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Record W2102564119 · doi:10.1109/92.974900

Efficient exponentiation using weakly dual basis

2001· article· en· W2102564119 on OpenAlexaff
Huapeng Wu, M.A. Hasan

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2001
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExponentiationFinite fieldPolynomial basisBasis (linear algebra)MathematicsModular exponentiationPolynomialNormal basisDual (grammatical number)Square (algebra)Topology (electrical circuits)Computer scienceDiscrete mathematicsPublic-key cryptographyCombinatoricsEncryptionGalois theoryMathematical analysisGeometry

Abstract

fetched live from OpenAlex

A new architecture for finite field exponentiation using weakly dual bases is presented. An extended bidirectional linear feedback shift register is designed to multiply an arbitrary field element with certain essential multiplicands in weakly dual basis (WDB). Each of these multiplications is done in one single clock cycle. It is shown that a bit parallel implementation of the WDB fourth power has complexities comparable to those of polynomial basis fourth power. The proposed structure can effectively speed up the computation of exponentiation and is expected to reduce the power consumption compared to the conventional square and multiply scheme. Compared to the structure for polynomial basis exponentiation, the new structure is thus advantageous in a system where the WDB is already available.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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

Citations2
Published2001
Admission routes1
Has abstractyes

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