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Record W2371742128

Adaptive joint source-channel coding and MAP decoding of error correction arithmetic codes

2007· article· en· W2371742128 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

VenueJournal of Communications · 2007
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsVariable-length codeDecoding methodsComputer scienceAlgorithmArithmetic codingShannon–Fano codingCoding gainAdaptive codingList decodingCoding (social sciences)Channel (broadcasting)Source codeTheoretical computer scienceContext-adaptive binary arithmetic codingConcatenated error correction codeMathematicsBlock codeTelecommunicationsData compressionStatistics
DOInot available

Abstract

fetched live from OpenAlex

The adaptive joint source/channel coding methodologies was investigated,and a novel joint source/channel coding/decoding system was proposed based on error correction arithmetic codes.At encoding,this system can achieve an adaptive source/channel integrative coding by utilizing the arithmetic codes with a forbidden symbol.In particular,the Markov source model was used,and the probability of the forbidden symbol was adjusted adaptively according to the channel state information(CSI)so as to provide an adaptive source coding;and then at decoing,an improved sequential search algorithm based on new MAP decoding metric was proposed.Unlike the conventional channel adaptive coding algorithm,only one parameter(ie.a forbidden symbol)must be need to adjust,and a continuous coding rate could be achieved theoretically.Some experimental results show that the proposed system can achieve a higher performance gain,comparing with systems proposed by Grangetto and the separate coding system.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.079
GPT teacher head0.316
Teacher spread0.237 · 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