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For each semantic search system, PathText 2 provides a query generation module that understands the capabilities of the system and implements a mapping between the semantics of the source pathway model and those applied in the semantic search system.
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This module implements a mapping bridge between the energy profile and the device available resource.
As the model structure implements a biunique mapping between a special set of border conditions and its parameters, a simple characterization procedure can be applied and the accuracy of the model is found to be independent of the boundary conditions.
Similarities between examples create a mapping between concepts.
Let be a mapping between linear spaces.
Let be a map between closed surfaces.
In addition, the implemented mapping between the input and output layer may not reflect the intrinsic structure of the data; these features may prevent the ANN methods from achieving better performances.
To address this problem, the feature extraction is implemented on a map-reduce framework.
Feature parsing implements compensation at the mapping between features and phonemic segments, not at a lexical level.
ISAS also implements a variable-length mapping mode and can be chosen in place of the X-MATE recursive mapping.
In such situation it is preferred to implement a simple "black box" (data-driven, or machine learning) model to identify a direct mapping between the inputs and outputs without detailed consideration of the internal structure of the physical process.
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