Sentence examples for sparse results from inspiring English sources

Perhaps, this could be largely due to the sparse results of ionospheric studies from the continent.

Normalisation tends to give more sparse results than difference, particularly for MCs I, due to division of low values.

To test the model performance on a substation close to the location of Ebre Observatory, where the geomagnetic data came from, a homogenous Earth and a couple of 1-D Earth conductivity models have been employed based on the sparse results of magnetotelluric surveys published in the geophysical literature.

According to the three steps in CS theory, in [3], DWT was utilized to make SAR imagery sparse and the random Gauss matrix after approximate QR decomposition was employed to complete the low-dimension measurement for sparse results based on the original SAR imagery divided into several sub-imageries.

The results reported in this paper are consistent with a number of sparse results and provide a rationale for observations such as that hyperglycemia inhibits hypoxia-induced stabilization of HIF-1α.

This is particularly interesting as the recent genome-wide association studies have yielded sparse results for RF-negative RA [ 6].

As in Fig. 9, OpenMVG gives the precise but very sparse result while the result of VisualSFM is much denser than the OpenMVG.

Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap.

The density of endogenously presented antigen-derived peptides on tumor cells is generally sparse, resulting in the inability of antigen-specific CTLs to work effectively.

However, the density of endogenously presented antigen-derived peptides on tumor cells is generally sparse, resulting in the inability of antigen-specific CTLs to work effectively.

High-throughput sequencing costs are often reported as attractively low, but additional significant costs are associated with optimizing the restriction enzyme-based DNA preparations for a new species of interest, applying an appropriate informatics pipeline to manage the huge amount of sequence data, and finally to call the SNPs from an often "sparse" resulting data matrix [ 57, 58].