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On Probabilistic Weight Distribution of Polar Codes

2013· article· en· W1985834029 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

VenueIEEE Communications Letters · 2013
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsPolar codeProbabilistic logicComputer scienceAlgorithmWeight distributionHamming codePolarCoding (social sciences)Hamming weightLinear codeTheoretical computer scienceMathematicsBlock codeDecoding methodsStatistics

Abstract

fetched live from OpenAlex

Polar code is a low-complexity and capacity-achieving code with applications in channel coding, source coding and secrecy. One way to evaluate the error performance of the finite-length polar 'channel' code, for instance, is to use Bonferroni-type (e.g., union bound) bounds requiring the Hamming weight distribution. Currently, there are no low-complexity algorithms for the polar code's spectra. In this paper, we propose a recursive probabilistic weight distribution (PWD) expression for polar codes. As this expression is complex, two polynomial-time approximations and a tree-based approach have been presented for a faster implementation of the PWD method with much accuracy.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.647
Threshold uncertainty score0.476

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 communication0.0000.000
Open science0.0030.000
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 head0.022
GPT teacher head0.265
Teacher spread0.243 · 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