State estimation for systems with unobservable packet losses: Approximate estimation, stability, and performance analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract For a system with packet losses, if the estimator can observe the status of packet losses, it is called a system with observable packet losses (an OPL system); otherwise, it is called a system with unobservable packet losses (a UPL system). We obtain the optimal estimator (OE) for UPL systems, which consists of an exponentially increasing number of items, and thus is computationally intractable. To address the computation issue, we design an approximate optimal estimator (AOE), which can be computed recursively. The proposed AOE features a theoretically‐proven stability condition and a theoretically‐guaranteed superiority to the optimal linear estimator (OLE). Specifically, for stability, we prove that for a stable UPL system, both the OE and the proposed AOE are stable; for performance, we show that both the OE and the proposed AOE are superior to the OLE in the mean sense. Then, we obtain a tight upper bound of the performance deviation between the OE and the proposed AOE. Finally, numerical examples are presented to illustrate the obtained results and the effectiveness of the proposed AOE in estimating system states when the packet‐loss status, that is, the private information of packet losses, cannot be observed.
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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 | 0.001 | 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