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In computing, a symbolic link (also symlink or soft link) is a file whose purpose is to point to a file or directory (called the "target") by specifying a path thereto. [ 1 ] Symbolic links are supported by POSIX and by most Unix-like operating systems , such as FreeBSD , Linux , and macOS .
In computing, a hard link is a directory entry (in a directory-based file system) that associates a name with a file.Thus, each file must have at least one hard link. Creating additional hard links for a file makes the contents of that file accessible via additional paths (i.e., via different names or in different directori
Hard-coded data typically can be modified only by editing the source code and recompiling the executable, although it can be changed in memory or on disk using a debugger or hex editor. Data that is hard-coded is best suited for unchanging pieces of information, such as physical constants, version numbers, and static text elements.
Soft – the usefulness of a result degrades after its deadline, thereby degrading the system's quality of service. Thus, the goal of a hard real-time system is to ensure that all deadlines are met, but for soft real-time systems the goal becomes meeting a certain subset of deadlines in order to optimize some application-specific criteria. The ...
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
Its hardlink sub-command can make hard links or list hard links associated with a file. [9] Another sub-command, reparsepoint, can query or delete reparse points, the file system objects that make up junction points, hard links, and symbolic links. [10] In addition, the following utilities can create NTFS links, even though they don't come with ...
Fuzzy clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster.. Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible.
Whereas a hard-decision decoder operates on data that take on a fixed set of possible values (typically 0 or 1 in a binary code), the inputs to a soft-decision decoder may take on a whole range of values in-between. This extra information indicates the reliability of each input data point, and is used to form better estimates of the original data.