MétaCan
Menu
Back to cohort
Record W2091011159 · doi:10.1080/0020716031000148205

Language equations for timed alternating finite automata

2003· article· en· W2091011159 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.

Bibliographic record

VenueInternational Journal of Computer Mathematics · 2003
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsComputationComputer scienceAutomatonFinite-state machineTimed automatonDeterministic automatonω-automatonSet (abstract data type)Quantum finite automataAsynchronous communicationTheoretical computer scienceAutomata theoryAlgorithmAlgebra over a fieldMathematicsProgramming languagePure mathematics

Abstract

fetched live from OpenAlex

Traditionally, finite state automata are untimed or asynchronous models of computation in which only the ordering of events, not the time at which events occur, would affect the result of a computation. For real-time systems, it is important to augment these models of computation with a notion of time. For this purpose timed automata have become a powerful canonical model for describing timed behaviors and an effective tool for modeling real-time computations. In this paper, we extend the notion of timed alternating finite automata (TAFA), a class of alternating finite automata (AFA) extended with a finite set of real-valued clocks, and we present an algebraic interpretation of TAFA which parallels that of timed regular expressions and language equations. We further extend the equational representation of AFA to describe timed alternating finite automata, and explore solutions for such equations over time languages.

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)0.001
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: Methods
Teacher disagreement score0.948
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.044
GPT teacher head0.351
Teacher spread0.308 · 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