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The map seed is unchanged, but the map generation algorithm has changed, leading to a discontinuity where new chunks are created beside old chunks. The map seed only has meaning in the context of the algorithm used to generate the map (that algorithm is often, [5] [6] based on Perlin noise). So if the map generation algorithm changes, the map ...
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
where the modulus m is a prime number or a power of a prime number, the multiplier a is an element of high multiplicative order modulo m (e.g., a primitive root modulo n), and the seed X 0 is coprime to m. Other names are multiplicative linear congruential generator (MLCG) [2] and multiplicative congruential generator (MCG).
In chunked transfer encoding, the data stream is divided into a series of non-overlapping "chunks". The chunks are sent out and received independently of one another. No knowledge of the data stream outside the currently-being-processed chunk is necessary for both the sender and the receiver at any given time.
Note that the system overhead associated with calculating and looking up hash values is primarily a function of the deduplication workflow. The reconstitution of files does not require this processing and any incremental performance penalty associated with re-assembly of data chunks is unlikely to impact application performance.
In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
A randomness extractor, often simply called an "extractor", is a function, which being applied to output from a weak entropy source, together with a short, uniformly random seed, generates a highly random output that appears independent from the source and uniformly distributed. [1]
Flood fill, also called seed fill, is a flooding algorithm that determines and alters the area connected to a given node in a multi-dimensional array with some matching attribute. It is used in the "bucket" fill tool of paint programs to fill connected, similarly colored areas with a different color, and in games such as Go and Minesweeper for ...