Joint optimization of power scheduling and rate‐distortion performance in<i>one‐helper</i>problem
Bibliographic record
Abstract
Abstract We consider the many‐help‐one problem, also called m‐helper problem, for the special case of m = 1 where one source provides partial side information to the fusion center (FC) to help reconstruction of the other correlated source. Both correlated sources communicate information about their observations to the FC through an orthogonal multiple access channel (MAC) without cooperating with each other. First, we characterize the optimal tradeoff between the transmission cost, that is, power, and the distortion D. Then, we consider a joint optimization of source coding and power scheduling from an information theory perspective, where the power scheduling is verified using Shannon capacity formula and the source‐coding problem is analyzed using rate‐distortion theory. We show that the joint optimization in the Gaussian one‐helper problem can be solved analytically. We provide closed‐form expressions for the optimal distortion and the optimal power scheduling in terms of the cost weights. Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".