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Record W3033468545 · doi:10.3934/amc.2020084

Involutory-Multiple-Lightweight MDS Matrices based on Cauchy-type Matrices

2020· article· en· W3033468545 on OpenAlex

Why this work is in the frame

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affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Mathematics of Communications · 2020
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMathematicsCauchy distributionCauchy matrixCombinatoricsMatrix (chemical analysis)Separable spaceArithmeticAlgebra over a fieldDiscrete mathematicsPure mathematicsMathematical analysis

Abstract

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<p style='text-indent:20px;'>One of the best methods for constructing maximum distance separable (<inline-formula><tex-math id="M1">\begin{document}$ \operatorname{MDS} $\end{document}</tex-math></inline-formula>) matrices is based on making use of Cauchy matrices. In this paper, by using some extensions of Cauchy matrices, we introduce several new forms of <inline-formula><tex-math id="M2">\begin{document}$ \operatorname{MDS} $\end{document}</tex-math></inline-formula> matrices over finite fields of characteristic 2. A known extension of a Cauchy matrix, called the Cauchy-like matrix, with application in coding theory was introduced in 1985. One of the main contributions of this paper is to apply Cauchy-like matrices to introduce <b><inline-formula><tex-math id="M3">\begin{document}$ 2n \times 2n $\end{document}</tex-math></inline-formula> involutory <inline-formula><tex-math id="M4">\begin{document}$ \operatorname{MDS} $\end{document}</tex-math></inline-formula> matrices</b> in the semi-Hadamard form which is a generalization of the previously known methods. We make use of Cauchy-like matrices to construct <b>multiple <inline-formula><tex-math id="M5">\begin{document}$ \operatorname{MDS} $\end{document}</tex-math></inline-formula> matrices</b> which can be used in the Feistel structures. We also introduce a new extension of Cauchy matrices to be referred to as <i>Cauchy-light matrices</i>. The introduced Cauchy-light matrices are applied to construct <inline-formula><tex-math id="M6">\begin{document}$ n \times n $\end{document}</tex-math></inline-formula> <inline-formula><tex-math id="M7">\begin{document}$ \operatorname{MDS} $\end{document}</tex-math></inline-formula> matrices having at least <inline-formula><tex-math id="M8">\begin{document}$ 3n-3 $\end{document}</tex-math></inline-formula> entries equal to the unit element <inline-formula><tex-math id="M9">\begin{document}$ 1 $\end{document}</tex-math></inline-formula>; such a matrix is called a <b>lightweight <inline-formula><tex-math id="M10">\begin{document}$ \operatorname{MDS} $\end{document}</tex-math></inline-formula> matrix</b> and can be used in the lightweight cryptography. A simple closed-form expression is given for the determinant of Cauchy-light matrices.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.293
Teacher spread0.261 · 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