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Given the rank-order operator denoted by f r,M [x i ], the output of ROBF y i can be then formulated as (1): y i = f r, M [ x i ] = Ran k n { x i - j | j ∈ M } (1).
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Design guidelines are then formulated which can be used in future works for detail plant design.
Thirty statements were then formulated and distributed to the group.
The approximated BER expressions are then formulated.
The predictive adaptive response (PAR) hypothesis was then formulated [7].
A critical comprehension was then formulated and reflected upon.
The selected information was then formulated into items.
The search for an adaptive circuit can be formulated then as a multiobjective optimization problem where the constraints are imposed by the circuit's dynamics.
Then they can be alternatively formulated as finding the zero point of the operator T x) = F x) + N C (x) where N C ( x ) = { y ∈ C | y, z - x ≤ 0, ∀ z ∈ C } if x ∈ C, ∅ otherwise.
For the problem GNEP, when the functions (f_{i}(cdot, x_{-i})) are convex and differentiable, then the problem can be equivalently formulated as the quasi-variational inequalities (1.2) by setting F x)=bigl nabla_{x_{i}}f_{i}(x bigr)_{i=1}^{N} and (K x)=prod_{i=1}^{N}K_{i}(x_{-i})).
Given an input matrix B = [b1,..., b L ] in R M×L of L signals, the problem then can be formulated as an optimization problem jointly over a dictionary A = [a1,..., a J ] in R M×J and the sparse representation matrix X = [x1,..., x J ] in R J×L, namely min A, X ∑ l = 1 L x l 0 s.
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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