Vérification temporelle des systèmes cycliques et acycliques basée sur lâanalyse des contraintes
Bibliographic record
Abstract
Nous présentons une nouvelle approche pour formuler et calculer le temps de séparation\ndes événements utilisé dans l’analyse et la vérification de différents systèmes cycliques et\nacycliques sous des contraintes linéaires-min-max avec des composants ayant des délais finis et\ninfinis. Notre approche consiste à formuler le problème sous la forme d’un programme entier\nmixte, puis à utiliser le solveur Cplex pour avoir les temps de séparation entre les événements.\nAfin de démontrer l’utilité en pratique de notre approche, nous l’avons utilisée pour la\nvérification et l’analyse d’une puce asynchrone d’Intel de calcul d’équations différentielles.\nComparée aux travaux précédents, notre approche est basée sur une formulation exacte et elle\npermet non seulement de calculer le maximum de séparation, mais aussi de trouver un\nordonnancement cyclique et de calculer les temps de séparation correspondant aux différentes\npériodes possibles de cet ordonnancement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".