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Record W2102922436 · doi:10.1142/s0218195900000048

POINT VISIBILITY GRAPHS AND ${\mathcal O}$-CONVEX COVER

2000· article· en· W2102922436 on OpenAlexaff
David Avis, Thomas C. Shermer

Bibliographic record

VenueInternational Journal of Computational Geometry & Applications · 2000
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCombinatoricsMathematicsVisibility graphPolygon (computer graphics)Visibility polygonRegular polygonVisibilityCardinality (data modeling)Uncountable setDiscrete mathematicsPolygon coveringComputer scienceGeometry

Abstract

fetched live from OpenAlex

A visibility relation can be viewed as a graph: the uncountable graph of a visibility relationship between points in a polygon P is called the point visibility graph (PVG) of P. In this paper we explore the use of perfect graphs to characterize tractable subproblems of visibility problems. Our main result is a characterization of which polygons are guaranteed to have weakly triangulated PVGs, under a generalized notion of visibility called [Formula: see text]-visibility. Let [Formula: see text] denote a set of line orientations. A connected point set P is called [Formula: see text]-convex if the intersection of P with any line with orientation in [Formula: see text] is connected. Two points in a polygon are said to be [Formula: see text]-visible if there is an [Formula: see text]-convex path between them inside the polygon. Let [Formula: see text] denote the set of orientations perpendicular to orientations in [Formula: see text]. Let [Formula: see text] be the set of orientations θ such that a "reflex" local maximum in the boundary of P exists with respect to θ. Our characterization of which polygons have weakly-triangulated PVGs is based on restricting the cardinality and span of [Formula: see text]. This characterization allows us to exhibit a class of polygons admitting a polynomial algorithm for [Formula: see text]-convex cover.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.272
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2000
Admission routes1
Has abstractyes

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