Binary matrix decompositions without tongue-and-groove underdosage for radiation therapy planning
Bibliographic record
Abstract
In the present paper we consider a particular case of the segmentation problem arising in the elaboration of radiation therapy plans. This problem consists in decomposing an integer matrix A into a nonnegative integer linear combination of some particular binary matrices called segments which represent fields that are deliverable with a multileaf collimator. For the radiation therapy context, it is desirable to find a decomposition that minimizes the beam-on time, that is the sum of the coefficients of the decomposition. Here we investigate a variant of this minimization problem with an additional constraint on the segments, called the tongue-and-groove constraint. Although this minimization problem under the condition that the used segments have to respect the tongue-and-groove constraint has already been studied, the complexity of it is still unknown. Here we prove that in the particular case where A is a binary matrix this problem is polynomially solvable. We provide a polynomial procedure that finds such a decomposition with minimal beam-on time. Furthermore, we show that the beam-on time of an optimal decomposition (but not the segmentation itself) can be found by determining the chromatic number of a related perfect graph.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".