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Record W2105030329 · doi:10.1109/tnet.2005.845551

Optimization of optical cross-connects with wave-mixing conversion

2005· article· en· W2105030329 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

VenueIEEE/ACM Transactions on Networking · 2005
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsMixing (physics)Topology (electrical circuits)Four-wave mixingSpace (punctuation)Network topologyWavelengthConstant (computer programming)PhysicsComputer scienceDiscrete mathematicsMathematicsCombinatoricsOpticsComputer networkNonlinear opticsQuantum mechanics

Abstract

fetched live from OpenAlex

This paper presents new constructions of multistage wave-mixing networks with arbitrary b/spl times/b space-switching elements, where b /spl ges/ 2. In these networks, for a size of F fiber links and W wavelengths per link, converter requirements are O(Flog/sub b/W) or O(FW/b) for rearrangeable nodes, and O(Flog/sub b/Wlog/sub b/(FW)) or O(FWlog/sub b/(FW)/b) for different types of strictly nonblocking nodes inspired from the Cantor topology. In all cases, the worst case number of cascaded conversion is O(log/sub b/W). When b=W /spl les/ F, the required number of converters, and the worst case number of cascaded conversions are respectively O(F) and O(1), and are both optimal up to a constant. The new networks generalize and improve upon previous wave-mixing networks based on 2/spl times/2 space switches.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.948

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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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