Stationarity Enforcement of Accelerator Based TRNG by Genetic Algorithm
Bibliographic record
Abstract
Random numbers generated by pseudo-random and true random number generators (TRNG), are used in a wide variety of applications. A TRNG relies on a nondeterministic source to produce random numbers. In this paper, we develop a TRNG using a heuristic evolutionary algorithm together with signal processing techniques. Our proposed algorithm consists of three phases:(i) Extraction: where a noise source(such as race conditions during concurrent memory accesses on a GPU) is sampled into random numbers, (ii) Exact Histogram Equalization: takes the output from (i) and delivers a specified output distribution, (iii) Stationarity Enforcement: using a genetic algorithm, the output of (ii) is permuted until the random numbers meet wide sense stationarity within a specified quality defined by Gaussian distribution with the expected standard deviation on the power spectral density of random numbers. We propose guideline parameters for the evolutionary algorithm to ensure fast convergence, within the first 100 generations, with a standard deviation over the specified quality level of less than 0.02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".