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Record W2296189763

Duality for Geometric Set Cover and Geometric Hitting Set Problems on Pseudodisks

2015· article· en· W2296189763 on OpenAlexaff
Stéphane Durocher, Robert Fraser

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCover (algebra)MathematicsDuality (order theory)Set cover problemSet (abstract data type)Dual (grammatical number)CombinatoricsCovering problemsObject (grammar)Point (geometry)Discrete mathematicsSet functionComputer scienceGeometryArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Given an instance of a geometric set cover problem on a set of points X and a set of objects R, the dual is a geo-metric hitting set problem on a set of points P and a set of objects Q, where there exists a one-to-one mapping from each xj ∈ X to a dual object Qj ∈ Q and for each Ri ∈ R to a dual point in pi ∈ P, so that a dual point pi is contained in a dual object Qj if and only if the cor-responding primal point xj is covered by the object Ri. In this work, we explore the setting of geometric dual-ity for geometric set cover problems on pseudodisks. We first show that there does not always exist a geometric dual on pseudodisks. We initiate the search for a char-acterization of the class of objects that may be dualized by identifying a sufficient (but not necessary) property for a dual to exist on distinct pseudodisks, called the pair-cover and crossing-quad free property. We show that such problems may be dualized into hitting set in-stances on pseudodisks by building a planar support for the dual instance, and then constructing an orthogo-nal drawing of the support which we transform into a dual set of pseudodisks. A corollary of these results is a PTAS for dualizable set cover problems using the PTAS for hitting set on pseudodisks. 1

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.105
GPT teacher head0.315
Teacher spread0.210 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations3
Published2015
Admission routes1
Has abstractyes

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