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A cognitive account would predict for our tasks that in the No Cover condition, rats should learn in the Extinction phase that the contingency has changed, which should manifest itself in a decrease of search time in the Test phase.
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We were given the tools for our task and three tiny ramekins of sauce.
For our task is to master globalisation in the interests of the poor and not just the rich; in the interests of protecting our environment and not degrading it; in the interests of increasing the sum of world prosperity and ensuring a more equitable distribution of it.
Therefore, feature reduction phase is necessary and important for our task.
However, for our task, we do not need the actual segmentation.
Despite this obvious disadvantage, it was expected that the modelling improvement would outweigh the generalization problem for our task.
In Section 2, we introduce the system overview considering the task and application model, prediction model, and proposed framework for our task allocation scheme in EH-WSNs.
This is a reasonable criterion for our task since the end goal of such an application would be target detection and tracking.
This is important for our task since it allows us to keep errors at a low level during filtering, rectifying, and mapping processes.
This characteristic is suitable for our task because it can be easily implemented in a real-time score-following system for mobile applications without fine-parameter tuning for each device and environment.
Recently, larger databases for action recognition have been proposed, i.e., UCF101 [56] with 101 categories and Sports1M [50] with 487 classes and one million clips; however, their categories are mainly related to sport activities and therefore not relevant for our task.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com