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In this paper, we use entropy functions to characterise the set of rate-capacity tuples achievable with either zero decoding error, or vanishing decoding error, for general network coding problems. We show that when sources are colocated,…

Information Theory · Computer Science 2015-03-19 Terence H. Chan , Alex Grant

We study quantum algorithms for verifying properties of the output probability distribution of a classical or quantum circuit, given access to the source code that generates the distribution. We consider the basic task of uniformity…

Quantum Physics · Physics 2024-11-08 Clément L. Canonne , Robin Kothari , Ryan O'Donnell

In this paper, both non-mixing and mixing local minima of the entropy are analyzed from the viewpoint of blind source separation (BSS); they correspond respectively to acceptable and spurious solutions of the BSS problem. The contribution…

Information Theory · Computer Science 2016-11-17 F. Vrins , D. -T. Pham , M. Verleysen

In this letter we consider the ensemble of codes formed by the serial concatenation of a Hamming code and two accumulate codes. We show that this ensemble is asymptotically good, in the sense that most codes in the ensemble have minimum…

Information Theory · Computer Science 2009-05-29 Alexandre Graell i Amat , Raphael Le Bidan

This paper starts by considering the minimization of the Renyi divergence subject to a constraint on the total variation distance. Based on the solution of this optimization problem, the exact locus of the points $\bigl( D(Q\|P_1),…

Information Theory · Computer Science 2015-10-27 Igal Sason

This paper presents new lower and upper bounds for the optimal compression of binary prefix codes in terms of the most probable input symbol, where compression efficiency is determined by the nonlinear codeword length objective of…

Information Theory · Computer Science 2008-09-09 Michael Baer

Hierarchical Clustering has been studied and used extensively as a method for analysis of data. More recently, Dasgupta [2016] defined a precise objective function. Given a set of $n$ data points with a weight function $w_{i,j}$ for each…

Data Structures and Algorithms · Computer Science 2021-11-15 Mirmahdi Rahgoshay , Mohammad R. Salavatipour

Entropic uncertainty relations are quantitative characterizations of Heisenberg's uncertainty principle, which make use of an entropy measure to quantify uncertainty. In quantum cryptography, they are often used as convenient tools in…

Quantum Physics · Physics 2012-06-22 Niek J. Bouman , Serge Fehr , Carlos González-Guillén , Christian Schaffner

The von Neumann entropy of an $n$-partite system $A_1^n$ given a system $B$ can be written as the sum of the von Neumann entropies of the individual subsystems $A_k$ given $A_1^{k-1}$ and $B$. While it is known that such a chain rule does…

Quantum Physics · Physics 2024-12-10 Ashutosh Marwah , Frédéric Dupuis

Let $H_1,H_2$ be complex Hilbert spaces and $T$ be a densely defined closed linear operator (not necessarily bounded). It is proved that for each $\epsilon>0$, there exists a bounded operator $S$ with $\|S\|\leq \epsilon$ such that $T+S$ is…

Functional Analysis · Mathematics 2016-09-23 S. H. Kulkarni , G. Ramesh

We characterize novel probability distributions for CSS codes. Such classes of error correcting codes, originally introduced by Calderbank, Shor, and Steane, are of great significance in advancing the fidelity of Quantum computation, with…

Quantum Physics · Physics 2025-10-06 Pete Rigas

Consider a finite set of sources, each producing i.i.d. observations that follow a unique probability distribution on a finite alphabet. We study the problem of matching a finite set of observed sequences to the set of sources under the…

Information Theory · Computer Science 2014-12-09 Jayakrishnan Unnikrishnan

It was recently shown that estimating the Shannon entropy $H({\rm p})$ of a discrete $k$-symbol distribution ${\rm p}$ requires $\Theta(k/\log k)$ samples, a number that grows near-linearly in the support size. In many applications $H({\rm…

Information Theory · Computer Science 2016-03-11 Jayadev Acharya , Alon Orlitsky , Ananda Theertha Suresh , Himanshu Tyagi

Accurate approximation of the sampling distribution of nonparametric kernel density estimators is crucial for many statistical inference problems. Since these estimators have complex asymptotic distributions, bootstrap methods are often…

Statistics Theory · Mathematics 2019-09-09 Todd A. Kuffner , Stephen M. -S. Lee , G. Alastair Young

We establish a general theory of optimality for block bootstrap distribution estimation for sample quantiles under a mild strong mixing assumption. In contrast to existing results, we study the block bootstrap for varying numbers of blocks.…

Statistics Theory · Mathematics 2017-10-10 Todd A. Kuffner , Stephen M. S. Lee , G. Alastair Young

The entropy accumulation theorem, and its subsequent generalized version, is a powerful tool in the security analysis of many device-dependent and device-independent cryptography protocols. However, it has the drawback that the finite-size…

Quantum Physics · Physics 2025-12-22 Amir Arqand , Thomas A. Hahn , Ernest Y. -Z. Tan

It is proven that a conjecture of Tao (2010) holds true for log-concave random variables on the integers: For every $n \geq 1$, if $X_1,\ldots,X_n$ are i.i.d. integer-valued, log-concave random variables, then $$ H(X_1+\cdots+X_{n+1}) \geq…

Probability · Mathematics 2023-10-19 Lampros Gavalakis

We consider the hashing of a set $X\subseteq U$ with $|X|=m$ using a simple tabulation hash function $h:U\to [n]=\{0,\dots,n-1\}$ and analyse the number of non-empty bins, that is, the size of $h(X)$. We show that the expected size of…

Data Structures and Algorithms · Computer Science 2018-11-01 Anders Aamand , Mikkel Thorup

Term Coding asks: given a finite system of term identities $\Gamma$ in $v$ variables, how large can its solution set be on an $n$--element alphabet, when we are free to choose the interpretations of the function symbols? This turns familiar…

Information Theory · Computer Science 2026-01-26 Søren Riis

We consider the hash function $h(x) = ((ax+b) \bmod p) \bmod n$ where $a,b$ are chosen uniformly at random from $\{0,1,\ldots,p-1\}$. We prove that when we use $h(x)$ in hashing with chaining to insert $n$ elements into a table of size $n$…

Data Structures and Algorithms · Computer Science 2017-06-12 Mathias Bæk Tejs Knudsen