Diffusion least-mean squares over distributed networks in the presence of MAC errors
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Bibliographic record
Abstract
This paper presents the formulation and steady-state analysis of the distributed estimation algorithms based on diffusion cooperation scheme, in which all nodes in the network communicate employing a non-ideal channel access mechanism. We formulate and study a two-node network and derive the closed-form expressions of the steady-state mean-square deviation (MSD). We also assess the mean performance and stability condition. The proposed analytical framework enables us to investigate the effects of the medium access control (MAC) layer performance on the behavior of the diffusion least-mean squares (LMS) algorithm in terms of the convergence speed and the steady-state error that is validated by performing Monte Carlo simulations. Simulation and analysis confirm that a high probability of collision at the MAC level, results in lower convergence speed and higher steady-state MSD for distributed estimation algorithm.
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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 | 0.000 |
| 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