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In this paper, we consider the group testing problem with adaptive test designs and noisy outcomes. We propose a computationally efficient four-stage procedure with components including random binning, identification of bins containing…

Computation · Statistics 2019-11-11 Jonathan Scarlett

In this paper, we treat the problem of testing for normality as a binary classification problem and construct a feedforward neural network that can successfully detect normal distributions by inspecting small samples from them. The…

Machine Learning · Statistics 2020-10-08 Miloš Simić

In this paper, we consider encoding strategies for the Z-channel with noiseless feedback. We analyze the combinatorial setting where the maximum number of errors inflicted by an adversary is proportional to the number of transmissions,…

Information Theory · Computer Science 2021-02-11 Christian Deppe , Vladimir Lebedev , Georg Maringer , Nikita Polyanskii

We begin a systematic study of the problem of the zero--error capacity of noisy binary channels with memory and solve some of the non--trivial cases.

Combinatorics · Mathematics 2016-02-22 Gérard Cohen , Emanuela Fachini , János Körner

A class of two-bit bit flipping algorithms for decoding low-density parity-check codes over the binary symmetric channel was proposed in [1]. Initial results showed that decoders which employ a group of these algorithms operating in…

Information Theory · Computer Science 2012-05-22 Dung Viet Nguyen , Bane Vasic , Michael W. Marcellin

The basic goal in combinatorial group testing is to identify a set of up to $d$ defective items within a large population of size $n \gg d$ using a pooling strategy. Namely, the items can be grouped together in pools, and a single…

Discrete Mathematics · Computer Science 2013-01-21 Mahdi Cheraghchi

We consider the problem of detecting the true quantum state among $r$ possible ones, based of measurements performed on $n$ copies of a finite-dimensional quantum system. A special case is the problem of discriminating between $r$…

Quantum Physics · Physics 2012-05-14 Michael Nussbaum , Arleta Szkoła

Group testing is an approach aimed at identifying up to $d$ defective items among a total of $n$ elements. This is accomplished by examining subsets to determine if at least one defective item is present. In our study, we focus on the…

Data Structures and Algorithms · Computer Science 2023-07-12 Nader H. Bshouty , Catherine A. Haddad-Zaknoon

The question of whether Reed-Muller (RM) codes achieve capacity on binary memoryless symmetric (BMS) channels has drawn attention since it was resolved positively for the binary erasure channel by Kudekar et al. in 2016. In 2021, Reeves and…

Information Theory · Computer Science 2025-02-11 Avijit Mandal , Henry D. Pfister

We address the problem of bounding below the probability of error under maximum likelihood decoding of a binary code with a known distance distribution used on a binary symmetric channel. An improved upper bound is given for the maximum…

Information Theory · Computer Science 2007-07-16 Alexander Barg , Andrew McGregor

We consider the problem of distributed binary hypothesis testing of two sequences that are generated by an i.i.d. doubly-binary symmetric source. Each sequence is observed by a different terminal. The two hypotheses correspond to different…

Information Theory · Computer Science 2018-01-03 Eli Haim , Yuval Kochman

We study the group testing problem with non-adaptive randomized algorithms. Several models have been discussed in the literature to determine how to randomly choose the tests. For a model ${\cal M}$, let $m_{\cal M}(n,d)$ be the minimum…

Machine Learning · Computer Science 2019-11-06 Nader H. Bshouty , George Haddad , Catherine A. Haddad-Zaknoon

In this paper, we introduce a variation of the group testing problem where each test is specified by an ordered subset of items and returns the first defective item in the specified order or returns null if there are no defectives. We refer…

Information Theory · Computer Science 2024-09-30 Waqar Mirza , Nikhil Karamchandani , Niranjan Balachandran

We consider the zero-error capacity of deletion channels. Specifically, we consider the setting where we choose a codebook ${\cal C}$ consisting of strings of $n$ bits, and our model of the channel corresponds to an adversary who may delete…

Information Theory · Computer Science 2011-02-02 Ian A. Kash , Michael Mitzenmacher , Justin Thaler , Jonathan Ullman

In this work, binary block-to-block distribution matching is considered. m independent and uniformly distributed bits are mapped to n output bits resembling a target product distribution. A rate R is called achieved by a sequence of…

Information Theory · Computer Science 2013-02-06 Georg Böcherer , Rana Ali Amjad

The input-constrained erasure channel with feedback is considered, where the binary input sequence contains no consecutive ones, i.e., it satisfies the $(1,\infty)$-RLL constraint. We derive the capacity for this setting, which can be…

Information Theory · Computer Science 2015-03-12 Oron Sabag , Haim H. Permuter , Navin Kashyap

Tight lower and upper bounds on the ratio of relative entropies of two probability distributions with respect to a common third one are established, where the three distributions are collinear in the standard $(n-1)$-simplex. These bounds…

Information Theory · Computer Science 2018-05-31 Shengtian Yang , Jun Chen

A zero-error coding scheme of asymptotic rate $ \log_2 (1+\sqrt{5}) - 1 $ was recently described for a communication channel composed of parallel asynchronous lines satisfying the so-called no switch assumption. We prove that this is in…

Information Theory · Computer Science 2020-08-13 Mladen Kovačević

The input-constrained binary erasure channel (BEC) with strictly causal feedback is studied. The channel input sequence must satisfy the $(0,k)$-runlength limited (RLL) constraint, i.e., no more than $k$ consecutive `$0$'s are allowed. The…

Information Theory · Computer Science 2017-12-08 Ori Peled , Oron Sabag , Haim H. Permuter

In this paper we suggest two statistical hypothesis tests for the regression function of binary classification based on conditional kernel mean embeddings. The regression function is a fundamental object in classification as it determines…

Machine Learning · Statistics 2022-06-22 Ambrus Tamás , Balázs Csanád Csáji
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