Bibliographic record
Abstract
We study the construction of the source-initiated (one-to-all) wireless broadcast tree to minimize the total required power for a given source node, a group of intended destination nodes and a given propagation constant, ie, the power attenuation constant /spl lambda/. The minimum energy broadcasting (MEB) problem has received much attention recently due to the two main challenges of mobile communication: the limited bandwidth of wireless networks and the limited power supply of mobile units. In a limited-bandwidth environment, push-based techniques, ie, broadcast schemes, appear to be a very effective way to allow mobile units to share the broadcast data on air. In a limited-energy environment, energy- efficient communication architectures and techniques are essential. We first give an insight analysis on the MEB problem and prove the NP-hardness of this problem. We then present an efficient heuristic called iterative maximum-branch minimization (IMBM) to approximate the construction of the minimum-energy broadcast tree, which fully utilizes the wireless broadcast advantage and demonstrates better performance compared with the related approaches. Due to the power-efficient way of the construction of the broadcast tree, the lifetime of the networks can be maximized.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".