A new algorithm for computing visibility graphs of polygonal obstacles in the plane
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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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".