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Specifically, we used a ratio of 1 positive to 10 negative examples, as this was the largest ratio for which there was no change in the estimated probability distributions for the negative class features (see section Representation of Biological Features used in the Classifier).
In other words, use aspirational language as well as concrete examples, as this shows you continue to be willing to learn and update your management skills.
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The parameter θ = 0.1 in this example as well.
However, the utility function in this example, as is typically the case, is largely arbitrary.
The examples are representative as this is certainly not an exhaustive account.
The discussion that follows uses the National Institutes of Health (NIH) system as example, as this is the largest source of biomedical research funding.
For example, as this license was first designed for limo drivers, the license stipulates that you need to drive a big car, longer than 4.5m.
Using Microsoft Windows Azure was advantageous in this example as this is a PaaS, negating the need to maintain, patch and update the underlying OS.
In the present study we chose oesophageal squamous cell carcinoma as an example as this type of cancer is both associated with chronic irritation and inflammation as well as viral infections.
Also check for both conformation (show lines) and working ability (Schutzhund or tracking for example) as this shows that the dogs look and act like Rottweilers!
So let's look at some examples as to how this thinking might apply.
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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