Optimized Implementation of Multirate Mixed-Criticality Synchronous Reactive Models
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
Model-based design using Synchronous Reactive (SR) models enables early design and verification of application functionality in a platform-independent manner, and the implementation on the target platform should guarantee the preservation of application semantic properties. Mixed-Criticality Scheduling (MCS) is an effective approach to addressing diverse certification requirements of safety-critical systems that integrate multiple subsystems with different levels of criticality. This article considers fixed-priority scheduling of mixed-criticality SR models, and considers two scheduling approaches: Adaptive MCS and Elastic MCS. We formulate the optimization problem of minimizing the total system cost of added functional delays in the implementation while guaranteeing schedulability, and present an optimal algorithm based on branch-and-bound search, and an efficient heuristic algorithm.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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