Capacity allocation in statistical multiplexing of ATM sources
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Bibliographic record
Abstract
The authors are concerned with the allocation of the available capacity of a statistical multiplexer to serve a number of heterogeneous on-off sources, with the cell loss rate as the performance criterion. In order to avoid using potentially lengthy simulations, they have derived computationally efficient bounds and asymptotic approximations for the cell loss rate. The union of all partitions of the available capacity which satisfies the capacity bound and the performance criterion is defined as the capacity region. Both linear approximation and nonlinear approximation of the capacity region are investigated. It is shown that the linear approximation is reasonably accurate when the activity factors of the sources are not too high (less than 0.8). For the case where the linear approximation appears too optimistic, a simple nonlinear approximation for determining the capacity region is suggested. The accuracy of the method is demonstrated using numerical examples.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it