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This paper studies expurgated random-coding bounds and exponents for channel coding with a given (possibly suboptimal) decoding rule. Variations of Gallager's analysis are presented, yielding several asymptotic and non-asymptotic bounds on…

Information Theory · Computer Science 2016-11-17 Jonathan Scarlett , Li Peng , Neri Merhav , Alfonso Martinez , Albert Guillén i Fàbregas

A variation of Gallager error-correcting codes is investigated using statistical mechanics. In codes of this type, a given message is encoded into a codeword which comprises Boolean sums of message bits selected by two randomly constructed…

Disordered Systems and Neural Networks · Physics 2009-10-31 Tatsuto Murayama , Yoshiyuki Kabashima , David Saad , Renato Vicente

This note points out a gap in the proof of one of the technical results in the paper "Asymptotic Invariants of Base Loci", that appeared in Ann. Inst. Fourier (Grenoble) 56 (2006), 1701-1734. We provide a correct proof of this result.

Algebraic Geometry · Mathematics 2023-10-02 Lawrence Ein , Robert Lazarsfeld , Mircea Mustata , Michael Nakamaye , Mihnea Popa

We correct a simple error in Percolation on random Johnson-Mehl tessellations and related models, Probability Theory and Related Fields 140 (2008), 417-468. (See also arXiv:math/0610716)

Probability · Mathematics 2010-02-08 Bela Bollobas , Oliver Riordan

I comment on Zaccone, Phys. Rev. Lett. {\bf 128}, 028002 (2022) highlighting a flaw in the derivation that led to a spurious divergent factor. This renders the derivation of the random close packing density invalid.

Soft Condensed Matter · Physics 2022-07-19 W. Till Kranz

We define the error exponent of the typical random code as the long-block limit of the negative normalized expectation of the logarithm of the error probability of the random code, as opposed to the traditional random coding error exponent,…

Information Theory · Computer Science 2017-08-25 Neri Merhav

The Annals of Applied Probability (2002) 12 1114-1137

Probability · Mathematics 2008-11-23 Jean B. Lasserre

We investigate random feature models in which neural networks sampled from a prescribed initialization ensemble are frozen and used as random features, with only the readout weights optimized. Adopting a statistical-physics viewpoint, we…

Machine Learning · Computer Science 2026-04-29 Taeyoung Kim

A Comment by R. Blumenfeld on our recent analytical solution for the random close packing density [A. Zaccone, Phys. Rev. Lett. 128, 028002 (2022)] is shown to be plagued by important errors and to contain incorrect statements.

Disordered Systems and Neural Networks · Physics 2022-08-25 Alessio Zaccone

In Part II we show that there exist quantum codes whose probability of undetected error falls exponentially with the length of the code and derive bounds on this exponent.The lower (existence) bound for stabilizer codes is proved by a…

Quantum Physics · Physics 2007-05-23 A. Ashikhmin , A. Barg , E. Knill , S. Litsyn

We derive various error exponents in the bee identification problem under two different decoding rules. Under na\"ive decoding, which decodes each bee independently of the others, we analyze a general discrete memoryless channel and a…

Information Theory · Computer Science 2020-11-20 Ran Tamir , Neri Merhav

Some of my previous publications were incomplete in the sense that non trivial zeros belonging to a particular type of fundamental domain have been inadvertently ignored. Due to this fact, I was brought to believe that computations done by…

Complex Variables · Mathematics 2016-02-05 Dorin Ghisa

We fill in a gap in the proof of the main theorem in our earlier paper [Ol]. At the same time, we prove a slightly stronger version of the theorem needed for another paper.

Group Theory · Mathematics 2021-09-17 Bob Oliver

This paper studies maximum likelihood(ML) decoding in error-correcting codes as rational maps and proposes an approximate ML decoding rule by using a Taylor expansion. The point for the Taylor expansion, which will be denoted by $p$ in the…

Dynamical Systems · Mathematics 2010-06-30 Kazunori Hayashi , Yasuaki Hiraoka

Upper and lower bounds on the error probability of linear codes under maximum-likelihood (ML) decoding are shortly surveyed and applied to ensembles of codes on graphs. For upper bounds, focus is put on Gallager bounding techniques and…

Information Theory · Computer Science 2007-07-13 Igal Sason , Shlomo Shamai

In this paper, we establish the list-decoding capacity theorem for sum-rank metric codes. This theorem implies the list-decodability theorem for random general sum-rank metric codes: Any random general sum-rank metric code with a rate not…

Information Theory · Computer Science 2025-03-14 Yang Liu , Anna Baumeister , Antonia Wachter-Zeh

Correction to Annals of Probability 29 (2001) 1612--1624 [doi:10.1214/aop/1015345764].

Probability · Mathematics 2007-05-23 Teddy Seidenfeld , Mark J. Schervish , Joseph B. Kadane

A new lower bound on the error probability of maximum likelihood decoding of a binary code on a binary symmetric channel was proved in Barg and McGregor (2004, cs.IT/0407011). It was observed in that paper that this bound leads to a new…

Information Theory · Computer Science 2007-07-13 Alexander Barg

The problem of guessing a random string is revisited. A close relation between guessing and compression is first established. Then it is shown that if the sequence of distributions of the information spectrum satisfies the large deviation…

Information Theory · Computer Science 2010-08-12 Manjesh Kumar Hanawal , Rajesh Sundaresan

This is a correction to the afore-mentioned paper in Duke Math. J. vol. 75 (1994), 99-119 by S. Keel, K. Matsuki, and J. McKernan. We completely rewrite Chapter 6 according to the original manuscript of the second author, in order to fix…

Algebraic Geometry · Mathematics 2007-05-23 Kenji Matsuki