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As predicted, the Munduruku compared and added large approximate numbers far beyond their naming range.
In order to decide whether two ESTs have a sufficiently large approximate overlap, we have to decide: how long the overlap should be (the window size); how we measure similarity or difference (what we mean by approximate); and what the error threshold should be (just how similar we want them to be).
We employ a single linkage algorithm: initially each sequence is put in a singleton cluster, and then pairs of sequences are compared; if they have a sufficiently large overlap (or a sufficiently large approximate overlap), the clusters to which they belong are merged.
Hover over the lower right corner until the cursor becomes a double-headed arrow and pull it open to become a large approximate square.
Similar(56)
Moreover, the effect of the smaller approximated coverage than the actual coverage is similar to the effect of the larger approximated coverage than actual coverage.
Here we address the question whether large intermediate approximate solutions reduce the final accuracy of these two-level (inner outer) iteration algorithms.
Assuming, under the hypothesis of sufficiently large to approximate an ordinary convolution, we have, from which the RDM decoder is able to recover the correct information bits.
ReE of X denotes related error of minimizer X. MAE of X denotes the mean absolute error of X. From the table and the figure, we can see that our formula can almost get the same results as the MM method when β is sufficiently large and approximate results similar as the MM method for other β.
In this system we apply the Chebyshev spectral collocation independently in the η direction by choosing (N_{eta}+1) Chebyshev-Gauss-Lobatto points (0=eta_{0}large to approximate the conditions at ∞.
Only one of six mice displayed a large neuroma (approximate thickness of 50 μm).
Assuming that allele frequencies in this large sample approximate the true population frequencies, we can estimate the error of the initial frequency estimates.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com