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The second-order achievable asymptotics in typical random number generation problems such as resolvability, intrinsic randomness, fixed-length source coding are considered. In these problems, several researchers have derived the first-order…

Information Theory · Computer Science 2013-01-01 Ryo Nomura , Te Sun Han

Universal fixed-to-variable lossless source coding for memoryless sources is studied in the finite blocklength and higher-order asymptotics regimes. Optimal third-order coding rates are derived for general fixed-to-variable codes and for…

Information Theory · Computer Science 2014-12-16 Oliver Kosut , Lalitha Sankar

The problem of determining the best achievable performance of arbitrary lossless compression algorithms is examined, when correlated side information is available at both the encoder and decoder. For arbitrary source-side information pairs,…

Information Theory · Computer Science 2020-07-15 Lampros Gavalakis , Ioannis Kontoyiannis

This paper studies the second-order asymptotics of coding rates for the discrete memoryless multiple-access channel with a fixed target error probability. Using constant-composition random coding, coded time-sharing, and a variant of…

Information Theory · Computer Science 2014-11-18 Jonathan Scarlett , Alfonso Martinez , Albert Guillén i Fàbregas

We treat a random number generation from an i.i.d. probability distribution of $P$ to that of $Q$. When $Q$ or $P$ is a uniform distribution, the problems have been well-known as the uniform random number generation and the resolvability…

Information Theory · Computer Science 2013-03-05 Wataru Kumagai , Masahito Hayashi

This paper considers the joint compression of a pair of correlated sources, where the encoder is allowed to access only one of the sources. The objective is to recover both sources under separate distortion constraints for each source while…

Information Theory · Computer Science 2024-11-07 Huiyuan Yang , Yuxuan Shi , Shuo Shao , Xiaojun Yuan

The simplest example of a quantum information source with memory is a mixed source which emits signals entirely from one of two memoryless quantum sources with given a priori probabilities. Considering a mixed source consisting of a general…

Quantum Physics · Physics 2016-06-27 Felix Leditzky , Nilanjana Datta

We introduce two variants of the information spectrum relative entropy defined by Tomamichel and Hayashi which have the particular advantage of satisfying the data-processing inequality, i.e. monotonicity under quantum operations. This…

Quantum Physics · Physics 2021-11-17 Nilanjana Datta , Felix Leditzky

Variable-length compression without prefix-free constraints and with side-information available at both encoder and decoder is considered. Instead of requiring the code to be error-free, we allow for it to have a non-vanishing error…

Information Theory · Computer Science 2020-08-24 Yuta Sakai , Vincent Y. F. Tan

This paper studies the joint data and semantics lossy compression problem, i.e., an extension of the hidden lossy source coding problem that entails recovering both the hidden and observable sources. We aim to study the nonasymptotic and…

Information Theory · Computer Science 2024-08-20 Huiyuan Yang , Yuxuan Shi , Shuo Shao , Xiaojun Yuan

Universal source coding at short blocklengths is considered for an exponential family of distributions. The \emph{Type Size} code has previously been shown to be optimal up to the third-order rate for universal compression of all memoryless…

Information Theory · Computer Science 2016-12-21 Nematollah Iri , Oliver Kosut

In this monograph, we review recent advances in second-order asymptotics for lossy source coding, which provides approximations to the finite blocklength performance of optimal codes. The monograph is divided into three parts. In part I, we…

Information Theory · Computer Science 2024-10-25 Lin Zhou , Mehul Motani

The problem of joint detection and lossless source coding is considered. We derive asymptotically optimal decision rules for deciding whether or not a sequence of observations has emerged from a desired information source, and to compress…

Information Theory · Computer Science 2016-11-17 Neri Merhav

This paper provides an extensive study of the behavior of the best achievable rate (and other related fundamental limits) in variable-length lossless compression. In the non-asymptotic regime, the fundamental limits of fixed-to-variable…

Information Theory · Computer Science 2012-12-13 Ioannis Kontoyiannis , Sergio Verdu

This paper characterizes the second-order coding rates for lossy source coding with side information available at both the encoder and the decoder. We first provide non-asymptotic bounds for this problem and then specialize the…

Information Theory · Computer Science 2014-10-13 Sy-Quoc Le , Vincent Y. F. Tan , Mehul Motani

This paper considers the problem of channel coding with a given (possibly suboptimal) maximum-metric decoding rule. A cost-constrained random-coding ensemble with multiple auxiliary costs is introduced, and is shown to achieve error…

Information Theory · Computer Science 2014-03-05 Jonathan Scarlett , Alfonso Martinez , Albert Guillén i Fàbregas

In this paper, we derive non-asymptotic achievability and converse bounds on the random number generation with/without side-information. Our bounds are efficiently computable in the sense that the computational complexity does not depend on…

Information Theory · Computer Science 2016-09-28 Masahito Hayashi , Shun Watanabe

The paper derives the optimal second-order coding rate for the continuous-time Poisson channel. We also obtain bounds on the third-order coding rate. This is the first instance of a second-order result for a continuous-time channel. The…

Information Theory · Computer Science 2020-08-18 Yuta Sakai , Vincent Y. F. Tan , Mladen Kovačević

This paper studies the second-order asymptotics of the discrete memoryless multiple-access channel with degraded message sets. For a fixed average error probability $\epsilon\in(0,1)$ and an arbitrary point on the boundary of the capacity…

Information Theory · Computer Science 2015-04-15 Jonathan Scarlett , Vincent Y. F. Tan

Motivated by real-world machine learning applications, we analyze approximations to the non-asymptotic fundamental limits of statistical classification. In the binary version of this problem, given two training sequences generated according…

Information Theory · Computer Science 2018-12-07 Lin Zhou , Vincent Y. F. Tan , Mehul Motani
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