Résolution d'équations en algèbre de Kleene - Applications à l'analyse de programmes
Bibliographic record
Abstract
Au fil des ans, l’algèbre de Kleene s’est avérée être un outil formel très pratique et\nflexible quant vient le temps de raisonner sur les programmes informatiques. Cependant,\nactuellement, la plupart des applications à l’analyse de programmes de l’algèbre de\nKleene se font en sélectionnant un problème précis et en voyant comment l’algèbre\nde Kleene permet de le résoudre, ce qui limite les applications possibles. L’objectif\nvisé par ce mémoire est de déterminer dans quelle mesure la résolution d’équations,\nen algèbre de Kleene, peut être utilisée en analyse de programmes. Une grande partie\nde ce mémoire est donc consacrée à la résolution de différents types d’équations dans\ndifférentes variantes de l’algèbre de Kleene. Puis nous montrons comment la vérification\nde programmes ainsi que la synthèse de contrôleurs peuvent tirer profit de la résolution\nd’équations en algèbre de Kleene.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".