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Record W1993598446 · doi:10.1142/s0129054102000960

AN EFFICIENT ALGORITHM FOR CONSTRUCTING MINIMAL COVER AUTOMATA FOR FINITE LANGUAGES

2002· article· en· W1993598446 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Foundations of Computer Science · 2002
Typearticle
Languageen
FieldComputer Science
Topicsemigroups and automata theory
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCover (algebra)Nondeterministic finite automatonDFA minimizationDeterministic finite automatonQuantum finite automataNested wordRegular languageFinite-state machineAutomatonDeterministic automatonω-automatonComputer scienceTime complexityAlgorithmFormal languageTimed automatonMathematicsTheoretical computer scienceAutomata theoryTwo-way deterministic finite automaton

Abstract

fetched live from OpenAlex

The concept of cover automata for finite languages was formally introduced in [3]. Cover automata have been studied as an efficient representation of finite languages. In [3], an algorithm was given to transform a DFA that accepts a finite language to a minimal deterministic finite cover automaton (DFCA) with the time complexity O(n 4 ), where n is the number of states of the given DFA. In this paper, we review the basic concept of cover automata and describe a new efficient transformation algorithm with the time complexity O(n 2 ), which is a significant improvement from the previous algorithm.

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.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.975
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.312
Teacher spread0.292 · 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