The issues that may surface, while modeling a current system using CAS, can be traced back to the complete knowledge of the system, its behaviors or states and the interaction of the subsystems; a problem of scale dealing with a very large state-space representation.
This platform provides components that make it simple to efficiently query very large datasets, draw common representations of genomic data, integrate with analysis tools, and share or publish fully interactive visualizations.
A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems.
The list of different types of phenomena that need to be processed during the generalization is very large, and the list of representations that one may want to derive for each of them is larger still.
However, most of the related studies mainly focus on learning features and representations from very large scale dataset relying on deep network architecture, which is doomed to fail with limited training samples due to its high complexity.
Rather than explicitly storing the very large reference vectors a hash table representation is chosen.
We favored the use of BAC engineered reporters because of their very large size allowing for a truer representation of endogenous gene expression.
In effect, we were trying to suggest that an animal could "stitch together" a representation of a very large environment by remapping its grids between segments.
The relatively low proportion of loci that were represented across both population samples was due to insufficient representation of their very large genome despite our protocol involving quanitification and compensation for this.
