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Tuning: Adjusting the value of the attributes of an existing ET. ET / E Mechanisms.
The method is based on assigning a maximum loss of one to an undesirable value of the attributes and zero loss to the most desirable value of the attributes in the available range of values of the attributes.
The actual value of the attributes is determined prior to the generation process and stays the same for each call to Tile and Sub.
Tuning: Adjusting the value of the attributes of an existing ET. ET / E Mechanisms Instantiating: Creating an E as an instance of ET, and tuning all of its attributes.
The entities are characterised by attributes such as delay time and arrival time and the value of the attributes can be stochastic and defined by a probability distribution function.
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TSPAN6 was selected as a value of the attribute 'Gene name' (see Fig. 1).
Measurement "forces" the subject to obtain one value of the attribute, the one measured by the observer.
The name of the attribute is "concern", and the value of the attribute is given by the variablel ${concern}.
0: The concept is not activated, or the value of the attribute has not yet been provided by the user.
This measure computes the discriminative power of individual attributes and returns the value of the attribute that can discriminate the largest number of training instances.
Similarly, in quadratic loss function the loss is assumed to be proportional to the square of the deviation of the attribute from the best value of the attribute.
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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