Sequential MCMC for spatial signal separation and restoration from an array of sensors
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the implementation of sequential Markov Chain Monte Carlo (MCMC) estimation, also known as particle filtering, to signal separation and restoration problems, using a passive array of sensors. This proposed method offers significant advantages: 1) the signals mixed at the array can be well-separated in space and restored in an online fashion, 2) the assumption of a stationary environment over the interval can be relaxed, 3) the estimated joint posterior distribution of all the unknown parameters can be used for statistical inference, and 4) the method can also be used to dynamically detect the number of signals throughout the observation period. The signals used in the simulation were mixed by a highly-nonlinear but structured steering-vector matrix. Simulation results demonstrated the effectiveness of the method in such a way that the true and restored signals were clearly separated and restored by the sequential MCMC method.
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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 | 0.001 |
| 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