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Tal Shinkar

dblp:238/0024 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2022
0000-0002-2590-5821ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes › burst error correction
cluster-correcting codes
0.612022
Clustering-Correcting Codes · IEEE Trans. Inf. Theory 2022
Coding theory
error-correcting codes
0.612022
Clustering-Correcting Codes · IEEE Trans. Inf. Theory 2022
Coding theory › error-correcting codes › storage coding
DNA storage
0.212022
Clustering-Correcting Codes · IEEE Trans. Inf. Theory 2022

Methods — techniques the papers use, named apart from their topics

code construction · 0.6
YearPublicationVenuePosition
2022 Clustering-Correcting Codes
Tal Shinkar, Eitan Yaakobi, Andreas Lenz 0001, Antonia Wachter-Zeh
IEEE Trans. Inf. Theory1
2019 Clustering-Correcting Codes
abstract
A new family of codes, calledclustering-correcting codes, is presented in this paper. This family of codes is motivated by the special structure of the data that is stored in DNA-based storage systems. The data stored in these systems has the form of unordered sequences, also calledstrands, and every strand is synthesized thousands to millions of times, where some of these copies are read back during sequencing. Due to the unordered structure of the strands, an important task in the decoding process is to place them in their correct order. This is usually accomplished by allocating part of the strand for an index. However, in the presence of errors in the index field, important information on the order of the strands may be lost. Clustering-correcting codes ensure that if the distance between the index fields of two strands is small, their data fields have large distance. It is shown how this property enables to place the strands together in their correct clusters even in the presence of errors. We present lower and upper bounds on the size of clustering-correcting codes and an explicit construction of these codes which uses only a single symbol of redundancy. The results are first presented for the Hamming metric and are then extended for the edit distance.
Tal Shinkar, Eitan Yaakobi, Andreas Lenz 0001, Antonia Wachter-Zeh
ISIT1