Efficient Snap Rounding with Integer Arithmetic.
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Bibliographic record
Abstract
In this paper we present a slightly modified definition of snap rounding, and provide two ecient algorithms that perform this rounding. The first algorithm takes n line segments as input and generates the set of snapped segments in O(|I| + c is(c)logn + |I m|), where |I| is the complexity of the unrounded arrangement I, is(c) is the number of segments that have an intersection or endpoint in pixel column c, and I m is the multi- set of snapped segment fragments. The second algo- rithm generates the rounded arrangement of segments in O(|I| + c is(c)logn + |I |logn), where |I | is the complexity of the rounded arrangement I. Both use simple integer arithmetic to compute the rounded ar- rangement by sweeping a strip of unit width through the arrangement, are robust, and are practical to im- plement. They improve upon existing algorithms, since existing running times either include a logarithmic fac- tor in |I|, (i.e., |I| logn), or depend upon the number of segments interacting within a particular hot pixel (is(h) and ed(h) (7), or |h| (3)), whereas ours are linear in |I| and depend upon the number of segments interacting in an entire hot column (is(c)), which is a much coarser partition of the plane.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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