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The approach includes a mild generalization to the case of harmonic maps defined using non-Riemannian connections in the target manifold and smooth strictly elliptic second-order differential operators in the domain.
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The following result is a mild generalization of a result of Kakavandi and Amini [[12], Lemma 2.1].
A mild generalization of the multivariable analog of the Mittag-Leffler function, which will play an important role in this paper, has been given by Saxena et al. [[7], p.547, Eq. (7.1)]: E ( ρ r ), λ ( γ r ), ( l r ) ( z 1, …, z r ) = ∑ k 1, …, k r = 0 ∞ ( γ 1 ) k 1 l 1 ⋯ ( γ r ) k r l r Γ ( k 1 ρ 1 + ⋯ + k r ρ r + λ ) z 1 k 1 ⋯ z r k r k 1 !
From only a well-designed random sample would a generalization to a population or subpopulation be valid.
There is a deliberate generalization to both of those lines.
Again, this is a generalization to demonstrate the idea.
This formula is a generalization to more than two populations.
A generalization to non-binary trees is discussed in Venkatachalam et al. (2010).
A generalization to Markov models of order D≥0 can be found in Zien et al. (2007).
We begin this section by proving a linear generating function for the polynomials Z n 1, …, n j ( α ; N 1, …, N j ) ( x 1, …, x j ; ρ 1, …, ρ j ) by means of the mild generalization of the multivariate analog of Mittag-Leffler functions.
Apple has just announced Snow Leopard 10.6.1, a mild update to everyone's favorite mild update.
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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