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Record W2968065474 · doi:10.2140/memocs.2019.7.189

The object detection by autonomous apparatus as a solution of the Buffon needle problem

2019· article· en· W2968065474 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMathematics and Mechanics of Complex Systems · 2019
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsnot available
FundersOtto von Guericke University MagdeburgCollege of Engineering, Michigan State UniversityUniversität Duisburg-EssenFreie Universität BerlinBilkent ÜniversitesiCentre National de la Recherche ScientifiqueUniversity of North Carolina at Chapel HillUniversität zu KölnUniversità degli Studi di PaviaUniversité de LyonUniversität WienAkademie Věd České RepublikyRussian Academy of SciencesRussian Foundation for Basic ResearchMcGill UniversityUniversidad Rey Juan CarlosLouisiana State UniversityUniversity of PittsburghIndian National Science AcademyMichigan State UniversityCarnegie Mellon UniversityVanderbilt UniversityWayne State University
KeywordsObject (grammar)Artificial intelligenceComputer visionComputer science

Abstract

fetched live from OpenAlex

The problem of object detection by autonomous apparatus is considered.The probabilistic formulation of the problem is proposed by means of a reduction to the classical Buffon problem.The latter naturally arises when the problem is formulated in the coordinate system associated with the apparatus.The problem of detection is considered for devices moving in the open space along a circle around one body, for vehicles patrolling along the linear boundary protecting the bodies, and for devices protecting the system of bodies.The problem of object detection was shown to admit an analysis in the presence of an asymptotic parameter determined by the ratio of the local size of the apparatus scanning area to the global size of the problem under consideration.For all problems, the minimum number of apparatuses that could detect a penetrating object with probability one was calculated.Communicated by Francesco dell'Isola.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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