Bibliographic record
Abstract
Large-scale networks consisting of similar similar subprocesses can conveniently be modeled as Parameterized Discrete Event Systems (PDES). This modeling is particularly useful when the number of subprocesses is arbitrary, unknown or time-varying. Unfortunately, key problems such as checking the nonblocking property in these networks are undecidable. Moreover, mathematical tools supporting analysis of these networks are very limited. In previous work, we introduced weak invariant simulation in its preliminary form as a new mathematical notion for analysis of deterministic PDES and for defining tractable subclasses of PDES. In this paper, we broaden the definition of weak invariant simulation for nondeterministic PDES and give more insight into its properties. We compare weak invariant simulation to other simulation relations in the literature. Moreover, we propose a method to check whether a process weakly invariantly simulates another process with respect to a specific subalphabet. The greatest lower bound of all weak invariant simulations between two processes is also introduced. To illustrate the significance of weak invariant simulation, we outline its application to deadlock analysis of parameterized networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".