The phrase "array representation" is correct and usable in written English.
It can be used in contexts related to computer science, mathematics, or data structures when discussing how data is organized or displayed in an array format.
Example: "In programming, understanding array representation is crucial for efficient data manipulation and retrieval."
Alternatives: "array format" or "array structure".
returns true only in implementations that do not have a specialized array representation for single floats distinct from that for other floats.
It shows that the performance profile of virtual execution is still highly influenced by the choice of both the multidimensional array representation and the VEE implementation, also contradicting some results of previous related works, as well as some beliefs of HPC programmers that come from their practice with native execution languages, such as C, C++, and Fortran.
Column fp in the similarity table stores a byte array representation of the fingerprint.
But the array representation in the computer (and, its counterpart, if it exists, in the brain) is not in itself visible, like a real picture would be.
The real model for the mental image is the underlying array representation in the computer's memory, physically instantiated as electronic states in RAM chips.
The array representation is constructed from a "deep representation" description, stored in the computer's equivalent of long term memory (as in figure 1 – the program had just two "images" hand-coded in: a car, as shown in figure 2, and a chair, in similar vein), and can thus be readily manipulated in various ways.
This is another way of saying that '(array type-specifier) and '(array,(upgraded-array-element-type 'type-specifier)) refer to the same set of specialized array representations.
For its simulation setup and output a mesoscopic simulation kernel software uses abstract matrix (array) representations for bead topology and connectivity.
The response was the fold change of intensity levels (MIs) for possible plus-HpaII and minus-HpaII combinations of array representations within the same sample.
In this paper, a bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is implemented.
Numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this bit-array representation method and suggest that the GA performance can be significantly improved by handling the design connectivity properly.
