + hyydrophobic moment profiles (created)
	--> single seq + msa profile (created)

(2) Different grammars, mixtures, and window sizes
	--> tied at mixture level not state level
	--> different mixture numbers (1,3)
	--> window sizes (7,11,15)

(3) PSICOV 
	--> contact map estimation
	--> look for significant local correlation as most people only search
		for non-local correlation for beta-strands
	--> data stored on server at /gluster/data/tmb_maps.tgz