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Record W3104878415

0 Improving the Sphere-Packing Bound for Binary Codes over Memoryless Symmetric Channels

2016· preprint· en· W3104878415 on OpenAlexaff
Kaveh Mahdaviani, S. Golnaz Shahidi, Shima Haddadi, Masoud Ardakani, Chintha Tellambura

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsQueen's UniversityUniversity of Alberta
Fundersnot available
KeywordsBinary symmetric channelUpper and lower boundsBinary numberHamming boundSphere packingBinary codeChannel codeCombinatoricsMathematicsCoding (social sciences)Channel (broadcasting)Code (set theory)Decoding methodsAlgorithmDiscrete mathematicsComputer scienceBlock codeLinear codeTelecommunicationsMathematical analysisGeometryStatisticsArithmetic
DOInot available

Abstract

fetched live from OpenAlex

A lower bound on the minimum required code length of binary codes is obtained.The bound is obtained based on observing a close relation between the Ulam's liar game and channel coding.In fact, Spencer's optimal solution to the game is used to derive this new bound which improves the famous Sphere-Packing Bound.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.003

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 head0.035
GPT teacher head0.294
Teacher spread0.259 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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