True Random Number Generator Using GPUs and Histogram Equalization Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Random numbers are used in a wide variety of applications from simulation and encryption to gambling and clinical trials. A good quality random number generator is an asset for applications like encryption, randomized designs and network and information security. Various mathematical models have been developed in the past to improve the quality of random numbers. It can be construed that in general to obtain random numbers of excellent quality, a complex mathematical model has to be used which can be a performance bottleneck. In this work, we propose a novel technique to implement a True Random Number Generator (TRNG) using sources of uncertainty found within Graphics Processing Units (GPUs) together with histogram equalization to obtain maximum entropy. We evaluate the random numbers generated by our approach using four tests. First, we measure the correlation values between two sequences of random numbers, second, we measure the entropy values, third, we use watermarking, an application used in network security and finally we use Monte Carlo analysis for pi-value calculation. Based on these quality measurements, our method has achieved better results than popular random number generators compared in this work. Furthermore, this approach is a massively scalable solution ideal for high performance computing implementations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it