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Record W2094490307 · doi:10.1109/iscc.2012.6249288

On static RWA in translucent optical networks

2012· article· en· W2094490307 on OpenAlex
Quazi Rahman, Subir Bandyopadhyay, Y.P. Aneja

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsInteger programmingHeuristicComputer scienceRouting and wavelength assignmentRouting (electronic design automation)Linear programmingInteger (computer science)SIGNAL (programming language)Computer networkWavelength-division multiplexingDistributed computingAlgorithmMathematical optimizationTopology (electrical circuits)WavelengthMathematicsOpticsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The distance an optical signal can travel before the signal quality degrades to a level that necessitates regeneration is called the optical reach. To establish a lightpath of length greater than the optical reach, it is necessary to regenerate optical signals at one or more nodes. In this paper we have proposed routing and wavelength assignment algorithms for translucent networks. For each request for a lightpath, the objective is to find a route from the source to the destination of the request, so that a translucent lightpath may be established that requires the fewest possible stages of regeneration. We have proposed an Integer Linear Programming (ILP) formulation that gives the optimum solutions for small networks. For larger networks, we have proposed an efficient heuristic. We have shown that the heuristic produces close-to-optimal solutions in a fraction of the time needed for the optimal solutions.

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 score0.752
Threshold uncertainty score0.386

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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