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Record W1984616519 · doi:10.1145/2680541

Stubborn Sets for Time Petri Nets

2015· article· en· W1984616519 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

VenueACM Transactions on Embedded Computing Systems · 2015
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPetri netPartial order reductionComputer scienceReduction (mathematics)Process architectureState (computer science)Set (abstract data type)Extension (predicate logic)EnumerationStochastic Petri netPetri dishTheoretical computer scienceAlgorithmModel checkingProgramming languageMathematicsDiscrete mathematics

Abstract

fetched live from OpenAlex

The main limitation of the verification approaches based on state enumeration is the state explosion problem. The partial order reduction techniques aim at attenuating this problem by reducing the number of transitions to be fired from each state while preserving properties of interest. Among the reduction techniques proposed in the literature, this article considers the stubborn set method of Petri nets and investigates its extension to time Petri nets. It establishes some useful sufficient conditions for stubborn sets, which preserve deadlocks and k-boundedness of places.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.480
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.074
GPT teacher head0.339
Teacher spread0.265 · 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