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Then we generalized this to obtain many positive solutions.
Then we generalized this instance, made it sound as though this is common practice and published the information to all of your readers.
Then we generalized this to include a combination of search volume at a given time-step, and the derivatives of search volume such as its rate-of-change over the past few time-steps.
Jointly with Yongshan Chen [30], we generalized this result to lifting a GS basis S ⊂ k [ Y ] ⊗ k ⟨ X ⟩, see Mikhalev and Zolotykh [170], to a GS basis of I d ( S, [ y i, y j ] for all ( i, j ) ) of k ⟨ Y ⟩ ⊗ k ⟨ X ⟩.
We generalized this situation to N line of treatments.
Since the proposed IBMD assesses disagreement between two observers only, we generalized this measure to include more than two observers.
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In this paper, we generalize this method by using generalized orthogonal designs, and we give another new method that creates and solves Diophantine equations over GF(p) in order to find suitable generator matrices for self-dual codes.
We generalize this concept to operator means or, more generally, operator connections as follows.
We generalize this concept as follows.
Then by induction we generalize this result for any.
We generalize this user association as an utility maximization problem.
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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