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Record W2069813451 · doi:10.1142/s021798491550030x

A promising nonlinear conjugate-gradient method proposed to design nonlinear domains with a disordered distribution

2015· article· en· W2069813451 on OpenAlex
Li-Ming Zhao, Guikuan Yue, Yun-Song Zhou, Fu-He Wang

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

VenueModern Physics Letters B · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotorefractive and Nonlinear Optics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNonlinear systemConjugate gradient methodNonlinear conjugate gradient methodConjugateDistribution (mathematics)Simulated annealingComputer scienceApplied mathematicsMatching (statistics)Materials scienceMathematical optimizationStatistical physicsMathematical analysisMathematicsAlgorithmPhysicsGradient descentArtificial intelligence

Abstract

fetched live from OpenAlex

A new method, namely the nonlinear conjugate-gradient (NCG) method, is proposed to design nonlinear domains with a disordered distribution, in which an efficient broadband second harmonic generation can be achieved simultaneously with high conversion efficiency. It is demonstrated by numerical simulation that the NCG method has obvious advantages in realizing the optimal quasi-phase-matching, in comparison with the traditional simulated annealing method.

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 score0.473
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
Bibliometrics0.0000.000
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 head0.276
Teacher spread0.248 · 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