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MO:DCA-P (Mixed Object:Document Content Architecture-Presentation), the Page Description Language file format that describes the text and graphics on a page. The 'Mixed Object' moniker refers to the fact that a MO:DCA file can contain multiple types of objects, including text, images, vector graphics, and even objects marked as 'barcodes'.
Before computers were invented, and thus becoming computerized (or digital) typesetting, font sizes were changed by replacing the characters with a different size of type. In letterpress printing, individual letters and punctuation marks were cast on small metal blocks, known as "sorts," and then arranged to form the text for a page.
Tab-separated values (TSV) is a simple, text-based file format for storing tabular data. [3] Records are separated by newlines , and values within a record are separated by tab characters . The TSV format is thus a delimiter-separated values format, similar to comma-separated values .
Prior to the advent of macOS, the classic Mac OS system regarded the content of a file (the data fork) to be a text file when its resource fork indicated that the type of the file was "TEXT". [7] Lines of classic Mac OS text files are terminated with CR characters. [8] Being a Unix-like system, macOS uses Unix format for text files. [8]
A MEX file is a type of computer file that provides an interface between MATLAB or Octave and functions written in C, C++ or Fortran.It stands for "MATLAB executable". When compiled, MEX files are dynamically loaded and allow external functions to be invoked from within MATLAB or Octave as if they were built-in functions.
Excel's storage of numbers in binary format also affects its accuracy. [3] To illustrate, the lower figure tabulates the simple addition 1 + x − 1 for several values of x. All the values of x begin at the 15 th decimal, so Excel must take them into account. Before calculating the sum 1 + x, Excel first approximates x as a binary number
Microprinting is the production of recognizable patterns or characters in a printed medium at a scale that typically requires magnification to read with the naked eye. To the unaided eye, the text may appear as a solid line.
The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013.