Dependency graph: An algorithm for analysis of generalized parameterized networks
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.
Bibliographic record
Abstract
In areas such as computer software and hardware, manufacturing systems and transportation, engineers encounter networks with arbitrarily large numbers of isomorphic subprocesses. Parameterized discrete event systems (PDES) provide a framework for modeling such networks. The analysis of PDES is a challenge as some key properties such as nonblocking and deadlock-freedom are undecidable. Previously we have established a procedure for deadlock analysis of a parameterized ring network of isomorphic subprocesses. Here we consider a network consisting of several parameterized sections with a more general topology. To model these networks we introduce Generalized Parameterized Discrete Event Systems (GPDES). The difficulty in analysis of a GPDES is the fact that some of the subprocesses interact with several parameterized sections of the network. Hence the analysis proposed in this paper involves careful study of interaction among different branches of the network. We investigate interactions among different components of the network, using a dependency graph. The dependency graph is a directed graph developed to characterize reachable deadlocks of proposed GPDES.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it