Tumor-tracking in radiotherapy: parameterization of sensor time-delay compensators and associated performance limitations
Bibliographic record
Abstract
Motivated by research into tumour tracking in radiotherapy, this paper considers the problem of constructing a linear time-invariant asymptotic estimator to predict the tumour location in real time. The challenge in the estimator design is to accommodate a time delay associated with the sensor, which in this case is an X-ray imager and associated image processor. The contributions of this paper are first, to show that the class of estimators which achieves perfect asymptotic estimation can be parameterized in a manner similar to the well-known Youla parameterization of stabilizing feedback controllers, and, second, to prove that there are fundamental limits on the performance levels that can be achieved. Aspects of performance considered include disturbance rejection, sensor noise rejection, and sensitivity to model uncertainty. The results are restricted to single-input single-output discrete-time linear time-invariant systems
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".