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Record W2726254666 · doi:10.1111/cgf.13242

Generalized Matryoshka: Computational Design of Nesting Objects

2017· article· en· W2726254666 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputer Graphics Forum · 2017
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNesting (process)Computer scienceObject (grammar)ReplicaScale (ratio)Variety (cybernetics)Orientation (vector space)Distributed computingTheoretical computer scienceComputer graphics (images)Artificial intelligenceMathematicsGeometryGeography

Abstract

fetched live from OpenAlex

Abstract This paper generalizes the self‐similar nesting of Matryoshka dolls (“Russian nesting dolls”) to arbitrary solid objects. We introduce the problem of finding the largest scale replica of an object that nests inside itself. Not only should the nesting object fit inside the larger copy without interpenetration, but also it should be possible to cut the larger copy in two and remove the smaller object without collisions. We present a GPU‐accelerated evaluation of nesting feasibility. This test can be conducted at interactive rates, providing feedback during manual design. Further, we may optimize for some or all of the nesting degrees of freedom (e.g., rigid motion of smaller object, cut orientation) to maximize the smaller object's scale while maintaining a feasible nesting. Our formulation and tools robustly handle imperfect geometric representations and generalize to the nesting of dissimilar objects in one another. We explore a variety of applications to aesthetic and functional shape design.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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