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Persistent data in the field of data processing denotes information that is infrequently accessed and unlikely to be modified. [ 1 ] Static data is information, for example a record , that does not change and may be intended to be permanent.
The main tool is persistent homology, an adaptation of homology to point cloud data. Persistent homology has been applied to many types of data across many fields. Moreover, its mathematical foundation is also of theoretical importance. The unique features of TDA make it a promising bridge between topology and geometry. [citation needed]
Variables are defined using the assignment operator, =. MATLAB is a weakly typed programming language because types are implicitly converted. [35] It is an inferred typed language because variables can be assigned without declaring their type, except if they are to be treated as symbolic objects, [36] and that their type can change.
The term "persistent" was first introduced by Atkinson and Morrison [1] in the sense of orthogonal persistence: they used an adjective rather than a verb to emphasize persistence as a property of the data, as distinct from an imperative action performed by a program. The use of the transitive verb "persist" (describing an action performed by a ...
Persistent homology is a method for computing topological features of a space at different spatial resolutions. More persistent features are detected over a wide range of spatial scales and are deemed more likely to represent true features of the underlying space rather than artifacts of sampling, noise, or particular choice of parameters.
The read-of-non-persistent-write problem is found for lock-free programs on persistent memory. As compare-and-swap (CAS) operations do not persist the written values to persistent memory, the modified data can be made visible by the cache coherence protocol to a concurrent observer before the modified data can be observed by a crash observer at persistent memory.
A Markov chain with two states, A and E. In probability, a discrete-time Markov chain (DTMC) is a sequence of random variables, known as a stochastic process, in which the value of the next variable depends only on the value of the current variable, and not any variables in the past.
A trend exists when there is a persistent increasing or decreasing direction in the data. The trend component does not have to be linear. [1], the cyclical component at time t, which reflects repeated but non-periodic fluctuations. The duration of these fluctuations depend on the nature of the time series.