Weak Invariant Simulation and Its Application to Analysis of Parameterized Networks
Bibliographic record
Abstract
Multi-process networks figure in many engineering applications. Parameterized discrete event systems provide a convenient means of modeling such networks when the number of subprocesses is arbitrary, unknown or time-varying. Unfortunately, some key properties of these networks, such as nonblocking and deadlock-freedom, are undecidable. Moreover, mathematical tools supporting analysis of these networks are limited. This paper introduces a novel mathematical notion, weak invariant simulation, which is adapted to the analysis of synchronous products of discrete event systems. Furthermore, it proposes an efficient method to check whether a finite-state generator weakly invariantly simulates another finite-state generator with respect to a specific subalphabet. This new simulation relation is used to define a tractable subclass of parameterized ring networks of isomorphic subprocesses in which deadlock-freedom is decidable. Within this framework, a procedure is given to determine the reachable deadlocked states of the network. The effectiveness of the procedure is demonstrated by the deadlock analysis of a version of the dining philosophers problem.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".