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Solution: divide one of the tall cells so that the row gets one rowspan=1 cell (and don't mind the eventual loss of text-centering). Then kill the border between them. Don't forget to fill the cell with nothing ({}). This being the only solution that correctly preserves the cell height, matching that of the reference seven row table.
Excel offers many user interface tweaks over the earliest electronic spreadsheets; however, the essence remains the same as in the original spreadsheet software, VisiCalc: the program displays cells organized in rows and columns, and each cell may contain data or a formula, with relative or absolute references to other cells. Excel 2.0 for ...
Adding the mw-collapsible class to a table automatically positions the toggle, and selects which parts to collapse. A common use is to make a collapsible layout table, which always displays an introduction or summary, but hides the rest of the content from immediate view.
This is a semi-humorous attempt to define the size at which a committee or other decision-making body becomes completely inefficient. In Parkinson's Law: The Pursuit of Progress , London: John Murray, 1958 a chapter is devoted to the basic question of what he called comitology : how committees, government cabinets, and other such bodies are ...
The following describes techniques to auto-widen, or expand, any wp:wikitable, based on each user's default text-size set for their browser or device.. The major technique is to pad columns with column-spacers, as groups of non-breaking spaces ( ) at the end of each column, such as by template {{ns|15}}, where those spaces will shift below the column on narrow screens, to move the columns ...
In computing, inline expansion, or inlining, is a manual or compiler optimization that replaces a function call site with the body of the called function. Inline expansion is similar to macro expansion, but occurs during compilation, without changing the source code (the text), while macro expansion occurs prior to compilation, and results in different text that is then processed by the compiler.
The data shown is a random sample of 10,000 points from a normal distribution with a mean of 0 and a standard deviation of 1. The data used to construct a histogram are generated via a function m i that counts the number of observations that fall into each of the disjoint categories (known as bins).
Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.