Expressiveness and analysis of Delayable Timed Petri Net*
Bibliographic record
Abstract
We consider an extension of Timed Petri Nets “à la Ramchandani” where the transitions are partitioned into delayable and non-delayable transitions which has proven to be suitable for the design of synchronous circuits. For this model called Delayable Timed Petri Net (DTPN), the firing delay of a non-delayable transition is strict whereas a delayable transition can miss its firing delay. Since the delays are natural numbers, this model can be studied as a discrete time model.We deal with the expressiveness of DTPN by a comparison with the well known Merlin's Time Petri Net model for which transitions can fire in a time interval. We show that DTPN are strictly more expressive w.r.t. weak timed bisimilarity than Merlin's model under the discrete-time semantics. We then deal with the symbolic reachability analysis of DTPN, for which we show the complexity of the successor symbolic state computation to be O(n). In addition, we propose a reduction of the number of edges to explore that preserves the markings and the firing sequences. The symbolic state space exploration is implemented in a prototype tool, which is evaluated on a classical TPN problem and a circuit design application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".