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Record W2975529812 · doi:10.5220/0008985100690074

Fast Fourier Transform based Force Histogram Computation for 3D Raster Data

2020· article· en· W2975529812 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHistogramComputer scienceComputationFourier transformRaster graphicsRaster dataArtificial intelligenceComputer visionComputer graphics (images)Fast Fourier transformAlgorithmMathematicsImage (mathematics)

Abstract

fetched live from OpenAlex

The force histogram is a quantitative representation of relative position between two objects. For 2D data, two algorithms are defined: a well-functioning line-based algorithm, and Fast Fourier Transform (FFT) based algorithm that has a high computational cost. The line-based algorithm has previously been extended to the 3D case, but found to be unstable, and affected by a variety of factors. This thesis presents an extension of the FFT-based algorithm to the 3D case along with an analysis that demonstrates that, with the exception of a few special cases, the computational time of the 3D FFT-based algorithm is less than the line-based version. In addition, the results included here shown that the FFT-based algorithm is independent of the number of directions, the types of forces, and the shapes of the objects (convex, concave, disjoint or overlapping).

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.995
Threshold uncertainty score0.333

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 communication0.0000.001
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 head0.052
GPT teacher head0.275
Teacher spread0.223 · 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

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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