Sentence examples for both methods in terms from inspiring English sources

Limitations of both methods in terms of k0 and diffusion constants are discussed.

We present a series of real data experiments to assess the behavior and effectiveness of both methods in terms of accuracy, robustness against errors and speed of convergence.

From Table 4, we can observe a certain parity for both methods in terms of ITF metric, with a slight advantage of the YouTube method [24], while the maintained pixels are in general comparable and, when lower, do not differ much.

Similarly, we evaluate both methods in terms of the average RMSD of the top 1, 2, 5 and 10% decoys, respectively.

We evaluated both methods in terms of average sensitivity (i.e. ratio of correctly learned edges to total number of edges in the original network) and specificity (i.e. ratio of correctly unconnected genes to total number of unconnected genes in the original network over 10 n generated networks for n S-genes.

We observed similar characteristics by both the culture methods in terms of the growth rate, gene expression profile, cell surface profile by fluorescence-activated cell sorting, surface glycan profile, and genomic DNA stability.

It significantly improves over both input methods in terms of the number of very good predictions (RS ≥ 0.6), achieving 77 such results.

A match between both computational methods in terms of predicted ground state multiplicity and geometry was found for 26 species, which translated into almost 80% agreement.

We investigate both processing methods in terms of accuracy and feasibility.

Looking at these results, we can see that MDLS is competitive with both other methods in terms of solution quality.

The experimental results show that our method outperforms the current methods both in terms of accuracy and response time.