Sentence examples for sparsity from from inspiring English sources

The small-world network parameters and areas under the integral curves were computed in the range of sparsity from 0.01 to 0.5.

For 64×256 partial Fourier matrices, the maximum recoverable sparsity from LP relaxation and the pick-2 algorithm are 3 and 4 respectively.

In particular, for a 600% increase in database image sparsity (from 10 m to 70 m), we show that the proposed method is able to maintain localization performance with only a 40% increase in localization latency (from 250 m to 350 m).

The main reason for this is because it combines their two main features: it combines the structured sparsity (from Group Lasso) and the low rank (from Trace Norm) into one unified framework, which implies that it is able to induce structured sparse-low-rank solutions which are biologically plausible: few active sources, with linear relations between them.

I think that the authors should either explore this directly using the model (by dissociating input sparsity from input strength to back up their qualitative assertions) or remove this consideration from the Discussion.

However, under the dichotomized categorization, an investigator might conclude that there was no substantial confounding by comorbidity (0.9 <RR c = 1.09 < 1.1), and may elect to exclude comorbidity from stratified tables (to avoid <span class="lh">sparsity) or from multivariate regression models (to improve parsimony).

OLAP cube normalisation controls the structural sparsity resulting from inter-dimensional functional dependencies.

Figure 4 Means of residual norm ||r (K') || 2 versus given sparsity K' obtained from 10,000 independent Monte-Carlo trials.

"Group sparsity" solutions from (2) have better representation and have been widely studied both for convex and nonconvex cases [1, 3, 6 9].

Moreover, from a fundamental physics perspective smoothness is enforced because interfaces separating distinct phases tend to be smooth to lower energy, and sparsity comes from the fact that, e.g., multiple structural phases cannot co-exist in the same location.

Association datasets are often collected to form sparse, bipartite networks, where sparsity arises from the reality that there are (typically) many more non-associations than associations in these data.