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Like Bloom filters, these structures achieve a small space overhead by accepting a small probability of false positives. In the case of "Bloomier filters", a false positive is defined as returning a result when the key is not in the map. The map will never return the wrong value for a key that is in the map.
The JS++ programming language is able to analyze if an array index or map key is out-of-bounds at compile time using existent types, which is a nominal type describing whether the index or key is within-bounds or out-of-bounds and guides code generation. Existent types have been shown to add only 1ms overhead to compile times.
As mentioned above, rows 1, 2, and 4 of G should look familiar as they map the data bits to their parity bits: p 1 covers d 1, d 2, d 4; p 2 covers d 1, d 3, d 4; p 3 covers d 2, d 3, d 4; The remaining rows (3, 5, 6, 7) map the data to their position in encoded form and there is only 1 in that row so it is an identical copy.
A perfect hash function for the four names shown A minimal perfect hash function for the four names shown. In computer science, a perfect hash function h for a set S is a hash function that maps distinct elements in S to a set of m integers, with no collisions.
In contrast, convolutional codes are typically decoded using soft-decision algorithms like the Viterbi, MAP or BCJR algorithms, which process (discretized) analog signals, and which allow for much higher error-correction performance than hard-decision decoding. Nearly all classical block codes apply the algebraic properties of finite fields ...
A lookup table, which contains, as keys, the case values and, as values, the part under the case statement. (In some languages, only actual data types are allowed as values in the lookup table. In other languages, it is also possible to assign functions as lookup table values, gaining the same flexibility as a real switch statement.
A map, sometimes referred to as a dictionary, consists of a key/value pair. The key is used to order the sequence, and the value is somehow associated with that key. For example, a map might contain keys representing every unique word in a text and values representing the number of times that word appears in the text.
Heteroskedasticity-consistent standard errors that differ from classical standard errors may indicate model misspecification. Substituting heteroskedasticity-consistent standard errors does not resolve this misspecification, which may lead to bias in the coefficients. In most situations, the problem should be found and fixed. [5]