Pseudo-random number generation using LSTMs

Jeong, Young-Seob; Oh, Kyo-Joong; Cho, Chung-Ki; Choi, Ho-Jin

doi:10.1007/s11227-020-03229-7

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Some methods of generating random numbers include physical processes and computational algorithms [5]. The use of chaos theory [6,7], quantum random number generators [8], and machine learning algorithms [9] for the generation of random numbers has also been proposed.…”

Section: Introductionmentioning

confidence: 99%

Evolving Patterns in Irrational Numbers Using Waiting Times between Digits

Ogunjo,

Kantz

2024

Fractal Fract

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There is an increasing interest in determining if there exist observable patterns or structures within the digits of irrational numbers. We extend this search by investigating the interval in position between two consecutive occurrences of the same digit, a kind of waiting time statistics. We characterise these by the burstiness measure which distinguishes if the inter-event times are periodic, bursty, or Poisson processes. Furthermore, the complexity–entropy plane was used to determine if the intervals are stochastic or chaotic. We analyse sequences of the first 1 million digits of the numbers π, e, 2, and ϕ. We find that the intervals between single, double, and triple digits are Poisson processes with a burstiness measure in the range −0.05≤B≤0.05 for the four numbers studied. This result is supported by a complexity–entropy plane analysis, which shows that the time intervals have the same characteristics as Gaussian noise. The four irrational numbers have identical degrees of complexity and burstiness in their inter-event analysis.

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Section: Introductionmentioning

confidence: 99%

Evolving Patterns in Irrational Numbers Using Waiting Times between Digits

Ogunjo,

Kantz

2024

Fractal Fract

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“…This challenge brings sensation for a limited ambitious community and provides a useful application in some areas. It is well known that the non-recurring digits of many irrational numbers, including Pi, have been guiding many advancedlevel random number generators (Jeong, Oh, Cho, & Choi, 2020). Moreover, the infinitude of the irrational's unorderly digits also recently inspiredany physicists to suggest a new interpretation of time (Wolchover, 2022).…”

Section: Introductionmentioning

confidence: 99%

Parallel Numerical Computation: A Comparative Study on Cpu-Gpu Performance in Pi Digits Computation

Tjandra

Lawalat

2022

pilar

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As the usage of GPU (Graphical Processing Unit) for non-graphical computation is rising, one important area is to study how the device helps improve numerical calculations. In this work, we present a time performance comparison between purely CPU (serial) and GPU-assisted (parallel) programs in numerical computation. Specifically, we design and implement the calculation of the hexadecimal -digit of the irrational number Pi in two ways: serial and parallel. Both programs are based upon the BBP formula for Pi in the form of infinite series identity. We then provide a detailed time performance analysis of both programs based on the magnitude. Our result shows that the GPU-assisted parallel algorithm ran a hundred times faster than the serial algorithm. To be more precise, we offer that as the value grows, the ratio between the execution time of the serial and parallel algorithms also increases. Moreover, when it is large enough, that is This GPU efficiency ratio converges to a constant, showing the GPU's maximally utilized capacity. On the other hand, for sufficiently small enough, the serial algorithm performed solely on the CPU works faster since the GPU's small usage of parallelism does not help much compared to the arithmetic complexity.

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