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In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables. These…

Machine Learning · Statistics 2018-09-27 Huangjie Zheng , Jiangchao Yao , Ya Zhang , Ivor W. Tsang , Jia Wang

We consider the process of diffusion or "pre-scrambling" of information in a quantum system. We define a measure for this spreading or "pre-scrambling" of the wavefunction in terms of a minimum probability threshold for the states in the…

High Energy Physics - Theory · Physics 2023-06-21 Oleg Kaikov

A few years ago we identified a recursion that works directly with the gaps among the generators in each stage of Eratosthenes sieve. This recursion provides explicit enumerations of sequences of gaps among the generators, which are known…

Number Theory · Mathematics 2013-12-10 Fred B. Holt , Helgi Rudd

We prove some results concerning the distribution of primes on the Riemann hypothesis. First, we prove the explicit result that there exists a prime in the interval $(x-\frac{4}{\pi} \sqrt{x} \log x,x]$ for all $x \geq 2$; this improves a…

Number Theory · Mathematics 2014-05-22 Adrian Dudek

Spiking networks that perform probabilistic inference have been proposed both as models of cortical computation and as candidates for solving problems in machine learning. However, the evidence for spike-based computation being in any way…

Neural and Evolutionary Computing · Computer Science 2017-10-12 Luziwei Leng , Roman Martel , Oliver Breitwieser , Ilja Bytschok , Walter Senn , Johannes Schemmel , Karlheinz Meier , Mihai A. Petrovici

Upper and lower bounds are obtained for the joint entropy of a collection of random variables in terms of an arbitrary collection of subset joint entropies. These inequalities generalize Shannon's chain rule for entropy as well as…

Information Theory · Computer Science 2024-05-07 Mokshay Madiman , Prasad Tetali

Sampling theory encompasses all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. In modern…

Information Theory · Computer Science 2015-03-19 Moshe Mishali , Yonina C. Eldar

The proliferation of science and technology has led to the prevalence of voluminous data sets that are distributed across multiple machines. It is an established fact that conventional statistical methodologies may be unfeasible in the…

Statistics Theory · Mathematics 2023-10-24 Lu Yan , Jiang Hu

Finding a target in a complex environment is a fundamental challenge in nature, from chemical reactions to sperm reaching an egg. An effective strategy to reduce the time needed to reach a target is to deploy many searchers, increasing the…

Statistical Mechanics · Physics 2025-07-15 Elisabetta Ellettari , Giacomo Nasuti , Alberto Bassanoni , Alessandro Vezzani , Raffaella Burioni

The goal of demonstrating a quantum advantage with currently available experimental systems is of utmost importance in quantum information science. While this remains elusive for quantum computation, the field of communication complexity…

Quantum Physics · Physics 2019-09-16 Niraj Kumar , Iordanis Kerenidis , Eleni Diamanti

Deep neural networks trained in an end-to-end manner are proven to be efficient in a wide range of machine learning tasks. However, there is one drawback of end-to-end learning: The learned features and information are implicitly…

Machine Learning · Computer Science 2024-06-07 Canlin Zhang , Xiuwen Liu

We study the gaps between consecutive prime numbers directly through Eratosthenes sieve. Using elementary methods, we identify a recursive relation for these gaps and for specific sequences of consecutive gaps, known as constellations.…

Number Theory · Mathematics 2007-06-07 Fred B. Holt

Bayesian model averaging, model selection and its approximations such as BIC are generally statistically consistent, but sometimes achieve slower rates og convergence than other methods such as AIC and leave-one-out cross-validation. On the…

Statistics Theory · Mathematics 2008-09-17 Tim van Erven , Peter Grunwald , Steven de Rooij

We analyse how the sampling dynamics of distributions evolve in score-based diffusion models using cross-fluctuations, a centered-moment statistic from statistical physics. Specifically, we show that starting from an unbiased isotropic…

Machine Learning · Computer Science 2026-05-04 Sai Niranjan Ramachandran , Manish Krishan Lal , Suvrit Sra

Machine learning theory has mostly focused on generalization to samples from the same distribution as the training data. Whereas a better understanding of generalization beyond the training distribution where the observed distribution…

Machine Learning · Statistics 2019-05-29 Matias Vera , Pablo Piantanida , Leonardo Rey Vega

We define a notion of stochastic domination between trees, where one tree dominates another if when the vertices of each are labeled with independent, identically distributed random variables, one tree is always more likely to contain a…

Probability · Mathematics 2007-05-23 Robin Pemantle , Yuval Peres

Spike bursting is a ubiquitous feature of all neuronal systems. Assuming the spiking states form an alphabet for a communication system, what is the optimal information processing rate? and what is the channel capacity? Here we demonstrate…

Neurons and Cognition · Quantitative Biology 2023-11-16 Bo Deng

We study arithmetic functions $\Phi(x;d,a)$, called prime running functions, whose value at $x$ sums the gaps between primes $p_k \equiv a\ (\text{mod}\ d)$ below $x$ and the next following prime $p_{k+1}$, up to $x$. (The following prime…

Number Theory · Mathematics 2020-06-25 Jaeyoon Kim

We consider $p$-variations in some membrane potential data --viewed as a function of the step size in case where $p$ is fixed, or viewed as a function of $p$ in case where the step size is fixed-- and compare their shape with results in…

Probability · Mathematics 2010-08-26 Reinhard Hoepfner

Graph spanners are sparse subgraphs which approximately preserve all pairwise shortest-path distances in an input graph. The notion of approximation can be additive, multiplicative, or both, and many variants of this problem have been…

Data Structures and Algorithms · Computer Science 2019-11-19 Manuel Fernandez , David P. Woodruff , Taisuke Yasuda