pseudo-random number generators是伪随机数发生器的意思,主要用于在系统需要随机数的时候,通过一系列种子值计算出来的伪随机数。因为生成一个真正意义上的“随机数”对于计算机来说是不可能的,伪随机数也只是尽可能地接近其应具有的随机性,但是因为有“种子值”,所以伪随机数在一定程度上是可控可预测的。 通过程序得...
^abGirish, Uma, and Ran Raz. "Eliminating Intermediate Measurements using Pseudorandom Generators." ...
pseudorandom generators existenceregular functionsAlgorithm design and analysisComputer scienceCouncilsA fresh view at the question of randomness was taken in the theory of computing: It has been postulated that a distribution is pseudorandom if it cannot be told apart from the uniform distribution by ...
As will be shown in this chapter, pseudo random generators based on fast superconducting switches present a new solution to this problem of generating noise power spectra. They allow the output noise power to be defined in terms of the measurements of a voltage, a resistance and a frequency. ...
Vadhan, Pseudorandom generators without the XOR lemma [abstract], in, Proceedings of the Fourteenth Annual IEEE Conference on Computational Complexity, Atlanta, GA, May 1999, p, 4. Google Scholar STV99b M. Sudan, L. Trevisan, and S. Vadhan, Pseudorandom generators without the XOR lemma [...
The Apache Commons RNG project provides pure-Java implementation of pseudo-random generators. Documentation More information can be found on theApache Commons RNG homepage. The Javadoc for each of the modules can be browsed: Commons RNG Client API ...
We construct pseudorandom generators for combinatorial shapes, which substantially generalize combinatorial rectangles, -biased spaces, 0/1 halfspaces, and 0/1 modular sums. Our generator uses seed length O(logm + log n + log2(1/eps)) to get error eps. When m = 2, this gives the fifirst...
Code of conduct BSD-3-Clause license PyTorch/CSPRNG torchcsprng is aPyTorch C++/CUDA extensionthat provides: AES128-bit encryption/decryption in two modes:ECBandCTR cryptographically secure pseudorandom number generatorsfor PyTorch. Design torchcsprng generates a random 128-bit key on CPU using one ...
Robust Pseudorandom Generators ∗ Yuval Ishai † Eyal Kushilevitz ‡ Xin Li § Rafail Ostrovsky ¶ Manoj Prabhakaran Amit Sahai ∗∗ David Zuckerman †† Abstract Let G : {0, 1} n →{0, 1} m be a pseudorandom generator. We say that a circuit implementa- tion of G is ...
In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; ...