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Record W2094358031 · doi:10.3934/dcds.2005.13.339

Structure of a class of traveling waves in delayed cellular neural networks

2005· article· en· W2094358031 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

VenueDiscrete and Continuous Dynamical Systems · 2005
Typearticle
Languageen
FieldComputer Science
TopicCellular Automata and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTraveling waveMonotonic functionMathematical analysisLattice (music)PhysicsMathematicsPlane (geometry)Partition (number theory)Artificial neural networkComplex planeComputer scienceCombinatoricsGeometryAcoustics

Abstract

fetched live from OpenAlex

This work investigates the structure of a class of traveling wave solutions of delayed cellular neural networks distributed in the one-dimensional integer lattice $\mathbb Z^1$. The dynamics of a given cell is characterized by instantaneous self-feedback and neighborhood interaction with its two left neighbors in which one is instantaneous and the other is distributively delayed due to, for example, finite switching speed and finite velocity of signal transmission. Applying the method of step with the aid of positive roots of the corresponding characteristic function of the profile equation, we can directly figure out the solution in explicit form. We then partition the parameter space $(\alpha, \beta)$-plane into four regions such that the qualitative properties of traveling waves can be completely determined for each region. In addition to the existence of monotonic traveling wave solutions, we also find that, for certain parameters, there exist non-monotonic traveling wave solutions such as camel-like waves with many critical points.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.413

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 head0.004
GPT teacher head0.205
Teacher spread0.200 · 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