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Abstract. We study different ways of determining the mean distance rn between a reference point and its nth neighbour among random points distributed with uniform density in a D-dimensional Euclidean space. First, we present a heuristic method; though this method provides only a crude mathematical result, it shows a simple way of estimating rn. Next, we describe two alternative means of deriving the exact expression of rn: we review the method using absolute probability and develop an alternative method using conditional probability. Finally, we obtain an approximation to rn from the mean volume between the reference point and its nth neighbour and compare it with the heuristic and exact results.

Abstract. The paper study counter-dependent pseudorandom number generators based on m-variate (m> 1) ergodic mappings of the space of 2-adic integers Z2. The sequence of internal states of these generators is defined by the recurrence law xi+1 = H B i (xi) mod 2 n, whereas their output sequence is zi = F B i (xi) mod 2 n; here xj, zj are m-dimensional vectors over Z2. It is shown how the results obtained for a univariate case could be extended to a multivariate case. 1.

Foundations of Cryptography. Basic Tools. Cambridge Univ – Goldreich - 2001

129 Uniform distribution of sequences – Kuipers, Niederreiter - 1974

For instance, the question of whether is equidistributed mod 1 is an old unsolved problem, equivalent to asking whether is normal base 10.

For instance, the question of whether is equidistributed mod 1 is an old unsolved problem, equivalent to asking whether is normal base 10.

[Incidentally, regarding the interactions between physics and number theory: physical intuition has proven to be quite useful in making accurate predictions about many mathematical objects, such as the distribution of zeroes of the Riemann zeta function, but has been significantly less useful in generating rigorous proofs of these predictions. In number theory, our ability to make accurate predictions on anything relating to the primes (or related objects) is now remarkably good, but our ability to actually prove these predictions rigorously lags behind quite significantly. So I doubt that the key to further rigorous progress on these problems lies with physics.]