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Record W4252940155 · doi:10.1109/dac.2003.1219000

Model order reduction of nonuniform transmission lines using integrated congruence transform

2004· article· en· W4252940155 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

VenueProceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451) · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsCarleton University
Fundersnot available
KeywordsOrthonormal basisBasis functionAlgorithmComputer scienceCongruence (geometry)Model order reductionOrthogonal basisBasis (linear algebra)MathematicsOrthonormalityTopology (electrical circuits)Mathematical optimizationProjection (relational algebra)Mathematical analysis

Abstract

fetched live from OpenAlex

This paper presents a new algorithm based on Integrated Congruence transform for the analysis of both uniform and nonuniform transmission lines. The key advantage of the proposed algorithm is that constructing a spanning orthonormal basis for the space-dependent moments is done without computing these moments explicitly. The proposed algorithm thus carries the numerical efficiency of Krylov-based projection techniques of lumped RLC networks to the domain of the distributed transmission line networks. The proposed algorithm can be used to construct an orthogonal basis for any set of moments related through a differential operator.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.615
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.052
GPT teacher head0.277
Teacher spread0.225 · 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