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The component traverses a given archetype or template and generates an example RM structure valid to the constraints.
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Unlike generative HMMs, discriminative HMMs can utilize both positive and negative examples when generating a model for each class.
For a given compound, the example template generates an HTML list of its chemical or other roles.
In order to generate a positive example, we retrieve a concrete phenomenon knowledge belonging to the target general phenomenon knowledge.
To generate a positive example, it swaps a material in the CWM and PSPM with another material that belongs to the same material class.
If the answer is sat, then the solver will generate a counter example, which depicts the violation of the property or formula f.
To generate a negative example, it swaps a material with another material that does not belong to the material class, with the consideration for choosing a similar material as often as possible.
In brief, we have generated a hypothetical example, in the table, of a situation in which the baseline (control) rates differ markedly between studies.
Numerical results are generated for an example case of a two-span sandwich beam.
We describe the principle of training set generating using an example.
Numerical results are generated for an example problem and sensitivity analysis is conducted to explore the relationships between parameters of the 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