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Crude subnetwork modules are initially encoded from the dependency rules.
Based on dependency rules, the server executes, in parallel if possible, prescribed post-processing tasks.
The novelty of the method lies in applying rough set theory for extracting dependency rules directly from a real-valued attribute table consisting of fuzzy membership values.
This helps in preserving all the class representative points in the dependency rules by adaptively applying a threshold that automatically takes care of the shape of membership functions.
In this study, we tested chimpanzees' ability to learn variable distance dependency rules in visual patterns in an operant task and then explored their ability to generalize such rules to previously unseen stimuli.
We found that chimpanzees can learn and generalize both types of dependency rules, indicating that the ability to encode both feature-based and arbitrary associative regularities over variable distances in the visual domain is not a human prerogative.
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None of the foils presented during these tests violated the dependency rule, but altered it in some way.
All further tests presented foils altering, but not violating, the dependency rule of the trained visual patterns.
For the generalization tests, we found that individuals CI (20/30, P < 0.05) and PE (21/30, P = 0.02) applied the dependency rule to a varying dependency distance (Test 2).
Half the stimuli for test 1 were consistent with the exposure rule (C1, index indicating test 1) and half represented violations (V1) of the dependency rule (table 1).
Our results show that chimpanzees are capable of representing a same feature dependency rule and also to perform some location-based computations with them.
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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