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Record W2088124680 · doi:10.1109/tit.2014.2332336

Private Broadcasting Over Independent Parallel Channels

2014· article· en· W2088124680 on OpenAlex
Ashish Khisti, Tie Liu

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Information Theory · 2014
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFadingBroadcasting (networking)GaussianCoding (social sciences)Computer scienceChannel codeBlock (permutation group theory)Encoding (memory)Theoretical computer scienceTopology (electrical circuits)MathematicsAlgorithmComputer networkDecoding methodsCombinatoricsStatistics

Abstract

fetched live from OpenAlex

We study broadcasting of two confidential messages to two groups of receivers over independent parallel subchannels. One group consists of an arbitrary number of receivers, interested in a common message, whereas the other group has only one receiver. Each message must be confidential from the receiver(s) in the other group. Each of the subchannels is assumed to be degraded in a certain fashion. While corner points of the capacity region of this setup were characterized in earlier works, we establish the complete capacity region, and show the optimality of a superposition coding technique. For Gaussian channels, we establish the optimality of a Gaussian input distribution by applying an extremal information inequality. By extending our coding scheme to block-fading channels, we demonstrate significant performance gains over a baseline time-sharing scheme.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.220
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it