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Record W4298309581 · doi:10.48550/arxiv.1701.05943

Structure of optimal strategies for remote estimation over\n Gilbert-Elliott channel with feedback

2017· preprint· W4298309581 on OpenAlex

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

VenuearXiv (Cornell University) · 2017
Typepreprint
Language
FieldComputer Science
TopicDistributed Sensor Networks and Detection Algorithms
Canadian institutionsMcGill University
Fundersnot available
KeywordsChannel (broadcasting)TransmitterAutoregressive modelTransmission (telecommunications)Kalman filterComputer scienceControl theory (sociology)Channel state informationMathematical optimizationMathematicsTelecommunicationsWirelessControl (management)StatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

We investigate remote estimation over a Gilbert-Elliot channel with feedback.\nWe assume that the channel state is observed by the receiver and fed back to\nthe transmitter with one unit delay. In addition, the transmitter gets ACK/NACK\nfeedback for successful/unsuccessful transmission. Using ideas from team\ntheory, we establish the structure of optimal transmission and estimation\nstrategies and identify a dynamic program to determine optimal strategies with\nthat structure. We then consider first-order autoregressive sources where the\nnoise process has unimodal and symmetric distribution. Using ideas from\nmajorization theory, we show that the optimal transmission strategy has a\nthreshold structure and the optimal estimation strategy is Kalman-like.\n

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 categoriesMeta-epidemiology (narrow)
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 score0.863
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
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 head0.047
GPT teacher head0.205
Teacher spread0.158 · 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