Sentence examples for zero valued from inspiring English sources

Furthermore, our implementation of the Karoui correction shows that several population eigenvalues are estimated as zero valued.

Zero valued relative error (i.e., mean of error distribution) and near unity value of correlation coefficient between measured and predicted oil FVF data confirms accomplishment of fuzzy modeling.

However, here is discussed only the case of boundary value conditions when the boundary functions are zero valued on the two bases of cylinder Q.

The first two EOF components together account for ∼93.78% of total variance of the original TEC data set, and it is found that the first EOF component represents a spatial variability of semi-annual variation and the second EOF component exhibits pronounced east west longitudinal difference with respect to zero valued geomagnetic declination line.

If boundary value conditions are zero valued on the edges of cylinder Q, then, under supplementary requirements for boundary functions on the bases of this cylinder (see conditions (iii) of Theorem 4), the solution of Problem D2 can be also continuous on the line (r=0) if (alpha<1), i.e., if equation (2) is weakly degenerate.

Also, this method can be used to identify zero valued fluxes (reactions for which both new lower and upper limits of the fluxes are zero).

If the boundary value conditions are non-zero valued on the edges of cylinder Q, then the solution of this problem is continuous on the line of degeneracy (r=0) only if (alpha>1), i.e., only in the case of the strong degeneracy of equation (2).   2.

In the following, we refer to a K-sparse signal such that only K out of its N transform coefficients are non-zero valued; besides, we denote by Ω the support of the K non-zero coefficients of α, satisfying Ω ⊆ L and having cardinality |Ω|=K

The mapping rule is that the elements for DC and zero-valued AC coefficients are set zero and those for nonzero AC coefficients are represented by the lengths of related VLI codes separately.

Spline analysis requires large datasets to generate closed contours, and constrained extrapolation to zero is not possible in the absence of zero-valued data points.

GFlasso extends the standard lasso [ 45] that uses an L 1 penalization to shrink the regression coefficients (or parameters for association strengths) towards zero and obtain a sparse estimate with many zero-valued coefficients for SNPs with no associations.