Middle Square Weyl Sequence RNG

Manipulating attentional load in sequence learning through random number generation

Implicit learning is often assumed to be an effortless process. However, some artificial grammar learning and sequence learning studies using dual tasks seem to suggest that attention is essential for implicit learning to occur. This discrepancy probably results from the specific type of secondary task that is used. Different secondary tasks may engage attentional resources differently and ther...

Bit recycling for scaling random number generators

Many Random Number Generators (RNG) are available nowadays; they are divided in two categories, hardware RNG, that provide “true” random numbers, and algorithmic RNG, that generate pseudo random numbers (PRNG). Both types usually generate random numbers (Xn)n as independent uniform samples in a range 0, . . . 2 − 1, with b = 8, 16, 32 or b = 64. In applications, it is instead sometimes desirabl...

The random number generation task: psychometric properties and normative data of an executive function task in a mixed sample.

We investigated the psychometric properties of the random number generation (RNG) task in four studies using a mixed sample of young adults (n = 306), middle-aged adults (n = 40), and patients diagnosed with schizophrenia (n = 26). Data in study 1 were best accounted for by a three-factor solution representing inhibition of stereotypical schemas (seriation), output inhibition (repetition), and ...

Fatou's Lemma and the Lebesgue's Convergence Theorem

For simplicity, we adopt the following rules: X denotes a non empty set, F denotes a sequence of partial functions from X into R with the same dom, s1, s2, s3 denote sequences of extended reals, x denotes an element of X, a, r denote extended real numbers, and n, m, k denote natural numbers. We now state several propositions: (1) If for every natural number n holds s2(n) ≤ s3(n), then inf rng s...

A Necessary Condition for Weyl-Heisenberg Frames