Bandwidth and Resource Allocation Optimization Through a Probabilistic Algorithm for Mobile TV
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we consider the problem of bandwidth and resource allocation optimization for delivering addressable advertising in traditional TV. Transmitting addressable advertising over a mobile TV platform via LTE broadcast with eMBMS, which is capable of efficiently supporting a large number of concurrent users within available network and spectrum constraints, has relevant structural similarities to delivering this advertising over traditional television systems, and so these results from the one area transfer to the other. We introduce a probabilistic algorithm for resource optimization and detail its exact recursive O(n2) (in the number of delivered programming networks) implementation together with a practical approximation. The proposed methods are evaluated on their performance against real historical data on TV programming and viewing, with the new methods showing significant improvement in terms of advertising revenue over methods currently used in the industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it