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These mappings will be used as base information by a system that extracts metaphors using supervised machine learning.
The so-called demiclosedness principle for nonexpansive mappings will be used.
The set of all full word mappings will be denoted by ℱ.
More information as regards admissible mappings will be presented in the next section.
Our approach combines the advantages of the strong topology (i.e., the involved mappings will be continuous) with the advantages of the weak topology (i.e., the maps will be weakly compact).
In view of these results and their numerous applications, it is natural to ask if convergence of the iterates of nonexpansive mappings will be preserved in the presence of computational errors.
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Kirk and Shahzad's results on set-valued mappings which will be extended are as follows.
We use quality level to cost mappings that will be explained in Section 'Pattern-driven resource configuration' below.
Let us finish this section with the modular definitions of some special mappings, which will be studied throughout.
In addition, several properties for arcwise connected cone-quasiconvex set-valued mappings, which will be used in the sequel, are discussed.
In this short section, we recall some definitions and known results concerning set-valued mappings which will be needed throughout this paper.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com