Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

277
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
277
Random Sampling Method01:09

Random Sampling Method

11.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.2K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

683
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
683
Randomized Experiments01:13

Randomized Experiments

7.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.1K
Random Variables01:09

Random Variables

12.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
12.4K
Random and Systematic Errors01:20

Random and Systematic Errors

11.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Residue‑resolved dynamically averaged interaction analysis of direct factor Xa inhibitors by MD‑FMO combination calculations.

Journal of computer-aided molecular design·2026
Same author

Correction to "Coarse-Grained Model of Disordered RNA for Simulations of Biomolecular Condensates".

Journal of chemical theory and computation·2026
Same author

Hamiltonian simulation for nonlinear partial differential equation by Schrödingerization.

Scientific reports·2026
Same author

Allosteric inhibition mechanism of PTP1B by DPM-1001 using molecular dynamics simulation.

Biophysical journal·2026
Same author

Enhanced Premelting at the Ice-Rubber Interface Using All-Atom Molecular Dynamics Simulation.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Water-ethanol separation with Janus tip charged carbon nanotubes.

Physical chemistry chemical physics : PCCP·2025

Related Experiment Video

Updated: Jul 26, 2025

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
00:09

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Published on: August 25, 2019

9.5K

Learned pseudo-random number generator: WGAN-GP for generating statistically robust random numbers.

Kiyoshiro Okada1,2, Katsuhiro Endo1, Kenji Yasuoka1

  • 1Department of Mechanical Engineering, Keio University, Yokohama, Japan.

Plos One
|June 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Wasserstein distance-based generative adversarial network (WGAN) to create pseudo-random number generators (PRNGs) that pass NIST statistical tests. The approach learns existing PRNGs without complex math, enabling easier generation of robust random number sequences.

More Related Videos

Optogenetic Random Mutagenesis Using Histone-miniSOG in C. elegans
04:51

Optogenetic Random Mutagenesis Using Histone-miniSOG in C. elegans

Published on: November 14, 2016

9.2K
Applying the RatWalker System for Gait Analysis in a Genetic Rat Model of Parkinson's Disease
04:08

Applying the RatWalker System for Gait Analysis in a Genetic Rat Model of Parkinson's Disease

Published on: January 18, 2021

2.9K

Related Experiment Videos

Last Updated: Jul 26, 2025

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
00:09

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Published on: August 25, 2019

9.5K
Optogenetic Random Mutagenesis Using Histone-miniSOG in C. elegans
04:51

Optogenetic Random Mutagenesis Using Histone-miniSOG in C. elegans

Published on: November 14, 2016

9.2K
Applying the RatWalker System for Gait Analysis in a Genetic Rat Model of Parkinson's Disease
04:08

Applying the RatWalker System for Gait Analysis in a Genetic Rat Model of Parkinson's Disease

Published on: January 18, 2021

2.9K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Pseudo-random number generators (PRNGs) are essential for unpredictable behaviors in machine learning, gaming, and cryptography.
  • Statistical test suites, like the NIST SP 800-22rev1a, are used to validate PRNG robustness and randomness.

Purpose of the Study:

  • To propose a Wasserstein distance-based generative adversarial network (WGAN) approach for generating PRNGs that fully satisfy the NIST test suite.
  • To enable the creation of PRNGs without requiring deep mathematical expertise, facilitating their "democratization".

Main Methods:

  • Utilized a WGAN architecture, learning the existing Mersenne Twister (MT) PRNG without explicit mathematical programming.
  • Removed dropout layers from the WGAN to ensure random number distribution across the entire feature space, mitigating overfitting with large datasets.
  • Employed cosine-function-based numbers with poor random properties as seed numbers for experimental evaluation.

Main Results:

  • The developed learned pseudo-random number generator (LPRNG) successfully transformed poor-quality seed numbers into sequences that fully satisfy the NIST test suite.
  • Demonstrated that the WGAN approach can learn PRNGs end-to-end, making them accessible without advanced mathematical knowledge.
  • Observed overfitting after approximately 450,000 learning trials, indicating a learning count limit for fixed-size neural networks.

Conclusions:

  • The proposed WGAN-based method offers a pathway to generate high-quality PRNGs that meet rigorous statistical standards.
  • This end-to-end learning approach democratizes PRNG development, enhancing unpredictability in critical information systems.
  • The study highlights the trade-offs in neural network training, including the existence of an optimal number of learning iterations.