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Record W2118695911 · doi:10.1142/s0218195910003256

FINDING ALL DOOR LOCATIONS THAT MAKE A ROOM SEARCHABLE

2010· article· en· W2118695911 on OpenAlexaff
Tsunehiko Kameda, John Z. Zhang

Bibliographic record

VenueInternational Journal of Computational Geometry & Applications · 2010
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsUniversity of LethbridgeSimon Fraser University
Fundersnot available
KeywordsPolygon (computer graphics)Boundary (topology)Simple polygonCombinatoricsPoint (geometry)MathematicsLine (geometry)Line segmentPolygon coveringComputer scienceAlgorithmComputer graphics (images)GeometryMonotone polygonMathematical analysisComputer network

Abstract

fetched live from OpenAlex

A room is a simple polygon with a prespecified point, called the door, on its boundary. Search starts at the door, in order to detect any intruder who was in the room at the time it started. Search may be conducted by two guards on the boundary who can detect any intruder that crosses the line connecting them, or by a single searcher with a flashlight, moving on the boundary. A room may or may not be searchable, depending on where the door is placed or no matter where the door is placed. The main contribution of this paper is an O(n log n) time algorithm for finding all intervals on the polygon boundary, if any, where the door can be placed for the resulting room to be searchable, where n is the number of sides of the given polygon.

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 categoriesMeta-epidemiology (narrow)
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 score0.733
Threshold uncertainty score1.000

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 communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.317
Teacher spread0.292 · 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.

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
Published2010
Admission routes1
Has abstractyes

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