Task selection and scheduling in multifunction multichannel radars
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 a multifunction radar, several tasks with differing parameters, such as tracking and surveillance, must be scheduled on a timeline. In an overload situation, the scheduling can become a challenging problem, as some of the tasks may need to be delayed or even dropped. With recent advancements in multichannel radars, e.g., multifrequency radars, it is possible to perform multiple tasks on different channels in parallel. This leads to us considering the NP-hard problem of optimal task scheduling for multiple channels. We extend previously proposed heuristic approaches to the multichannel case; but our main contribution is an optimal solution based on the branch-and-bound (B&B) method. The heuristics are suboptimal but have the advantage of low computational complexity. On the other hand, the optimal solution provides significantly better performance than the heuristics, but has high computational burden and is likely impractical for real-time scheduling. However, the B&B approach does provide the performance upper bound on heuristics and can be used to train a cognitive task scheduler.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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