A new algorithm for computing visibility graphs of polygonal obstacles in the plane
Why this work is in the frame
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Bibliographic record
Abstract
Given a set of $h$ pairwise disjoint polygonal obstacles with a total of $n$ vertices in the plane, the vertex-vertex visibility graph is an undirected graph whose nodes are vertices of the obstacles and whose edges are pairs of visible vertices. The vertex-edge and edge-edge visibility graphs are defined similarly. Ghosh and Mount gave a well-known output-sensitive $O(n\log n+k)$ time algorithm for computing these visibility graphs, where $k$ is the size of the corresponding graph. By developing new techniques based on an extended corridor structure, we augment Ghosh and Mount’s algorithm to build these visibility graphs in $O(n+h\log h+k)$ time, after the free space is triangulated. The new algorithm improves Ghosh and Mount’s algorithm by reducing its additive $O(n\log n)$ time factor to $O(n + h\log h)$. Like Ghosh and Mount’s algorithm, our algorithm can also compute several important structures such as the funnel structure and the enhanced visibility graph, which may have other applications.
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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 | 0.001 | 0.002 |
| 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