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Record W2130589109 · doi:10.1109/20.952648

Application of a wavelet transform in eigenvalue problems for electromagnetic field computations

2001· article· en· W2130589109 on OpenAlexaff
K.R. Shao, Jiankun Yang, J.D. Lavers

Bibliographic record

VenueIEEE Transactions on Magnetics · 2001
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEigenvalues and eigenvectorsDimension (graph theory)ComputationMatrix (chemical analysis)Wavelet transformWaveletSeries (stratigraphy)Field (mathematics)MathematicsApplied mathematicsAlgorithmMathematical analysisComputer sciencePhysicsPure mathematicsMaterials scienceQuantum mechanicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we present the wavelet transform (WT) method to reduce the dimension of a matrix without sacrificing too much precision on eigenvalues. By using the WT, the original matrix is split into two matrices, with each being a quarter the size of the original one. One of the derived matrices contains the smaller eigenvalues, while the other has larger ones. Moreover, this procedure can be taken successively, one can have a series of reduced order matrices.

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)0.000
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: none
Teacher disagreement score0.929
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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 designOther design
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

Citations2
Published2001
Admission routes1
Has abstractyes

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