Modeling and Analysis of Cellular Neural Networks Based on Memcapacitor
Why this work is in the frame
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Bibliographic record
Abstract
Introducing the memcapacitor into the Cellular Neural Network (CNN), the Memcapacitor-Cellular Neural Network (MC-CNN) model with infinitely many equilibrium points is constructed. A series of dynamical behaviors of the MC-CNN are investigated by various nonlinear system analysis means. It is shown that the system has a large maximum Lyapunov exponent in a specific parameter range. And with the variation of parameters, the system is able to produce many different phase trajectories of the attractor. Multistability is also found in the system. The pseudo-randomness of the MC-CNN is calculated by Spectral Entropy (SE) complexity algorithm. The final hardware results proves the physical realizability of the system. The MC-CNN model is intended to provide guidance for neural networks and cryptographic strategies based on the memcapacitor.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it