The Gaussian Diamond-Wiretap Channel with Conferencing Relays
Bibliographic record
Abstract
In this letter, we consider the Gaussian diamond-wiretap channel with conferencing links between relays for the following three cases: 1) both conferencing links are confidential; 2) both conferencing links are public; and 3) one conferencing link is confidential, the other link is public, and the legitimate parties do not know which link is confidential or public. For all the cases, we establish the exact secure degrees of freedom (d.o.f.). Our results show that if at least one of conferencing links is confidential, the secure d.o.f. improves upon the case of no conferencing. In these cases of fully and partially confidential conferencing, it is shown to be sufficient to use the conferencing links only for transmitting noise symbols. On the other hand, if conferencing information is fully revealed to the eavesdropper, it is shown that there is no gain in secure d.o.f. due to conferencing, even if the conferencing information is also known to the legitimate destination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".