Sentence examples for use of such representations from inspiring English sources

The use of such representations and processes, which we refer to as the algorithmic approach, is uniquely suited for problems requiring strong models, i.e., tasks and domains that are well defined, and/or involve close interaction with the environment.

Specific expectations were based on findings with nonimpaired readers showing that the left OT cortex is engaged by lexical route processes (i.e., storage of orthographic word representations and use of such representations for whole-word recognition), but even more so by sublexical route processes [39], [43].

Our behavioral tests assess the use of such representations.

Despite the well known advantages that a space-variant representation of the visual signal offers, the required adaptation of the algorithms developed in the Cartesian domain, before applying them in the log-polar space, has limited a wide use of such representation in visual processing applications.

Our method uses appropriately scaled function values in lieu of such representations.

One type of such representations are power-law models.

In construction of such representation, the unitary equivalence formalism is used [13].

The use of such a representation of the area restrictions allow us to solve several challenging test problems to optimality with a guarantee that the final area of each department is within an ε% error of the required area.

This is the simplest form of contour parameterisation, and if an AER system that makes use of such a representation outperforms a system that models the distribution of the parameter values (without taking into account any temporal information), then it is reasonable to suppose that the shape of the parameter contours contain emotion-specific information.

However, the use of such network representation for prediction requires the determination of the specific scale of the PIN that one has to consider for unveiling the individual protein's function.

We show that the use of such distributions allows for representation principles not requiring too hard data aggregation, but still admitting efficient evaluation of decision situations.