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Agent-based models (ABMs) are widely used to estimate causal treatment effects via paired counterfactual simulation. A standard variance reduction technique is common random numbers (CRNs), which couples replicates across intervention…

Methodology · Statistics 2026-03-13 Vince Buffalo , Carl A. B. Pearson , Daniel Klein

"A generator is a parser of randomness." This perspective on generators for random data structures is well established as folklore in the programming languages community, but it has apparently never been formalized, nor have its…

Programming Languages · Computer Science 2022-03-02 Harrison Goldstein , Benjamin C. Pierce

We present a simple approach to realize truly random number generation based on measurement of the phase noise of a single mode vertical cavity surface emitting laser (VCSEL). The true randomness of the quantum phase noise originates from…

Quantum Physics · Physics 2013-05-29 Hong Guo , Wenzhuo Tang , Yu Liu , Wei Wei

Using error correcting codes and fault tolerant techniques, it is possible, at least in theory, to produce logical qubits with significantly lower error rates than the underlying physical qubits. Suppose, however, that the gates that act on…

Quantum Physics · Physics 2016-12-06 M. B. Hastings

As a kind of basic machine learning method, clustering algorithms group data points into different categories based on their similarity or distribution. We present a clustering algorithm by finding hyper-planes to distinguish the data…

Computer Vision and Pattern Recognition · Computer Science 2020-04-28 Luhong Diao , Jinying Gao1 , Manman Deng

A new approach to the generation of random sequences and two dimensional random patterns is proposed in this paper in which random sequences are generated by making use of either Delaunay triangulation or Voronoi diagrams drawn from random…

Cryptography and Security · Computer Science 2011-04-12 Chakradhara Reddy Chinthapanti

In this paper, we present a universal scheme for transforming an arbitrary algorithm for biased 2-face coins to generate random bits from the general source of an m-sided die, hence enabling the application of existing algorithms to general…

Information Theory · Computer Science 2012-09-05 Hongchao Zhou , Jehoshua Bruck

A systematic study of the probability distribution of superimposed random codes is presented through the use of generating functions. Special attention is paid to the cases of either uniformly distributed but not necessarily independent or…

Databases · Computer Science 2008-06-19 Bernd Günther

A novel fast recursive coding technique is proposed. It operates with only integer values not longer 8 bits and is multiplication free. Recursion the algorithm is based on indirectly provides rather effective coding of symbols for very…

Information Theory · Computer Science 2007-08-22 Nikolay Ponomarenko , Vladimir Lukin , Karen Egiazarian , Jaakko Astola , Boris Y Ryabko

Programs with floating-point computations are often derived from mathematical models or designed with the semantics of the real numbers in mind. However, for a given input, the computed path with floating-point numbers may differ from the…

Programming Languages · Computer Science 2016-08-08 Hélène Collavizza , Claude Michel , Michel Rueher

Machine learning (ML) frameworks rely heavily on pseudorandom number generators (PRNGs) for tasks such as data shuffling, weight initialization, dropout, and optimization. Yet, the statistical quality and reproducibility of these…

Other Computer Science · Computer Science 2025-07-08 Benjamin A. Antunes

Random numbers are important in many activities, including communication, encryption, science, gambling, finance, and decision-making. There is a strong demand for a hardware random number generator that could support cryptographic…

A C library for random number generation, Randompack, is presented. The library implements several modern random number generators (engines), including xoshiro256, PCG64, Philox, ranlux++, and sfc64; 14 continuous distributions including…

Applications · Statistics 2026-05-11 Kristján Jónasson

Quantum random number generators are a burgeoning technology used for a variety of applications, including modern security and encryption systems. Typical methods exploit an entropy source combined with an extraction or bit generation…

It is shown how correlations in the generalized feedback shift-register (GFSR) random-number generator are greatly diminished when the number of feedback taps is increased from two to four (or more) and the tap offsets are lengthened.…

Disordered Systems and Neural Networks · Physics 2009-10-30 Robert M. Ziff

The paper develops techniques in order to construct computer programs, pseudorandom number generators (PRNG), that produce uniformly distributed sequences. The paper exploits an approach that treats standard processor instructions…

Dynamical Systems · Mathematics 2011-11-15 Vladimir Anashin

Quantum random number generation is a technique to generate random numbers by extracting randomness from specific quantum processes. As for practical random number generators, they are required not only to have no information leakage but…

Information Theory · Computer Science 2020-11-10 Zehao Zhao , Xiongfeng Ma , Hongyi Zhou

We introduce a new variant of Nodal Virtual Element spaces that mimics the "Serendipity Finite Element Methods" (whose most popular example is the 8-node quadrilateral) and allows to reduce (often in a significant way) the number of…

Numerical Analysis · Mathematics 2015-10-30 L. Beirao da Veiga , F. Brezzi , L. D. Marini , A. Russo

The logistic map is one of the simple systems exhibiting order to chaos transition. In this work we have investigated the possibility of using the logistic map in the chaotic regime ({\sc logmap}) for a pseudo random number generator. To…

Condensed Matter · Physics 2007-05-23 S. C. Phatak , S. Suresh Rao

We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error.…

Machine Learning · Computer Science 2016-10-31 Felix X. Yu , Ananda Theertha Suresh , Krzysztof Choromanski , Daniel Holtmann-Rice , Sanjiv Kumar