MétaCan
Menu
Back to cohort

Towards Modular Scattering-Based Design of Dissipative Networks with Time Delays

2025· article· W7123349136 on OpenAlexaff
Ilia G. Polushin

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicNeural Networks Stability and Synchronization
Canadian institutionsWestern University
Fundersnot available
KeywordsDissipative systemInterconnectionModular designQuadratic equationTopology (electrical circuits)Stability (learning theory)Control theory (sociology)

Abstract

fetched live from OpenAlex

Results related to modular scattering-based design of complex dissipative networks in the case where at least some of the subsystems are interconnected over delayed communication channels are presented. Conditions for well-posedness of dissipative networks with delayed communication channels are formulated in terms of subsystems behaviors. Conditions on the subsystems that guarantee an interconnection to satisfy the target property of weak dissipativity with a quadratic supply rate and additional internal stability-like properties are established in the form of a linear matrix inequality as well as in the form of a graph separation condition. A procedure for the design of scattering transformations is developed which guarantees that the scattering-based interconnection with communication delays satisfies the target property. Iterative application of the proposed techniques allows for modular design of large scale dissipative networks with overall stability properties in the presence of communication delays.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicNeural Networks Stability and SynchronizationFrench-language works237,207