Since the HEN synthesis problem contains nonlinear equations and constraints, the mathematical programming difficulties for large-sized HEN are long computational time and divergence problem.
We tested three of the sectors properties using computational means: statistical and divergence independences and structural continuity.
Here, we have taken the eQTL mapping paradigm to the single nucleotide level by predicting specific nucleotide differences that cause gene expression divergence using computational predictions of TFBSs.
In fact, comparison of results obtained with several of these computational tools show significant divergences.
Our data show that this divergence in the computational processes necessary for foveal and peripheral recognition is evident even in early visual cortex.
The main advantage of the Rényi divergence is the small computational burden.
The results of intra-class correlation coefficients from the test-retest experiment were consistent with previous research employing KL divergence, but with low computational complexity.
In particular, the closed-form ATWLS method had similar MSE average performance to that of the proposed iteration-based method, preserving the advantages of low computational complexity and avoidance of the divergence problem of the solution.
Furthermore, in this case there exists an analytical solution for modified Jensen-Rényi divergence, which reduces the computational burden of the algorithm and improves the accuracy of the solution.
However, in many cases, these individual fields have developed their own methods for both acquiring and interpreting images based on new hardware and computational approaches, leading to a rapid divergence in what is considered state-of-the-art in imaging.
