Complex Fuzzy Dynamical Graphs and their Applications in Signals Processing
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we introduce the concepts of complex fuzzy dynamic graphs, complex fuzzy diagonal matrices and complex fuzzy Laplacian matrices. We use these graphs and their laplacian matrices as mathematical framework for applications in Sciences, especially signals processing. We define absolute average eigenvalues of the Complex Laplacian matrices and explore the properties of these matrices with their eigenvalues. We develop an algorithm using the absolute eigenvalues of the Laplacian matrices and apply this algorithm to signal and systems. Our study begins by establishing the theoretical foundation of complex fuzzy dynamic graphs, highlighting their role to model within dynamic systems including two dimensional uncertainties. We investigates the complex fuzzy Laplacian matrices obtain from these graphs. Our main focus is on the absolute eigenvalues of these matrices, which hold a vital role into the graph’s structural characteristics and behavior. In the context of signals processing, the research demonstrates how these absolute eigenvalues serve as essential matrices for system characterization. This study presents novel methods to analyze signals on complex fuzzy dynamic graphs. These methods are particularly relevant in scenarios where signals are influenced by dynamic and uncertain environments.
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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 | 0.000 | 0.001 |
| 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