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We can use the measures defined above to investigate a number of interesting problems: 1) Comparison of Providers Using criteria such as coverage, we can compare providers directly.
Given a collection of references and similarity measures defined above, we still design an algorithmic process to traverse and compare all identity pairs and eventually reveal all underlying individuals.
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We constructed the distance measure defined above to be a one-dimensional variant of the AUC.
The quantitative measure of goodness-of-fit defined above is coherent with the curves' visual aspects: for the Size Model the distance between the model and the data ranges from 17%to20%0% for f i data (16% to 26% for s i data) whereas for the Age Model it ranges from 51% to 93% for f i data (45% to 125% for s i ).
Step 1: Calculate the image quality measures defined (Equations 3 and 4) above for each of the input images.
Potential managers offer a contract based on a potential workers' performance measure as defined above.
The quality filtering procedure makes use of the maximum semantic similarity measure, as defined above.
The Proportional Hazards model estimated using the methods of Fine and Grey or by using standard software as defined above measures the difference in the survival curves over time.
For the measure (nu ) as defined above, one can easily deduce that (|nu |(B =sum _{mathbf{k}: mathbf{y}_mathbf{k}in B}|w_{mathbf{k}}|) for any subset (Bsubset mathbb T ^q).
and μ P ( X 0 = x ) = p ( x ), x ∈ S, { X t, t ∈ T } will be called S-valued Markov chains indexed by an infinite tree T with initial distribution (1) and transition matrix (2) under probability measure μ P. Let { X t, t ∈ T } be Markov chains indexed by tree T under probability measure μ P defined above.
Methodologically, the present study has been focused on the most prominent linkage disequilibrium measure, D as defined above.
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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