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Record W4400035848 · doi:10.1109/tit.2024.3417894

A New Version of q-Ary Varshamov-Tenengolts Codes With More Efficient Encoders: The Differential VT Codes and The Differential Shifted VT Codes

2024· article· en· W4400035848 on OpenAlexfundno aff
Tuan Thanh Nguyen, Kui Cai, Paul H. Siegel

Bibliographic record

VenueIEEE Transactions on Information Theory · 2024
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsnot available
FundersMinistry of Advanced Education and Technology
KeywordsDifferential codingBlock codeLuby transform codeEncoderTurbo codeLinear codeComputer scienceDifferential (mechanical device)Raptor codeConcatenated error correction codeAlgorithmMathematicsDiscrete mathematicsDecoding methodsPhysics

Abstract

fetched live from OpenAlex

The problem of correcting deletions and insertions has recently received significantly increased attention due to the DNA-based data storage technology, which suffers from deletions and insertions with extremely high probability. In this work, we study the problem of constructing non-binary burst-deletion/insertion correcting codes. Particularly, for the quaternary alphabet, our designed codes are suited for correcting a burst of deletions/insertions in DNA storage. Non-binary codes correcting a single deletion or insertion were introduced by Tenengolts (1984), and the results were extended to correct a fixed-length burst of deletions or insertions by Schoeny et al. (2017). Recently, Wang et al. (2021) proposed constructions of non-binary codes of length n, correcting a burst of length at most two for q-ary alphabets with redundancy$\log n+O(\log q \log \log n)$bits, for arbitrary even q. The common idea in those constructions is to convert non-binary sequences into binary sequences, and the error decoding algorithms for the q-ary sequences are mainly based on the success of recovering the corresponding binary sequences, respectively. In this work, we look at a natural solution that the error detection and correction algorithms are performed directly over q-ary sequences, and for certain cases, our codes provide a more efficient encoder with lower redundancy than the best-known encoder in the literature. Particularly, (Single-error correction codes) We first present a new version of non-binary VT codes that are capable of correcting a single deletion or single insertion, providing an alternative simpler and more efficient encoder of the construction by Tenengolts (1984). Our construction is based on the differential vector, and the codes are referred to as the differential VT codes. In addition, we provide linear-time algorithms that encode user messages into these codes of length n over the q-ary alphabet for$q \geqslant 2$with at most$\lceil \log _{q} n\rceil +1$redundant symbols, while the optimal redundancy required is at least$\log _{q} n+\log _{q} (q-1)$symbols. Our designed encoder reduces the redundancy of the best-known encoder of Tenengolts (1984) by at least 2 redundant symbols or equivalently$2\log _{2} q$bits. (Burst-error correction codes) We use the idea of the binary shifted VT codes to define the q-ary differential shifted VT codes, and propose non-binary codes correcting a burst of up to two deletions (or two insertions) with redundancy$\log n+3\log \log n+ O(\log q)$bits, which improves a recent result of Wang et al. (2021) with redundancy$\log n+O(\log q \log \log n)$bits for all$q\geqslant 8$. We then extend the construction to design non-binary codes correcting a burst of either exactly or at most t deletions (or insertions) for arbitrary$t\geqslant 2$.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.202
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2024
Admission routes1
Has abstractyes

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