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Suppose that we are given an infinite binary sequence which is random for a Bernoulli measure of parameter $p$. By the law of large numbers, the frequency of zeros in the sequence tends to~$p$, and thus we can get better and better…

Logic · Mathematics 2018-10-18 Laurent Bienvenu , Santiago Figueira , Benoit Monin , Alexander Shen

Suppose we are given a computably enumerable object arise from algorithmic randomness or computable analysis. We are interested in the strength of oracles which can compute an object that approximates this c.e. object. It turns out that,…

Logic · Mathematics 2019-12-09 Noam Greenberg , Joseph S. Miller , Andre Nies

In "Reliable Communication in the Absence of a Common Clock" (Yeung et al., 2009), the authors introduce general run-length sets, which form a class of constrained systems that permit run-lengths from a countably infinite set. For a…

Information Theory · Computer Science 2010-01-14 Georg Böcherer , Rudolf Mathar , Valdemar Cardoso da Rocha Junior , Cecilio Pimentel

We study recurrence, and multiple recurrence, properties along the $k$-th powers of a given set of integers. We show that the property of recurrence for some given values of $k$ does not give any constraint on the recurrence for the other…

Dynamical Systems · Mathematics 2014-02-26 Nikos Frantzikinakis , Emmanuel Lesigne , Mate Wierdl

Given a problem which is intractable for both quantum and classical algorithms, can we find a sub-problem for which quantum algorithms provide an exponential advantage? We refer to this problem as the "sculpting problem." In this work, we…

Quantum Physics · Physics 2015-12-15 Scott Aaronson , Shalev Ben-David

We classify all irreducible admissible representations of three Olshanski pairs connected to the infinite symmetric group. In particular, our methods yield two simple proofs of the classical Thoma's description of the characters of the…

Representation Theory · Mathematics 2007-05-23 Andrei Okounkov

In the theory of algorithmic randomness, one of the central notions is that of computable randomness. An infinite binary sequence X is computably random if no recursive martingale (strategy) can win an infinite amount of money by betting on…

Computer Science and Game Theory · Computer Science 2015-05-18 Laurent Bienvenu , Frank Stephan , Jason Teutsch

Motivation: Combining the results of different experiments to exhibit complex patterns or to improve statistical power is a typical aim of data integration. The starting point of the statistical analysis often comes as sets of p-values…

Methodology · Statistics 2021-12-02 Tristan Mary-Huard , Sarmistha Das , Indranil Mukhopadhyay , Stéphane Robin

In combinatorics, the probabilistic method is a very powerful tool to prove the existence of combinatorial objects with interesting and useful properties. Explicit constructions of objects with such properties are often very difficult, or…

Computational Complexity · Computer Science 2007-05-23 Luca Trevisan

This paper examines the classical matching distribution arising in the "problem of coincidences". We generalise the classical matching distribution with a preliminary round of allocation where items are correctly matched with some fixed…

Other Statistics · Statistics 2021-12-24 Ben O'Neill

Wolfram's Principle of Computational Equivalence (PCE) implies that universal complexity abounds in nature. This paper comprises three sections. In the first section we consider the question why there are so many universal phenomena around.…

Logic in Computer Science · Computer Science 2012-11-09 Joost J. Joosten

We introduce an algorithm for the uniform generation of infinite runs in concurrent systems under a partial order probabilistic semantics. We work with trace monoids as concurrency models. The algorithm outputs on-the-fly approximations of…

Combinatorics · Mathematics 2017-12-07 Samy Abbes , Vincent Jugé

A new characterization of provably recursive functions of first-order arithmetic is described. Its main feature is using only terms consisting of 0, the successor S and variables in the quantifier rules, namely, universal elimination and…

Logic in Computer Science · Computer Science 2012-01-06 Evgeny Makarov

In finite probability theory, events are subsets of the outcome set. Subsets can be represented by 1-dimensional column vectors. By extending the representation of events to two dimensional matrices, we can introduce "superposition events."…

Quantum Physics · Physics 2020-06-18 David Ellerman

In the modern Bayesian view classical probability theory is simply an extension of conventional logic, i.e., a quantitative tool that allows for consistent reasoning in the presence of uncertainty. Classical theory presupposes, however,…

Quantum Physics · Physics 2007-06-20 Jochen Rau

For a probability distribution $P$ on an at most countable alphabet $\mathcal A$, this article gives finite sample bounds for the expected occupancy counts $\mathbb E K_{n,r}$ and probabilities $\mathbb E M_{n,r}$. Both upper and lower…

Statistics Theory · Mathematics 2016-11-17 Geoffrey Decrouez , Michael Grabchak , Quentin Paris

Recurrence properties of systems and associated sets of integers that suffice for recurrence are classical objects in topological dynamics. We describe relations between recurrence in different sorts of systems, study ways to formulate…

Dynamical Systems · Mathematics 2014-08-13 Bernard Host , Bryna Kra , Alejandro Maass

In this paper I propose a new way of measuring linguistic productivity that objectively assesses the ability of an affix to be used to coin new complex words and, unlike other popular measures, is not directly dependent upon token…

Computation and Language · Computer Science 2023-08-25 Sergei Monakhov

A hypothesis testing algorithm is replicable if, when run on two different samples from the same distribution, it produces the same output with high probability. This notion, defined by by Impagliazzo, Lei, Pitassi, and Sorell [STOC'22],…

Data Structures and Algorithms · Computer Science 2025-09-05 Anders Aamand , Maryam Aliakbarpour , Justin Y. Chen , Shyam Narayanan , Sandeep Silwal

Choice functions constitute a simple, direct and very general mathematical framework for modelling choice under uncertainty. In particular, they are able to represent the set-valued choices that appear in imprecise-probabilistic decision…

Artificial Intelligence · Computer Science 2019-05-22 Jasper De Bock , Gert de Cooman
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