Sparse Robust Learning From Flipped Bits
Bibliographic record
Abstract
In wireless sensor networks (WSNs), distributed sensors are often constrained by their limited battery energy and radio spectrum for transmission. This paper investigates an on-line parameter estimation problem of linear regression in a WSN, where each sensor is restricted to send a one-bit message +1/-1 to a fusion center in order to satisfy the spectrum and power constraints. Moreover, sensor nodes communicate with the fusion center over noisy links, which can randomly flip the binary message sent from each sensor to the fusion center. With the flipped bit stream, robust and sparse-robust learning algorithms respectively are proposed. In the proposed algorithms, the parameter estimation over a WSN with the imperfect binary communication is formulated hierarchically as Bayesian learning, and is equivalent to an expectation maximization realized by using the recursive least-squares methods. Theoretical and empirical research is carried out to assess the performance of the proposed algorithms, and a practical application of the proposed algorithms in estimation and tracking of frequencies of multiple sinusoids is also presented. These theoretical analysis and experimental results demonstrate the effectiveness of the proposed algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".