The main contribution of this paper concerns criteria for obtaining optimal inputs in this context.
In the previous work on rank-2 NMF [32] that takes a term-document matrix as input in the context of text clustering, sparse dense matrix multiplication (SpMM) was the main computational bottleneck for computing the solution.
We explore the limits of this methodology, as related to network complexity (size and number of node inputs) in the context of synchronously updated random Boolean networks.
We chose this approach for generating the wMRP source trees because the parsimony ratchet we used for the MRP input trees was too slow in this context (given the number of bootstrap replicates we analyzed).
The term re-annotation reflects the use of R EPEATM ASKER annotation as input to RE ANNOTATE, and in this context it means neither the prediction of previously unannotated sequence features (similarity hits) nor the detection of false positives in the original annotation.
Similarly, risk assessments are model-based estimates and rely on water quality data as input, and so must be interpreted in this context.
In this context, the input is a set R of rooted triples on X, and the goal is to compute a rooted phylogenetic network N that contains all the rooted triples in R and is optimal in some sense.
There was no auditory input in the black context, whereas, in the red orange context, subjects heard 2 sounds of different pitch, presented over headphones, which changed synchronously with the color of the screen.
In this context, the only input is the target quality, for example, defined in terms of peak signal-to-noise ratio (PSNR) for each frame.
In this context, the characterization of uncertainty in the input variables is crucial.
