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Record W3115550904

Zipper Codes: High-rate Spatially-coupled Codes with Algebraic Component Codes

2020· dissertation· en· W3115550904 on OpenAlex
Alvin Y. Sukmadji

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTSpace · 2020
Typedissertation
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaTechnische Universität München
KeywordsComponent (thermodynamics)Block codeConcatenated error correction codeComputer scienceLinear codeMathematicsZipperTornado codeAlgebraic numberTheoretical computer scienceAlgorithmPhysicsDecoding methods
DOInot available

Abstract

fetched live from OpenAlex

Zipper codes, a new framework for describing spatially-coupled product-like codes, are introduced. This framework encompasses many types of codes such as staircase codes and braided block codes. New types of codes such as tiled and delayed diagonal zipper codes are also introduced. Simulation results show that these new type of codes achieve comparable performance to staircase codes while requiring less memory. This thesis also analyzes the types of stall patterns that can arise in zipper codes and how they affect the error floor. Finally, the impact of error-and-erasure decoding in zipper codes is also studied. Software simulation results show that adding erasure symbols improves the coding gain by around 0.1 dB with only a small increase in memory overhead and decoding complexity.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.288
Teacher spread0.272 · 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