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Record W4314946952 · doi:10.1109/cdc51059.2022.9993369

Modular Scattering-Based Design of Dissipative Networks

2022· article· en· W4314946952 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

Venue2022 IEEE 61st Conference on Decision and Control (CDC) · 2022
Typearticle
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsDissipative systemModular designComputer scienceGeneralityInterconnectionGraphDissipative solitonTopology (electrical circuits)Dissipative operatorTheoretical computer scienceMathematicsPhysicsNonlinear systemTelecommunications

Abstract

fetched live from OpenAlex

Results towards modular design of complex dissipative networks are presented. Graph separation condition for proper interconnection of dissipative networks are given, and methods for design of scattering transformations that guarantees the fulfilment of the graph separation condition are presented. To ensure full generality of the results, behavioral framework for modeling of dissipative networks is utilized. Properly interconnected dissipative networks can be used as modular blocks that can be further interconnected with other dissipative networks thus allowing for iterative design of complex networks of dissipative systems.

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 score0.692
Threshold uncertainty score0.783

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 head0.210
Teacher spread0.194 · 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