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The difference is that in standard models we let plural variables range over the full power set of $D$, whereas plural variables range over a set $S$ of non-empty subsets of $D$, provided only that $S$ contains all sets definable by some formula.
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In our model we let the units contain a certain (possibly unbounded) charge, which can be exchanged with other neurons under stochastic dynamics.
In our model, we let concealment costs capture costs associated with concealing taxable income due to third-party reporting or even morality, guilt, or shame.
To apply the physical model, we let the SINR threshold be the case that the packet error rate is, that is,, where is the bit error rate of DBPSK [26].
In the model we let β = cp and β' = cp'.
For the latter model, we let the regression coefficient between reservoir and virus-carrying index be 1.00.
In the second model, we let Y be a binary outcome variable and X be a normally distributed covariate.
If in the DD model, we let p be very small and r very large, dense graphs will result, with many bicliques.
In our model, we let T ij denote the natural logarithm of DUP for the jth individual in ward i (i = 1,..., K = 32 ; j = 1,..., n i, ∑ i = 1 K n i = 314 ).
As in the MGdNdS model, we let λ and η vary among lineages, jointly with L other quantitative traits, whereas ρ and π are assumed to be constant across the entire phylogeny.
To derive an estimate for psurf under this model, we let N denote the number of individuals present in the rightmost cell in generation P. Then (5) The first term on the right-hand side of equation (5), (1/ N) prun, is the probability that the mutant is the first candidate chosen and does succeed in propagating.
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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