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Consequently, and after applying normalization of Lemma 1, the effective correlation matrix, (widehat {mathbf {Sigma }}_{K}), has coincidence in its eigenvalues.
If S ( X ) is a closed subset of X, then on the lines of Theorem 3.1, we can show that the pair ( A, S ) has coincidence point, say u, i.e. A u = S u = z.
On the other hand, by [[7], Lemma 3.10], every essential coincidence class is a singleton and has coincidence index sign ( det Λ Λ ^ ( Id ) ). Remark that sign sign ( det Λ Λ ^ ( Id ) ) = 1.
Similar(57)
Therefore, these results all have coincidence with common sense.
The message might be that merely having coincidence in evaluative outlook is enough to satisfy (4) and (5).
Since C has the coincidence property, (s_{alpha} phi r_{alpha}) has a coincidence and therefore (see [3] (Lemma 6.3)) (r_{alpha} s_{alpha} phi) has a coincidence.
Now Theorem 1.1 guarantees that (j s phi h) has a coincidence and therefore (see [3] (Lemma 6.3)) (h j s phi) has a coincidence.
She has by coincidence or design made the bombshell announcement at a very opportune moment.
Then ϕ has a coincidence.
Then the pair has a coincidence point.
Then, (A, S) has a coincidence point.
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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