MétaCan
Menu
Back to cohort

An Encoder-Only Transformer to Generate Power Patterns from Far-Field Performance Criteria

2022· article· en· W4281392512 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue2022 16th European Conference on Antennas and Propagation (EuCAP) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsComputer scienceNear and far fieldRadiation patternAntenna apertureTransformerElectronic engineeringAlgorithmElectrical engineeringVoltageEngineeringAntenna (radio)OpticsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This paper proposes a method for generation of Maxwellian far-field power patterns, to be used as input for a variety of electromagnetic inversion algorithms. Typically inversion source and scattering algorithms require a desired power pattern to reconstruct equivalent currents and/or material properties, but it is more common for the designer to provide only far-field pattern specifications such as beam directions, null directions, or half-power beamwidth. By using a method that represents an aperture antenna with a grid of infinitesimal dipoles, a set of training data is created consisting of current distributions (with varying phase and amplitude) and their corresponding power patterns as well as power pattern specifications. An encoder-only transformer neural-network model is then trained to recreate the full power pattern given only the specifications.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.990

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)0.0110.000

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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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