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Record W2093320800 · doi:10.1145/2406336.2406343

Reducing Interleaving Semantics Redundancy in Reachability Analysis of Time Petri Nets

2013· article· en· W2093320800 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 · 2013
Typearticle
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsPolytechnique Montréal
FundersDivision of Computer and Network SystemsNational Science Foundation
KeywordsReachabilityPetri netInterleavingRedundancy (engineering)Computer scienceSemantics (computer science)State (computer science)Theoretical computer scienceSet (abstract data type)Stochastic Petri netModel checkingAutomatonState spaceAlgorithmProgramming languageMathematicsOperating system

Abstract

fetched live from OpenAlex

The main problem of verification techniques based on exploration of (reachable) state space is the state explosion problem. In timed models, abstract states reached by different interleavings of the same set of transitions are, in general, different and their union is not necessarily an abstract state. To attenuate this state explosion, it would be interesting to reduce the redundancy caused by the interleaving semantics by agglomerating all these abstract states whenever their union is an abstract state. This article considers the time Petri net model and establishes some sufficient conditions that ensure that this union is an abstract state. In addition, it proposes a procedure to compute this union without computing beforehand intermediate abstract states. Finally, it shows how to use this result to improve the reachability analysis.

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: Empirical · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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