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In this paper we consider the two-dimensional (2D) rectangular packing problem, where a fixed set of items have to be allocated on a single object.
This provides a possible means of overcoming DIF associated with cultural differences in QoL assessment which cannot be avoided where a fixed set of items is employed.
The present task differs from standard working memory tasks in that subjects are not engaged in rehearsal (i.e., retaining a fixed set of items across a delay) but, rather, are continually making recognition decisions about a constantly changing probe item.
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How can a fixed set of knowledge and resources be deployed to manage this uncertainty?
In a classification setting the network is asked to provide a prediction among a fixed set of distinct classes.
Therefore, the number of items needs to be fixed for comparison of C α of several candidate sets of items.
Influence of items on some other items might not be the same as the association between these sets of items.
Booklet 1 and 2 contained two distinct sets of items.
Question is evaluated absolutely, relatively, or absolutely and relatively whether a correspondence exists between item-item or item-set of items.
Item sets A and B have half their items in common, as do item sets B and C, item sets C and D and item sets A and D. The items in common between two sets of items are known as 'anchors' and allow all items and patients to be calibrated on the same scale.
Internal representations of APNNs are sparse binary vectors of fixed dimensionality for items of various complexity and generality.
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CEO of Professional Science Editing for Scientists @ prosciediting.com