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Sonic artifact, in sound and music production, sonic material that is accidental or unwanted, resulting from the editing of another sound. Visual artifact, in imaging, any unwanted visual alteration introduced by the imaging equipment. Compression artifact, in computer graphics, distortion of media by the data compression.
The size of the compressed data structure is typically highly dependent upon the information entropy of the data being represented. Important examples of compressed data structures include the compressed suffix array [1] [2] and the FM-index, [3] both of which can represent an arbitrary text of characters T for pattern matching.
Array, a sequence of elements of the same type stored contiguously in memory; Record (also called a structure or struct), a collection of fields . Product type (also called a tuple), a record in which the fields are not named
Lossy data compression involves discarding some of the media's data so that it becomes small enough to be stored within the desired disk space or transmitted (streamed) within the available bandwidth (known as the data rate or bit rate). If the compressor cannot store enough data in the compressed version, the result is a loss of quality, or ...
For a more comprehensive listing of data structures, see List of data structures. The comparisons in this article are organized by abstract data type . As a single concrete data structure may be used to implement many abstract data types, some data structures may appear in multiple comparisons (for example, a hash map can be used to implement ...
Unlike general lossless data compression algorithms, succinct data structures retain the ability to use them in-place, without decompressing them first. A related notion is that of a compressed data structure, insofar as the size of the stored or encoded data similarly depends upon the specific content of the data itself.
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.
Regression dilution, also known as regression attenuation, is the biasing of the linear regression slope towards zero (the underestimation of its absolute value), caused by errors in the independent variable. Consider fitting a straight line for the relationship of an outcome variable y to a predictor variable x, and estimating the slope of the ...